From 89214a1c48adaa96f9a816aa292b559dd78de318 Mon Sep 17 00:00:00 2001 From: Raghav Dixit <34462078+raghavdixit99@users.noreply.github.com> Date: Fri, 17 May 2024 12:15:52 -0400 Subject: [PATCH 01/16] Langchain cloud oss example update (#188) --- examples/Code-Documentation-QA-Bot/main.ipynb | 143 ++++++++++-------- examples/Code-Documentation-QA-Bot/test.py | 92 ----------- 2 files changed, 82 insertions(+), 153 deletions(-) delete mode 100644 examples/Code-Documentation-QA-Bot/test.py diff --git a/examples/Code-Documentation-QA-Bot/main.ipynb b/examples/Code-Documentation-QA-Bot/main.ipynb index c066ef3..fd2cdef 100644 --- a/examples/Code-Documentation-QA-Bot/main.ipynb +++ b/examples/Code-Documentation-QA-Bot/main.ipynb @@ -23,12 +23,22 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, + "id": "e8a49c31", + "metadata": {}, + "outputs": [], + "source": [ + "! pip install -U langchain langchain-openai" + ] + }, + { + "cell_type": "code", + "execution_count": 23, "id": "66638d6c", "metadata": {}, "outputs": [], "source": [ - "!pip install -qq openai==0.28 langchain==0.0.354 tiktoken unstructured pandas lancedb" + "! pip install -qq tiktoken unstructured pandas lancedb" ] }, { @@ -41,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "id": "58ee1868", "metadata": {}, "outputs": [], @@ -49,6 +59,7 @@ "import openai\n", "import os\n", "\n", + "\n", "# Configuring the environment variable OPENAI_API_KEY\n", "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n", "\n", @@ -56,7 +67,7 @@ " os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n", "openai.api_key = os.environ[\"OPENAI_API_KEY\"]\n", "\n", - "assert len(openai.Model.list()[\"data\"]) > 0" + "# assert len(openai.models.list()[\"data\"]) > 0" ] }, { @@ -78,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "b55d22f1", "metadata": {}, "outputs": [], @@ -91,10 +102,9 @@ "from pathlib import Path\n", "\n", "from langchain.document_loaders import UnstructuredHTMLLoader\n", - "from langchain.embeddings import OpenAIEmbeddings\n", "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain.vectorstores import LanceDB\n", - "from langchain.llms import OpenAI\n", + "from langchain_openai import OpenAI, OpenAIEmbeddings\n", "from langchain.chains import RetrievalQA" ] }, @@ -117,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "id": "7da77e75", "metadata": {}, "outputs": [], @@ -142,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "id": "d171d062", "metadata": {}, "outputs": [], @@ -196,14 +206,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "f5f683a7-123b-4e9e-a60b-115bc1340a66", "metadata": {}, "outputs": [], "source": [ + "from tqdm import tqdm\n", + "\n", "docs = []\n", "docs_path = Path(\"docs.pkl\")\n", - "for p in Path(\"numpy_docs\").rglob(\"*.html\"):\n", + "for p in tqdm(Path(\"numpy_docs\").rglob(\"*.html\")):\n", " if p.is_dir():\n", " continue\n", " loader = UnstructuredHTMLLoader(p)\n", @@ -227,21 +239,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "c019d728-bb65-494a-b4a9-73a62bf8e155", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2699" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "len(docs)" ] @@ -258,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "id": "82230563", "metadata": {}, "outputs": [], @@ -278,29 +279,52 @@ "source": [ "# Storing\n", "\n", - "Let's connect to LanceDB so we can store our documents. We'll create a Table to store them in:" + "Let's connect to LanceDB so we can store our documents, It requires 0 setup !" + ] + }, + { + "cell_type": "markdown", + "id": "7ed0c792", + "metadata": {}, + "source": [ + "### For LanceDB cloud use the below cell :" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "id": "74780a58", "metadata": {}, "outputs": [], "source": [ - "db = lancedb.connect(\"lancedb\")\n", - "table = db.create_table(\n", - " \"numpy_docs\",\n", - " data=[\n", - " {\n", - " \"vector\": embeddings.embed_query(\"Hello World\"),\n", - " \"text\": \"Hello World\",\n", - " \"id\": \"1\",\n", - " }\n", - " ],\n", - " mode=\"overwrite\",\n", + "vectorstore = LanceDB(\n", + " embedding=embeddings,\n", + " uri=\"db://test\", # your remote database URI\n", + " api_key=\"sk_...\",\n", + " region=\"us-east-x-xxx\", # the cloud region you have configured\n", + " table_name=\"langchain_vectorstore\", # Optional, defaults to \"vectors\"\n", + " mode=\"overwrite\", # Optional, defaults to \"overwrite\"\n", ")\n", - "docsearch = LanceDB.from_documents(documents, embeddings, connection=table)" + "\n", + "doc_ids = vectorstore.add_documents(documents=documents)" + ] + }, + { + "cell_type": "markdown", + "id": "9d6cf585", + "metadata": {}, + "source": [ + "### For LanceDB local use the below cell :" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "71e15e3b", + "metadata": {}, + "outputs": [], + "source": [ + "vectorstore = LanceDB.from_documents(documents=documents, embedding=embeddings)" ] }, { @@ -313,13 +337,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "id": "6a5891ad", "metadata": {}, "outputs": [], "source": [ "qa = RetrievalQA.from_chain_type(\n", - " llm=OpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever()\n", + " llm=OpenAI(), chain_type=\"stuff\", retriever=vectorstore.as_retriever()\n", ")" ] }, @@ -341,68 +365,71 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "id": "70d88316", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "' The NumPy library is an open source Python library that is used in many fields of science and engineering. It provides efficient data structures for multidimensional arrays and matrices, along with a vast array of mathematical functions to operate on them. It is a core component of the scientific Python and PyData ecosystems, and is used by everyone from beginners to experienced researchers in various applications. It is also interoperable with other Python libraries such as SciPy, Pandas, and OpenCV, making it a crucial tool for scientific computing in Python. '" + "{'query': 'tell me about the numpy library?',\n", + " 'result': ' The NumPy library is an open-source Python library that provides efficient data structures and mathematical functions for working with multidimensional arrays and matrices. It is widely used in the fields of science and engineering and is a fundamental component of the scientific Python ecosystem. It can be installed using a scientific Python distribution or through the use of pip or conda. '}" ] }, - "execution_count": 23, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = \"tell me about the numpy library?\"\n", - "qa.run(query)" + "qa.invoke(query)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 12, "id": "85a0397c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'\\n\\n1.22.0'" + "{'query': \"What's the current version of numpy?\",\n", + " 'result': ' The current version of NumPy is 1.21.6, according to the context provided.'}" ] }, - "execution_count": 24, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = \"What's the current version of numpy?\"\n", - "qa.run(query)" + "qa.invoke(query)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 13, "id": "923f86c6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "' Linear algebra operations can be performed using the numpy.linalg library.'" + "{'query': 'What kind of linear algebra related operations can be done in numpy?',\n", + " 'result': ' Numpy provides a variety of linear algebra related operations, including decompositions, matrix eigenvalues, norms and other numbers, solving equations and inverting matrices, and linear algebra on several matrices at once.'}" ] }, - "execution_count": 25, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = \"What kind of linear algebra related operations can be done in numpy?\"\n", - "qa.run(query)" + "qa.invoke(query)" ] }, { @@ -410,14 +437,8 @@ "id": "f8958d1b-0ad6-44d6-bca0-d81771c564a1", "metadata": {}, "source": [ - "Thanks" + "Thanks !" ] - }, - { - "cell_type": "markdown", - "id": "e6a53efb", - "metadata": {}, - "source": [] } ], "metadata": { @@ -436,7 +457,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.10.13" }, "vscode": { "interpreter": { diff --git a/examples/Code-Documentation-QA-Bot/test.py b/examples/Code-Documentation-QA-Bot/test.py deleted file mode 100644 index f89682e..0000000 --- a/examples/Code-Documentation-QA-Bot/test.py +++ /dev/null @@ -1,92 +0,0 @@ -import pytest -import os -import openai -import argparse -import lancedb -import re -import pickle -import requests -import zipfile -from pathlib import Path -from main import get_document_title - -from langchain.document_loaders import BSHTMLLoader -from langchain.embeddings import OpenAIEmbeddings -from langchain.text_splitter import RecursiveCharacterTextSplitter -from langchain.vectorstores import LanceDB -from langchain.llms import OpenAI -from langchain.chains import RetrievalQA - -# TESTING =============================================================== - - -@pytest.fixture -def mock_embed(monkeypatch): - def mock_embed_query(query, x): - return [0.5, 0.5] - - monkeypatch.setattr(OpenAIEmbeddings, "embed_query", mock_embed_query) - - -def test_main(mock_embed): - os.mkdir("./tmp") - args = argparse.Namespace(query="test", openai_key="test") - os.environ["OPENAI_API_KEY"] = "test" - - docs_path = Path("docs.pkl") - docs = [] - - pandas_docs = requests.get( - "https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip" - ) - with open("./tmp/pandas.documentation.zip", "wb") as f: - f.write(pandas_docs.content) - - file = zipfile.ZipFile("./tmp/pandas.documentation.zip") - file.extractall(path="./tmp/pandas_docs") - - if not docs_path.exists(): - for p in Path("./tmp/pandas_docs/pandas.documentation").rglob("*.html"): - print(p) - if p.is_dir(): - continue - loader = BSHTMLLoader(p, open_encoding="utf8") - raw_document = loader.load() - - m = {} - m["title"] = get_document_title(raw_document[0]) - m["version"] = "2.0rc0" - raw_document[0].metadata = raw_document[0].metadata | m - raw_document[0].metadata["source"] = str(raw_document[0].metadata["source"]) - docs = docs + raw_document - - with docs_path.open("wb") as fh: - pickle.dump(docs, fh) - else: - with docs_path.open("rb") as fh: - docs = pickle.load(fh) - - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=1000, - chunk_overlap=200, - ) - documents = text_splitter.split_documents(docs) - - db = lancedb.connect("./tmp/lancedb") - table = db.create_table( - "pandas_docs", - data=[ - { - "vector": OpenAIEmbeddings().embed_query("Hello World"), - "text": "Hello World", - "id": "1", - } - ], - mode="overwrite", - ) - # docsearch = LanceDB.from_documents(documents, OpenAIEmbeddings, connection=table) - - # qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever()) - - # result = qa.run(args.query) - # print(result) From 7cdfb0d04a81d1afe9bff68d5c146b251a6d499c Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Sat, 25 May 2024 23:54:17 +0530 Subject: [PATCH 02/16] Tutorials/lama parser complex pdf & text data (#191) * Create README.md * Add files via upload * Update README.md * Update README.md --- README.md | 1 + tutorials/Advace_RAG_LlamaParser/README.md | 19 + tutorials/Advace_RAG_LlamaParser/main.ipynb | 3560 +++++++++++++++++++ 3 files changed, 3580 insertions(+) create mode 100644 tutorials/Advace_RAG_LlamaParser/README.md create mode 100644 tutorials/Advace_RAG_LlamaParser/main.ipynb diff --git a/README.md b/README.md index ec240e4..d9cb320 100644 --- a/README.md +++ b/README.md @@ -109,6 +109,7 @@ Looking to get started with LLMs, vectorDBs, and the world of Generative AI? The | [Fine-Tuning LLM using PEFT & QLoRA](./tutorials/fine-tuning_LLM_with_PEFT_QLoRA) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/fine-tuning_LLM_with_PEFT_QLoRA/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/optimizing-llms-a-step-by-step-guide-to-fine-tuning-with-peft-and-qlora-22eddd13d25b) | | [Context-Aware Chatbot using Llama 2 & LanceDB](./tutorials/chatbot_using_Llama2_&_lanceDB) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755) | | [Better RAG with FLARE](./tutorials/better-rag-FLAIR) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/better-rag-FLAIR/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/) | +| [Extracting Complex PDFs containing tables-text -LlamaParse ](./tutorials/Advace_RAG_LlamaParser) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Advace_RAG_LlamaParser/main.ipynb)[![LLM](https://img.shields.io/badge/openai-api-white)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | diff --git a/tutorials/Advace_RAG_LlamaParser/README.md b/tutorials/Advace_RAG_LlamaParser/README.md new file mode 100644 index 0000000..53730af --- /dev/null +++ b/tutorials/Advace_RAG_LlamaParser/README.md @@ -0,0 +1,19 @@ + +## Advanced RAG: Extracting Complex PDFs containing tables & Text Using LlamaParse +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Advace_RAG_LlamaParser/main.ipynb) + +This repository contains code and examples for comparing LangChain, LlamaIndex, and LlamaParse in extracting data from PDFs, especially those with complex tables and text. + +Overview +In this project, we explore: + +* Q&A on PDF data using LangChain + +* Q&A on PDF data using LlamaIndex + +* Q&A on PDF data using LlamaIndex with LlamaParse + +The results of each method are compared & code is available in our collab notebook + + + diff --git a/tutorials/Advace_RAG_LlamaParser/main.ipynb b/tutorials/Advace_RAG_LlamaParser/main.ipynb new file mode 100644 index 0000000..eea1220 --- /dev/null +++ b/tutorials/Advace_RAG_LlamaParser/main.ipynb @@ -0,0 +1,3560 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "This notebook is compairsion of langchain & lamaindex for better extraction of pdf with table & text\n", + "\n", + "\n", + "here we have coverd\n", + "\n", + "1.langchain RAG\n", + "2.Lamaindex RAG\n", + "\n", + "3.Langchain wiht lamaparser\n", + "4.Lamaindex with lamaparser\n", + "\n", + "\n", + "from above this methid will get idea about which is our best method for table extraction\n" + ], + "metadata": { + "id": "7wD8dJo-WZH7" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HGcMcXLF7zoM", + "outputId": "b4f6876b-0531-4bce-c1a6-1615c77322a2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting llama-index\n", + " Downloading llama_index-0.10.37-py3-none-any.whl (6.8 kB)\n", + "Collecting llama-index-core\n", + " Downloading llama_index_core-0.10.37.post1-py3-none-any.whl (15.4 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.4/15.4 MB\u001b[0m \u001b[31m40.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hCollecting llama-index-embeddings-openai\n", + " Downloading llama_index_embeddings_openai-0.1.9-py3-none-any.whl (6.0 kB)\n", + "Collecting llama-parse\n", + " Downloading llama_parse-0.4.3-py3-none-any.whl (7.7 kB)\n", + 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jinja2->spacy<4.0.0,>=3.7.1->llama-index-core<0.11.0,>=0.10.1->llama-index-vector-stores-lancedb) (2.1.5)\n", + "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<4.0.0,>=3.7.1->llama-index-core<0.11.0,>=0.10.1->llama-index-vector-stores-lancedb) (1.1.1)\n", + "Installing collected packages: ratelimiter, semver, py, overrides, deprecation, retry, pylance, lancedb, llama-index-vector-stores-lancedb\n", + "Successfully installed deprecation-2.1.0 lancedb-0.5.7 llama-index-vector-stores-lancedb-0.1.3 overrides-7.7.0 py-1.11.0 pylance-0.9.18 ratelimiter-1.2.0.post0 retry-0.9.2 semver-3.0.2\n" + ] + } + ], + "source": [ + "%pip install llama-index llama-index-core llama-index-embeddings-openai llama-parse\n", + "%pip install llama-index-postprocessor-flag-embedding-reranker\n", + "%pip install git+https://github.com/FlagOpen/FlagEmbedding.git\n", + "%pip install llama-index-vector-stores-lancedb\n", + "%pip install --upgrade --quiet langchain langchain-community langchainhub langchain-openai langchain-chroma bs4 lancedb\n", + "%pip install unstructured" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NqK1g8Bg7zlZ" + }, + "outputs": [], + "source": [ + "# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio\n", + "import os\n", + "import nest_asyncio\n", + "nest_asyncio.apply()\n", + "\n", + "\n", + "\n", + "# API access to llama-cloud\n", + "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-\"\n", + "# Using OpenAI API for embeddings/llms\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-\"\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Download the pdf. it has both tables & text ." + ], + "metadata": { + "id": "4OmWRDtAKONC" + } + }, + { + "cell_type": "code", + "source": [ + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10q/uber_10q_march_2022.pdf' -O './uber_10q_march_2022.pdf'" + ], + "metadata": { + "id": "smCjT2FIj9Fo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# 1. Langchain with Q&A on PDF" + ], + "metadata": { + "id": "1I--ouSiTGvj" + } + }, + { + "cell_type": "code", + "source": [ + "import bs4\n", + "from langchain import hub\n", + "from langchain_community.document_loaders import WebBaseLoader\n", + "from langchain_openai import ChatOpenAI\n", + "from langchain_community.document_loaders import PyPDFLoader\n", + "from langchain.vectorstores import LanceDB\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" + ], + "metadata": { + "id": "4ysMDhHiR2bG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n", + "\n", + "loader = PyPDFLoader(\"/content/uber_10q_march_2022.pdf\")\n", + "docs = loader.load()\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", + "splits = text_splitter.split_documents(docs)\n", + "vectorstore = LanceDB.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n", + "\n", + "# Retrieve and generate using the relevant snippets of the blog.\n", + "retriever = vectorstore.as_retriever()\n", + "prompt = hub.pull(\"rlm/rag-prompt\")\n", + "\n", + "def format_docs(docs):\n", + " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", + "\n", + "\n", + "rag_chain = (\n", + " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n", + " | prompt\n", + " | llm\n", + " | StrOutputParser()\n", + ")\n", + "\n", + "qa_langchain_query1 = \" what is the net loss value attributable to Uber compared to last year?\"\n", + "rag_chain.invoke(qa_langchain_query1)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "id": "4emkzsCqSMTe", + "outputId": "d122f7f4-9072-4a4f-acf8-3d3b39755328" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'The net loss value attributable to Uber Technologies, Inc. for the period was $5.9 billion, compared to $108 million in the same period the previous year. This represents a significant increase in net loss year-over-year.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "source": [ + "qa_langchain_query2 = \"how is the Cash paid for Income taxes, net of refunds from Supplemental disclosures of cash flow information?\"\n", + "rag_chain.invoke(qa_langchain_query2)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "id": "G4qR6GxTSMWO", + "outputId": "38fff0fe-377f-4b10-aa4f-362712e539d9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"I don't know.\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "source": [ + "qa_langchain_query3 = \"give me detailed charts of intangible assets, net as of December 31, 2021 and March 31, 2022\"\n", + "rag_chain.invoke(qa_langchain_query3)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "id": "b3DM-lCnSMZG", + "outputId": "4c97e6f3-d610-447f-ba4f-62715ae274a1" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"I don't have detailed charts of intangible assets, net as of December 31, 2021 and March 31, 2022.\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "FOR QUERY 2 & QUERY 3 we are not getting the answer,LETS TRY LAMAINDEX" + ], + "metadata": { + "id": "pwZzEShGwwvT" + } + }, + { + "cell_type": "markdown", + "source": [ + "# 2 . # Lamaindex with Q&A on PDF" + ], + "metadata": { + "id": "GVD2sPBEcRE3" + } + }, + { + "cell_type": "code", + "source": [ + "import textwrap\n", + "from llama_index.vector_stores.lancedb import LanceDBVectorStore\n", + "from llama_index.core import SimpleDirectoryReader, Document, StorageContext\n", + "from llama_index.core import VectorStoreIndex\n", + "from llama_index.llms.openai import OpenAI\n", + "from llama_index.embeddings.openai import OpenAIEmbedding\n", + "from llama_index.core import VectorStoreIndex\n", + "from llama_index.core import SimpleDirectoryReader\n", + "from llama_index.postprocessor.flag_embedding_reranker import (\n", + " FlagEmbeddingReranker,\n", + ")" + ], + "metadata": { + "id": "H__qJIWYdmgY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from llama_index.vector_stores.lancedb import LanceDBVectorStore\n", + "\n", + "vector_store_pdf = LanceDBVectorStore(uri=\"/tmp/lancedb_lamaindex\")" + ], + "metadata": { + "id": "BCNNrAw9Dklk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "from llama_index.postprocessor.flag_embedding_reranker import (\n", + " FlagEmbeddingReranker,\n", + ")\n", + "\n", + "reader = SimpleDirectoryReader(input_dir=\"/content/data_pdf/\")\n", + "\n", + "documents_pdf_loader = reader.load_data()\n", + "\n", + "from llama_index.vector_stores.lancedb import LanceDBVectorStore\n", + "\n", + "vector_store_pdf = LanceDBVectorStore(uri=\"/tmp/lancedb_lamaindex\")\n", + "storage_context_pdf = StorageContext.from_defaults(vector_store=vector_store_pdf)\n", + "lance_index_pdf = VectorStoreIndex.from_documents(\n", + " documents_pdf_loader, storage_context=storage_context_pdf\n", + ")\n", + "\n", + "\n", + "reranker = FlagEmbeddingReranker(\n", + " top_n=5,\n", + " model=\"BAAI/bge-reranker-large\",\n", + ")\n", + "\n", + "Lance_index_query_pdf = lance_index_pdf.as_query_engine(\n", + " similarity_top_k=10, node_postprocessors=[reranker]\n", + ")\n", + "\n", + "qa_lama_query1 = \"how is the Cash paid for Income taxes, net of refunds from Supplemental disclosures of cash flow information?\"\n", + "output1 = Lance_index_query_pdf.query(qa_lama_query1)\n", + "print(output1.response)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 393, + "referenced_widgets": [ + "0ab0af38e5c54405a48fd40de7bfe606", + "17300f8bea7644f09907376fab719f92", + "f2bbc601684446b5be521833592a319f", + "326bb35286ba4d23a5e3b8b364c7a44f", + "d19be0dd23344eb2887605d88f80c142", + "2e3f44b38e6842a9bd9db4c5e6cc6bcf", + "738a133fb9204208913431c2a35fef5c", + "d407aa9ec48347d9b5536a69fe9f5dc7", + "d779e07ce42c4f088dea05f8fd22922f", + "1700dc0afbb14e4cb85da9eedfca8605", + "498b64a4f33046c9a65b5a5486376359", + "f7de0a6aa31c41de90f45e862fe4ce15", + "a034635cd3d94f8b89b56e6b972daa63", + "5bec1c5f11434bf481862e67f73403f0", + "bd254eea803c4e44b60783f05c5c1ca1", + "1bbdd18be2e74c7f8916c95a1e5cf511", + "0f2a76e74d74459182402f92fbb946c9", + "d3d91529e30a4d67881a782d7eb54060", + "b69deb8741ad47229438548949e2da42", + "f894d72c0e19494abb732b2db46ced98", + "6fe509c6157e4ddbb1cfcf319cf04fac", + "de859b9a94684d39ac8a7aef81058a3b", + "09f4df92faa844b28fef90c3620b09e1", + "d0a4ce2181c84af8b277058689a35641", + "faaff37bf60e4870a9cbebd20d3afc6e", + "f55e2cf0eb34482da1902adb652fca5a", + "55ee9cd902c147ae97c4deea101f25e8", + "f3f8c3dc8a404bd4af55b105f98267d7", + "e13aaa64b9c848f9a67f61295fb546fe", + "2a6b2dacce79419b9a2469b6362abca8", + "3c63c0a7eac54b0fb72775e3fb6ba891", + "61b79e900bfb4b849cfa740236cf6e44", + "57ae83a64b224117a6d01573d3437540", + "e9394568881e456c99758e52813428f6", + "8779121983404d48ab560c1e0d2300f9", + "48550e72c11c4da7985434d6df76df28", + "62c949dbc1714614b00254b3b3ee80c1", + "1c7cdfc1b5ec488dbb08e75b5c89dc0e", + "12eb26078d544f3abe72e91835cc14fc", + "77e3dff7c22343848b9ad097e47b16df", + "cb5a5f4ddfe14e37a6807a74005b01f3", + "9492f5081dd841099c26140b03ef791a", + "86d744b64d604e388d3aadfbbf84eb1c", + "5eda98429678436daa918640f2193bd9", + "2ff3c80449634437b0d88f9a87932b14", + "69a388720e4346e6a98e77c06acb1089", + "22407e9553af4c00a93aa9085ca10b67", + "edd1f2ad4cca4cb093f3d2ab09ebbe85", + "bfb0b78895c34af1abf9a8d669c24aeb", + "356acddfa87f4c1b808245a7880a2ff4", + "f8502cc7258f4efeb43d9e2b0b5e91b2", + "1d645f1f896140158cc30061c0e67080", + "14467a4613e74d819502803825959e5e", + "cdc69d3551bd4bb183d30361b164d0ac", + "17173ad4ef464e9fb4611c2c9c611736", + "edb9212d6a3540b19de36e895b6179e7", + "edada2634ba54b9290fe904edc9905f0", + "1447b1135b994f14866cd17a34124ff6", + "9b8c980b418146e992ec2e5cfde8bf6b", + "45793d371304439db504cb04d4913911", + "2b190420fb764d06bd468c2914773fa8", + "c4829c7eed1541129e4944ad7784b43d", + "74e2c433490840a0a4caa296bd3521f0", + "7d690fb215c64c7a81cdce7156456ca2", + "cd48bd077b9c4a7b822a79fa66b65031", + "b246fdb13dc84c02b266565bb75e621f" + ] + }, + "id": "1BLK8QPhcyMh", + "outputId": "fd0df920-77f7-453f-8b06-3cb35e054467" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/443 [00:00 Date: Mon, 27 May 2024 08:49:48 +0530 Subject: [PATCH 03/16] formatting fixes (#192) --- README.md | 2 +- tutorials/Advace_RAG_LlamaParser/README.md | 9 +- tutorials/Advace_RAG_LlamaParser/main.ipynb | 6915 +++++++++---------- 3 files changed, 3447 insertions(+), 3479 deletions(-) diff --git a/README.md b/README.md index d9cb320..576de81 100644 --- a/README.md +++ b/README.md @@ -109,7 +109,7 @@ Looking to get started with LLMs, vectorDBs, and the world of Generative AI? The | [Fine-Tuning LLM using PEFT & QLoRA](./tutorials/fine-tuning_LLM_with_PEFT_QLoRA) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/fine-tuning_LLM_with_PEFT_QLoRA/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/optimizing-llms-a-step-by-step-guide-to-fine-tuning-with-peft-and-qlora-22eddd13d25b) | | [Context-Aware Chatbot using Llama 2 & LanceDB](./tutorials/chatbot_using_Llama2_&_lanceDB) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755) | | [Better RAG with FLARE](./tutorials/better-rag-FLAIR) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/better-rag-FLAIR/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/) | -| [Extracting Complex PDFs containing tables-text -LlamaParse ](./tutorials/Advace_RAG_LlamaParser) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Advace_RAG_LlamaParser/main.ipynb)[![LLM](https://img.shields.io/badge/openai-api-white)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | +| [Extracting Complex tables-text from PDFs using LlamaParse ](./tutorials/Advace_RAG_LlamaParser) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Advace_RAG_LlamaParser/main.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![LlamaCloud](https://img.shields.io/badge/Llama-api-pink)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | diff --git a/tutorials/Advace_RAG_LlamaParser/README.md b/tutorials/Advace_RAG_LlamaParser/README.md index 53730af..50a5d25 100644 --- a/tutorials/Advace_RAG_LlamaParser/README.md +++ b/tutorials/Advace_RAG_LlamaParser/README.md @@ -1,10 +1,9 @@ - ## Advanced RAG: Extracting Complex PDFs containing tables & Text Using LlamaParse [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Advace_RAG_LlamaParser/main.ipynb) -This repository contains code and examples for comparing LangChain, LlamaIndex, and LlamaParse in extracting data from PDFs, especially those with complex tables and text. +This example contains code and examples for comparing LangChain, LlamaIndex, and LlamaParse in extracting data from PDFs, especially those with complex tables and text. -Overview +### Overview In this project, we explore: * Q&A on PDF data using LangChain @@ -13,7 +12,7 @@ In this project, we explore: * Q&A on PDF data using LlamaIndex with LlamaParse -The results of each method are compared & code is available in our collab notebook - +The results of each method are compared in colab notebook +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Advace_RAG_LlamaParser/main.ipynb) diff --git a/tutorials/Advace_RAG_LlamaParser/main.ipynb b/tutorials/Advace_RAG_LlamaParser/main.ipynb index eea1220..c303d0f 100644 --- a/tutorials/Advace_RAG_LlamaParser/main.ipynb +++ b/tutorials/Advace_RAG_LlamaParser/main.ipynb @@ -1,3560 +1,3529 @@ { - "cells": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "7wD8dJo-WZH7" + }, + "source": [ + "This notebook compares Langchain & Llamaindex for understand which method is best extraction of table & text from PDF in the following\n", + "\n", + "\n", + "Here we have covered\n", + "\n", + "1. Langchain RAG\n", + "2. Llamaindex RAG\n", + "3. Langchain wiht llamaparser\n", + "4. Llamaindex with llamaparser\n", + "\n", + "\n", + "from above this method will get idea about which is best method for table extraction for the following data used\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HGcMcXLF7zoM", + "outputId": "b4f6876b-0531-4bce-c1a6-1615c77322a2" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "This notebook is compairsion of langchain & lamaindex for better extraction of pdf with table & text\n", - "\n", - "\n", - "here we have coverd\n", - "\n", - "1.langchain RAG\n", - "2.Lamaindex RAG\n", - "\n", - "3.Langchain wiht lamaparser\n", - "4.Lamaindex with lamaparser\n", - "\n", - "\n", - "from above this methid will get idea about which is our best method for table extraction\n" - ], - "metadata": { - "id": "7wD8dJo-WZH7" - } + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting llama-index\n", + " Downloading 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llama-index llama-index-core llama-index-embeddings-openai llama-parse\n", + "%pip install llama-index-postprocessor-flag-embedding-reranker\n", + "%pip install git+https://github.com/FlagOpen/FlagEmbedding.git\n", + "%pip install llama-index-vector-stores-lancedb\n", + "%pip install --upgrade --quiet langchain langchain-community langchainhub langchain-openai langchain-chroma bs4 lancedb\n", + "%pip install unstructured" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NqK1g8Bg7zlZ" + }, + "outputs": [], + "source": [ + "# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio\n", + "import os\n", + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()\n", + "\n", + "# API access to llama-cloud\n", + "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-...\"\n", + "# Using OpenAI API for embeddings/llms\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4OmWRDtAKONC" + }, + "source": [ + "### Download the PDF (contains both tables & text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "smCjT2FIj9Fo" + }, + "outputs": [], + "source": [ + "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10q/uber_10q_march_2022.pdf' -O './uber_10q_march_2022.pdf'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1I--ouSiTGvj" + }, + "source": [ + "# 1. Langchain with Q&A on PDF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4ysMDhHiR2bG" + }, + "outputs": [], + "source": [ + "import bs4\n", + "from langchain import hub\n", + "from langchain_community.document_loaders import WebBaseLoader\n", + "from langchain_openai import ChatOpenAI\n", + "from langchain_community.document_loaders import PyPDFLoader\n", + "from langchain.vectorstores import LanceDB\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 }, + "id": "4emkzsCqSMTe", + "outputId": "d122f7f4-9072-4a4f-acf8-3d3b39755328" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - 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"Installing collected packages: ratelimiter, semver, py, overrides, deprecation, retry, pylance, lancedb, llama-index-vector-stores-lancedb\n", - "Successfully installed deprecation-2.1.0 lancedb-0.5.7 llama-index-vector-stores-lancedb-0.1.3 overrides-7.7.0 py-1.11.0 pylance-0.9.18 ratelimiter-1.2.0.post0 retry-0.9.2 semver-3.0.2\n" - ] - } - ], - "source": [ - "%pip install llama-index llama-index-core llama-index-embeddings-openai llama-parse\n", - "%pip install llama-index-postprocessor-flag-embedding-reranker\n", - "%pip install git+https://github.com/FlagOpen/FlagEmbedding.git\n", - "%pip install llama-index-vector-stores-lancedb\n", - "%pip install --upgrade --quiet langchain langchain-community langchainhub langchain-openai langchain-chroma bs4 lancedb\n", - "%pip install unstructured" + "text/plain": [ + "'The net loss value attributable to Uber Technologies, Inc. for the period was $5.9 billion, compared to $108 million in the same period the previous year. This represents a significant increase in net loss year-over-year.'" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n", + "\n", + "loader = PyPDFLoader(\"/content/uber_10q_march_2022.pdf\")\n", + "docs = loader.load()\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", + "splits = text_splitter.split_documents(docs)\n", + "vectorstore = LanceDB.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n", + "\n", + "# Retrieve and generate using the relevant snippets of the blog.\n", + "retriever = vectorstore.as_retriever()\n", + "prompt = hub.pull(\"rlm/rag-prompt\")\n", + "\n", + "\n", + "def format_docs(docs):\n", + " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", + "\n", + "\n", + "rag_chain = (\n", + " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n", + " | prompt\n", + " | llm\n", + " | StrOutputParser()\n", + ")\n", + "\n", + "qa_langchain_query1 = (\n", + " \" what is the net loss value attributable to Uber compared to last year?\"\n", + ")\n", + "rag_chain.invoke(qa_langchain_query1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 }, + "id": "G4qR6GxTSMWO", + "outputId": "38fff0fe-377f-4b10-aa4f-362712e539d9" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NqK1g8Bg7zlZ" + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" }, - "outputs": [], - "source": [ - "# llama-parse is async-first, running the async code in a notebook requires the use of nest_asyncio\n", - "import os\n", - "import nest_asyncio\n", - "nest_asyncio.apply()\n", - "\n", - "\n", - "\n", - "# API access to llama-cloud\n", - "os.environ[\"LLAMA_CLOUD_API_KEY\"] = \"llx-\"\n", - "# Using OpenAI API for embeddings/llms\n", - "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-\"\n", - "\n", - "\n" + "text/plain": [ + "\"I don't know.\"" ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qa_langchain_query2 = \"how is the Cash paid for Income taxes, net of refunds from Supplemental disclosures of cash flow information?\"\n", + "rag_chain.invoke(qa_langchain_query2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 }, + "id": "b3DM-lCnSMZG", + "outputId": "4c97e6f3-d610-447f-ba4f-62715ae274a1" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "### Download the pdf. it has both tables & text ." - ], - "metadata": { - "id": "4OmWRDtAKONC" - } - }, - { - "cell_type": "code", - "source": [ - "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10q/uber_10q_march_2022.pdf' -O './uber_10q_march_2022.pdf'" - ], - "metadata": { - "id": "smCjT2FIj9Fo" + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# 1. Langchain with Q&A on PDF" - ], - "metadata": { - "id": "1I--ouSiTGvj" - } + "text/plain": [ + "\"I don't have detailed charts of intangible assets, net as of December 31, 2021 and March 31, 2022.\"" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qa_langchain_query3 = \"give me detailed charts of intangible assets, net as of December 31, 2021 and March 31, 2022\"\n", + "rag_chain.invoke(qa_langchain_query3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pwZzEShGwwvT" + }, + "source": [ + "FOR QUERY 2 & QUERY 3 we are not getting the answer\n", + "\n", + "**LETS TRY LLAMAINDEX**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GVD2sPBEcRE3" + }, + "source": [ + "# 2 . Llamaindex with Q&A on PDF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "H__qJIWYdmgY" + }, + "outputs": [], + "source": [ + "import textwrap\n", + "from llama_index.vector_stores.lancedb import LanceDBVectorStore\n", + "from llama_index.core import SimpleDirectoryReader, Document, StorageContext\n", + "from llama_index.core import VectorStoreIndex\n", + "from llama_index.llms.openai import OpenAI\n", + "from llama_index.embeddings.openai import OpenAIEmbedding\n", + "from llama_index.core import VectorStoreIndex\n", + "from llama_index.core import SimpleDirectoryReader\n", + "from llama_index.postprocessor.flag_embedding_reranker import (\n", + " FlagEmbeddingReranker,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BCNNrAw9Dklk" + }, + "outputs": [], + "source": [ + "from llama_index.vector_stores.lancedb import LanceDBVectorStore\n", + "\n", + "vector_store_pdf = LanceDBVectorStore(uri=\"/tmp/lancedb_lamaindex\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 393, + "referenced_widgets": [ + "0ab0af38e5c54405a48fd40de7bfe606", + "17300f8bea7644f09907376fab719f92", + "f2bbc601684446b5be521833592a319f", + "326bb35286ba4d23a5e3b8b364c7a44f", + "d19be0dd23344eb2887605d88f80c142", + "2e3f44b38e6842a9bd9db4c5e6cc6bcf", + "738a133fb9204208913431c2a35fef5c", + "d407aa9ec48347d9b5536a69fe9f5dc7", + "d779e07ce42c4f088dea05f8fd22922f", + "1700dc0afbb14e4cb85da9eedfca8605", + "498b64a4f33046c9a65b5a5486376359", + "f7de0a6aa31c41de90f45e862fe4ce15", + "a034635cd3d94f8b89b56e6b972daa63", + "5bec1c5f11434bf481862e67f73403f0", + "bd254eea803c4e44b60783f05c5c1ca1", + "1bbdd18be2e74c7f8916c95a1e5cf511", + "0f2a76e74d74459182402f92fbb946c9", + "d3d91529e30a4d67881a782d7eb54060", + "b69deb8741ad47229438548949e2da42", + "f894d72c0e19494abb732b2db46ced98", + "6fe509c6157e4ddbb1cfcf319cf04fac", + "de859b9a94684d39ac8a7aef81058a3b", + "09f4df92faa844b28fef90c3620b09e1", + "d0a4ce2181c84af8b277058689a35641", + "faaff37bf60e4870a9cbebd20d3afc6e", + "f55e2cf0eb34482da1902adb652fca5a", + "55ee9cd902c147ae97c4deea101f25e8", + "f3f8c3dc8a404bd4af55b105f98267d7", + "e13aaa64b9c848f9a67f61295fb546fe", + "2a6b2dacce79419b9a2469b6362abca8", + "3c63c0a7eac54b0fb72775e3fb6ba891", + "61b79e900bfb4b849cfa740236cf6e44", + "57ae83a64b224117a6d01573d3437540", + "e9394568881e456c99758e52813428f6", + "8779121983404d48ab560c1e0d2300f9", + "48550e72c11c4da7985434d6df76df28", + "62c949dbc1714614b00254b3b3ee80c1", + "1c7cdfc1b5ec488dbb08e75b5c89dc0e", + "12eb26078d544f3abe72e91835cc14fc", + "77e3dff7c22343848b9ad097e47b16df", + "cb5a5f4ddfe14e37a6807a74005b01f3", + "9492f5081dd841099c26140b03ef791a", + "86d744b64d604e388d3aadfbbf84eb1c", + "5eda98429678436daa918640f2193bd9", + "2ff3c80449634437b0d88f9a87932b14", + "69a388720e4346e6a98e77c06acb1089", + "22407e9553af4c00a93aa9085ca10b67", + "edd1f2ad4cca4cb093f3d2ab09ebbe85", + "bfb0b78895c34af1abf9a8d669c24aeb", + "356acddfa87f4c1b808245a7880a2ff4", + "f8502cc7258f4efeb43d9e2b0b5e91b2", + "1d645f1f896140158cc30061c0e67080", + "14467a4613e74d819502803825959e5e", + "cdc69d3551bd4bb183d30361b164d0ac", + "17173ad4ef464e9fb4611c2c9c611736", + "edb9212d6a3540b19de36e895b6179e7", + "edada2634ba54b9290fe904edc9905f0", + "1447b1135b994f14866cd17a34124ff6", + "9b8c980b418146e992ec2e5cfde8bf6b", + "45793d371304439db504cb04d4913911", + "2b190420fb764d06bd468c2914773fa8", + "c4829c7eed1541129e4944ad7784b43d", + "74e2c433490840a0a4caa296bd3521f0", + "7d690fb215c64c7a81cdce7156456ca2", + "cd48bd077b9c4a7b822a79fa66b65031", + "b246fdb13dc84c02b266565bb75e621f" + ] }, + "id": "1BLK8QPhcyMh", + "outputId": "fd0df920-77f7-453f-8b06-3cb35e054467" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "import bs4\n", - "from langchain import hub\n", - "from langchain_community.document_loaders import WebBaseLoader\n", - "from langchain_openai import ChatOpenAI\n", - "from langchain_community.document_loaders import PyPDFLoader\n", - "from langchain.vectorstores import LanceDB\n", - "from langchain_core.output_parsers import StrOutputParser\n", - "from langchain_core.runnables import RunnablePassthrough\n", - "from langchain_openai import OpenAIEmbeddings\n", - "from langchain_text_splitters import RecursiveCharacterTextSplitter" - ], - "metadata": { - "id": "4ysMDhHiR2bG" - }, - "execution_count": null, - "outputs": [] + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "code", - "source": [ - "\n", - "\n", - "llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\")\n", - "\n", - "loader = PyPDFLoader(\"/content/uber_10q_march_2022.pdf\")\n", - "docs = loader.load()\n", - "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", - "splits = text_splitter.split_documents(docs)\n", - "vectorstore = LanceDB.from_documents(documents=splits, embedding=OpenAIEmbeddings())\n", - "\n", - "# Retrieve and generate using the relevant snippets of the blog.\n", - "retriever = vectorstore.as_retriever()\n", - "prompt = hub.pull(\"rlm/rag-prompt\")\n", - "\n", - "def format_docs(docs):\n", - " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", - "\n", - "\n", - "rag_chain = (\n", - " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n", - " | prompt\n", - " | llm\n", - " | StrOutputParser()\n", - ")\n", - "\n", - "qa_langchain_query1 = \" what is the net loss value attributable to Uber compared to last year?\"\n", - "rag_chain.invoke(qa_langchain_query1)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 54 - }, - "id": "4emkzsCqSMTe", - "outputId": "d122f7f4-9072-4a4f-acf8-3d3b39755328" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0ab0af38e5c54405a48fd40de7bfe606", + "version_major": 2, + "version_minor": 0 }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'The net loss value attributable to Uber Technologies, Inc. for the period was $5.9 billion, compared to $108 million in the same period the previous year. This represents a significant increase in net loss year-over-year.'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 9 - } + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/443 [00:00 Date: Mon, 27 May 2024 13:41:36 +0530 Subject: [PATCH 04/16] updated image (#193) --- assets/superagent-autogen.png | Bin 107325 -> 77846 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/assets/superagent-autogen.png b/assets/superagent-autogen.png index d41d2484f1cf1c9ea2ec935b7ef1ff6d6cf0b84c..9908c624b19ebecd57b44328729b3ed146faa159 100644 GIT binary patch literal 77846 zcmeFZRX|kT8#ihZlG5F+NOvmTA&qo*cM1qdcSwk|3<3f&ba#t1Lw6(H@U40O=es+1 z=k|ERfE)InwVw6FFD61oNg5r680E>6C+M;=5+9yCc~1M}$`1yp)RYFKirKlx*w*9DD-o+>|Pcm|0)CpFE*_A}jG; z-79N#sF`@z(CKh&K|HA*KJ?GJv}Krbwd#}B?49@WEc zd9#)76)kPbG(XnPReCsg>c7KkN5GBGel-;z|2f`Y@Yb{bk&iz->D4Q|=&;e#F`GXA zr2}S{=GJL15M7W2_!;<)-G(;)f3K*dVqReX 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z-++6YaY#LMbcL|}NO+^!@;pB#>P-j3zDiJsEfyJ*&|mJM?J3VpIERwn!O#5~ULI)cgvJjgs;4{w!PFJBiH+5M`h z_*qw{X`Geqp&DUzCsWxGE6WxQ~qRQ{n?vyZCwgCj4G0{d?&>*fyrT!!do{{7`r727XKOh1es?_!h99*52cjnA5jeOk0(g~OXWrQ xf9c1FWBo75nE(G@9;AUDuH^rNE7p3Ctox;Uf;WQQ2MPR Date: Mon, 27 May 2024 14:10:20 +0530 Subject: [PATCH 05/16] imagebind demo img (#194) --- README.md | 2 +- assets/imagebind-demo.png | Bin 0 -> 446589 bytes examples/imagebind_demo/README.md | 2 ++ 3 files changed, 3 insertions(+), 1 deletion(-) create mode 100644 assets/imagebind-demo.png diff --git a/README.md b/README.md index 576de81..017253e 100644 --- a/README.md +++ b/README.md @@ -64,7 +64,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [SuperAgent Autogen](/examples/SuperAgent_Autogen) |Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|| [Sentiment Analysis : Analysing Hotel Reviews](/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6)| | [Facial Recognition](./examples/facial_recognition) | Open In Colab [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| -| [Imagebind demo app](/examples/imagebind_demo/) | hf spaces [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| +| [Imagebind demo app](./examples/imagebind_demo/) | hf spaces 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zZ;sEZPOs-#&8c#^^IJir_o`Av7$u}{C?Rd=|9|!QIlMaNdO+59=XhQ{EfG*3WU-pi z1lAmwHE_)yZ97ExttW=_$$J9Xr^&K4cv?r#5q_q*WfXD4%JaS5v{#VXXNZ@=$5{Vt zBZxfyf2rl0(`JnaZI}IC7f(7E%|Yk8*x%`-^VP>t*Q*yfV&w)uP%4P`_lEh`h|S1I zuJwNnMZHG-Bi*R}?teV~F-#65eI@+&{^kg{$?2u#I$*eNTssi|=y&?xCmwYe1>#q6 zkH1N1JO>q;1~ruHqy01H!{yDB-Rtdm%Rko_+Qp|~#d`k?lJv>2b`;3Bljs3K0sg5l zLoS$zi1@~I&c93z>Z*?l3F_0WhEu4Y{wATB>GyRr&jJmuCkUSPYUl4sKG9I2o_P@5 z;2x|wT@Y{zKUG5h%Ha@AoaRou#LPAgtlhP!tKzg$^)BZ@k(2$aeRDR*;F82%Jirs6 z&}cE7+NdPYe5jzxECHwqlET!cJ~YGRi`JE5W`Eu4f!1IA$8012 zoV;p;escY1<{la07k~FVyjlO}8pMlTz~6^A*9)nz{_g)2O@sd3{ZFxLCOrB3_|dDe e|8w#w) + ## Usage you can run it locally by cloning the project as mentioned below, or access via Spaces: hf spaces From 418124f8174c5ac6118d8195df125d16de065160 Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Tue, 28 May 2024 11:56:17 +0530 Subject: [PATCH 06/16] updated autogen notebook (#195) --- examples/SuperAgent_Autogen/main.ipynb | 332 ++++++++---------- examples/parent_document_retriever/main.ipynb | 6 +- 2 files changed, 151 insertions(+), 187 deletions(-) diff --git a/examples/SuperAgent_Autogen/main.ipynb b/examples/SuperAgent_Autogen/main.ipynb index 9278654..13047b0 100644 --- a/examples/SuperAgent_Autogen/main.ipynb +++ b/examples/SuperAgent_Autogen/main.ipynb @@ -11,55 +11,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3poVgyh-bZJ-", - "outputId": "ad799a6e-7eec-4e14-dae3-f7e86c9e67cc" + "outputId": "7244e7cf-8eca-481d-fd05-d21a82460d47" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.8/88.8 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m811.8/811.8 kB\u001b[0m \u001b[31m9.3 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"%pip install pyautogen~=0.1.0 langchain openai tiktoken lancedb pypdf -q -U" + "%pip install pyautogen~=0.1.0 langchain langchain_community openai tiktoken lancedb pypdf -q -U" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "id": "0tLTTT9ucFEb" }, "outputs": [], "source": [ - "from langchain.embeddings import OpenAIEmbeddings\n", + "from langchain_community.embeddings import OpenAIEmbeddings\n", "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "from langchain.document_loaders import PyPDFLoader\n", "from langchain.memory import ConversationBufferMemory\n", @@ -80,60 +78,88 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { - "id": "6RuVu12whCG0" + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6RuVu12whCG0", + "outputId": "1300d37d-65ae-4e24-cbfb-d1cc701db28d" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pyautogen in /usr/local/lib/python3.10/dist-packages (0.1.14)\n", + "Requirement already satisfied: diskcache in /usr/local/lib/python3.10/dist-packages (from pyautogen) (5.6.3)\n", + "Requirement already satisfied: flaml in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.1.2)\n", + "Requirement already satisfied: openai<1 in /usr/local/lib/python3.10/dist-packages (from pyautogen) (0.28.1)\n", + "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from pyautogen) (1.0.1)\n", + "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.4.0)\n", + "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (2.31.0)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (4.66.4)\n", + "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (3.9.5)\n", + "Requirement already satisfied: NumPy>=1.17 in /usr/local/lib/python3.10/dist-packages (from flaml->pyautogen) (1.25.2)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.7)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (2.0.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (2024.2.2)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.3.1)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (23.2.0)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.4.1)\n", + "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (6.0.5)\n", + "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.9.4)\n", + "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (4.0.3)\n" + ] + } + ], "source": [ "!pip install pyautogen" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": { "id": "sUFdvTyVh8xF" }, "outputs": [], "source": [ "import lancedb\n", + "import os\n", + "\n", + "# setup OPENAI API KEY\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-....\"\n", "\n", - "embeddings = OpenAIEmbeddings(openai_api_key=\"sk-yourapikey\")" + "embeddings = OpenAIEmbeddings()" ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "kztlyFIXU8m-" - }, - "source": [] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ODKg12trdhX-", - "outputId": "a7041322-f633-496c-a8e8-126a81cbb5d9" + "outputId": "4736d14d-454c-4458-98c4-d2d0d2edceb9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "--2024-02-11 04:40:16-- https://pdf.usaid.gov/pdf_docs/PA00TBCT.pdf\n", - "Resolving pdf.usaid.gov (pdf.usaid.gov)... 23.7.61.67, 2600:1408:ec00:380::1923, 2600:1408:ec00:38f::1923\n", - "Connecting to pdf.usaid.gov (pdf.usaid.gov)|23.7.61.67|:443... connected.\n", + "--2024-05-28 05:17:50-- https://pdf.usaid.gov/pdf_docs/PA00TBCT.pdf\n", + "Resolving pdf.usaid.gov (pdf.usaid.gov)... 23.4.180.157, 2600:1408:5400:197::1923, 2600:1408:5400:183::1923\n", + "Connecting to pdf.usaid.gov (pdf.usaid.gov)|23.4.180.157|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 6419525 (6.1M) [application/pdf]\n", "Saving to: ‘food.pdf’\n", "\n", - "food.pdf 100%[===================>] 6.12M --.-KB/s in 0.1s \n", + "food.pdf 100%[===================>] 6.12M 27.0MB/s in 0.2s \n", "\n", - "2024-02-11 04:40:16 (42.7 MB/s) - ‘food.pdf’ saved [6419525/6419525]\n", + "2024-05-28 05:17:51 (27.0 MB/s) - ‘food.pdf’ saved [6419525/6419525]\n", "\n" ] } @@ -143,14 +169,40 @@ "!wget -O food.pdf https://pdf.usaid.gov/pdf_docs/PA00TBCT.pdf" ] }, + { + "cell_type": "markdown", + "metadata": { + "id": "yV0pNPiRPy8h" + }, + "source": [ + "# create file name with OAI_CONFIG_LIT.json" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "yWDhjTDMcFBi" + }, + "outputs": [], + "source": [ + "# create file name with OAI_CONFIG_LIT.\n", + "import json\n", + "\n", + "config = [{\"model\": \"gpt-4\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n", + "\n", + "with open(\"OAI_CONFIG_LIT.json\", \"w\") as fp:\n", + " json.dump(config, fp)" + ] + }, { "cell_type": "markdown", "metadata": { "id": "1oC3NAFyd4Kb" }, "source": [ - "create OAI_CONFIG_LIST.json file in pwd & upload\n", - "in it\n", + "**create OAI_CONFIG_LIST.json file in pwd & upload\n", + "in it**\n", "\n", "\n", "[\n", @@ -163,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": { "id": "H1bRXWu-cE_C" }, @@ -179,30 +231,9 @@ ")" ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "yV0pNPiRPy8h" - }, - "source": [ - "# create file name with OAI_CONFIG_LIT.json & put below authentications code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "yWDhjTDMcFBi" - }, - "outputs": [], - "source": [ - "# create file name with OAI_CONFIG_LIT.\n", - "[{\"model\": \"gpt-4\", \"api_key\": \"sk-yourapikey\"}]" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": { "id": "5gapqmsscFG-" }, @@ -218,70 +249,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5dLkCqa0dLXV", - "outputId": "28ab5984-c875-4281-95e5-d48bfdd12e99" + "id": "5dLkCqa0dLXV" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.embeddings.openai.OpenAIEmbeddings` was deprecated in langchain-community 0.1.0 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAIEmbeddings`.\n", - " warn_deprecated(\n" - ] - } - ], + "outputs": [], "source": [ "import lancedb\n", "\n", - "embeddings = OpenAIEmbeddings(openai_api_key=\"sk-yourapikey\")\n", - "\n", - "db = lancedb.connect(\"/tmp/lancedb\")\n", - "table = db.create_table(\n", - " \"my_table\",\n", - " data=[\n", - " {\n", - " \"vector\": embeddings.embed_query(\"Hello food\"),\n", - " \"text\": \"Hello food\",\n", - " \"id\": \"1\",\n", - " }\n", - " ],\n", - " mode=\"overwrite\",\n", - ")\n", + "embeddings = OpenAIEmbeddings()\n", "\n", - "vectorstore = LanceDB.from_documents(docs, embeddings, connection=table)" + "vectorstore = LanceDB.from_documents(documents=docs, embedding=embeddings)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YMBoF5kucFMJ", - "outputId": "13d7edab-5f3d-4698-fe6f-40f33dcd865a" + "id": "YMBoF5kucFMJ" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.llms.openai.OpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAI`.\n", - " warn_deprecated(\n" - ] - } - ], + "outputs": [], "source": [ "qa = ConversationalRetrievalChain.from_llm(\n", " OpenAI(\n", " temperature=0,\n", - " openai_api_key=\"sk-yourapikey\",\n", " ),\n", " vectorstore.as_retriever(),\n", " memory=ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True),\n", @@ -290,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": { "id": "HjSVygLIcSEX" }, @@ -303,34 +294,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 160 + "height": 90 }, "id": "XCqxSaQSepsW", - "outputId": "c1fc1bdc-9f2e-467b-cb51-fdde3fc964ae" + "outputId": "309a74fe-05c6-4d78-ad3e-e720b77d6e3c" }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n", - " warn_deprecated(\n" - ] - }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ - "' Good food is food that provides the recommended amounts of nutrients for the body to perform all its physiological activities. It is important to eat the right food, at the right time, in the right amounts, and prepared correctly in order to maintain a balanced diet and promote good nutrition. Good food is essential for physical and cognitive development and can improve overall health and quality of life.'" + "' Good food is any type of food that provides the recommended amounts of nutrients for the body to perform its physiological activities. It should be eaten at the right time, in the right amounts, and prepared correctly. Good food is important for physical and cognitive development, and can help prevent health problems. Foods can also be classified according to their functions in the body, such as energy-giving foods, body-building foods, and protective foods.'" ] }, - "execution_count": 18, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -342,13 +325,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BY4Fz-l7cUCA", - "outputId": "3a10b926-3659-46d3-d76b-7e083daf8fca" + "outputId": "e90c9b52-ab8e-4176-e249-fba0f20cae68" }, "outputs": [ { @@ -357,16 +340,16 @@ "text": [ "Requirement already satisfied: pyautogen in /usr/local/lib/python3.10/dist-packages (0.1.14)\n", "Requirement already satisfied: diskcache in /usr/local/lib/python3.10/dist-packages (from pyautogen) (5.6.3)\n", - "Requirement already satisfied: flaml in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.1.1)\n", + "Requirement already satisfied: flaml in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.1.2)\n", "Requirement already satisfied: openai<1 in /usr/local/lib/python3.10/dist-packages (from pyautogen) (0.28.1)\n", "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from pyautogen) (1.0.1)\n", "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.4.0)\n", "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (2.31.0)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (4.66.1)\n", - "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (3.9.3)\n", - "Requirement already satisfied: NumPy>=1.17.0rc1 in /usr/local/lib/python3.10/dist-packages (from flaml->pyautogen) (1.23.5)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (4.66.4)\n", + "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (3.9.5)\n", + "Requirement already satisfied: NumPy>=1.17 in /usr/local/lib/python3.10/dist-packages (from flaml->pyautogen) (1.25.2)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.3.2)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.6)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.7)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (2024.2.2)\n", "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.3.1)\n", @@ -393,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": { "id": "Vca8Y_khcUID" }, @@ -425,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": { "id": "1XHjzIYAcfE7" }, @@ -451,13 +434,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hOZKxakHchZ4", - "outputId": "7ca36fe0-a211-409a-abb0-57e3cd05e429" + "outputId": "20693c38-773a-47d2-bfc4-8311c7ef4d1d" }, "outputs": [ { @@ -475,7 +458,6 @@ "\n", "***** Suggested function Call: answer_food_question *****\n", "Arguments: \n", - "\n", "{\n", " \"question\": \"what is good food?\"\n", "}\n", @@ -487,13 +469,13 @@ "user_proxy (to assistant):\n", "\n", "***** Response from calling function \"answer_food_question\" *****\n", - " Good food is food that is able to provide the recommended amounts of nutrients for the body to perform all its physiological activities. It is important for our health and well-being because it helps us maintain a balanced diet, promotes physical and cognitive development, and protects us from foodborne illnesses. Good food also ensures that we have enough energy for physical activity and basic body functions, and it helps us maintain a healthy weight. Additionally, good food can improve our overall quality of life and productivity.\n", + " Good food is food that is able to provide the recommended amounts of nutrients for the body to perform all its physiological activities. It is important because it helps with physical and cognitive development, promotes good health, and improves the quality of life. Good food should be eaten at the right time, in the right amounts, and prepared correctly. It can also be classified into different categories based on its function in the body, such as energy-giving foods, body-building foods, and protective foods.\n", "*****************************************************************\n", "\n", "--------------------------------------------------------------------------------\n", "assistant (to user_proxy):\n", "\n", - "Good food is food that is able to provide the recommended amounts of nutrients for the body to perform all its physiological activities. It is important for our health and well-being because it helps us maintain a balanced diet, promotes physical and cognitive development, and protects us from foodborne illnesses. Good food also ensures that we have enough energy for physical activity and basic body functions, and it helps us maintain a healthy weight. Additionally, good food can improve our overall quality of life and productivity.\n", + "Good food is food that provides the recommended amounts of nutrients for the body to perform all its physiological activities. It is important because it helps with physical and cognitive development, promotes good health, and improves the quality of life. Good food should be eaten at the right time, in the right amounts, and prepared correctly. It can also be classified into different categories based on its function in the body, such as energy-giving foods, body-building foods, and protective foods.\n", "\n", "TERMINATE\n", "\n", @@ -518,13 +500,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UDXo2V06fNjz", - "outputId": "37cf6766-9b68-4a81-e0c6-245d2af28a30" + "outputId": "057425b5-f0c1-4132-cdc6-fd03197e95c3" }, "outputs": [ { @@ -549,26 +531,26 @@ " - Deficiency Symptoms: Osteoporosis, rickets in children, muscle cramps, dental problems.\n", "\n", "2. Iron:\n", - " - Sources: Red meat, poultry, eggs, fruits, green vegetables, fortified bread.\n", - " - Functions: Essential for the production of red blood cells, helps in oxygen transport.\n", - " - Deficiency Symptoms: Anemia, fatigue, weakness, immune system problems.\n", + " - Sources: Red meat, poultry, fish, legumes, fortified cereals.\n", + " - Functions: Essential for the production of red blood cells, carries oxygen in the blood.\n", + " - Deficiency Symptoms: Anemia, fatigue, weakness, pale skin, shortness of breath.\n", "\n", - "3. Magnesium:\n", - " - Sources: Nuts, seeds, whole grains, green leafy vegetables, fish, beans, yogurt.\n", - " - Functions: Helps in over 300 enzyme reactions, including regulation of blood pressure, supports immune system.\n", - " - Deficiency Symptoms: Loss of appetite, nausea, fatigue, weakness, muscle cramps, numbness.\n", + "3. Potassium:\n", + " - Sources: Bananas, oranges, cantaloupes, raisins, nuts, fish, chicken, beef, and pork.\n", + " - Functions: Helps maintain fluid balance, nerve transmission, muscle contractions.\n", + " - Deficiency Symptoms: Weakness, fatigue, muscle cramps, constipation.\n", "\n", - "4. Potassium:\n", - " - Sources: Bananas, oranges, cantaloupe, honeydew, apricots, grapefruit, cooked spinach, cooked broccoli, potatoes, sweet potatoes, mushrooms, peas, cucumbers, zucchini, eggplant, pumpkins, leafy greens.\n", - " - Functions: Maintains fluid balance, helps in nerve transmission and muscle contraction.\n", - " - Deficiency Symptoms: Fatigue, weakness, constipation, muscle cramps.\n", + "4. Magnesium:\n", + " - Sources: Green leafy vegetables, nuts, seeds, whole grains, fish.\n", + " - Functions: Involved in over 300 enzymatic reactions in the body including energy production, protein synthesis, muscle and nerve function.\n", + " - Deficiency Symptoms: Loss of appetite, nausea, fatigue, weakness, muscle cramps, numbness and tingling.\n", "\n", "5. Zinc:\n", - " - Sources: Meat, shellfish, legumes, seeds, nuts, dairy, eggs, whole grains.\n", - " - Functions: Necessary for immune function, protein synthesis, DNA synthesis, cell division, wound healing.\n", - " - Deficiency Symptoms: Growth retardation, loss of appetite, impaired immune function, hair loss, diarrhea, delayed sexual maturation.\n", + " - Sources: Meat, shellfish, legumes, seeds, nuts, dairy, eggs.\n", + " - Functions: Supports immune function, protein synthesis, wound healing, DNA synthesis, and cell division.\n", + " - Deficiency Symptoms: Loss of appetite, impaired immune function, hair loss, diarrhea, delayed sexual maturation.\n", "\n", - "Please note that this is not an exhaustive list and there are other essential minerals as well. Also, the symptoms of deficiency can vary from person to person and can often be symptoms of other conditions as well. Always consult with a healthcare provider for accurate information.\n", + "Please note that this is not an exhaustive list and there are many other essential minerals that the body needs. It's also important to remember that while these minerals are essential for health, they should be consumed in moderation as too much can also lead to health problems. Always consult with a healthcare provider or a registered dietitian for personalized advice.\n", "\n", "--------------------------------------------------------------------------------\n", "user_proxy (to assistant):\n", @@ -592,13 +574,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UrlFGYW0g0sJ", - "outputId": "e82396e5-3dca-402c-889c-394557aeea0d" + "outputId": "0fbf3ed8-0103-4904-9737-2a41e6e1cf0d" }, "outputs": [ { @@ -628,22 +610,13 @@ "user_proxy (to assistant):\n", "\n", "***** Response from calling function \"answer_food_question\" *****\n", - " Foods that are rich in Vitamin A, such as yellow/orange fruits and vegetables, dark green and deep yellow fruits and vegetables, liver, egg yolk, dairy products, and margarine can help maintain healthy eyes.\n", + " Fruits and vegetables, particularly dark green leafy vegetables and yellow fruits, are considered protective and can help keep eyes healthy.\n", "*****************************************************************\n", "\n", "--------------------------------------------------------------------------------\n", "assistant (to user_proxy):\n", "\n", - "Foods that are rich in Vitamin A can help maintain healthy eyes. These include:\n", - "\n", - "1. Yellow/orange fruits and vegetables: These include carrots, sweet potatoes, pumpkins, and apricots.\n", - "2. Dark green and deep yellow fruits and vegetables: These include spinach, kale, and other leafy greens.\n", - "3. Liver: This is a great source of Vitamin A.\n", - "4. Egg yolk: This is another good source of Vitamin A.\n", - "5. Dairy products: These include milk, cheese, and yogurt.\n", - "6. Margarine: This is also a good source of Vitamin A.\n", - "\n", - "Including these foods in your diet can help keep your eyes healthy.\n", + "Fruits and vegetables, particularly dark green leafy vegetables and yellow fruits, are considered protective and can help keep eyes healthy. These foods are rich in vitamins A, C, E, and minerals like Copper and Zinc which are essential for eye health. Foods like carrots, sweet potatoes, spinach, kale, and other dark green leafy vegetables; and fish like salmon and tuna are good for eye health.\n", "\n", "TERMINATE\n", "\n", @@ -665,15 +638,6 @@ "\"\"\",\n", ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "En6-kvjcjaid" - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/parent_document_retriever/main.ipynb b/examples/parent_document_retriever/main.ipynb index eead31e..9b7e51a 100644 --- a/examples/parent_document_retriever/main.ipynb +++ b/examples/parent_document_retriever/main.ipynb @@ -109,9 +109,9 @@ "metadata": {}, "outputs": [], "source": [ - "os.environ[\"OPENAI_API_KEY\"] = (\n", - " \"YOUR_API_KEY_HERE\" # NEEDED if you run LLM Experiment below\n", - ")" + "os.environ[\n", + " \"OPENAI_API_KEY\"\n", + "] = \"YOUR_API_KEY_HERE\" # NEEDED if you run LLM Experiment below" ] }, { From ca7afbaacecd4ca8548ec5872d6c5ef60958f00f Mon Sep 17 00:00:00 2001 From: Vipul Maheshwari Date: Tue, 28 May 2024 11:56:30 +0530 Subject: [PATCH 07/16] movie recommendation using Doc2Vec embeddings (#190) --- .../README.md | 9 + ...ommendation_with_doc2vec_and_lancedb.ipynb | 594 ++++++++++++++++++ 2 files changed, 603 insertions(+) create mode 100644 examples/movie-recommendation-with-genres/README.md create mode 100644 examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb diff --git a/examples/movie-recommendation-with-genres/README.md b/examples/movie-recommendation-with-genres/README.md new file mode 100644 index 0000000..f1af0bf --- /dev/null +++ b/examples/movie-recommendation-with-genres/README.md @@ -0,0 +1,9 @@ +# Movie Recommendation using Emebeddings and VectorDB + +![image](https://github.com/vipul-maheshwari/vipul-maheshwari.github.io/blob/main/images/movie-recommendation-using-rag/doc2vec_final.png?raw=true) + +This example provides a comprehensive guide on creating a movie recommendation system by leveraging the power of Embeddings and VectorDB. We'll explore how combining these two techniques can significantly enhance the recommendation experience, addressing key challenges faced by traditional systems. + +Colab walkthrough - Open In Colab + +[Read the Blog Post](https://blog.lancedb.com/movie-recommendation-system-using-lancedb-and-doc2vec/) diff --git a/examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb b/examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb new file mode 100644 index 0000000..fdb29a7 --- /dev/null +++ b/examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb @@ -0,0 +1,594 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "K45xhdPRsZJV" + }, + "source": [ + "# Movie Recommendation System using Doc2vec Embeddings and Vector DB" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XUj6NXD0sdgf" + }, + "source": [ + "This Colab notebook aims to illustrate the process of creating a recommendation system using embeddings and a Vector DB.\n", + "\n", + "This approach involves combining the various movie genres or characteristics of a movie to form Doc2Vec embeddings, which offer a comprehensive portrayal of the movie content.\n", + "\n", + "These embeddings serve dual purposes: they can either be directly inputted into a classification model for genre classification or stored in a VectorDB. By storing embeddings in a VectorDB, efficient retrieval and query search for recommendations become possible at a later stage.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qEa74a_Wtpc7" + }, + "source": [ + "### Installing the relevant dependencies\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hyde90IntuFi" + }, + "outputs": [], + "source": [ + "!pip install torch scikit-learn lancedb nltk gensim lancedb scipy==1.12 kaggle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "shPjHTZbtxTh" + }, + "source": [ + "## Kaggle Configuration and Data Needs\n", + "\n", + "We are using a movies metadata data which is being uploaded on the Kaggle. To download the dataset and use it for our recommendation system, we will need a `kaggle.json` file containing our creds.\n", + "\n", + "You can download the `kaggle.json` file from your Kaggle account settings. Follow these steps and make your life easy.\n", + "\n", + "1. Go to Kaggle and log in to your account.\n", + "2. Navigate to Your Account Settings and click on your profile picture in the top right corner of the page, Now From the dropdown menu, select `Account`.\n", + "3. Scroll down to the `API` section, Click on `Create New API Token`. This will download a file named kaggle.json to your computer.\n", + "\n", + "Once you have the `kaggle.json` file, you need to upload it here on colab data space. After uploading the `kaggle.json` file, run the following code to set up the credentials and download the dataset in `data` directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6Tl2qzgKsWtF" + }, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "\n", + "# Assuming kaggle.json is uploaded to the current directory\n", + "with open('kaggle.json') as f:\n", + " kaggle_credentials = json.load(f)\n", + "\n", + "os.environ['KAGGLE_USERNAME'] = kaggle_credentials['username']\n", + "os.environ['KAGGLE_KEY'] = kaggle_credentials['key']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8va-0of3sU0x" + }, + "outputs": [], + "source": [ + "from kaggle.api.kaggle_api_extended import KaggleApi\n", + "\n", + "# Initialize the Kaggle API\n", + "api = KaggleApi()\n", + "api.authenticate()\n", + "\n", + "# Specify the dataset you want to download\n", + "dataset = 'rounakbanik/the-movies-dataset'\n", + "destination = 'data/'\n", + "\n", + "# Create the destination directory if it doesn't exist\n", + "if not os.path.exists(destination):\n", + " os.makedirs(destination)\n", + "\n", + "# Download the dataset\n", + "api.dataset_download_files(dataset, path=destination, unzip=True)\n", + "\n", + "print(f\"Dataset {dataset} downloaded to {destination}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hBYzad3lrY4e", + "outputId": "5a8f7983-80be-47e0-aa9c-ae4e10495c1e" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1000/1000 [00:00<00:00, 5050.83it/s]\n", + "100%|██████████| 1000/1000 [00:00<00:00, 5161.29it/s]\n", + "100%|██████████| 1000/1000 [00:00<00:00, 5006.18it/s]\n", + "100%|██████████| 1000/1000 [00:00<00:00, 5222.83it/s]\n", + "100%|██████████| 1000/1000 [00:00<00:00, 5216.24it/s]\n", + "100%|██████████| 1000/1000 [00:00<00:00, 5171.35it/s]\n", + "100%|██████████| 1000/1000 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], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.utils.data import DataLoader, TensorDataset\n", + "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n", + "from nltk.tokenize import word_tokenize\n", + "from sklearn.preprocessing import MultiLabelBinarizer\n", + "from sklearn.model_selection import train_test_split\n", + "from tqdm import tqdm\n", + "\n", + "# Read data from CSV file\n", + "movie_data = pd.read_csv('/Users/vipul/Nova/Projects/genre_spectrum/movies_metadata.csv', low_memory=False)\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "\n", + "def preprocess_data(movie_data_chunk):\n", + " tagged_docs = []\n", + " valid_indices = []\n", + " movie_info = []\n", + "\n", + " # Wrap your loop with tqdm\n", + " for i, row in tqdm(movie_data_chunk.iterrows(), total=len(movie_data_chunk)):\n", + " try:\n", + " # Constructing movie text\n", + " movies_text = ''\n", + " movies_text += \"Overview: \" + row['overview'] + '\\n'\n", + " genres = ', '.join([genre['name'] for genre in eval(row['genres'])])\n", + " movies_text += \"Overview: \" + row['overview'] + '\\n'\n", + " movies_text += \"Genres: \" + genres + '\\n'\n", + " movies_text += \"Title: \" + row['title'] + '\\n'\n", + " tagged_docs.append(TaggedDocument(words=word_tokenize(movies_text.lower()), tags=[str(i)]))\n", + " valid_indices.append(i)\n", + " movie_info.append((row['title'], genres))\n", + " except Exception as e:\n", + " continue\n", + "\n", + " return tagged_docs, valid_indices, movie_info\n", + "\n", + "def train_doc2vec_model(tagged_data, num_epochs=20):\n", + " # Initialize Doc2Vec model\n", + " doc2vec_model = Doc2Vec(vector_size=100, min_count=2, epochs=num_epochs)\n", + " doc2vec_model.build_vocab(tqdm(tagged_data, desc=\"Building Vocabulary\"))\n", + " for epoch in range(num_epochs):\n", + " doc2vec_model.train(tqdm(tagged_data, desc=f\"Epoch {epoch+1}\"), total_examples=doc2vec_model.corpus_count, epochs=doc2vec_model.epochs)\n", + "\n", + " return doc2vec_model\n", + "\n", + "# Preprocess data and extract genres for the first 1000 movies\n", + "chunk_size = 1000\n", + "tagged_data = []\n", + "valid_indices = []\n", + "movie_info = []\n", + "for chunk_start in range(0, len(movie_data), chunk_size):\n", + " movie_data_chunk = movie_data.iloc[chunk_start:chunk_start+chunk_size]\n", + " chunk_tagged_data, chunk_valid_indices, chunk_movie_info = preprocess_data(movie_data_chunk)\n", + " tagged_data.extend(chunk_tagged_data)\n", + " valid_indices.extend(chunk_valid_indices)\n", + " movie_info.extend(chunk_movie_info)\n", + "\n", + "doc2vec_model = train_doc2vec_model(tagged_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VryHT1zVuEp0" + }, + "source": [ + "### Training a Neural Network for the Genre Classification Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3pVNy2UKt5lu" + }, + "outputs": [], + "source": [ + "# Extract genre labels for the valid indices\n", + "genres_list = []\n", + "for i in valid_indices:\n", + " row = movie_data.loc[i]\n", + " genres = [genre['name'] for genre in eval(row['genres'])]\n", + " genres_list.append(genres)\n", + "\n", + "mlb = MultiLabelBinarizer()\n", + "genre_labels = mlb.fit_transform(genres_list)\n", + "\n", + "embeddings = []\n", + "for i in valid_indices:\n", + " embeddings.append(doc2vec_model.dv[str(i)])\n", + "X_train, X_test, y_train, y_test = train_test_split(embeddings, genre_labels, test_size=0.2, random_state=42)\n", + "\n", + "X_train_np = np.array(X_train, dtype=np.float32)\n", + "y_train_np = np.array(y_train, dtype=np.float32)\n", + "X_test_np = np.array(X_test, dtype=np.float32)\n", + "y_test_np = np.array(y_test, dtype=np.float32)\n", + "\n", + "X_train_tensor = torch.tensor(X_train_np)\n", + "y_train_tensor = torch.tensor(y_train_np)\n", + "X_test_tensor = torch.tensor(X_test_np)\n", + "y_test_tensor = torch.tensor(y_test_np)\n", + "\n", + "class GenreClassifier(nn.Module):\n", + " def __init__(self, input_size, output_size):\n", + " super(GenreClassifier, self).__init__()\n", + " self.fc1 = nn.Linear(input_size, 512)\n", + " self.bn1 = nn.BatchNorm1d(512)\n", + " self.fc2 = nn.Linear(512, 256)\n", + " self.bn2 = nn.BatchNorm1d(256)\n", + " self.fc3 = nn.Linear(256, 128)\n", + " self.bn3 = nn.BatchNorm1d(128)\n", + " self.fc4 = nn.Linear(128, output_size)\n", + " self.relu = nn.ReLU()\n", + " self.dropout = nn.Dropout(p=0.2) # Adjust the dropout rate as needed\n", + "\n", + " def forward(self, x):\n", + " x = self.fc1(x)\n", + " x = self.bn1(x)\n", + " x = self.relu(x)\n", + " x = self.dropout(x)\n", + " x = self.fc2(x)\n", + " x = self.bn2(x)\n", + " x = self.relu(x)\n", + " x = self.dropout(x)\n", + " x = self.fc3(x)\n", + " x = self.bn3(x)\n", + " x = self.relu(x)\n", + " x = self.dropout(x)\n", + " x = self.fc4(x)\n", + " return x\n", + "\n", + "# Move model to the selected device\n", + "model = GenreClassifier(input_size=100, output_size=len(mlb.classes_)).to(device)\n", + "\n", + "# Define loss function and optimizer\n", + "criterion = nn.BCEWithLogitsLoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.001)\n", + "\n", + "# Training loop\n", + "epochs = 50\n", + "batch_size = 64\n", + "\n", + "train_dataset = TensorDataset(X_train_tensor.to(device), y_train_tensor.to(device))\n", + "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", + "\n", + "for epoch in range(epochs):\n", + " model.train()\n", + " running_loss = 0.0\n", + " for inputs, labels in train_loader:\n", + " inputs, labels = inputs.to(device), labels.to(device) # Move data to device\n", + " optimizer.zero_grad()\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " running_loss += loss.item() * inputs.size(0)\n", + " epoch_loss = running_loss / len(train_loader.dataset)\n", + " print(f'Epoch [{epoch + 1}/{epochs}], Loss: {epoch_loss:.4f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yV8lTDYIubEQ" + }, + "source": [ + "### Testing the `model` to see if our model is able to predict the genres for the movies from the test dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "73D3aqdJuct8" + }, + "outputs": [], + "source": [ + "from sklearn.metrics import f1_score\n", + "\n", + "model.eval()\n", + "with torch.no_grad():\n", + " X_test_tensor, y_test_tensor = X_test_tensor.to(device), y_test_tensor.to(device) # Move test data to device\n", + " outputs = model(X_test_tensor)\n", + " test_loss = criterion(outputs, y_test_tensor)\n", + " print(f'Test Loss: {test_loss.item():.4f}')\n", + "\n", + "\n", + "thresholds = [0.1] * len(mlb.classes_)\n", + "thresholds_tensor = torch.tensor(thresholds, device=device).unsqueeze(0)\n", + "\n", + "# Convert the outputs to binary predictions using varying thresholds\n", + "predicted_labels = (outputs > thresholds_tensor).cpu().numpy()\n", + "\n", + "# Convert binary predictions and actual labels to multi-label format\n", + "predicted_multilabels = mlb.inverse_transform(predicted_labels)\n", + "actual_multilabels = mlb.inverse_transform(y_test_np)\n", + "\n", + "# Print the Predicted and Actual Labels for each movie\n", + "for i, (predicted, actual) in enumerate(zip(predicted_multilabels, actual_multilabels)):\n", + " print(f'Movie {i+1}:')\n", + " print(f' Predicted Labels: {predicted}')\n", + " print(f' Actual Labels: {actual}')\n", + "\n", + "\n", + "# Compute F1-score\n", + "f1 = f1_score(y_test_np, predicted_labels, average='micro')\n", + "print(f'F1-score: {f1:.4f}')\n", + "\n", + "# Saving the trained model\n", + "torch.save(model.state_dict(), 'trained_model.pth')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kZrHpMm4un0G" + }, + "source": [ + "### Storing the Doc2Vec Embeddings into LanceDB VectorDatabase" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BTTNb9irrY4h" + }, + "outputs": [], + "source": [ + "import lancedb\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "data = []\n", + "\n", + "for i in valid_indices:\n", + " embedding = doc2vec_model.dv[str(i)]\n", + " title, genres = movie_info[valid_indices.index(i)]\n", + " data.append({\"title\": title, \"genres\": genres, \"vector\": embedding.tolist()})\n", + "\n", + "db = lancedb.connect(\".db\")\n", + "tbl = db.create_table(\"doc2vec_embeddings\", data, mode=\"Overwrite\")\n", + "db[\"doc2vec_embeddings\"].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ciUFn7uQrY4i" + }, + "outputs": [], + "source": [ + "def get_recommendations(title):\n", + " pd_data = pd.DataFrame(data)\n", + " result = (\n", + " tbl.search(pd_data[pd_data[\"title\"] == title][\"vector\"].values[0]).metric(\"cosine\")\n", + " .limit(10)\n", + " .to_pandas()\n", + " )\n", + " return result[[\"title\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Kz-JGsTuwmk" + }, + "source": [ + "### D-Day : Let's generate some recommendations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uw_El12JrY4j", + "outputId": "c245bab5-7966-4fd1-ec72-37f708c3b570" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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We'll also demonstrate the use of LangChain and LanceDB Cloud using the OpenAI API. In this example we'll use **Numpy 1.26** documentation, but, this could be replaced for your own docs as well. +Colab walkthrough - Open In Colab + + +### Set credentials +if you would like to set api key through an environment variable: +``` +export LANCEDB_API_KEY="sk_..." +``` +or +``` +import os +import getpass + +os.environ["LANCEDB_API_KEY"] = getpass.getpass("Enter Your LANCEDB API Key:") +``` + +replace the following lines in main.py with your project slug and api key" +``` +db_url="db://your-project-slug-name" +api_key="sk_..." +region="us-east-1" +``` + +### Run the script +```python +OPENAI_API_KEY=... python main.py --query "what is a vectordb?" +``` + +### Javascript +Run the script +```javascript +OPENAI_API_KEY=... node index.js +``` \ No newline at end of file diff --git a/examples/Code-Documentation-QA-Bot/lancedb_cloud/main.ipynb b/examples/Code-Documentation-QA-Bot/lancedb_cloud/main.ipynb new file mode 100644 index 0000000..53036e6 --- /dev/null +++ b/examples/Code-Documentation-QA-Bot/lancedb_cloud/main.ipynb @@ -0,0 +1,484 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "13cb272e", + "metadata": {}, + "source": [ + "# Code documentation Q&A bot example with LangChain\n", + "![picture](https://lancedb.github.io/lancedb/assets/ecosystem-illustration.png)\n", + "\n", + "This Q&A bot will allow you to query your own documentation easily using questions. We'll also demonstrate the use of LangChain and LanceDB using the OpenAI API.\n", + "\n", + "In this example we'll **Numpy 1.26** documentation, but, this could be replaced for your own docs as well" + ] + }, + { + "cell_type": "markdown", + "id": "9a0e829a", + "metadata": { + "id": "wgPbKbpumkhH" + }, + "source": [ + "## Credentials\n", + "\n", + "Copy and paste the project name and the api key from your project page.\n", + "These will be used later to [connect to LanceDB Cloud](#scroll-to=5q8m6GMD7sGu)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6553603f", + "metadata": { + "id": "rqEXT5-fmofw" + }, + "outputs": [], + "source": [ + "project_slug = \"your-project-slug\" # @param {type:\"string\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "36ef9c45", + "metadata": { + "id": "5LYmBomPmswi" + }, + "outputs": [], + "source": [ + "api_key = \"sk_...\" # @param {type:\"string\"}" + ] + }, + { + "cell_type": "markdown", + "id": "33ba6af1", + "metadata": { + "id": "Xs6tr6CMnBrr" + }, + "source": [ + "You can also set the LANCEDB_API_KEY as an environment variable. More details can be found **here**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Le27BWs2vDbB" + }, + "source": [ + "Since we will be using OPENAI API, let us set the OPENAI API KEY as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-2-fyVPKu9fl" + }, + "outputs": [], + "source": [ + "openai_api_key = \"sk-...\" # @param {type:\"string\"}" + ] + }, + { + "cell_type": "markdown", + "id": "1991331f-4316-417a-b693-e2f27cbe9ea7", + "metadata": {}, + "source": [ + "### Installing dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8a49c31", + "metadata": {}, + "outputs": [], + "source": [ + "! pip install -U langchain langchain-openai langchain-community" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66638d6c", + "metadata": { + "id": "QR9W53zStdlz" + }, + "outputs": [], + "source": [ + "! pip install -qq tiktoken unstructured pandas lancedb" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0QQL4lm8lTzg" + }, + "source": [ + "### Importing libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vP6d6JUShgqo" + }, + "outputs": [], + "source": [ + "import openai\n", + "import os\n", + "import re\n", + "import pickle\n", + "import requests\n", + "import zipfile\n", + "from pathlib import Path\n", + "\n", + "from langchain.document_loaders import UnstructuredHTMLLoader\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain.vectorstores import LanceDB\n", + "from langchain_openai import OpenAI, OpenAIEmbeddings\n", + "from langchain.chains import RetrievalQA\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = openai_api_key\n", + "assert openai.models.list() is not None" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8eKRYd2F7v5n" + }, + "source": [ + "# Get the data\n", + "To make this easier, we've downloaded Numpy documentation and stored the raw HTML files for you to download. Once the docs are downloaded, we then use LangChain's HTML document readers to parse them and store them in LanceDB as a vector store, along with relevant metadata.\n", + "By default we use numpy docs, but you can replace this with your own docs as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "l0ezDr7suAf_" + }, + "outputs": [], + "source": [ + "numpy_docs = requests.get(\"https://numpy.org/doc/1.26/numpy-html.zip\")\n", + "with open(\"numpy-html.zip\", \"wb\") as f:\n", + " f.write(numpy_docs.content)\n", + "\n", + "file = zipfile.ZipFile(\"numpy-html.zip\")\n", + "file = file.extractall(path=\"numpy_docs\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HJf8xZmX8VJC" + }, + "source": [ + "We'll create a simple **helper function** that can help to extract metadata, so it can used later when querying with filters. In this case, we want to keep the lineage of the uri or path for each document that has been processed:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5aljyqpUiViE" + }, + "outputs": [], + "source": [ + "# Pre-processing and loading the documentation\n", + "\n", + "# Next, let's pre-process and load the documentation. To make sure we don't need to do this repeatedly if we were updating code,\n", + "# we're caching it using pickle so we can retrieve it again (this could take a few minutes to run the first time you do it).\n", + "# We'll also add some more metadata to the docs here such as the title and version of the code:\n", + "\n", + "\n", + "def get_document_title(document_list):\n", + " titles = []\n", + " for doc in document_list:\n", + " if \"metadata\" in doc and \"source\" in doc[\"metadata\"]:\n", + " m = str(doc[\"metadata\"][\"source\"])\n", + " title = re.findall(\"numpy_docs(.*).html\", m)\n", + " print(title)\n", + " if title:\n", + " titles.append(title[0])\n", + " else:\n", + " titles.append(\"\")\n", + " else:\n", + " titles.append(\"\")\n", + " return titles" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PCufm9Xr8eWp" + }, + "source": [ + "# Pre-processing and loading the documents\n", + "\n", + "Next, let's pre-process and load the documents. To make sure we don't need to do this repeatedly while updating code, we're caching it using pickle so it can be retrieved again (this could take a few minutes to run the first time you do it). We'll also add extra metadata to the docs here such as the title and version of the code:\n", + "\n", + "*Note*: This step might take up to 10 minutes to run!\n", + "*Note*: If there is some issue with nltk package, kindly try using\n", + "```\n", + "import nltk\n", + "nltk.download('punkt')\n", + "```\n", + "or try to manually install the [nltk_data](https://github.com/nltk/nltk_data/tree/gh-pages) package and unzip the **punkt tokenizer** zip and the **averaged_perceptron_tagger** zip file in the packages folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 443 + }, + "id": "964Z2sZA247g", + "outputId": "236df468-a630-4691-85a4-886835cfc02d" + }, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "\n", + "docs = []\n", + "docs_path = Path(\"docs.pkl\")\n", + "for p in tqdm(Path(\"numpy_docs\").rglob(\"*.html\")):\n", + " if p.is_dir():\n", + " continue\n", + " loader = UnstructuredHTMLLoader(p)\n", + " raw_document = loader.load()\n", + " # docs.append(raw_document)\n", + " title = get_document_title(raw_document)\n", + " m = {\"title\": title}\n", + " if raw_document:\n", + " raw_document[0].metadata.update(m)\n", + " raw_document[0].metadata[\"source\"] = str(raw_document[0].metadata[\"source\"])\n", + " docs.extend(raw_document)\n", + "\n", + "\n", + "if docs:\n", + " with open(docs_path, \"wb\") as fh:\n", + " pickle.dump(docs, fh)\n", + "else:\n", + " with open(docs_path, \"rb\") as fh:\n", + " docs = pickle.load(fh)\n", + "\n", + "len(docs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cntAuaUU_TER" + }, + "source": [ + "# Generating emebeddings from our docs\n", + "\n", + "Now that we have our raw documents loaded, we need to pre-process them to generate embeddings:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dHw2DSAj3u9B" + }, + "outputs": [], + "source": [ + "text_splitter = RecursiveCharacterTextSplitter(\n", + " chunk_size=1000,\n", + " chunk_overlap=200,\n", + ")\n", + "documents = text_splitter.split_documents(docs)\n", + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IiM4DJvC_2dV" + }, + "source": [ + "# Store data in LanceDB Cloud\n", + "\n", + "Let's connect to LanceDB so we can store our documents, It requires 0 setup !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GV77SSi-AK0v" + }, + "outputs": [], + "source": [ + "uri = \"db://\" + project_slug\n", + "table_name = \"langchain_vectorstore\"\n", + "\n", + "vectorstore = LanceDB(\n", + " embedding=embeddings,\n", + " uri=uri, # your remote database URI\n", + " api_key=api_key,\n", + " region=\"us-east-1\",\n", + " table_name=table_name, # Optional, defaults to \"vectors\"\n", + " mode=\"overwrite\", # Optional, defaults to \"overwrite\"\n", + ")\n", + "\n", + "doc_ids = vectorstore.add_documents(documents=documents)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sZOUxfqzXr1m" + }, + "source": [ + "Now let's create our RetrievalQA chain using the LanceDB vector store:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4nDltKClAhhU" + }, + "outputs": [], + "source": [ + "qa = RetrievalQA.from_chain_type(\n", + " llm=OpenAI(), chain_type=\"stuff\", retriever=vectorstore.as_retriever()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xoS-WKXMXvvR" + }, + "source": [ + "And thats it! We're all setup. The next step is to run some queries, let's try a few:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7SKSlyq2iwpK" + }, + "source": [ + "# Query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6aSZr8fCXx9s", + "outputId": "ac5b5663-d45f-48c0-9f0a-f272e1a3ec2d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'query': 'tell me about the numpy library?',\n", + " 'result': ' The NumPy library is an open source Python library that is used for working with numerical data in Python. It contains multidimensional array and matrix data structures, and provides methods for efficient operations on these arrays. It is widely used in various fields of science and engineering and is a core component of the scientific Python and PyData ecosystems. It also offers a large library of high-level mathematical functions for working with arrays and matrices. '}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = \"tell me about the numpy library?\"\n", + "qa.invoke(query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EtBw5EH7lv9_", + "outputId": "1745f881-fa15-44b5-e692-b702babce734" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'query': \"What's the current version of numpy?\",\n", + " 'result': '\\nThe current version of numpy is 1.16.4.'}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = \"What's the current version of numpy?\"\n", + "qa.invoke(query)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fR4CmF9ylvzw", + "outputId": "1b33bb78-4b3f-4dea-addd-75f56eb4e5e6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'query': 'What kind of linear algebra related operations can be done in numpy?',\n", + " 'result': ' The numpy package provides various operations related to linear algebra, such as decompositions, matrix eigenvalues, norms, solving equations and inverting matrices, and performing linear algebra on several matrices at once. It also has support for logic functions, masked array operations, mathematical functions, matrix library, miscellaneous routines, padding arrays, polynomials, random sampling, set routines, sorting, searching, counting, statistics, and window functions.'}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = \"What kind of linear algebra related operations can be done in numpy?\"\n", + "qa.invoke(query)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/Code-Documentation-QA-Bot/lancedb_cloud/main.py b/examples/Code-Documentation-QA-Bot/lancedb_cloud/main.py new file mode 100644 index 0000000..e9eb960 --- /dev/null +++ b/examples/Code-Documentation-QA-Bot/lancedb_cloud/main.py @@ -0,0 +1,136 @@ +# %% [markdown] +# # Code documentation Q&A bot example with LangChain +# +# This Q&A bot will allow you to query your own documentation easily using questions. We'll also demonstrate the use of LangChain and LanceDB using the OpenAI API. +# +# In this example we'll use Pandas 2.0 documentation, but, this could be replaced for your own docs as well + +import argparse +import os +import pickle +import re +import zipfile +from pathlib import Path + +import openai +import requests +from langchain.chains import RetrievalQA +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_community.document_loaders import BSHTMLLoader +from langchain_community.vectorstores import LanceDB +from langchain_openai import OpenAI, OpenAIEmbeddings + + +def get_document_title(document_list): + titles = [] + for doc in document_list: + if "metadata" in doc and "source" in doc["metadata"]: + m = str(doc["metadata"]["source"]) + title = re.findall("numpy_docs(.*).html", m) + if title: + titles.append(title[0]) + else: + titles.append("") + else: + titles.append("") + return titles + + +def arg_parse(): + default_query = "tell me about the numpy library?" + # default_query = "What's the current version of numpy?" + + parser = argparse.ArgumentParser(description="Code Documentation QA Bot") + parser.add_argument( + "--query", type=str, default=default_query, help="query to search" + ) + parser.add_argument("--openai-key", type=str, help="OpenAI API Key") + args = parser.parse_args() + + if not args.openai_key: + if "OPENAI_API_KEY" not in os.environ: + raise ValueError( + "OPENAI_API_KEY environment variable not set. Please set it or pass --openai_key" + ) + else: + openai.api_key = args.openai_key + + return args + + +def pre_process(): + from tqdm import tqdm + + docs = [] + docs_path = Path("docs.pkl") + for p in tqdm(Path("numpy_docs").rglob("*.html")): + if p.is_dir(): + continue + # loader = UnstructuredHTMLLoader(p) + loader = BSHTMLLoader(p, open_encoding="utf8") + raw_document = loader.load() + # docs.append(raw_document) + title = get_document_title(raw_document) + m = {"title": title} + if raw_document: + raw_document[0].metadata.update(m) + raw_document[0].metadata["source"] = str(raw_document[0].metadata["source"]) + docs.extend(raw_document) + + if docs: + with open(docs_path, "wb") as fh: + pickle.dump(docs, fh) + else: + with open(docs_path, "rb") as fh: + docs = pickle.load(fh) + + return docs + + +if __name__ == "__main__": + args = arg_parse() + + numpy_docs = requests.get("https://numpy.org/doc/1.26/numpy-html.zip") + with open("numpy-html.zip", "wb") as f: + f.write(numpy_docs.content) + + file = zipfile.ZipFile("numpy-html.zip") + file = file.extractall(path="numpy_docs") + + docs = pre_process() + + print("Loaded {} documents".format(len(docs))) + text_splitter = RecursiveCharacterTextSplitter( + chunk_size=1000, + chunk_overlap=200, + ) + documents = text_splitter.split_documents(docs) + embeddings = OpenAIEmbeddings() + + db_url = "db://your-project-slug" + api_key = "sk_..." + region = "us-east-1" + table_name = "langchain_vectorstore" + + vectorstore = LanceDB( + embedding=embeddings, + uri=db_url, # your remote database URI + api_key=api_key, + region="us-east-1", + table_name=table_name, # Optional, defaults to "vectors" + mode="overwrite", # Optional, defaults to "overwrite" + ) + + # insert documents in batches + batch_size = 10000 + for i in range(0, len(documents), batch_size): + print(f"ingesting batch of {i} : {i+batch_size}") + batch = documents[i : i + batch_size] + vectorstore.add_documents(batch) + + qa = RetrievalQA.from_chain_type( + llm=OpenAI(), chain_type="stuff", retriever=vectorstore.as_retriever() + ) + + result = qa.run(args.query) + print(result) diff --git a/examples/Code-Documentation-QA-Bot/lancedb_cloud/requirements.txt b/examples/Code-Documentation-QA-Bot/lancedb_cloud/requirements.txt new file mode 100644 index 0000000..79d2c50 --- /dev/null +++ b/examples/Code-Documentation-QA-Bot/lancedb_cloud/requirements.txt @@ -0,0 +1,8 @@ +argparse +openai +langchain-community +langchain-openai +lancedb +unstructured +tiktoken +polars From d6ba79d7def6533b1358ec9d6c122371f69a798b Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Wed, 29 May 2024 13:52:17 +0530 Subject: [PATCH 09/16] evaluating RAG with RAGAs (#197) * assests and app name * update README * demo gifs * talk with github codespaces * talk with github codespaces * gitignore * linted * added version * link fix * added local llm tag * crag * link fix * lint * llm tags * non-clickable badge * non-clickable badge * fix * tutorial llm tags * added instructions and fix * colab fix * fix * formatted * hybrid search and rag colab * colab format * python test * node test * python test * blog link update * rag mlx * myntra search engine app * link fix * CrewAI Example * lint * node test * node test * node test * added readme * support for Gemini Pro * fix * chunking techniques * lint * Locally RAG from Scratch * lint * llama3 added * link finx * sdk manual cli chatbot phidata * sdk manual cli chatbot phidata * link fix * tags * advanced * update readme * remove key * lint * formatting fixes * lint * updated image * added demo image * change autogen notebook * lint * lint * rag evaluation with ragas --- README.md | 1 + assets/rag_evaluation_flow.png | Bin 0 -> 98126 bytes .../Evaluating_RAG_with_RAGAs.ipynb | 1158 ++++++++++++++++ examples/Evaluating_RAG_with_RAGAs/README.md | 13 + ...ommendation_with_doc2vec_and_lancedb.ipynb | 1192 +++++++++-------- 5 files changed, 1778 insertions(+), 586 deletions(-) create mode 100644 assets/rag_evaluation_flow.png create mode 100644 examples/Evaluating_RAG_with_RAGAs/Evaluating_RAG_with_RAGAs.ipynb create mode 100644 examples/Evaluating_RAG_with_RAGAs/README.md diff --git a/README.md b/README.md index 017253e..11d18ae 100644 --- a/README.md +++ b/README.md @@ -59,6 +59,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301/) | | [Instruct-Multitask](./examples/instruct-multitask) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543)| | [Evaluating Prompts with Prompttools](/examples/prompttools-eval-prompts/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| | +| [Evaluating RAG with RAGAs](./examples/Evaluating_RAG_with_RAGAs/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| | | [AI Agents: Reducing Hallucination](/examples/reducing_hallucinations_ai_agents/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/reducing_hallucinations_ai_agents/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/reducing_hallucinations_ai_agents/index.js) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f/)| | [AI Trends Searcher with CrewAI](./examples/AI-Trends-with-CrewAI/) |Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/track-ai-trends-crewai-agents-rag/)| | [SuperAgent Autogen](/examples/SuperAgent_Autogen) |Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|| diff --git a/assets/rag_evaluation_flow.png b/assets/rag_evaluation_flow.png new file mode 100644 index 0000000000000000000000000000000000000000..4b74b6cb81bf180d1e70ba3bcf4ae0d6bafae77e GIT binary patch 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Evaluating RAG with RAGAs using GPT-4o\n", + "\n", + "Ragas is a **framework for evaluating Retrieval Augmented Generation (RAG) pipelines**.\n", + "\n", + "Ragas provides you with the tools/metrics based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. Ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.\n", + "\n", + "GPT4-o is used as an LLM to generate responses out of semantically close context chunks.\n", + "\n", + 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)" + ], + "metadata": { + "id": "AwhxwHTf4VZp" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1mr4fXemsYci", + "outputId": "dd51d890-7da2-45ec-b14f-d96169bb8bdf" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m973.5/973.5 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.6/320.6 kB\u001b[0m \u001b[31m38.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.9/18.9 MB\u001b[0m \u001b[31m71.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta 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ragas -q" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Setup `OPENAI_API_KEY` as an environment variable" + ], + "metadata": { + "id": "z8hT0Jn74ZmT" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-proj-...\"" + ], + "metadata": { + "id": "YHgQd_1rI04R" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Load .txt file and convert them into chunks" + ], + "metadata": { + "id": "mM0tf_vo6GbI" + } + }, + { + "cell_type": "code", + "source": [ + "import requests\n", + "from langchain.document_loaders import TextLoader\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "\n", + "url = \"https://raw.githubusercontent.com/hwchase17/chroma-langchain/master/state_of_the_union.txt\"\n", + "res = requests.get(url)\n", + "with open(\"state_of_the_union.txt\", \"w\") as f:\n", + " f.write(res.text)\n", + "\n", + "# Load the data\n", + "loader = TextLoader(\"./state_of_the_union.txt\")\n", + "documents = loader.load()\n", + "\n", + "# Chunk the data\n", + "text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=10)\n", + "chunks = text_splitter.split_documents(documents)" + ], + "metadata": { + "id": "IkLbg-_1I3Rt", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4248c952-c719-4a30-ee7e-06d2f1b17449" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:langchain_text_splitters.base:Created a chunk of size 215, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 232, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 242, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 219, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 304, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 205, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 332, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 215, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 203, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 281, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 201, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 250, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 325, which is longer than the specified 200\n", + "WARNING:langchain_text_splitters.base:Created a chunk of size 242, which is longer than the specified 200\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Setup Retriever\n", + "\n", + "Retriever utilizes **LanceDB** for scalable vector search and advanced retrieval in RAG, delivering blazing fast performance for searching large sets of embeddings." + ], + "metadata": { + "id": "pgetSLZXEJ2Q" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.embeddings import OpenAIEmbeddings\n", + "from langchain.vectorstores import LanceDB\n", + "import lancedb\n", + "\n", + "openai_embed = OpenAIEmbeddings()\n", + "\n", + "# Setup lancedb\n", + "db = lancedb.connect(\"/tmp/lancedb\")\n", + "table = db.create_table(\n", + " \"raga_eval\",\n", + " data=[{\"vector\": openai_embed.embed_query(\"Hello World\"), \"text\": \"Hello World\"}],\n", + " mode=\"overwrite\",\n", + ")\n", + "\n", + "# Populate vector database\n", + "vectorstore = LanceDB.from_documents(\n", + " client=table, documents=chunks, embedding=openai_embed, by_text=False\n", + ")\n", + "\n", + "# Define vectorstore as retriever to enable semantic search\n", + "retriever = vectorstore.as_retriever()" + ], + "metadata": { + "id": "2PYhU_vvJC0P" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Setup RAG Pipeline with Prompt template" + ], + "metadata": { + "id": "9CFnNEfuExj7" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.prompts import ChatPromptTemplate\n", + "from langchain.schema.runnable import RunnablePassthrough\n", + "from langchain.schema.output_parser import StrOutputParser\n", + "\n", + "# Define LLM\n", + "llm = ChatOpenAI(model_name=\"gpt-4o\", temperature=0)\n", + "\n", + "# Define Prompt template\n", + "template = \"\"\"You are an assistant for question-answering tasks.\n", + "Use the following pieces of retrieved context to answer the question.\n", + "If you don't know the answer, just say that you don't know.\n", + "Use two sentences maximum and keep the answer concise.\n", + "Question: {question}\n", + "Context: {context}\n", + "Answer:\n", + "\"\"\"\n", + "\n", + "prompt = ChatPromptTemplate.from_template(template)\n", + "\n", + "# Setup RAG pipeline\n", + "rag_chain = (\n", + " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", + " | prompt\n", + " | llm\n", + " | StrOutputParser()\n", + ")" + ], + "metadata": { + "id": "-TiQhbNyLSKv" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Sample Questions with their Expected Answers\n", + "\n", + "Define a set of questions with their answers for creating dataset including ground truth, generated answers with their context using which they are generated." + ], + "metadata": { + "id": "Ge8JtkNXFXhI" + } + }, + { + "cell_type": "code", + "source": [ + "from datasets import Dataset\n", + "\n", + "questions = [\n", + " \"What did the president say about Justice Breyer?\",\n", + " \"What did the president say about Intel's CEO?\",\n", + " \"What did the president say about gun violence?\",\n", + "]\n", + "ground_truth = [\n", + " \"The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.\",\n", + " \"The president said that Pat Gelsinger is ready to increase Intel's investment to $100 billion.\",\n", + " \"The president asked Congress to pass proven measures to reduce gun violence.\",\n", + "]\n", + "answers = []\n", + "contexts = []\n", + "\n", + "# Inference\n", + "for query in questions:\n", + " answers.append(rag_chain.invoke(query))\n", + " contexts.append(\n", + " [docs.page_content for docs in retriever.get_relevant_documents(query)]\n", + " )\n", + "\n", + "# To dict\n", + "data = {\n", + " \"question\": questions,\n", + " \"answer\": answers,\n", + " \"contexts\": contexts,\n", + " \"ground_truth\": ground_truth,\n", + "}\n", + "\n", + "# Convert dict to dataset\n", + "dataset = Dataset.from_dict(data)" + ], + "metadata": { + "id": "PGiU57QJMP0J" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### RAGA Evaluation Pipeline\n", + "\n", + "Simple pipeline of RAGA for evaluation with the listed metrics to understand and evaluate the RAG system.\n", + "\n", + "**Metrics** on which we will evaulate are answer_correctness,\n", + "faithfulness,\n", + "answer_similarity,\n", + "context_precision,\n", + "context_utilization,\n", + "context_recall,\n", + "context_relevancy,\n", + "answer_relevancy, and\n", + "context_entity_recall" + ], + "metadata": { + "id": "szBZ1nwkFruF" + } + }, + { + "cell_type": "code", + "source": [ + "from ragas import evaluate\n", + "from ragas.metrics import (\n", + " answer_correctness,\n", + " faithfulness,\n", + " answer_similarity,\n", + " context_precision,\n", + " context_utilization,\n", + " context_recall,\n", + " context_relevancy,\n", + " answer_relevancy,\n", + " context_entity_recall,\n", + ")\n", + "\n", + "\n", + "# evaluating dataest on listed metrics\n", + "result = evaluate(\n", + " dataset=dataset,\n", + " metrics=[\n", + " answer_correctness,\n", + " faithfulness,\n", + " answer_similarity,\n", + " context_precision,\n", + " context_utilization,\n", + " context_recall,\n", + " context_relevancy,\n", + " answer_relevancy,\n", + " context_entity_recall,\n", + " ],\n", + ")\n", + "\n", + "\n", + "df = result.to_pandas()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "91f4187ef74b4c0791fa9058899f7454", + "d9fb5f1092e24ba59cb768842ff8f828", + "6441a4ce0f644c51aa6c140de43ed31d", + "a6b459a38e5c4386b85ee7ebc0e302a4", + "cf96076499974020b541a541648028f4", + "82cd48cdf6f144e19cb3ef0a0553b689", + "8e537aa094004828b08a55a94cbd7dff", + "55a45baafaad4a4ca2cad720da0a80aa", + "7f2a940f7f114e439f63e5e900748511", + "50282721d29447c89bc05559a99183dd", + "23c5a724d0fa40efac45f1fe8fc0b9c5" + ] + }, + "id": "Samkm2TnMUQA", + "outputId": "f50e72d4-55fd-4f74-f0f6-334af8353ae2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Evaluating: 0%| | 0/27 [00:00\n", + "
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0What did the president say about Justice Breyer?The president honored Justice Stephen Breyer a...[And I did that 4 days ago, when I nominated C...The president said that Justice Breyer has ded...0.4154871.00.9119481.01.01.00.2000000.8415890.500000
1What did the president say about Intel's CEO?The president said that Intel’s CEO, Pat Gelsi...[Intel’s CEO, Pat Gelsinger, who is here tonig...The president said that Pat Gelsinger is ready...0.6199980.00.9801031.01.01.00.0909090.8970840.750000
2What did the president say about gun violence?The president called on Congress to pass prove...[And I ask Congress to pass proven measures to...The president asked Congress to pass proven me...0.6062301.00.9248951.01.01.00.2500000.9148880.666667
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He also mentioned that Circuit Court of Appeals Judge Ketanji Brown Jackson, who he nominated, will continue Justice Breyer\\u2019s legacy of excellence.\",\n \"The president said that Intel\\u2019s CEO, Pat Gelsinger, told him they are ready to increase their investment from $20 billion to $100 billion.\",\n \"The president called on Congress to pass proven measures to reduce gun violence, including universal background checks and banning assault weapons and high-capacity magazines. He also questioned why individuals on a terrorist list should be able to purchase a weapon and advocated for repealing the liability shield that protects gun manufacturers from being sued.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"contexts\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ground_truth\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"The president said that Justice Breyer has dedicated his life to serve the country and thanked him for his service.\",\n \"The president said that Pat Gelsinger is ready to increase Intel's investment to $100 billion.\",\n \"The president asked Congress to pass proven measures to reduce gun violence.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"answer_correctness\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.11430741128276067,\n \"min\": 0.4154869951100285,\n \"max\": 0.6199979663207625,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.4154869951100285,\n 0.6199979663207625,\n 0.6062297668831289\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"faithfulness\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.5773502691896258,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"answer_similarity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.03619563595630723,\n \"min\": 0.911947980440114,\n \"max\": 0.9801032234987175,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.911947980440114,\n 0.9801032234987175\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"context_precision\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.8867515847990063e-11,\n \"min\": 0.9999999999,\n \"max\": 0.99999999995,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.9999999999,\n 0.99999999995\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"context_utilization\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.8867515847990063e-11,\n \"min\": 0.9999999999,\n \"max\": 0.99999999995,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.9999999999,\n 0.99999999995\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"context_recall\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 1.0,\n \"max\": 1.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"context_relevancy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.08135390156762909,\n \"min\": 0.09090909090909091,\n \"max\": 0.25,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"answer_relevancy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.038230490911004,\n \"min\": 0.8415890798965432,\n \"max\": 0.9148879952296768,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.8415890798965432\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"context_entity_recall\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12729376960740932,\n \"min\": 0.4999999975,\n \"max\": 0.7499999981250001,\n \"num_unique_values\": 3,\n \"samples\": [\n 0.4999999975\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 22 + } + ] + } + ] +} \ No newline at end of file diff --git a/examples/Evaluating_RAG_with_RAGAs/README.md b/examples/Evaluating_RAG_with_RAGAs/README.md new file mode 100644 index 0000000..98c7069 --- /dev/null +++ b/examples/Evaluating_RAG_with_RAGAs/README.md @@ -0,0 +1,13 @@ +# Evaluating RAG with RAGAs and GPT-4o +Open In Colab + + +Ragas is a **framework for evaluating Retrieval Augmented Generation (RAG) pipelines**. + +Ragas provides you with the tools/metrics based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. Ragas can be integrated with your CI/CD to provide continuous checks to ensure performance. + +GPT4-o is used as an LLM to generate responses out of semantically close context chunks. + +![flow](../../assets/rag_evaluation_flow.png) + +Try it out on Colab - Open In Colab \ No newline at end of file diff --git a/examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb b/examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb index fdb29a7..2a75b19 100644 --- a/examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb +++ b/examples/movie-recommendation-with-genres/movie_recommendation_with_doc2vec_and_lancedb.ipynb @@ -1,594 +1,614 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "K45xhdPRsZJV" - }, - "source": [ - "# Movie Recommendation System using Doc2vec Embeddings and Vector DB" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XUj6NXD0sdgf" - }, - "source": [ - "This Colab notebook aims to illustrate the process of creating a recommendation system using embeddings and a Vector DB.\n", - "\n", - "This approach involves combining the various movie genres or characteristics of a movie to form Doc2Vec embeddings, which offer a comprehensive portrayal of the movie content.\n", - "\n", - "These embeddings serve dual purposes: they can either be directly inputted into a classification model for genre classification or stored in a VectorDB. By storing embeddings in a VectorDB, efficient retrieval and query search for recommendations become possible at a later stage.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qEa74a_Wtpc7" - }, - "source": [ - "### Installing the relevant dependencies\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "hyde90IntuFi" - }, - "outputs": [], - "source": [ - "!pip install torch scikit-learn lancedb nltk gensim lancedb scipy==1.12 kaggle" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "shPjHTZbtxTh" - }, - "source": [ - "## Kaggle Configuration and Data Needs\n", - "\n", - "We are using a movies metadata data which is being uploaded on the Kaggle. To download the dataset and use it for our recommendation system, we will need a `kaggle.json` file containing our creds.\n", - "\n", - "You can download the `kaggle.json` file from your Kaggle account settings. Follow these steps and make your life easy.\n", - "\n", - "1. Go to Kaggle and log in to your account.\n", - "2. Navigate to Your Account Settings and click on your profile picture in the top right corner of the page, Now From the dropdown menu, select `Account`.\n", - "3. Scroll down to the `API` section, Click on `Create New API Token`. This will download a file named kaggle.json to your computer.\n", - "\n", - "Once you have the `kaggle.json` file, you need to upload it here on colab data space. After uploading the `kaggle.json` file, run the following code to set up the credentials and download the dataset in `data` directory" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "6Tl2qzgKsWtF" - }, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "\n", - "# Assuming kaggle.json is uploaded to the current directory\n", - "with open('kaggle.json') as f:\n", - " kaggle_credentials = json.load(f)\n", - "\n", - "os.environ['KAGGLE_USERNAME'] = kaggle_credentials['username']\n", - "os.environ['KAGGLE_KEY'] = kaggle_credentials['key']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8va-0of3sU0x" - }, - "outputs": [], - "source": [ - "from kaggle.api.kaggle_api_extended import KaggleApi\n", - "\n", - "# Initialize the Kaggle API\n", - "api = KaggleApi()\n", - "api.authenticate()\n", - "\n", - "# Specify the dataset you want to download\n", - "dataset = 'rounakbanik/the-movies-dataset'\n", - "destination = 'data/'\n", - "\n", - "# Create the destination directory if it doesn't exist\n", - "if not os.path.exists(destination):\n", - " os.makedirs(destination)\n", - "\n", - "# Download the dataset\n", - "api.dataset_download_files(dataset, path=destination, unzip=True)\n", - "\n", - "print(f\"Dataset {dataset} downloaded to {destination}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "hBYzad3lrY4e", - "outputId": "5a8f7983-80be-47e0-aa9c-ae4e10495c1e" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1000/1000 [00:00<00:00, 5050.83it/s]\n", - "100%|██████████| 1000/1000 [00:00<00:00, 5161.29it/s]\n", - "100%|██████████| 1000/1000 [00:00<00:00, 5006.18it/s]\n", - "100%|██████████| 1000/1000 [00:00<00:00, 5222.83it/s]\n", - "100%|██████████| 1000/1000 [00:00<00:00, 5216.24it/s]\n", - "100%|██████████| 1000/1000 [00:00<00:00, 5171.35it/s]\n", - "100%|██████████| 1000/1000 [00:00<00:00, 5109.78it/s]\n", - 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"from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n", - "from nltk.tokenize import word_tokenize\n", - "from sklearn.preprocessing import MultiLabelBinarizer\n", - "from sklearn.model_selection import train_test_split\n", - "from tqdm import tqdm\n", - "\n", - "# Read data from CSV file\n", - "movie_data = pd.read_csv('/Users/vipul/Nova/Projects/genre_spectrum/movies_metadata.csv', low_memory=False)\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "\n", - "def preprocess_data(movie_data_chunk):\n", - " tagged_docs = []\n", - " valid_indices = []\n", - " movie_info = []\n", - "\n", - " # Wrap your loop with tqdm\n", - " for i, row in tqdm(movie_data_chunk.iterrows(), total=len(movie_data_chunk)):\n", - " try:\n", - " # Constructing movie text\n", - " movies_text = ''\n", - " movies_text += \"Overview: \" + row['overview'] + '\\n'\n", - " genres = ', '.join([genre['name'] for genre in eval(row['genres'])])\n", - " movies_text += \"Overview: \" + row['overview'] + '\\n'\n", - " movies_text += \"Genres: \" + genres + '\\n'\n", - " movies_text += \"Title: \" + row['title'] + '\\n'\n", - " tagged_docs.append(TaggedDocument(words=word_tokenize(movies_text.lower()), tags=[str(i)]))\n", - " valid_indices.append(i)\n", - " movie_info.append((row['title'], genres))\n", - " except Exception as e:\n", - " continue\n", - "\n", - " return tagged_docs, valid_indices, movie_info\n", - "\n", - "def train_doc2vec_model(tagged_data, num_epochs=20):\n", - " # Initialize Doc2Vec model\n", - " doc2vec_model = Doc2Vec(vector_size=100, min_count=2, epochs=num_epochs)\n", - " doc2vec_model.build_vocab(tqdm(tagged_data, desc=\"Building Vocabulary\"))\n", - " for epoch in range(num_epochs):\n", - " doc2vec_model.train(tqdm(tagged_data, desc=f\"Epoch {epoch+1}\"), total_examples=doc2vec_model.corpus_count, epochs=doc2vec_model.epochs)\n", - "\n", - " return doc2vec_model\n", - "\n", - "# Preprocess data and extract genres for the first 1000 movies\n", - "chunk_size = 1000\n", - "tagged_data = []\n", - "valid_indices = []\n", - "movie_info = []\n", - "for chunk_start in range(0, len(movie_data), chunk_size):\n", - " movie_data_chunk = movie_data.iloc[chunk_start:chunk_start+chunk_size]\n", - " chunk_tagged_data, chunk_valid_indices, chunk_movie_info = preprocess_data(movie_data_chunk)\n", - " tagged_data.extend(chunk_tagged_data)\n", - " valid_indices.extend(chunk_valid_indices)\n", - " movie_info.extend(chunk_movie_info)\n", - "\n", - "doc2vec_model = train_doc2vec_model(tagged_data)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VryHT1zVuEp0" - }, - "source": [ - "### Training a Neural Network for the Genre Classification Task" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "3pVNy2UKt5lu" - }, - "outputs": [], - "source": [ - "# Extract genre labels for the valid indices\n", - "genres_list = []\n", - "for i in valid_indices:\n", - " row = movie_data.loc[i]\n", - " genres = [genre['name'] for genre in eval(row['genres'])]\n", - " genres_list.append(genres)\n", - "\n", - "mlb = MultiLabelBinarizer()\n", - "genre_labels = mlb.fit_transform(genres_list)\n", - "\n", - "embeddings = []\n", - "for i in valid_indices:\n", - " embeddings.append(doc2vec_model.dv[str(i)])\n", - "X_train, X_test, y_train, y_test = train_test_split(embeddings, genre_labels, test_size=0.2, random_state=42)\n", - "\n", - "X_train_np = np.array(X_train, dtype=np.float32)\n", - "y_train_np = np.array(y_train, dtype=np.float32)\n", - "X_test_np = np.array(X_test, dtype=np.float32)\n", - "y_test_np = np.array(y_test, dtype=np.float32)\n", - "\n", - "X_train_tensor = torch.tensor(X_train_np)\n", - "y_train_tensor = torch.tensor(y_train_np)\n", - "X_test_tensor = torch.tensor(X_test_np)\n", - "y_test_tensor = torch.tensor(y_test_np)\n", - "\n", - "class GenreClassifier(nn.Module):\n", - " def __init__(self, input_size, output_size):\n", - " super(GenreClassifier, self).__init__()\n", - " self.fc1 = nn.Linear(input_size, 512)\n", - " self.bn1 = nn.BatchNorm1d(512)\n", - " self.fc2 = nn.Linear(512, 256)\n", - " self.bn2 = nn.BatchNorm1d(256)\n", - " self.fc3 = nn.Linear(256, 128)\n", - " self.bn3 = nn.BatchNorm1d(128)\n", - " self.fc4 = nn.Linear(128, output_size)\n", - " self.relu = nn.ReLU()\n", - " self.dropout = nn.Dropout(p=0.2) # Adjust the dropout rate as needed\n", - "\n", - " def forward(self, x):\n", - " x = self.fc1(x)\n", - " x = self.bn1(x)\n", - " x = self.relu(x)\n", - " x = self.dropout(x)\n", - " x = self.fc2(x)\n", - " x = self.bn2(x)\n", - " x = self.relu(x)\n", - " x = self.dropout(x)\n", - " x = self.fc3(x)\n", - " x = self.bn3(x)\n", - " x = self.relu(x)\n", - " x = self.dropout(x)\n", - " x = self.fc4(x)\n", - " return x\n", - "\n", - "# Move model to the selected device\n", - "model = GenreClassifier(input_size=100, output_size=len(mlb.classes_)).to(device)\n", - "\n", - "# Define loss function and optimizer\n", - "criterion = nn.BCEWithLogitsLoss()\n", - "optimizer = optim.Adam(model.parameters(), lr=0.001)\n", - "\n", - "# Training loop\n", - "epochs = 50\n", - "batch_size = 64\n", - "\n", - "train_dataset = TensorDataset(X_train_tensor.to(device), y_train_tensor.to(device))\n", - "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", - "\n", - "for epoch in range(epochs):\n", - " model.train()\n", - " running_loss = 0.0\n", - " for inputs, labels in train_loader:\n", - " inputs, labels = inputs.to(device), labels.to(device) # Move data to device\n", - " optimizer.zero_grad()\n", - " outputs = model(inputs)\n", - " loss = criterion(outputs, labels)\n", - " loss.backward()\n", - " optimizer.step()\n", - " running_loss += loss.item() * inputs.size(0)\n", - " epoch_loss = running_loss / len(train_loader.dataset)\n", - " print(f'Epoch [{epoch + 1}/{epochs}], Loss: {epoch_loss:.4f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "yV8lTDYIubEQ" - }, - "source": [ - "### Testing the `model` to see if our model is able to predict the genres for the movies from the test dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "73D3aqdJuct8" - }, - "outputs": [], - "source": [ - "from sklearn.metrics import f1_score\n", - "\n", - "model.eval()\n", - "with torch.no_grad():\n", - " X_test_tensor, y_test_tensor = X_test_tensor.to(device), y_test_tensor.to(device) # Move test data to device\n", - " outputs = model(X_test_tensor)\n", - " test_loss = criterion(outputs, y_test_tensor)\n", - " print(f'Test Loss: {test_loss.item():.4f}')\n", - "\n", - "\n", - "thresholds = [0.1] * len(mlb.classes_)\n", - "thresholds_tensor = torch.tensor(thresholds, device=device).unsqueeze(0)\n", - "\n", - "# Convert the outputs to binary predictions using varying thresholds\n", - "predicted_labels = (outputs > thresholds_tensor).cpu().numpy()\n", - "\n", - "# Convert binary predictions and actual labels to multi-label format\n", - "predicted_multilabels = mlb.inverse_transform(predicted_labels)\n", - "actual_multilabels = mlb.inverse_transform(y_test_np)\n", - "\n", - "# Print the Predicted and Actual Labels for each movie\n", - "for i, (predicted, actual) in enumerate(zip(predicted_multilabels, actual_multilabels)):\n", - " print(f'Movie {i+1}:')\n", - " print(f' Predicted Labels: {predicted}')\n", - " print(f' Actual Labels: {actual}')\n", - "\n", - "\n", - "# Compute F1-score\n", - "f1 = f1_score(y_test_np, predicted_labels, average='micro')\n", - "print(f'F1-score: {f1:.4f}')\n", - "\n", - "# Saving the trained model\n", - "torch.save(model.state_dict(), 'trained_model.pth')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kZrHpMm4un0G" - }, - "source": [ - "### Storing the Doc2Vec Embeddings into LanceDB VectorDatabase" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "BTTNb9irrY4h" - }, - "outputs": [], - "source": [ - "import lancedb\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "data = []\n", - "\n", - "for i in valid_indices:\n", - " embedding = doc2vec_model.dv[str(i)]\n", - " title, genres = movie_info[valid_indices.index(i)]\n", - " data.append({\"title\": title, \"genres\": genres, \"vector\": embedding.tolist()})\n", - "\n", - "db = lancedb.connect(\".db\")\n", - "tbl = db.create_table(\"doc2vec_embeddings\", data, mode=\"Overwrite\")\n", - "db[\"doc2vec_embeddings\"].head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ciUFn7uQrY4i" - }, - "outputs": [], - "source": [ - "def get_recommendations(title):\n", - " pd_data = pd.DataFrame(data)\n", - " result = (\n", - " tbl.search(pd_data[pd_data[\"title\"] == title][\"vector\"].values[0]).metric(\"cosine\")\n", - " .limit(10)\n", - " .to_pandas()\n", - " )\n", - " return result[[\"title\"]]" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "K45xhdPRsZJV" + }, + "source": [ + "# Movie Recommendation System using Doc2vec Embeddings and Vector DB" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XUj6NXD0sdgf" + }, + "source": [ + "This Colab notebook aims to illustrate the process of creating a recommendation system using embeddings and a Vector DB.\n", + "\n", + "This approach involves combining the various movie genres or characteristics of a movie to form Doc2Vec embeddings, which offer a comprehensive portrayal of the movie content.\n", + "\n", + "These embeddings serve dual purposes: they can either be directly inputted into a classification model for genre classification or stored in a VectorDB. By storing embeddings in a VectorDB, efficient retrieval and query search for recommendations become possible at a later stage.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qEa74a_Wtpc7" + }, + "source": [ + "### Installing the relevant dependencies\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hyde90IntuFi" + }, + "outputs": [], + "source": [ + "!pip install torch scikit-learn lancedb nltk gensim lancedb scipy==1.12 kaggle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "shPjHTZbtxTh" + }, + "source": [ + "## Kaggle Configuration and Data Needs\n", + "\n", + "We are using a movies metadata data which is being uploaded on the Kaggle. To download the dataset and use it for our recommendation system, we will need a `kaggle.json` file containing our creds.\n", + "\n", + "You can download the `kaggle.json` file from your Kaggle account settings. Follow these steps and make your life easy.\n", + "\n", + "1. Go to Kaggle and log in to your account.\n", + "2. Navigate to Your Account Settings and click on your profile picture in the top right corner of the page, Now From the dropdown menu, select `Account`.\n", + "3. Scroll down to the `API` section, Click on `Create New API Token`. This will download a file named kaggle.json to your computer.\n", + "\n", + "Once you have the `kaggle.json` file, you need to upload it here on colab data space. After uploading the `kaggle.json` file, run the following code to set up the credentials and download the dataset in `data` directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6Tl2qzgKsWtF" + }, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "\n", + "# Assuming kaggle.json is uploaded to the current directory\n", + "with open(\"kaggle.json\") as f:\n", + " kaggle_credentials = json.load(f)\n", + "\n", + "os.environ[\"KAGGLE_USERNAME\"] = kaggle_credentials[\"username\"]\n", + "os.environ[\"KAGGLE_KEY\"] = kaggle_credentials[\"key\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8va-0of3sU0x" + }, + "outputs": [], + "source": [ + "from kaggle.api.kaggle_api_extended import KaggleApi\n", + "\n", + "# Initialize the Kaggle API\n", + "api = KaggleApi()\n", + "api.authenticate()\n", + "\n", + "# Specify the dataset you want to download\n", + "dataset = \"rounakbanik/the-movies-dataset\"\n", + "destination = \"data/\"\n", + "\n", + "# Create the destination directory if it doesn't exist\n", + "if not os.path.exists(destination):\n", + " os.makedirs(destination)\n", + "\n", + "# Download the dataset\n", + "api.dataset_download_files(dataset, path=destination, unzip=True)\n", + "\n", + "print(f\"Dataset {dataset} downloaded to {destination}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hBYzad3lrY4e", + "outputId": "5a8f7983-80be-47e0-aa9c-ae4e10495c1e" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "8Kz-JGsTuwmk" - }, - "source": [ - "### D-Day : Let's generate some recommendations" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1000/1000 [00:00<00:00, 5050.83it/s]\n", + "100%|██████████| 1000/1000 [00:00<00:00, 5161.29it/s]\n", + "100%|██████████| 1000/1000 [00:00<00:00, 5006.18it/s]\n", + "100%|██████████| 1000/1000 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21206.01it/s]\n", + "Epoch 19: 100%|██████████| 44506/44506 [00:02<00:00, 20086.00it/s]\n", + "Epoch 20: 100%|██████████| 44506/44506 [00:02<00:00, 20943.08it/s]\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.utils.data import DataLoader, TensorDataset\n", + "from gensim.models.doc2vec import Doc2Vec, TaggedDocument\n", + "from nltk.tokenize import word_tokenize\n", + "from sklearn.preprocessing import MultiLabelBinarizer\n", + "from sklearn.model_selection import train_test_split\n", + "from tqdm import tqdm\n", + "\n", + "# Read data from CSV file\n", + "movie_data = pd.read_csv(\n", + " \"/Users/vipul/Nova/Projects/genre_spectrum/movies_metadata.csv\", low_memory=False\n", + ")\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "\n", + "def preprocess_data(movie_data_chunk):\n", + " tagged_docs = []\n", + " valid_indices = []\n", + " movie_info = []\n", + "\n", + " # Wrap your loop with tqdm\n", + " for i, row in tqdm(movie_data_chunk.iterrows(), total=len(movie_data_chunk)):\n", + " try:\n", + " # Constructing movie text\n", + " movies_text = \"\"\n", + " movies_text += \"Overview: \" + row[\"overview\"] + \"\\n\"\n", + " genres = \", \".join([genre[\"name\"] for genre in eval(row[\"genres\"])])\n", + " movies_text += \"Overview: \" + row[\"overview\"] + \"\\n\"\n", + " movies_text += \"Genres: \" + genres + \"\\n\"\n", + " movies_text += \"Title: \" + row[\"title\"] + \"\\n\"\n", + " tagged_docs.append(\n", + " TaggedDocument(words=word_tokenize(movies_text.lower()), tags=[str(i)])\n", + " )\n", + " valid_indices.append(i)\n", + " movie_info.append((row[\"title\"], genres))\n", + " except Exception as e:\n", + " continue\n", + "\n", + " return tagged_docs, valid_indices, movie_info\n", + "\n", + "\n", + "def train_doc2vec_model(tagged_data, num_epochs=20):\n", + " # Initialize Doc2Vec model\n", + " doc2vec_model = Doc2Vec(vector_size=100, min_count=2, epochs=num_epochs)\n", + " doc2vec_model.build_vocab(tqdm(tagged_data, desc=\"Building Vocabulary\"))\n", + " for epoch in range(num_epochs):\n", + " doc2vec_model.train(\n", + " tqdm(tagged_data, desc=f\"Epoch {epoch+1}\"),\n", + " total_examples=doc2vec_model.corpus_count,\n", + " epochs=doc2vec_model.epochs,\n", + " )\n", + "\n", + " return doc2vec_model\n", + "\n", + "\n", + "# Preprocess data and extract genres for the first 1000 movies\n", + "chunk_size = 1000\n", + "tagged_data = []\n", + "valid_indices = []\n", + "movie_info = []\n", + "for chunk_start in range(0, len(movie_data), chunk_size):\n", + " movie_data_chunk = movie_data.iloc[chunk_start : chunk_start + chunk_size]\n", + " chunk_tagged_data, chunk_valid_indices, chunk_movie_info = preprocess_data(\n", + " movie_data_chunk\n", + " )\n", + " tagged_data.extend(chunk_tagged_data)\n", + " valid_indices.extend(chunk_valid_indices)\n", + " movie_info.extend(chunk_movie_info)\n", + "\n", + "doc2vec_model = train_doc2vec_model(tagged_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VryHT1zVuEp0" + }, + "source": [ + "### Training a Neural Network for the Genre Classification Task" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3pVNy2UKt5lu" + }, + "outputs": [], + "source": [ + "# Extract genre labels for the valid indices\n", + "genres_list = []\n", + "for i in valid_indices:\n", + " row = movie_data.loc[i]\n", + " genres = [genre[\"name\"] for genre in eval(row[\"genres\"])]\n", + " genres_list.append(genres)\n", + "\n", + "mlb = MultiLabelBinarizer()\n", + "genre_labels = mlb.fit_transform(genres_list)\n", + "\n", + "embeddings = []\n", + "for i in valid_indices:\n", + " embeddings.append(doc2vec_model.dv[str(i)])\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " embeddings, genre_labels, test_size=0.2, random_state=42\n", + ")\n", + "\n", + "X_train_np = np.array(X_train, dtype=np.float32)\n", + "y_train_np = np.array(y_train, dtype=np.float32)\n", + "X_test_np = np.array(X_test, dtype=np.float32)\n", + "y_test_np = np.array(y_test, dtype=np.float32)\n", + "\n", + "X_train_tensor = torch.tensor(X_train_np)\n", + "y_train_tensor = torch.tensor(y_train_np)\n", + "X_test_tensor = torch.tensor(X_test_np)\n", + "y_test_tensor = torch.tensor(y_test_np)\n", + "\n", + "\n", + "class GenreClassifier(nn.Module):\n", + " def __init__(self, input_size, output_size):\n", + " super(GenreClassifier, self).__init__()\n", + " self.fc1 = nn.Linear(input_size, 512)\n", + " self.bn1 = nn.BatchNorm1d(512)\n", + " self.fc2 = nn.Linear(512, 256)\n", + " self.bn2 = nn.BatchNorm1d(256)\n", + " self.fc3 = nn.Linear(256, 128)\n", + " self.bn3 = nn.BatchNorm1d(128)\n", + " self.fc4 = nn.Linear(128, output_size)\n", + " self.relu = nn.ReLU()\n", + " self.dropout = nn.Dropout(p=0.2) # Adjust the dropout rate as needed\n", + "\n", + " def forward(self, x):\n", + " x = self.fc1(x)\n", + " x = self.bn1(x)\n", + " x = self.relu(x)\n", + " x = self.dropout(x)\n", + " x = self.fc2(x)\n", + " x = self.bn2(x)\n", + " x = self.relu(x)\n", + " x = self.dropout(x)\n", + " x = self.fc3(x)\n", + " x = self.bn3(x)\n", + " x = self.relu(x)\n", + " x = self.dropout(x)\n", + " x = self.fc4(x)\n", + " return x\n", + "\n", + "\n", + "# Move model to the selected device\n", + "model = GenreClassifier(input_size=100, output_size=len(mlb.classes_)).to(device)\n", + "\n", + "# Define loss function and optimizer\n", + "criterion = nn.BCEWithLogitsLoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.001)\n", + "\n", + "# Training loop\n", + "epochs = 50\n", + "batch_size = 64\n", + "\n", + "train_dataset = TensorDataset(X_train_tensor.to(device), y_train_tensor.to(device))\n", + "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", + "\n", + "for epoch in range(epochs):\n", + " model.train()\n", + " running_loss = 0.0\n", + " for inputs, labels in train_loader:\n", + " inputs, labels = inputs.to(device), labels.to(device) # Move data to device\n", + " optimizer.zero_grad()\n", + " outputs = model(inputs)\n", + " loss = criterion(outputs, labels)\n", + " loss.backward()\n", + " optimizer.step()\n", + " running_loss += loss.item() * inputs.size(0)\n", + " epoch_loss = running_loss / len(train_loader.dataset)\n", + " print(f\"Epoch [{epoch + 1}/{epochs}], Loss: {epoch_loss:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yV8lTDYIubEQ" + }, + "source": [ + "### Testing the `model` to see if our model is able to predict the genres for the movies from the test dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "73D3aqdJuct8" + }, + "outputs": [], + "source": [ + "from sklearn.metrics import f1_score\n", + "\n", + "model.eval()\n", + "with torch.no_grad():\n", + " X_test_tensor, y_test_tensor = X_test_tensor.to(device), y_test_tensor.to(\n", + " device\n", + " ) # Move test data to device\n", + " outputs = model(X_test_tensor)\n", + " test_loss = criterion(outputs, y_test_tensor)\n", + " print(f\"Test Loss: {test_loss.item():.4f}\")\n", + "\n", + "\n", + "thresholds = [0.1] * len(mlb.classes_)\n", + "thresholds_tensor = torch.tensor(thresholds, device=device).unsqueeze(0)\n", + "\n", + "# Convert the outputs to binary predictions using varying thresholds\n", + "predicted_labels = (outputs > thresholds_tensor).cpu().numpy()\n", + "\n", + "# Convert binary predictions and actual labels to multi-label format\n", + "predicted_multilabels = mlb.inverse_transform(predicted_labels)\n", + "actual_multilabels = mlb.inverse_transform(y_test_np)\n", + "\n", + "# Print the Predicted and Actual Labels for each movie\n", + "for i, (predicted, actual) in enumerate(zip(predicted_multilabels, actual_multilabels)):\n", + " print(f\"Movie {i+1}:\")\n", + " print(f\" Predicted Labels: {predicted}\")\n", + " print(f\" Actual Labels: {actual}\")\n", + "\n", + "\n", + "# Compute F1-score\n", + "f1 = f1_score(y_test_np, predicted_labels, average=\"micro\")\n", + "print(f\"F1-score: {f1:.4f}\")\n", + "\n", + "# Saving the trained model\n", + "torch.save(model.state_dict(), \"trained_model.pth\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kZrHpMm4un0G" + }, + "source": [ + "### Storing the Doc2Vec Embeddings into LanceDB VectorDatabase" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BTTNb9irrY4h" + }, + "outputs": [], + "source": [ + "import lancedb\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "data = []\n", + "\n", + "for i in valid_indices:\n", + " embedding = doc2vec_model.dv[str(i)]\n", + " title, genres = movie_info[valid_indices.index(i)]\n", + " data.append({\"title\": title, \"genres\": genres, \"vector\": embedding.tolist()})\n", + "\n", + "db = lancedb.connect(\".db\")\n", + "tbl = db.create_table(\"doc2vec_embeddings\", data, mode=\"Overwrite\")\n", + "db[\"doc2vec_embeddings\"].head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ciUFn7uQrY4i" + }, + "outputs": [], + "source": [ + "def get_recommendations(title):\n", + " pd_data = pd.DataFrame(data)\n", + " result = (\n", + " tbl.search(pd_data[pd_data[\"title\"] == title][\"vector\"].values[0])\n", + " .metric(\"cosine\")\n", + " .limit(10)\n", + " .to_pandas()\n", + " )\n", + " return result[[\"title\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8Kz-JGsTuwmk" + }, + "source": [ + "### D-Day : Let's generate some recommendations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uw_El12JrY4j", + "outputId": "c245bab5-7966-4fd1-ec72-37f708c3b570" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "uw_El12JrY4j", - "outputId": 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"kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } From e6fd9eadd8b7909c735a9df61b76a15a9a626ab0 Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Mon, 3 Jun 2024 10:31:22 +0530 Subject: [PATCH 10/16] README update (#198) --- README.md | 1 + assets/movie-recommendation-with-genre.png | Bin 0 -> 215245 bytes .../movie-recommendation-with-genres/README.md | 6 +++--- tutorials/cohere-reranker/README.md | 4 ++-- 4 files changed, 6 insertions(+), 5 deletions(-) create mode 100644 assets/movie-recommendation-with-genre.png diff --git a/README.md b/README.md index 11d18ae..6df6a08 100644 --- a/README.md +++ b/README.md @@ -47,6 +47,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Multimodal CLIP: Youtube videos](/examples/multimodal_video_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_video_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)| | [Multimodal Image + Text Search](/examples/multimodal_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)| | [Movie Recommender](/examples/movie-recommender/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/movie-recommender/main.py) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | +| [Movie Recommender with Genre](./examples/movie-recommendation-with-genres/) | Open In Colab [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/movie-recommendation-system-using-lancedb-and-doc2vec/)| | [Product Recommender](./examples/product-recommender/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/product-recommender/main.py)[![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| | | [Arxiv paper recommender](/examples/arxiv-recommender) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/arxiv-recommender/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | | [Improve RAG with Re-ranking](/examples/RAG_Reranking/) | Open In Colab [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)| diff --git 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zjd;DZex>w!&zx(ggB}O>u3Om`BsI3Bd-1HFw<|u*S~MB01Qh-?&nl%ecF#$U68>7V zGWgfcf}DE2ulF}T{JDK{cYMtN^Sf0s4Gj(O4*iI(*X;UvhQUG+MTHTz+L2G%&c7;9 zY`>`V^@3R6Eq%ib-YW+2*H^Wo0~4_Ch=h__03OqZ=nhgTIJ0a^$Lim%bqE){>-#b4 z*SPOJ8s&ZN$NNXm>W^O<(EX|ZP+!{U_O?Y0BeWYw+%nwwW@ADBQtdvUSwFvQeR{TM zjzGQH?{O!$#7nAciKza1dK8Hceme5x+(Cc6oBqh4-~L2@<%0p|^YaB+31vH&4}AQZ zH0t0B+rD*wNin>_$Q`LMW5go7bzaliqv#j2taI1=gu26y!dFB+rMlt;>29*HP4|sa zq5Z*}OejL~fPGpa1=yeq8~MTX3-=SL3Nt%D-Rx-%sS$M*OR>5SqVu<=X##i>FlO|Nf)U f|F0ix=oe~}cFDavsK9vu|CyUu8UJPIy!(Fub&OxK literal 0 HcmV?d00001 diff --git a/examples/movie-recommendation-with-genres/README.md b/examples/movie-recommendation-with-genres/README.md index f1af0bf..f79e4e4 100644 --- a/examples/movie-recommendation-with-genres/README.md +++ b/examples/movie-recommendation-with-genres/README.md @@ -1,9 +1,9 @@ # Movie Recommendation using Emebeddings and VectorDB -![image](https://github.com/vipul-maheshwari/vipul-maheshwari.github.io/blob/main/images/movie-recommendation-using-rag/doc2vec_final.png?raw=true) +![alt text](../../assets/movie-recommendation-with-genre.png) This example provides a comprehensive guide on creating a movie recommendation system by leveraging the power of Embeddings and VectorDB. We'll explore how combining these two techniques can significantly enhance the recommendation experience, addressing key challenges faced by traditional systems. -Colab walkthrough - Open In Colab +Colab walkthrough - Open In Colab -[Read the Blog Post](https://blog.lancedb.com/movie-recommendation-system-using-lancedb-and-doc2vec/) +[Read the Blog Post](https://blog.lancedb.com/movie-recommendation-system-using-lancedb-and-doc2vec/) \ No newline at end of file diff --git a/tutorials/cohere-reranker/README.md b/tutorials/cohere-reranker/README.md index 6f5802b..c2c8fe7 100644 --- a/tutorials/cohere-reranker/README.md +++ b/tutorials/cohere-reranker/README.md @@ -1,8 +1,8 @@ -Code for "Benchmarking Cohere Rerankers with LanceDB" +# Benchmarking Cohere Rerankers with LanceDB Screenshot-2024-05-06-at-6 06 30-PM -### [Read the blog](blog.lancedb.com) +### [Read the blog](https://blog.lancedb.com/benchmarking-cohere-reranker-with-lancedb/) ## Setup ``` From 40e36c7bb24122a3525f25a653e99f58b381a954 Mon Sep 17 00:00:00 2001 From: Ayush Chaurasia Date: Thu, 6 Jun 2024 16:21:39 +0530 Subject: [PATCH 11/16] Add local illustrator to DBRX chatbot (#199) * update * format * update * update --------- Co-authored-by: Ayush --- .../databricks_DBRX_website_bot/README.md | 11 +- .../databricks_DBRX_website_bot/__init__.py | 0 .../diffusion_mlx/__init__.py | 306 ++++++++++++ .../diffusion_mlx/clip.py | 116 +++++ .../diffusion_mlx/config.py | 65 +++ .../diffusion_mlx/model_io.py | 330 +++++++++++++ .../diffusion_mlx/sampler.py | 105 ++++ .../diffusion_mlx/tokenizer.py | 100 ++++ .../diffusion_mlx/unet.py | 461 ++++++++++++++++++ .../diffusion_mlx/vae.py | 274 +++++++++++ .../databricks_DBRX_website_bot/gen_image.py | 142 ++++++ examples/databricks_DBRX_website_bot/gui.py | 76 +++ examples/databricks_DBRX_website_bot/main.py | 58 ++- .../requirements.txt | 10 +- 14 files changed, 2044 insertions(+), 10 deletions(-) create mode 100644 examples/databricks_DBRX_website_bot/__init__.py create mode 100644 examples/databricks_DBRX_website_bot/diffusion_mlx/__init__.py create mode 100644 examples/databricks_DBRX_website_bot/diffusion_mlx/clip.py create mode 100644 examples/databricks_DBRX_website_bot/diffusion_mlx/config.py create mode 100644 examples/databricks_DBRX_website_bot/diffusion_mlx/model_io.py create mode 100644 examples/databricks_DBRX_website_bot/diffusion_mlx/sampler.py create mode 100644 examples/databricks_DBRX_website_bot/diffusion_mlx/tokenizer.py create mode 100644 examples/databricks_DBRX_website_bot/diffusion_mlx/unet.py create mode 100644 examples/databricks_DBRX_website_bot/diffusion_mlx/vae.py create mode 100644 examples/databricks_DBRX_website_bot/gen_image.py create mode 100644 examples/databricks_DBRX_website_bot/gui.py diff --git a/examples/databricks_DBRX_website_bot/README.md b/examples/databricks_DBRX_website_bot/README.md index e99db29..18d3883 100644 --- a/examples/databricks_DBRX_website_bot/README.md +++ b/examples/databricks_DBRX_website_bot/README.md @@ -15,7 +15,7 @@ export DATABRICKS_TOKEN= DATABRICKS_SERVING_ENDPOINT= ``` -3. Run the application +3. Run the application in CLI mode ``` python main.py ``` @@ -25,3 +25,12 @@ Accepted arguments: - `embed_model`: Huggingface model to use for embeddings. Default is `mixedbread-ai/mxbai-embed-large-v1`. - `uri`: URI of the vector store. Default is `~/tmp/lancedb_hogwarts`. - `force_create_embeddings`: Whether to force create embeddings. Default is `False`. +- `illustrate`: Whether to illustrate the responses. Default is `True`. + +4. Run the application in GUI mode +``` +streamlit run gui.py +``` + +## MLX SDXL +The MLX SDXL implementation is taken from MLX [examples repo](https://github.com/ml-explore/mlx-examples/tree/main/stable_diffusion). The implementation is modified a bit to make it work faster with the current application. \ No newline at end of file diff --git a/examples/databricks_DBRX_website_bot/__init__.py b/examples/databricks_DBRX_website_bot/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/examples/databricks_DBRX_website_bot/diffusion_mlx/__init__.py b/examples/databricks_DBRX_website_bot/diffusion_mlx/__init__.py new file mode 100644 index 0000000..f266816 --- /dev/null +++ b/examples/databricks_DBRX_website_bot/diffusion_mlx/__init__.py @@ -0,0 +1,306 @@ +# Copyright © 2023-2024 Apple Inc. + +import time +from typing import Optional, Tuple + +import mlx.core as mx + +from .model_io import ( + _DEFAULT_MODEL, + load_autoencoder, + load_diffusion_config, + load_text_encoder, + load_tokenizer, + load_unet, +) +from .sampler import SimpleEulerAncestralSampler, SimpleEulerSampler + + +class StableDiffusion: + def __init__(self, model: str = _DEFAULT_MODEL, float16: bool = True): + self.dtype = mx.float16 if float16 else mx.float32 + self.diffusion_config = load_diffusion_config(model) + self.unet = load_unet(model, float16) + self.text_encoder = load_text_encoder(model, float16) + self.autoencoder = load_autoencoder(model, False) + self.sampler = SimpleEulerSampler(self.diffusion_config) + self.tokenizer = load_tokenizer(model) + + def ensure_models_are_loaded(self): + mx.eval(self.unet.parameters()) + mx.eval(self.text_encoder.parameters()) + mx.eval(self.autoencoder.parameters()) + + def _tokenize(self, tokenizer, text: str, negative_text: Optional[str] = None): + # Tokenize the text + tokens = [tokenizer.tokenize(text)] + if negative_text is not None: + tokens += [tokenizer.tokenize(negative_text)] + lengths = [len(t) for t in tokens] + N = max(lengths) + tokens = [t + [0] * (N - len(t)) for t in tokens] + tokens = mx.array(tokens) + + return tokens + + def _get_text_conditioning( + self, + text: str, + n_images: int = 1, + cfg_weight: float = 7.5, + negative_text: str = "", + ): + # Tokenize the text + tokens = self._tokenize( + self.tokenizer, text, (negative_text if cfg_weight > 1 else None) + ) + + # Compute the features + conditioning = self.text_encoder(tokens).last_hidden_state + + # Repeat the conditioning for each of the generated images + if n_images > 1: + conditioning = mx.repeat(conditioning, n_images, axis=0) + + return conditioning + + def _denoising_step( + self, x_t, t, t_prev, conditioning, cfg_weight: float = 7.5, text_time=None + ): + x_t_unet = mx.concatenate([x_t] * 2, axis=0) if cfg_weight > 1 else x_t + t_unet = mx.broadcast_to(t, [len(x_t_unet)]) + eps_pred = self.unet( + x_t_unet, t_unet, encoder_x=conditioning, text_time=text_time + ) + + if cfg_weight > 1: + eps_text, eps_neg = eps_pred.split(2) + eps_pred = eps_neg + cfg_weight * (eps_text - eps_neg) + + x_t_prev = self.sampler.step(eps_pred, x_t, t, t_prev) + + return x_t_prev + + def _denoising_loop( + self, + x_T, + T, + conditioning, + num_steps: int = 50, + cfg_weight: float = 7.5, + text_time=None, + ): + x_t = x_T + for t, t_prev in self.sampler.timesteps( + num_steps, start_time=T, dtype=self.dtype + ): + x_t = self._denoising_step( + x_t, t, t_prev, conditioning, cfg_weight, text_time + ) + yield x_t + + def generate_latents( + self, + text: str, + n_images: int = 1, + num_steps: int = 50, + cfg_weight: float = 7.5, + negative_text: str = "", + latent_size: Tuple[int] = (64, 64), + seed=None, + ): + # Set the PRNG state + seed = int(time.time()) if seed is None else seed + mx.random.seed(seed) + + # Get the text conditioning + conditioning = self._get_text_conditioning( + text, n_images, cfg_weight, negative_text + ) + + # Create the latent variables + x_T = self.sampler.sample_prior( + (n_images, *latent_size, self.autoencoder.latent_channels), dtype=self.dtype + ) + + # Perform the denoising loop + yield from self._denoising_loop( + x_T, self.sampler.max_time, conditioning, num_steps, cfg_weight + ) + + def generate_latents_from_image( + self, + image, + text: str, + n_images: int = 1, + strength: float = 0.8, + num_steps: int = 50, + cfg_weight: float = 7.5, + negative_text: str = "", + seed=None, + ): + # Set the PRNG state + seed = int(time.time()) if seed is None else seed + mx.random.seed(seed) + + # Define the num steps and start step + start_step = self.sampler.max_time * strength + num_steps = int(num_steps * strength) + + # Get the text conditioning + conditioning = self._get_text_conditioning( + text, n_images, cfg_weight, negative_text + ) + + # Get the latents from the input image and add noise according to the + # start time. + x_0, _ = self.autoencoder.encode(image[None]) + x_0 = mx.broadcast_to(x_0, (n_images,) + x_0.shape[1:]) + x_T = self.sampler.add_noise(x_0, mx.array(start_step)) + + # Perform the denoising loop + yield from self._denoising_loop( + x_T, start_step, conditioning, num_steps, cfg_weight + ) + + def decode(self, x_t): + x = self.autoencoder.decode(x_t) + x = mx.clip(x / 2 + 0.5, 0, 1) + return x + + +class StableDiffusionXL(StableDiffusion): + def __init__(self, model: str = _DEFAULT_MODEL, float16: bool = False): + super().__init__(model, float16) + + self.sampler = SimpleEulerAncestralSampler(self.diffusion_config) + + self.text_encoder_1 = self.text_encoder + self.tokenizer_1 = self.tokenizer + del self.tokenizer, self.text_encoder + + self.text_encoder_2 = load_text_encoder( + model, + float16, + model_key="text_encoder_2", + ) + self.tokenizer_2 = load_tokenizer( + model, + merges_key="tokenizer_2_merges", + vocab_key="tokenizer_2_vocab", + ) + + def ensure_models_are_loaded(self): + mx.eval(self.unet.parameters()) + mx.eval(self.text_encoder_1.parameters()) + mx.eval(self.text_encoder_2.parameters()) + mx.eval(self.autoencoder.parameters()) + + def _get_text_conditioning( + self, + text: str, + n_images: int = 1, + cfg_weight: float = 7.5, + negative_text: str = "", + ): + tokens_1 = self._tokenize( + self.tokenizer_1, + text, + (negative_text if cfg_weight > 1 else None), + ) + tokens_2 = self._tokenize( + self.tokenizer_2, + text, + (negative_text if cfg_weight > 1 else None), + ) + + conditioning_1 = self.text_encoder_1(tokens_1) + conditioning_2 = self.text_encoder_2(tokens_2) + conditioning = mx.concatenate( + [conditioning_1.hidden_states[-2], conditioning_2.hidden_states[-2]], + axis=-1, + ) + pooled_conditioning = conditioning_2.pooled_output + + if n_images > 1: + conditioning = mx.repeat(conditioning, n_images, axis=0) + pooled_conditioning = mx.repeat(pooled_conditioning, n_images, axis=0) + + return conditioning, pooled_conditioning + + def generate_latents( + self, + text: str, + n_images: int = 1, + num_steps: int = 2, + cfg_weight: float = 0.0, + negative_text: str = "", + latent_size: Tuple[int] = (64, 64), + seed=None, + ): + # Set the PRNG state + seed = int(time.time()) if seed is None else seed + mx.random.seed(seed) + + # Get the text conditioning + conditioning, pooled_conditioning = self._get_text_conditioning( + text, n_images, cfg_weight, negative_text + ) + text_time = ( + pooled_conditioning, + mx.array([[512, 512, 0, 0, 512, 512.0]] * len(pooled_conditioning)), + ) + + # Create the latent variables + x_T = self.sampler.sample_prior( + (n_images, *latent_size, self.autoencoder.latent_channels), dtype=self.dtype + ) + + # Perform the denoising loop + yield from self._denoising_loop( + x_T, + self.sampler.max_time, + conditioning, + num_steps, + cfg_weight, + text_time=text_time, + ) + + def generate_latents_from_image( + self, + image, + text: str, + n_images: int = 1, + strength: float = 0.8, + num_steps: int = 2, + cfg_weight: float = 0.0, + negative_text: str = "", + seed=None, + ): + # Set the PRNG state + seed = seed or int(time.time()) + mx.random.seed(seed) + + # Define the num steps and start step + start_step = self.sampler.max_time * strength + num_steps = int(num_steps * strength) + + # Get the text conditioning + conditioning, pooled_conditioning = self._get_text_conditioning( + text, n_images, cfg_weight, negative_text + ) + text_time = ( + pooled_conditioning, + mx.array([[512, 512, 0, 0, 512, 512.0]] * len(pooled_conditioning)), + ) + + # Get the latents from the input image and add noise according to the + # start time. + x_0, _ = self.autoencoder.encode(image[None]) + x_0 = mx.broadcast_to(x_0, (n_images,) + x_0.shape[1:]) + x_T = self.sampler.add_noise(x_0, mx.array(start_step)) + + # Perform the denoising loop + yield from self._denoising_loop( + x_T, start_step, conditioning, num_steps, cfg_weight, text_time=text_time + ) diff --git a/examples/databricks_DBRX_website_bot/diffusion_mlx/clip.py b/examples/databricks_DBRX_website_bot/diffusion_mlx/clip.py new file mode 100644 index 0000000..b5e11fd --- /dev/null +++ b/examples/databricks_DBRX_website_bot/diffusion_mlx/clip.py @@ -0,0 +1,116 @@ +# Copyright © 2023-2024 Apple Inc. + +from dataclasses import dataclass +from typing import List, Optional + +import mlx.core as mx +import mlx.nn as nn + +from .config import CLIPTextModelConfig + +_ACTIVATIONS = {"quick_gelu": nn.gelu_fast_approx, "gelu": nn.gelu} + + +@dataclass +class CLIPOutput: + # The last_hidden_state indexed at the EOS token and possibly projected if + # the model has a projection layer + pooled_output: Optional[mx.array] = None + + # The full sequence output of the transformer after the final layernorm + last_hidden_state: Optional[mx.array] = None + + # A list of hidden states corresponding to the outputs of the transformer layers + hidden_states: Optional[List[mx.array]] = None + + +class CLIPEncoderLayer(nn.Module): + """The transformer encoder layer from CLIP.""" + + def __init__(self, model_dims: int, num_heads: int, activation: str): + super().__init__() + + self.layer_norm1 = nn.LayerNorm(model_dims) + self.layer_norm2 = nn.LayerNorm(model_dims) + + self.attention = nn.MultiHeadAttention(model_dims, num_heads) + # Add biases to the attention projections to match CLIP + self.attention.query_proj.bias = mx.zeros(model_dims) + self.attention.key_proj.bias = mx.zeros(model_dims) + self.attention.value_proj.bias = mx.zeros(model_dims) + self.attention.out_proj.bias = mx.zeros(model_dims) + + self.linear1 = nn.Linear(model_dims, 4 * model_dims) + self.linear2 = nn.Linear(4 * model_dims, model_dims) + + self.act = _ACTIVATIONS[activation] + + def __call__(self, x, attn_mask=None): + y = self.layer_norm1(x) + y = self.attention(y, y, y, attn_mask) + x = y + x + + y = self.layer_norm2(x) + y = self.linear1(y) + y = self.act(y) + y = self.linear2(y) + x = y + x + + return x + + +class CLIPTextModel(nn.Module): + """Implements the text encoder transformer from CLIP.""" + + def __init__(self, config: CLIPTextModelConfig): + super().__init__() + + self.token_embedding = nn.Embedding(config.vocab_size, config.model_dims) + self.position_embedding = nn.Embedding(config.max_length, config.model_dims) + self.layers = [ + CLIPEncoderLayer(config.model_dims, config.num_heads, config.hidden_act) + for i in range(config.num_layers) + ] + self.final_layer_norm = nn.LayerNorm(config.model_dims) + + if config.projection_dim is not None: + self.text_projection = nn.Linear( + config.model_dims, config.projection_dim, bias=False + ) + + def _get_mask(self, N, dtype): + indices = mx.arange(N) + mask = indices[:, None] < indices[None] + mask = mask.astype(dtype) * (-6e4 if dtype == mx.float16 else -1e9) + return mask + + def __call__(self, x): + # Extract some shapes + B, N = x.shape + eos_tokens = x.argmax(-1) + + # Compute the embeddings + x = self.token_embedding(x) + x = x + self.position_embedding.weight[:N] + + # Compute the features from the transformer + mask = self._get_mask(N, x.dtype) + hidden_states = [] + for l in self.layers: + x = l(x, mask) + hidden_states.append(x) + + # Apply the final layernorm and return + x = self.final_layer_norm(x) + last_hidden_state = x + + # Select the EOS token + pooled_output = x[mx.arange(len(x)), eos_tokens] + if "text_projection" in self: + pooled_output = self.text_projection(pooled_output) + + return CLIPOutput( + pooled_output=pooled_output, + last_hidden_state=last_hidden_state, + hidden_states=hidden_states, + ) diff --git a/examples/databricks_DBRX_website_bot/diffusion_mlx/config.py b/examples/databricks_DBRX_website_bot/diffusion_mlx/config.py new file mode 100644 index 0000000..6715757 --- /dev/null +++ b/examples/databricks_DBRX_website_bot/diffusion_mlx/config.py @@ -0,0 +1,65 @@ +# Copyright © 2023-2024 Apple Inc. + +from dataclasses import dataclass +from typing import Optional, Tuple + + +@dataclass +class AutoencoderConfig: + in_channels: int = 3 + out_channels: int = 3 + latent_channels_out: int = 8 + latent_channels_in: int = 4 + block_out_channels: Tuple[int] = (128, 256, 512, 512) + layers_per_block: int = 2 + norm_num_groups: int = 32 + scaling_factor: float = 0.18215 + + +@dataclass +class CLIPTextModelConfig: + num_layers: int = 23 + model_dims: int = 1024 + num_heads: int = 16 + max_length: int = 77 + vocab_size: int = 49408 + projection_dim: Optional[int] = None + hidden_act: str = "quick_gelu" + + +@dataclass +class UNetConfig: + in_channels: int = 4 + out_channels: int = 4 + conv_in_kernel: int = 3 + conv_out_kernel: int = 3 + block_out_channels: Tuple[int] = (320, 640, 1280, 1280) + layers_per_block: Tuple[int] = (2, 2, 2, 2) + mid_block_layers: int = 2 + transformer_layers_per_block: Tuple[int] = (1, 1, 1, 1) + num_attention_heads: Tuple[int] = (5, 10, 20, 20) + cross_attention_dim: Tuple[int] = (1024,) * 4 + norm_num_groups: int = 32 + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "CrossAttnDownBlock2D", + "DownBlock2D", + ) + up_block_types: Tuple[str] = ( + "UpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + "CrossAttnUpBlock2D", + ) + addition_embed_type: Optional[str] = None + addition_time_embed_dim: Optional[int] = None + projection_class_embeddings_input_dim: Optional[int] = None + + +@dataclass +class DiffusionConfig: + beta_schedule: str = "scaled_linear" + beta_start: float = 0.00085 + beta_end: float = 0.012 + num_train_steps: int = 1000 diff --git a/examples/databricks_DBRX_website_bot/diffusion_mlx/model_io.py b/examples/databricks_DBRX_website_bot/diffusion_mlx/model_io.py new file mode 100644 index 0000000..2c2227d --- /dev/null +++ b/examples/databricks_DBRX_website_bot/diffusion_mlx/model_io.py @@ -0,0 +1,330 @@ +# Copyright © 2023-2024 Apple Inc. + +import json +from typing import Optional + +import mlx.core as mx +from huggingface_hub import hf_hub_download +from mlx.utils import tree_unflatten + +from .clip import CLIPTextModel +from .config import AutoencoderConfig, CLIPTextModelConfig, DiffusionConfig, UNetConfig +from .tokenizer import Tokenizer +from .unet import UNetModel +from .vae import Autoencoder + +_DEFAULT_MODEL = "stabilityai/stable-diffusion-2-1-base" +_MODELS = { + # See https://huggingface.co/stabilityai/sdxl-turbo for the model details and license + "stabilityai/sdxl-turbo": { + "unet_config": "unet/config.json", + "unet": "unet/diffusion_pytorch_model.safetensors", + "text_encoder_config": "text_encoder/config.json", + "text_encoder": "text_encoder/model.safetensors", + "text_encoder_2_config": "text_encoder_2/config.json", + "text_encoder_2": "text_encoder_2/model.safetensors", + "vae_config": "vae/config.json", + "vae": "vae/diffusion_pytorch_model.safetensors", + "diffusion_config": "scheduler/scheduler_config.json", + "tokenizer_vocab": "tokenizer/vocab.json", + "tokenizer_merges": "tokenizer/merges.txt", + "tokenizer_2_vocab": "tokenizer_2/vocab.json", + "tokenizer_2_merges": "tokenizer_2/merges.txt", + }, + # See https://huggingface.co/stabilityai/stable-diffusion-2-1-base for the model details and license + "stabilityai/stable-diffusion-2-1-base": { + "unet_config": "unet/config.json", + "unet": "unet/diffusion_pytorch_model.safetensors", + "text_encoder_config": "text_encoder/config.json", + "text_encoder": "text_encoder/model.safetensors", + "vae_config": "vae/config.json", + "vae": "vae/diffusion_pytorch_model.safetensors", + "diffusion_config": "scheduler/scheduler_config.json", + "tokenizer_vocab": "tokenizer/vocab.json", + "tokenizer_merges": "tokenizer/merges.txt", + }, +} + + +def map_unet_weights(key, value): + # Map up/downsampling + if "downsamplers" in key: + key = key.replace("downsamplers.0.conv", "downsample") + if "upsamplers" in key: + key = key.replace("upsamplers.0.conv", "upsample") + + # Map the mid block + if "mid_block.resnets.0" in key: + key = key.replace("mid_block.resnets.0", "mid_blocks.0") + if "mid_block.attentions.0" in key: + key = key.replace("mid_block.attentions.0", "mid_blocks.1") + if "mid_block.resnets.1" in key: + key = key.replace("mid_block.resnets.1", "mid_blocks.2") + + # Map attention layers + if "to_k" in key: + key = key.replace("to_k", "key_proj") + if "to_out.0" in key: + key = key.replace("to_out.0", "out_proj") + if "to_q" in key: + key = key.replace("to_q", "query_proj") + if "to_v" in key: + key = key.replace("to_v", "value_proj") + + # Map transformer ffn + if "ff.net.2" in key: + key = key.replace("ff.net.2", "linear3") + if "ff.net.0" in key: + k1 = key.replace("ff.net.0.proj", "linear1") + k2 = key.replace("ff.net.0.proj", "linear2") + v1, v2 = mx.split(value, 2) + + return [(k1, v1), (k2, v2)] + + if "conv_shortcut.weight" in key: + value = value.squeeze() + + # Transform the weights from 1x1 convs to linear + if len(value.shape) == 4 and ("proj_in" in key or "proj_out" in key): + value = value.squeeze() + + if len(value.shape) == 4: + value = value.transpose(0, 2, 3, 1) + value = value.reshape(-1).reshape(value.shape) + + return [(key, value)] + + +def map_clip_text_encoder_weights(key, value): + # Remove prefixes + if key.startswith("text_model."): + key = key[11:] + if key.startswith("embeddings."): + key = key[11:] + if key.startswith("encoder."): + key = key[8:] + + # Map attention layers + if "self_attn." in key: + key = key.replace("self_attn.", "attention.") + if "q_proj." in key: + key = key.replace("q_proj.", "query_proj.") + if "k_proj." in key: + key = key.replace("k_proj.", "key_proj.") + if "v_proj." in key: + key = key.replace("v_proj.", "value_proj.") + + # Map ffn layers + if "mlp.fc1" in key: + key = key.replace("mlp.fc1", "linear1") + if "mlp.fc2" in key: + key = key.replace("mlp.fc2", "linear2") + + return [(key, value)] + + +def map_vae_weights(key, value): + # Map up/downsampling + if "downsamplers" in key: + key = key.replace("downsamplers.0.conv", "downsample") + if "upsamplers" in key: + key = key.replace("upsamplers.0.conv", "upsample") + + # Map attention layers + if "to_k" in key: + key = key.replace("to_k", "key_proj") + if "to_out.0" in key: + key = key.replace("to_out.0", "out_proj") + if "to_q" in key: + key = key.replace("to_q", "query_proj") + if "to_v" in key: + key = key.replace("to_v", "value_proj") + + # Map the mid block + if "mid_block.resnets.0" in key: + key = key.replace("mid_block.resnets.0", "mid_blocks.0") + if "mid_block.attentions.0" in key: + key = key.replace("mid_block.attentions.0", "mid_blocks.1") + if "mid_block.resnets.1" in key: + key = key.replace("mid_block.resnets.1", "mid_blocks.2") + + # Map the quant/post_quant layers + if "quant_conv" in key: + key = key.replace("quant_conv", "quant_proj") + value = value.squeeze() + + # Map the conv_shortcut to linear + if "conv_shortcut.weight" in key: + value = value.squeeze() + + if len(value.shape) == 4: + value = value.transpose(0, 2, 3, 1) + value = value.reshape(-1).reshape(value.shape) + + return [(key, value)] + + +def _flatten(params): + return [(k, v) for p in params for (k, v) in p] + + +def _load_safetensor_weights(mapper, model, weight_file, float16: bool = False): + dtype = mx.float16 if float16 else mx.float32 + weights = mx.load(weight_file) + weights = _flatten([mapper(k, v.astype(dtype)) for k, v in weights.items()]) + model.update(tree_unflatten(weights)) + + +def _check_key(key: str, part: str): + if key not in _MODELS: + raise ValueError( + f"[{part}] '{key}' model not found, choose one of {{{','.join(_MODELS.keys())}}}" + ) + + +def load_unet(key: str = _DEFAULT_MODEL, float16: bool = False): + """Load the stable diffusion UNet from Hugging Face Hub.""" + _check_key(key, "load_unet") + + # Download the config and create the model + unet_config = _MODELS[key]["unet_config"] + with open(hf_hub_download(key, unet_config)) as f: + config = json.load(f) + + n_blocks = len(config["block_out_channels"]) + model = UNetModel( + UNetConfig( + in_channels=config["in_channels"], + out_channels=config["out_channels"], + block_out_channels=config["block_out_channels"], + layers_per_block=[config["layers_per_block"]] * n_blocks, + transformer_layers_per_block=config.get( + "transformer_layers_per_block", (1,) * 4 + ), + num_attention_heads=( + [config["attention_head_dim"]] * n_blocks + if isinstance(config["attention_head_dim"], int) + else config["attention_head_dim"] + ), + cross_attention_dim=[config["cross_attention_dim"]] * n_blocks, + norm_num_groups=config["norm_num_groups"], + down_block_types=config["down_block_types"], + up_block_types=config["up_block_types"][::-1], + addition_embed_type=config.get("addition_embed_type", None), + addition_time_embed_dim=config.get("addition_time_embed_dim", None), + projection_class_embeddings_input_dim=config.get( + "projection_class_embeddings_input_dim", None + ), + ) + ) + + # Download the weights and map them into the model + unet_weights = _MODELS[key]["unet"] + weight_file = hf_hub_download(key, unet_weights) + _load_safetensor_weights(map_unet_weights, model, weight_file, float16) + + return model + + +def load_text_encoder( + key: str = _DEFAULT_MODEL, + float16: bool = False, + model_key: str = "text_encoder", + config_key: Optional[str] = None, +): + """Load the stable diffusion text encoder from Hugging Face Hub.""" + _check_key(key, "load_text_encoder") + + config_key = config_key or (model_key + "_config") + + # Download the config and create the model + text_encoder_config = _MODELS[key][config_key] + with open(hf_hub_download(key, text_encoder_config)) as f: + config = json.load(f) + + with_projection = "WithProjection" in config["architectures"][0] + + model = CLIPTextModel( + CLIPTextModelConfig( + num_layers=config["num_hidden_layers"], + model_dims=config["hidden_size"], + num_heads=config["num_attention_heads"], + max_length=config["max_position_embeddings"], + vocab_size=config["vocab_size"], + projection_dim=config["projection_dim"] if with_projection else None, + hidden_act=config.get("hidden_act", "quick_gelu"), + ) + ) + + # Download the weights and map them into the model + text_encoder_weights = _MODELS[key][model_key] + weight_file = hf_hub_download(key, text_encoder_weights) + _load_safetensor_weights(map_clip_text_encoder_weights, model, weight_file, float16) + + return model + + +def load_autoencoder(key: str = _DEFAULT_MODEL, float16: bool = False): + """Load the stable diffusion autoencoder from Hugging Face Hub.""" + _check_key(key, "load_autoencoder") + + # Download the config and create the model + vae_config = _MODELS[key]["vae_config"] + with open(hf_hub_download(key, vae_config)) as f: + config = json.load(f) + + model = Autoencoder( + AutoencoderConfig( + in_channels=config["in_channels"], + out_channels=config["out_channels"], + latent_channels_out=2 * config["latent_channels"], + latent_channels_in=config["latent_channels"], + block_out_channels=config["block_out_channels"], + layers_per_block=config["layers_per_block"], + norm_num_groups=config["norm_num_groups"], + scaling_factor=config.get("scaling_factor", 0.18215), + ) + ) + + # Download the weights and map them into the model + vae_weights = _MODELS[key]["vae"] + weight_file = hf_hub_download(key, vae_weights) + _load_safetensor_weights(map_vae_weights, model, weight_file, float16) + + return model + + +def load_diffusion_config(key: str = _DEFAULT_MODEL): + """Load the stable diffusion config from Hugging Face Hub.""" + _check_key(key, "load_diffusion_config") + + diffusion_config = _MODELS[key]["diffusion_config"] + with open(hf_hub_download(key, diffusion_config)) as f: + config = json.load(f) + + return DiffusionConfig( + beta_start=config["beta_start"], + beta_end=config["beta_end"], + beta_schedule=config["beta_schedule"], + num_train_steps=config["num_train_timesteps"], + ) + + +def load_tokenizer( + key: str = _DEFAULT_MODEL, + vocab_key: str = "tokenizer_vocab", + merges_key: str = "tokenizer_merges", +): + _check_key(key, "load_tokenizer") + + vocab_file = hf_hub_download(key, _MODELS[key][vocab_key]) + with open(vocab_file, encoding="utf-8") as f: + vocab = json.load(f) + + merges_file = hf_hub_download(key, _MODELS[key][merges_key]) + with open(merges_file, encoding="utf-8") as f: + bpe_merges = f.read().strip().split("\n")[1 : 49152 - 256 - 2 + 1] + bpe_merges = [tuple(m.split()) for m in bpe_merges] + bpe_ranks = dict(map(reversed, enumerate(bpe_merges))) + + return Tokenizer(bpe_ranks, vocab) diff --git a/examples/databricks_DBRX_website_bot/diffusion_mlx/sampler.py b/examples/databricks_DBRX_website_bot/diffusion_mlx/sampler.py new file mode 100644 index 0000000..ff4433d --- /dev/null +++ b/examples/databricks_DBRX_website_bot/diffusion_mlx/sampler.py @@ -0,0 +1,105 @@ +# Copyright © 2023 Apple Inc. + +import mlx.core as mx + +from .config import DiffusionConfig + + +def _linspace(a, b, num): + x = mx.arange(0, num) / (num - 1) + return (b - a) * x + a + + +def _interp(y, x_new): + """Interpolate the function defined by (arange(0, len(y)), y) at positions x_new.""" + x_low = x_new.astype(mx.int32) + x_high = mx.minimum(x_low + 1, len(y) - 1) + + y_low = y[x_low] + y_high = y[x_high] + delta_x = x_new - x_low + y_new = y_low * (1 - delta_x) + delta_x * y_high + + return y_new + + +class SimpleEulerSampler: + """A simple Euler integrator that can be used to sample from our diffusion models. + + The method ``step()`` performs one Euler step from x_t to x_t_prev. + """ + + def __init__(self, config: DiffusionConfig): + # Compute the noise schedule + if config.beta_schedule == "linear": + betas = _linspace( + config.beta_start, config.beta_end, config.num_train_steps + ) + elif config.beta_schedule == "scaled_linear": + betas = _linspace( + config.beta_start**0.5, config.beta_end**0.5, config.num_train_steps + ).square() + else: + raise NotImplementedError(f"{config.beta_schedule} is not implemented.") + + alphas = 1 - betas + alphas_cumprod = mx.cumprod(alphas) + + self._sigmas = mx.concatenate( + [mx.zeros(1), ((1 - alphas_cumprod) / alphas_cumprod).sqrt()] + ) + + @property + def max_time(self): + return len(self._sigmas) - 1 + + def sample_prior(self, shape, dtype=mx.float32, key=None): + noise = mx.random.normal(shape, key=key) + return ( + noise * self._sigmas[-1] * (self._sigmas[-1].square() + 1).rsqrt() + ).astype(dtype) + + def add_noise(self, x, t, key=None): + noise = mx.random.normal(x.shape, key=key) + s = self.sigmas(t) + return (x + noise * s) * (s.square() + 1).rsqrt() + + def sigmas(self, t): + return _interp(self._sigmas, t) + + def timesteps(self, num_steps: int, start_time=None, dtype=mx.float32): + start_time = start_time or (len(self._sigmas) - 1) + assert 0 < start_time <= (len(self._sigmas) - 1) + steps = _linspace(start_time, 0, num_steps + 1).astype(dtype) + return list(zip(steps, steps[1:])) + + def step(self, eps_pred, x_t, t, t_prev): + sigma = self.sigmas(t).astype(eps_pred.dtype) + sigma_prev = self.sigmas(t_prev).astype(eps_pred.dtype) + + dt = sigma_prev - sigma + x_t_prev = (sigma.square() + 1).sqrt() * x_t + eps_pred * dt + + x_t_prev = x_t_prev * (sigma_prev.square() + 1).rsqrt() + + return x_t_prev + + +class SimpleEulerAncestralSampler(SimpleEulerSampler): + def step(self, eps_pred, x_t, t, t_prev): + sigma = self.sigmas(t).astype(eps_pred.dtype) + sigma_prev = self.sigmas(t_prev).astype(eps_pred.dtype) + + sigma2 = sigma.square() + sigma_prev2 = sigma_prev.square() + sigma_up = (sigma_prev2 * (sigma2 - sigma_prev2) / sigma2).sqrt() + sigma_down = (sigma_prev2 - sigma_up**2).sqrt() + + dt = sigma_down - sigma + x_t_prev = (sigma2 + 1).sqrt() * x_t + eps_pred * dt + noise = mx.random.normal(x_t_prev.shape).astype(x_t_prev.dtype) + x_t_prev = x_t_prev + noise * sigma_up + + x_t_prev = x_t_prev * (sigma_prev2 + 1).rsqrt() + + return x_t_prev diff --git a/examples/databricks_DBRX_website_bot/diffusion_mlx/tokenizer.py b/examples/databricks_DBRX_website_bot/diffusion_mlx/tokenizer.py new file mode 100644 index 0000000..ae9b967 --- /dev/null +++ b/examples/databricks_DBRX_website_bot/diffusion_mlx/tokenizer.py @@ -0,0 +1,100 @@ +# Copyright © 2023 Apple Inc. + +import regex + + +class Tokenizer: + """A simple port of CLIPTokenizer from https://github.com/huggingface/transformers/ .""" + + def __init__(self, bpe_ranks, vocab): + self.bpe_ranks = bpe_ranks + self.vocab = vocab + self.pat = regex.compile( + r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", + regex.IGNORECASE, + ) + + self._cache = {self.bos: self.bos, self.eos: self.eos} + + @property + def bos(self): + return "<|startoftext|>" + + @property + def bos_token(self): + return self.vocab[self.bos] + + @property + def eos(self): + return "<|endoftext|>" + + @property + def eos_token(self): + return self.vocab[self.eos] + + def bpe(self, text): + if text in self._cache: + return self._cache[text] + + unigrams = list(text[:-1]) + [text[-1] + ""] + unique_bigrams = set(zip(unigrams, unigrams[1:])) + + if not unique_bigrams: + return unigrams + + # In every iteration try to merge the two most likely bigrams. If none + # was merged we are done. + # + # Ported from https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py + while unique_bigrams: + bigram = min( + unique_bigrams, key=lambda pair: self.bpe_ranks.get(pair, float("inf")) + ) + if bigram not in self.bpe_ranks: + break + + new_unigrams = [] + skip = False + for a, b in zip(unigrams, unigrams[1:]): + if skip: + skip = False + continue + + if (a, b) == bigram: + new_unigrams.append(a + b) + skip = True + + else: + new_unigrams.append(a) + + if not skip: + new_unigrams.append(b) + + unigrams = new_unigrams + unique_bigrams = set(zip(unigrams, unigrams[1:])) + + self._cache[text] = unigrams + + return unigrams + + def tokenize(self, text, prepend_bos=True, append_eos=True): + if isinstance(text, list): + return [self.tokenize(t, prepend_bos, append_eos) for t in text] + + # Lower case cleanup and split according to self.pat. Hugging Face does + # a much more thorough job here but this should suffice for 95% of + # cases. + clean_text = regex.sub(r"\s+", " ", text.lower()) + tokens = regex.findall(self.pat, clean_text) + + # Split the tokens according to the byte-pair merge file + bpe_tokens = [ti for t in tokens for ti in self.bpe(t)] + + # Map to token ids and return + tokens = [self.vocab[t] for t in bpe_tokens] + if prepend_bos: + tokens = [self.bos_token] + tokens + if append_eos: + tokens.append(self.eos_token) + + return tokens diff --git a/examples/databricks_DBRX_website_bot/diffusion_mlx/unet.py b/examples/databricks_DBRX_website_bot/diffusion_mlx/unet.py new file mode 100644 index 0000000..ec2915e --- /dev/null +++ b/examples/databricks_DBRX_website_bot/diffusion_mlx/unet.py @@ -0,0 +1,461 @@ +# Copyright © 2023 Apple Inc. + +import math +from typing import Optional + +import mlx.core as mx +import mlx.nn as nn + +from .config import UNetConfig + + +def upsample_nearest(x, scale: int = 2): + B, H, W, C = x.shape + x = mx.broadcast_to(x[:, :, None, :, None, :], (B, H, scale, W, scale, C)) + x = x.reshape(B, H * scale, W * scale, C) + + return x + + +class TimestepEmbedding(nn.Module): + def __init__(self, in_channels: int, time_embed_dim: int): + super().__init__() + + self.linear_1 = nn.Linear(in_channels, time_embed_dim) + self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim) + + def __call__(self, x): + x = self.linear_1(x) + x = nn.silu(x) + x = self.linear_2(x) + + return x + + +class TransformerBlock(nn.Module): + def __init__( + self, + model_dims: int, + num_heads: int, + hidden_dims: Optional[int] = None, + memory_dims: Optional[int] = None, + ): + super().__init__() + + self.norm1 = nn.LayerNorm(model_dims) + self.attn1 = nn.MultiHeadAttention(model_dims, num_heads) + self.attn1.out_proj.bias = mx.zeros(model_dims) + + memory_dims = memory_dims or model_dims + self.norm2 = nn.LayerNorm(model_dims) + self.attn2 = nn.MultiHeadAttention( + model_dims, num_heads, key_input_dims=memory_dims + ) + self.attn2.out_proj.bias = mx.zeros(model_dims) + + hidden_dims = hidden_dims or 4 * model_dims + self.norm3 = nn.LayerNorm(model_dims) + self.linear1 = nn.Linear(model_dims, hidden_dims) + self.linear2 = nn.Linear(model_dims, hidden_dims) + self.linear3 = nn.Linear(hidden_dims, model_dims) + + def __call__(self, x, memory, attn_mask, memory_mask): + # Self attention + y = self.norm1(x) + y = self.attn1(y, y, y, attn_mask) + x = x + y + + # Cross attention + y = self.norm2(x) + y = self.attn2(y, memory, memory, memory_mask) + x = x + y + + # FFN + y = self.norm3(x) + y_a = self.linear1(y) + y_b = self.linear2(y) + y = y_a * nn.gelu(y_b) + y = self.linear3(y) + x = x + y + + return x + + +class Transformer2D(nn.Module): + """A transformer model for inputs with 2 spatial dimensions.""" + + def __init__( + self, + in_channels: int, + model_dims: int, + encoder_dims: int, + num_heads: int, + num_layers: int = 1, + norm_num_groups: int = 32, + ): + super().__init__() + + self.norm = nn.GroupNorm(norm_num_groups, in_channels, pytorch_compatible=True) + self.proj_in = nn.Linear(in_channels, model_dims) + self.transformer_blocks = [ + TransformerBlock(model_dims, num_heads, memory_dims=encoder_dims) + for i in range(num_layers) + ] + self.proj_out = nn.Linear(model_dims, in_channels) + + def __call__(self, x, encoder_x, attn_mask, encoder_attn_mask): + # Save the input to add to the output + input_x = x + dtype = x.dtype + + # Perform the input norm and projection + B, H, W, C = x.shape + x = self.norm(x.astype(mx.float32)).astype(dtype).reshape(B, -1, C) + x = self.proj_in(x) + + # Apply the transformer + for block in self.transformer_blocks: + x = block(x, encoder_x, attn_mask, encoder_attn_mask) + + # Apply the output projection and reshape + x = self.proj_out(x) + x = x.reshape(B, H, W, C) + + return x + input_x + + +class ResnetBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: Optional[int] = None, + groups: int = 32, + temb_channels: Optional[int] = None, + ): + super().__init__() + + out_channels = out_channels or in_channels + + self.norm1 = nn.GroupNorm(groups, in_channels, pytorch_compatible=True) + self.conv1 = nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if temb_channels is not None: + self.time_emb_proj = nn.Linear(temb_channels, out_channels) + self.norm2 = nn.GroupNorm(groups, out_channels, pytorch_compatible=True) + self.conv2 = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + if in_channels != out_channels: + self.conv_shortcut = nn.Linear(in_channels, out_channels) + + def __call__(self, x, temb=None): + dtype = x.dtype + + if temb is not None: + temb = self.time_emb_proj(nn.silu(temb)) + + y = self.norm1(x.astype(mx.float32)).astype(dtype) + y = nn.silu(y) + y = self.conv1(y) + if temb is not None: + y = y + temb[:, None, None, :] + y = self.norm2(y.astype(mx.float32)).astype(dtype) + y = nn.silu(y) + y = self.conv2(y) + + x = y + (x if "conv_shortcut" not in self else self.conv_shortcut(x)) + + return x + + +class UNetBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + prev_out_channels: Optional[int] = None, + num_layers: int = 1, + transformer_layers_per_block: int = 1, + num_attention_heads: int = 8, + cross_attention_dim=1280, + resnet_groups: int = 32, + add_downsample=True, + add_upsample=True, + add_cross_attention=True, + ): + super().__init__() + + # Prepare the in channels list for the resnets + if prev_out_channels is None: + in_channels_list = [in_channels] + [out_channels] * (num_layers - 1) + else: + in_channels_list = [prev_out_channels] + [out_channels] * (num_layers - 1) + res_channels_list = [out_channels] * (num_layers - 1) + [in_channels] + in_channels_list = [ + a + b for a, b in zip(in_channels_list, res_channels_list) + ] + + # Add resnet blocks that also process the time embedding + self.resnets = [ + ResnetBlock2D( + in_channels=ic, + out_channels=out_channels, + temb_channels=temb_channels, + groups=resnet_groups, + ) + for ic in in_channels_list + ] + + # Add optional cross attention layers + if add_cross_attention: + self.attentions = [ + Transformer2D( + in_channels=out_channels, + model_dims=out_channels, + num_heads=num_attention_heads, + num_layers=transformer_layers_per_block, + encoder_dims=cross_attention_dim, + ) + for i in range(num_layers) + ] + + # Add an optional downsampling layer + if add_downsample: + self.downsample = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=2, padding=1 + ) + + # or upsampling layer + if add_upsample: + self.upsample = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + def __call__( + self, + x, + encoder_x=None, + temb=None, + attn_mask=None, + encoder_attn_mask=None, + residual_hidden_states=None, + ): + output_states = [] + + for i in range(len(self.resnets)): + if residual_hidden_states is not None: + x = mx.concatenate([x, residual_hidden_states.pop()], axis=-1) + + x = self.resnets[i](x, temb) + + if "attentions" in self: + x = self.attentions[i](x, encoder_x, attn_mask, encoder_attn_mask) + + output_states.append(x) + + if "downsample" in self: + x = self.downsample(x) + output_states.append(x) + + if "upsample" in self: + x = self.upsample(upsample_nearest(x)) + output_states.append(x) + + return x, output_states + + +class UNetModel(nn.Module): + """The conditional 2D UNet model that actually performs the denoising.""" + + def __init__(self, config: UNetConfig): + super().__init__() + + self.conv_in = nn.Conv2d( + config.in_channels, + config.block_out_channels[0], + config.conv_in_kernel, + padding=(config.conv_in_kernel - 1) // 2, + ) + + self.timesteps = nn.SinusoidalPositionalEncoding( + config.block_out_channels[0], + max_freq=1, + min_freq=math.exp( + -math.log(10000) + 2 * math.log(10000) / config.block_out_channels[0] + ), + scale=1.0, + cos_first=True, + full_turns=False, + ) + self.time_embedding = TimestepEmbedding( + config.block_out_channels[0], + config.block_out_channels[0] * 4, + ) + + if config.addition_embed_type == "text_time": + self.add_time_proj = nn.SinusoidalPositionalEncoding( + config.addition_time_embed_dim, + max_freq=1, + min_freq=math.exp( + -math.log(10000) + + 2 * math.log(10000) / config.addition_time_embed_dim + ), + scale=1.0, + cos_first=True, + full_turns=False, + ) + self.add_embedding = TimestepEmbedding( + config.projection_class_embeddings_input_dim, + config.block_out_channels[0] * 4, + ) + + # Make the downsampling blocks + block_channels = [config.block_out_channels[0]] + list( + config.block_out_channels + ) + self.down_blocks = [ + UNetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=config.block_out_channels[0] * 4, + num_layers=config.layers_per_block[i], + transformer_layers_per_block=config.transformer_layers_per_block[i], + num_attention_heads=config.num_attention_heads[i], + cross_attention_dim=config.cross_attention_dim[i], + resnet_groups=config.norm_num_groups, + add_downsample=(i < len(config.block_out_channels) - 1), + add_upsample=False, + add_cross_attention="CrossAttn" in config.down_block_types[i], + ) + for i, (in_channels, out_channels) in enumerate( + zip(block_channels, block_channels[1:]) + ) + ] + + # Make the middle block + self.mid_blocks = [ + ResnetBlock2D( + in_channels=config.block_out_channels[-1], + out_channels=config.block_out_channels[-1], + temb_channels=config.block_out_channels[0] * 4, + groups=config.norm_num_groups, + ), + Transformer2D( + in_channels=config.block_out_channels[-1], + model_dims=config.block_out_channels[-1], + num_heads=config.num_attention_heads[-1], + num_layers=config.transformer_layers_per_block[-1], + encoder_dims=config.cross_attention_dim[-1], + ), + ResnetBlock2D( + in_channels=config.block_out_channels[-1], + out_channels=config.block_out_channels[-1], + temb_channels=config.block_out_channels[0] * 4, + groups=config.norm_num_groups, + ), + ] + + # Make the upsampling blocks + block_channels = ( + [config.block_out_channels[0]] + + list(config.block_out_channels) + + [config.block_out_channels[-1]] + ) + self.up_blocks = [ + UNetBlock2D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=config.block_out_channels[0] * 4, + prev_out_channels=prev_out_channels, + num_layers=config.layers_per_block[i] + 1, + transformer_layers_per_block=config.transformer_layers_per_block[i], + num_attention_heads=config.num_attention_heads[i], + cross_attention_dim=config.cross_attention_dim[i], + resnet_groups=config.norm_num_groups, + add_downsample=False, + add_upsample=(i > 0), + add_cross_attention="CrossAttn" in config.up_block_types[i], + ) + for i, (in_channels, out_channels, prev_out_channels) in reversed( + list( + enumerate( + zip(block_channels, block_channels[1:], block_channels[2:]) + ) + ) + ) + ] + + self.conv_norm_out = nn.GroupNorm( + config.norm_num_groups, + config.block_out_channels[0], + pytorch_compatible=True, + ) + self.conv_out = nn.Conv2d( + config.block_out_channels[0], + config.out_channels, + config.conv_out_kernel, + padding=(config.conv_out_kernel - 1) // 2, + ) + + def __call__( + self, + x, + timestep, + encoder_x, + attn_mask=None, + encoder_attn_mask=None, + text_time=None, + ): + # Compute the time embeddings + temb = self.timesteps(timestep).astype(x.dtype) + temb = self.time_embedding(temb) + + # Add the extra text_time conditioning + if text_time is not None: + text_emb, time_ids = text_time + emb = self.add_time_proj(time_ids).flatten(1).astype(x.dtype) + emb = mx.concatenate([text_emb, emb], axis=-1) + emb = self.add_embedding(emb) + temb = temb + emb + + # Preprocess the input + x = self.conv_in(x) + + # Run the downsampling part of the unet + residuals = [x] + for block in self.down_blocks: + x, res = block( + x, + encoder_x=encoder_x, + temb=temb, + attn_mask=attn_mask, + encoder_attn_mask=encoder_attn_mask, + ) + residuals.extend(res) + + # Run the middle part of the unet + x = self.mid_blocks[0](x, temb) + x = self.mid_blocks[1](x, encoder_x, attn_mask, encoder_attn_mask) + x = self.mid_blocks[2](x, temb) + + # Run the upsampling part of the unet + for block in self.up_blocks: + x, _ = block( + x, + encoder_x=encoder_x, + temb=temb, + attn_mask=attn_mask, + encoder_attn_mask=encoder_attn_mask, + residual_hidden_states=residuals, + ) + + # Postprocess the output + dtype = x.dtype + x = self.conv_norm_out(x.astype(mx.float32)).astype(dtype) + x = nn.silu(x) + x = self.conv_out(x) + + return x diff --git a/examples/databricks_DBRX_website_bot/diffusion_mlx/vae.py b/examples/databricks_DBRX_website_bot/diffusion_mlx/vae.py new file mode 100644 index 0000000..5fd47f1 --- /dev/null +++ b/examples/databricks_DBRX_website_bot/diffusion_mlx/vae.py @@ -0,0 +1,274 @@ +# Copyright © 2023 Apple Inc. + +import math +from typing import List + +import mlx.core as mx +import mlx.nn as nn + +from .config import AutoencoderConfig +from .unet import ResnetBlock2D, upsample_nearest + + +class Attention(nn.Module): + """A single head unmasked attention for use with the VAE.""" + + def __init__(self, dims: int, norm_groups: int = 32): + super().__init__() + + self.group_norm = nn.GroupNorm(norm_groups, dims, pytorch_compatible=True) + self.query_proj = nn.Linear(dims, dims) + self.key_proj = nn.Linear(dims, dims) + self.value_proj = nn.Linear(dims, dims) + self.out_proj = nn.Linear(dims, dims) + + def __call__(self, x): + B, H, W, C = x.shape + + y = self.group_norm(x) + + queries = self.query_proj(y).reshape(B, H * W, C) + keys = self.key_proj(y).reshape(B, H * W, C) + values = self.value_proj(y).reshape(B, H * W, C) + + scale = 1 / math.sqrt(queries.shape[-1]) + scores = (queries * scale) @ keys.transpose(0, 2, 1) + attn = mx.softmax(scores, axis=-1) + y = (attn @ values).reshape(B, H, W, C) + + y = self.out_proj(y) + x = x + y + + return x + + +class EncoderDecoderBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + num_layers: int = 1, + resnet_groups: int = 32, + add_downsample=True, + add_upsample=True, + ): + super().__init__() + + # Add the resnet blocks + self.resnets = [ + ResnetBlock2D( + in_channels=in_channels if i == 0 else out_channels, + out_channels=out_channels, + groups=resnet_groups, + ) + for i in range(num_layers) + ] + + # Add an optional downsampling layer + if add_downsample: + self.downsample = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=2, padding=0 + ) + + # or upsampling layer + if add_upsample: + self.upsample = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + + def __call__(self, x): + for resnet in self.resnets: + x = resnet(x) + + if "downsample" in self: + x = mx.pad(x, [(0, 0), (0, 1), (0, 1), (0, 0)]) + x = self.downsample(x) + + if "upsample" in self: + x = self.upsample(upsample_nearest(x)) + + return x + + +class Encoder(nn.Module): + """Implements the encoder side of the Autoencoder.""" + + def __init__( + self, + in_channels: int, + out_channels: int, + block_out_channels: List[int] = [64], + layers_per_block: int = 2, + resnet_groups: int = 32, + ): + super().__init__() + + self.conv_in = nn.Conv2d( + in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1 + ) + + channels = [block_out_channels[0]] + list(block_out_channels) + self.down_blocks = [ + EncoderDecoderBlock2D( + in_channels, + out_channels, + num_layers=layers_per_block, + resnet_groups=resnet_groups, + add_downsample=i < len(block_out_channels) - 1, + add_upsample=False, + ) + for i, (in_channels, out_channels) in enumerate(zip(channels, channels[1:])) + ] + + self.mid_blocks = [ + ResnetBlock2D( + in_channels=block_out_channels[-1], + out_channels=block_out_channels[-1], + groups=resnet_groups, + ), + Attention(block_out_channels[-1], resnet_groups), + ResnetBlock2D( + in_channels=block_out_channels[-1], + out_channels=block_out_channels[-1], + groups=resnet_groups, + ), + ] + + self.conv_norm_out = nn.GroupNorm( + resnet_groups, block_out_channels[-1], pytorch_compatible=True + ) + self.conv_out = nn.Conv2d(block_out_channels[-1], out_channels, 3, padding=1) + + def __call__(self, x): + x = self.conv_in(x) + + for l in self.down_blocks: + x = l(x) + + x = self.mid_blocks[0](x) + x = self.mid_blocks[1](x) + x = self.mid_blocks[2](x) + + x = self.conv_norm_out(x) + x = nn.silu(x) + x = self.conv_out(x) + + return x + + +class Decoder(nn.Module): + """Implements the decoder side of the Autoencoder.""" + + def __init__( + self, + in_channels: int, + out_channels: int, + block_out_channels: List[int] = [64], + layers_per_block: int = 2, + resnet_groups: int = 32, + ): + super().__init__() + + self.conv_in = nn.Conv2d( + in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1 + ) + + self.mid_blocks = [ + ResnetBlock2D( + in_channels=block_out_channels[-1], + out_channels=block_out_channels[-1], + groups=resnet_groups, + ), + Attention(block_out_channels[-1], resnet_groups), + ResnetBlock2D( + in_channels=block_out_channels[-1], + out_channels=block_out_channels[-1], + groups=resnet_groups, + ), + ] + + channels = list(reversed(block_out_channels)) + channels = [channels[0]] + channels + self.up_blocks = [ + EncoderDecoderBlock2D( + in_channels, + out_channels, + num_layers=layers_per_block, + resnet_groups=resnet_groups, + add_downsample=False, + add_upsample=i < len(block_out_channels) - 1, + ) + for i, (in_channels, out_channels) in enumerate(zip(channels, channels[1:])) + ] + + self.conv_norm_out = nn.GroupNorm( + resnet_groups, block_out_channels[0], pytorch_compatible=True + ) + self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1) + + def __call__(self, x): + x = self.conv_in(x) + + x = self.mid_blocks[0](x) + x = self.mid_blocks[1](x) + x = self.mid_blocks[2](x) + + for l in self.up_blocks: + x = l(x) + + x = self.conv_norm_out(x) + x = nn.silu(x) + x = self.conv_out(x) + + return x + + +class Autoencoder(nn.Module): + """The autoencoder that allows us to perform diffusion in the latent space.""" + + def __init__(self, config: AutoencoderConfig): + super().__init__() + + self.latent_channels = config.latent_channels_in + self.scaling_factor = config.scaling_factor + self.encoder = Encoder( + config.in_channels, + config.latent_channels_out, + config.block_out_channels, + config.layers_per_block, + resnet_groups=config.norm_num_groups, + ) + self.decoder = Decoder( + config.latent_channels_in, + config.out_channels, + config.block_out_channels, + config.layers_per_block + 1, + resnet_groups=config.norm_num_groups, + ) + + self.quant_proj = nn.Linear( + config.latent_channels_out, config.latent_channels_out + ) + self.post_quant_proj = nn.Linear( + config.latent_channels_in, config.latent_channels_in + ) + + def decode(self, z): + z = z / self.scaling_factor + return self.decoder(self.post_quant_proj(z)) + + def encode(self, x): + x = self.encoder(x) + x = self.quant_proj(x) + mean, logvar = x.split(2, axis=-1) + mean = mean * self.scaling_factor + logvar = logvar + 2 * math.log(self.scaling_factor) + + return mean, logvar + + def __call__(self, x, key=None): + mean, logvar = self.encode(x) + z = mx.random.normal(mean.shape, key=key) * mx.exp(0.5 * logvar) + mean + x_hat = self.decode(z) + + return dict(x_hat=x_hat, z=z, mean=mean, logvar=logvar) diff --git a/examples/databricks_DBRX_website_bot/gen_image.py b/examples/databricks_DBRX_website_bot/gen_image.py new file mode 100644 index 0000000..0e80f3a --- /dev/null +++ b/examples/databricks_DBRX_website_bot/gen_image.py @@ -0,0 +1,142 @@ +from diffusers import ( + StableDiffusionPipeline, + StableDiffusionXLPipeline, + AutoPipelineForText2Image, +) +import mlx.core as mx +from diffusion_mlx import StableDiffusion, StableDiffusionXL +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np +import time + +SUPPORTS_NEGATIVE_PROMPT = False +GLOBAL_NEGATIVE_PROMPT = ( + "3d, cartoon, anime, (deformed eyes, nose, ears, nose), bad anatomy, ugly, text" +) +RESPONSE_TO_DIFFUSER_PROMPT = "Get minimal text (no longer than 70 tokesn) describe the response and use it as a prompt for a diffuser: {} | avoid adding text to the image |" + +""" +MODEL_MAP = { + "runway_diffusion_v1": "runwayml/stable-diffusion-v1-5", + "sdxl": "stabilityai/stable-diffusion-xl-base-1.0", +} + +def load_model(model_id="runway_diffusion_v1"): + global MODEL_PIPE, SUPPORTS_NEGATIVE_PROMPT + if model_id == "runway_diffusion_v1": + MODEL_PIPE = StableDiffusionPipeline.from_pretrained(MODEL_MAP[model_id]) + elif model_id == "sdxl": + MODEL_PIPE = StableDiffusionXLPipeline.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", variant="fp16", use_safetensors=True + ) + SUPPORTS_NEGATIVE_PROMPT = True + elif model_id == "sdxl-turbo": + MODEL_PIPE = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", variant="fp16") + + + +def generate_image(prompt, model_id="runway_diffusion_v1"): + prompt += " | avoid adding text to the image |" + image = MODEL_PIPE(prompt).images[0] if not SUPPORTS_NEGATIVE_PROMPT else MODEL_PIPE(prompt, negative_prompt=GLOBAL_NEGATIVE_PROMPT).images[0] + return image +""" + +### MLX version +import mlx.core as mx +import mlx.nn as nn + + +def load_models(model="sdxl", float16=True, quantize=True, preload_models=True): + # Load the models + if model == "sdxl": + model = StableDiffusionXL("stabilityai/sdxl-turbo", float16=float16) + if quantize: + nn.quantize( + model.text_encoder_1, + class_predicate=lambda _, m: isinstance(m, nn.Linear), + ) + nn.quantize( + model.text_encoder_2, + class_predicate=lambda _, m: isinstance(m, nn.Linear), + ) + nn.quantize(model.unet, group_size=32, bits=8) + steps = 2 + else: + model = StableDiffusion( + "stabilityai/stable-diffusion-2-1-base", float16=float16 + ) + if quantize: + nn.quantize( + model.text_encoder, + class_predicate=lambda _, m: isinstance(m, nn.Linear), + ) + nn.quantize(model.unet, group_size=32, bits=8) + steps = 50 + + # Ensure that models are read in memory if needed + if preload_models: + model.ensure_models_are_loaded() + + return model, steps + + +def generate_image(model, steps, prompt, verbose=True): + # Generate the latent vectors using diffusion + time1 = time.time() + latents = model.generate_latents( + prompt, + n_images=1, + num_steps=steps, + negative_text=GLOBAL_NEGATIVE_PROMPT, + ) + for x_t in tqdm(latents, total=steps): + mx.eval(x_t) + + # The following is not necessary but it may help in memory + # constrained systems by reusing the memory kept by the unet and the text + # encoders. + + # if model == "sdxl": + # del MODEL_PIPE.text_encoder_1 + # del MODEL_PIPE.text_encoder_2 + # else: + # del MODEL_PIPE.text_encoder + # del sd.unet + # del sd.sampler + peak_mem_unet = mx.metal.get_peak_memory() / 1024**3 + + # Decode them into images + decoded = [] + for i in tqdm(range(0, 1, 1)): + decoded.append(model.decode(x_t[i : i + 1])) + mx.eval(decoded[-1]) + peak_mem_overall = mx.metal.get_peak_memory() / 1024**3 + + # Arrange them on a grid + x = mx.concatenate(decoded, axis=0) + x = mx.pad(x, [(0, 0), (8, 8), (8, 8), (0, 0)]) + B, H, W, C = x.shape + x = x.reshape(1, B, H, W, C).transpose(0, 2, 1, 3, 4) + x = x.reshape(1 * H, B * W, C) + x = (x * 255).astype(mx.uint8) + + time2 = time.time() + if verbose: + print(f"Time taken to generate the image: {time2 - time1:.3f}s") + # Save them to disc + im = Image.fromarray(np.array(x)) + + # Report the peak memory used during generation + if verbose: + print(f"Peak memory used for the unet: {peak_mem_unet:.3f}GB") + print(f"Peak memory used overall: {peak_mem_overall:.3f}GB") + + return im + + +if __name__ == "__main__": + load_models() + generate_image("A cartoon of a cute cat", verbose=True) + generate_image("Hogwartz school of witchcraft and wizardry", verbose=True) diff --git a/examples/databricks_DBRX_website_bot/gui.py b/examples/databricks_DBRX_website_bot/gui.py new file mode 100644 index 0000000..95360a0 --- /dev/null +++ b/examples/databricks_DBRX_website_bot/gui.py @@ -0,0 +1,76 @@ +import streamlit as st +from main import build_RAG +from gen_image import generate_image, RESPONSE_TO_DIFFUSER_PROMPT +from llama_index.core import Settings + + +def add_to_session(key, value): + st.session_state[key] = value + + +def main(): + st.title("Databricks DBRX Website Bot") + if st.session_state.get("query_engine") is None: + context = st.text_area( + "Enter the link to the context", + value="https://harrypotter.fandom.com/wiki/Hogwarts_School_of_Witchcraft_and_Wizardry", + ) + illustrate = st.checkbox("Illustrate") + steps = st.selectbox("Select the number of steps for diffusion", (1, 2)) + build_rag = st.button("Build RAG") + query_engine, model = None, None + if build_rag: + query_engine, model, _ = build_RAG( + context, + "mixedbread-ai/mxbai-embed-large-v1", + "~/tmp/lancedb_hogwarts_12", + False, + illustrate, + "sdxl", + ) + add_to_session("query_engine", query_engine) + add_to_session("model", model) + add_to_session("steps", steps or 1) + add_to_session("illustrate", illustrate) + print("steps", steps) + st._experimental_rerun() + else: + query_engine = st.session_state["query_engine"] + model = st.session_state["model"] + steps = st.session_state["steps"] + illustrate = st.session_state["illustrate"] + col1, col2 = st.columns(2) + with col1: + query = st.text_input( + "Enter a question", + value="What is Hogwarts?", + label_visibility="collapsed", + ) + with col2: + enter = st.button("Enter") + if enter: + response = query_engine.chat(query) + if illustrate: + with col1: + st.write("Response") + st.write(response.response) + with col2: + st.write("Illustration") + with st.spinner("waiting"): + image = generate_image( + model, + steps, + Settings.llm.complete( + RESPONSE_TO_DIFFUSER_PROMPT.format( + str(response.response) + ) + ).text, + ) + st.image(image) + else: + st.write("Response") + st.write(response) + + +if __name__ == "__main__": + main() diff --git a/examples/databricks_DBRX_website_bot/main.py b/examples/databricks_DBRX_website_bot/main.py index cef72f9..9dad226 100644 --- a/examples/databricks_DBRX_website_bot/main.py +++ b/examples/databricks_DBRX_website_bot/main.py @@ -5,6 +5,9 @@ from llama_index.vector_stores.lancedb import LanceDBVectorStore from llama_index.llms.databricks import Databricks from llama_index.embeddings.huggingface import HuggingFaceEmbedding +from gen_image import load_models, generate_image, RESPONSE_TO_DIFFUSER_PROMPT + +MODEL, STEPS = None, None def get_doc_from_url(url): @@ -17,22 +20,24 @@ def build_RAG( embed_model="mixedbread-ai/mxbai-embed-large-v1", uri="~/tmp/lancedb_hogwart", force_create_embeddings=False, + illustrate=True, + diffuser_model="sdxl", ): Settings.embed_model = HuggingFaceEmbedding(model_name=embed_model) Settings.llm = Databricks(model="databricks-dbrx-instruct") - + if illustrate: + print("Loading sdxl model") + model, steps = load_models(diffuser_model) + # This is a hack to tradeoff between speed and quality + steps = 1 + print("Model loaded") documents = get_doc_from_url(url) vector_store = LanceDBVectorStore(uri=uri) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents(documents, storage_context=storage_context) query_engine = index.as_chat_engine() - print("Ask a question relevant to the given context:") - while True: - query = input() - response = query_engine.chat(query) - print(response) - print("\n") + return query_engine, model, steps if __name__ == "__main__": @@ -61,5 +66,42 @@ def build_RAG( default=False, help="Force create embeddings", ) + parser.add_argument( + "--diffuser_model", + type=str, + default="sdxl", + help="Model ID", + ) + + parser.add_argument( + "--illustrate", + type=bool, + default=True, + help="Annotate", + ) args = parser.parse_args() - build_RAG(args.url, args.embed_model, args.uri, args.force_create_embeddings) + # hardcode model because no one should use sd + args.diffuser_model = "sdxl" + query_engine, model, steps = build_RAG( + args.url, + args.embed_model, + args.uri, + args.force_create_embeddings, + args.illustrate, + args.diffuser_model, + ) + + print("Ask a question relevant to the given context:") + while True: + query = input() + response = query_engine.chat(query) + print(response) + print("\n Illustrating the response...:") + image = generate_image( + model, + steps, + Settings.llm.complete( + RESPONSE_TO_DIFFUSER_PROMPT.format(str(response.response)) + ).text, + ) + image.show() diff --git a/examples/databricks_DBRX_website_bot/requirements.txt b/examples/databricks_DBRX_website_bot/requirements.txt index a25f1f6..f5719f6 100644 --- a/examples/databricks_DBRX_website_bot/requirements.txt +++ b/examples/databricks_DBRX_website_bot/requirements.txt @@ -2,4 +2,12 @@ llama-index llama-index-llms-databricks llama-index-embeddings-huggingface llama-index-readers-web -llama-index-vector-stores-lancedb \ No newline at end of file +llama-index-vector-stores-lancedb +diffusers +mlx>=0.11 +huggingface-hub +regex +numpy +tqdm +Pillow +streamlit \ No newline at end of file From d9af7f924db8e23a5140fe462bc3e340e4ee6663 Mon Sep 17 00:00:00 2001 From: QianZhu Date: Thu, 6 Jun 2024 19:13:01 -0700 Subject: [PATCH 12/16] Llamaindex demo (#200) --- .../lancedb_cloud/README.md | 6 - .../lancedb_cloud/main.ipynb | 12 +- .../LlamaIndex-demo/lancedb_cloud/README.md | 29 ++ .../LlamaIndex-demo/lancedb_cloud/main.ipynb | 392 ++++++++++++++++++ .../LlamaIndex-demo/lancedb_cloud/main.py | 89 ++++ .../lancedb_cloud/requirements.txt | 5 + 6 files changed, 521 insertions(+), 12 deletions(-) create mode 100644 examples/LlamaIndex-demo/lancedb_cloud/README.md create mode 100644 examples/LlamaIndex-demo/lancedb_cloud/main.ipynb create mode 100644 examples/LlamaIndex-demo/lancedb_cloud/main.py create mode 100644 examples/LlamaIndex-demo/lancedb_cloud/requirements.txt diff --git a/examples/Code-Documentation-QA-Bot/lancedb_cloud/README.md b/examples/Code-Documentation-QA-Bot/lancedb_cloud/README.md index 0700bbf..88b6eca 100644 --- a/examples/Code-Documentation-QA-Bot/lancedb_cloud/README.md +++ b/examples/Code-Documentation-QA-Bot/lancedb_cloud/README.md @@ -30,10 +30,4 @@ region="us-east-1" ### Run the script ```python OPENAI_API_KEY=... python main.py --query "what is a vectordb?" -``` - -### Javascript -Run the script -```javascript -OPENAI_API_KEY=... node index.js ``` \ No newline at end of file diff --git a/examples/Code-Documentation-QA-Bot/lancedb_cloud/main.ipynb b/examples/Code-Documentation-QA-Bot/lancedb_cloud/main.ipynb index 53036e6..5b40c69 100644 --- a/examples/Code-Documentation-QA-Bot/lancedb_cloud/main.ipynb +++ b/examples/Code-Documentation-QA-Bot/lancedb_cloud/main.ipynb @@ -20,7 +20,7 @@ "id": "wgPbKbpumkhH" }, "source": [ - "## Credentials\n", + "### Credentials\n", "\n", "Copy and paste the project name and the api key from your project page.\n", "These will be used later to [connect to LanceDB Cloud](#scroll-to=5q8m6GMD7sGu)" @@ -151,7 +151,7 @@ "id": "8eKRYd2F7v5n" }, "source": [ - "# Get the data\n", + "### Get the data\n", "To make this easier, we've downloaded Numpy documentation and stored the raw HTML files for you to download. Once the docs are downloaded, we then use LangChain's HTML document readers to parse them and store them in LanceDB as a vector store, along with relevant metadata.\n", "By default we use numpy docs, but you can replace this with your own docs as well." ] @@ -218,7 +218,7 @@ "id": "PCufm9Xr8eWp" }, "source": [ - "# Pre-processing and loading the documents\n", + "### Pre-processing and loading the documents\n", "\n", "Next, let's pre-process and load the documents. To make sure we don't need to do this repeatedly while updating code, we're caching it using pickle so it can be retrieved again (this could take a few minutes to run the first time you do it). We'll also add extra metadata to the docs here such as the title and version of the code:\n", "\n", @@ -278,7 +278,7 @@ "id": "cntAuaUU_TER" }, "source": [ - "# Generating emebeddings from our docs\n", + "### Generating emebeddings from our docs\n", "\n", "Now that we have our raw documents loaded, we need to pre-process them to generate embeddings:" ] @@ -305,7 +305,7 @@ "id": "IiM4DJvC_2dV" }, "source": [ - "# Store data in LanceDB Cloud\n", + "### Store data in LanceDB Cloud\n", "\n", "Let's connect to LanceDB so we can store our documents, It requires 0 setup !" ] @@ -370,7 +370,7 @@ "id": "7SKSlyq2iwpK" }, "source": [ - "# Query" + "### Query" ] }, { diff --git a/examples/LlamaIndex-demo/lancedb_cloud/README.md b/examples/LlamaIndex-demo/lancedb_cloud/README.md new file mode 100644 index 0000000..636ab13 --- /dev/null +++ b/examples/LlamaIndex-demo/lancedb_cloud/README.md @@ -0,0 +1,29 @@ +# LlamaIndex and LanceDB Cloud Demo + +In this demo, we are going to show how to use LanceDB Cloud to perform vector searches in LlamaIndex + + +### Set credentials +if you would like to set api key through an environment variable: +``` +export LANCEDB_API_KEY="sk_..." +``` +or +``` +import os +import getpass + +os.environ["LANCEDB_API_KEY"] = getpass.getpass("Enter Your LANCEDB API Key:") +``` + +replace the following lines in main.py with your project slug and api key" +``` +db_url="db://your-project-slug-name" +api_key="sk_..." +region="us-east-1" +``` + +### Run the script +```python +OPENAI_API_KEY=... python main.py +``` \ No newline at end of file diff --git a/examples/LlamaIndex-demo/lancedb_cloud/main.ipynb b/examples/LlamaIndex-demo/lancedb_cloud/main.ipynb new file mode 100644 index 0000000..c206a8c --- /dev/null +++ b/examples/LlamaIndex-demo/lancedb_cloud/main.ipynb @@ -0,0 +1,392 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "13cb272e", + "metadata": {}, + "source": [ + "# Vector search with LanceDB Cloud and LlamaIndex \n" + ] + }, + { + "cell_type": "markdown", + "id": "9a0e829a", + "metadata": { + "id": "wgPbKbpumkhH" + }, + "source": [ + "### Credentials\n", + "\n", + "Copy and paste the project name and the api key from your project page.\n", + "These will be used later to [connect to LanceDB Cloud](#scroll-to=5q8m6GMD7sGu)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6553603f", + "metadata": { + "id": "rqEXT5-fmofw" + }, + "outputs": [], + "source": [ + "project_slug = \"your-project-slug\" # @param {type:\"string\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "36ef9c45", + "metadata": { + "id": "5LYmBomPmswi" + }, + "outputs": [], + "source": [ + "api_key = \"sk_...\" # @param {type:\"string\"}" + ] + }, + { + "cell_type": "markdown", + "id": "33ba6af1", + "metadata": { + "id": "Xs6tr6CMnBrr" + }, + "source": [ + "You can also set the LANCEDB_API_KEY as an environment variable. More details can be found **here**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Le27BWs2vDbB" + }, + "source": [ + "Since we will be using OPENAI API, let us set the OPENAI API KEY as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-2-fyVPKu9fl" + }, + "outputs": [], + "source": [ + "openai_api_key = \"sk-...\" # @param {type:\"string\"}" + ] + }, + { + "cell_type": "markdown", + "id": "1991331f-4316-417a-b693-e2f27cbe9ea7", + "metadata": {}, + "source": [ + "### Installing dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8a49c31", + "metadata": {}, + "outputs": [], + "source": [ + "! pip install llama-index-vector-stores-lancedb llama-index-readers-file llama-index-embeddings-openai llama-index-llms-openai" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0QQL4lm8lTzg" + }, + "source": [ + "### Importing libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vP6d6JUShgqo" + }, + "outputs": [], + "source": [ + "import openai\n", + "import logging\n", + "import sys\n", + "# Uncomment to see debug logs\n", + "# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n", + "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n", + "\n", + "from llama_index.core import SimpleDirectoryReader, Document, StorageContext\n", + "from llama_index.core import VectorStoreIndex\n", + "from llama_index.vector_stores.lancedb import LanceDBVectorStore\n", + "import textwrap\n", + "\n", + "openai.api_key = openai_api_key\n", + "assert openai.models.list() is not None" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8eKRYd2F7v5n" + }, + "source": [ + "### Download the data\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "l0ezDr7suAf_" + }, + "outputs": [], + "source": [ + "! mkdir -p 'data/paul_graham/'\n", + "! wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n", + "! ls 'data/paul_graham/'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HJf8xZmX8VJC" + }, + "source": [ + "Load the documents stored in the data/paul_graham/ using the SimpleDirectoryReader:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5aljyqpUiViE" + }, + "outputs": [], + "source": [ + "documents = SimpleDirectoryReader(\"data/paul_graham/\").load_data()\n", + "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].hash)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IiM4DJvC_2dV" + }, + "source": [ + "### Store data in LanceDB Cloud\n", + "\n", + "Let's connect to LanceDB so we can store our documents, It requires 0 setup !" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GV77SSi-AK0v" + }, + "outputs": [], + "source": [ + "uri = \"db://\" + project_slug\n", + "table_name = \"llamaindex_vectorstore\" #optional, default table name is \"vectors\" \n", + "\n", + "vector_store = LanceDBVectorStore( \n", + " uri=uri, # your remote DB URI\n", + " api_key=\"sk_..\", # lancedb cloud api key\n", + " region=\"your-region\" # the region you configured\n", + " ...\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sZOUxfqzXr1m" + }, + "source": [ + "### Create an index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4nDltKClAhhU" + }, + "outputs": [], + "source": [ + "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + "\n", + "index = VectorStoreIndex.from_documents(\n", + " documents, storage_context=storage_context\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xoS-WKXMXvvR" + }, + "source": [ + "And thats it! We're all setup. The next step is to run some queries, let's try a few:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7SKSlyq2iwpK" + }, + "source": [ + "### Query the index\n", + "We can now ask questions using the created index. Filtering can be enabled via `MetadataFilters` or use native lance `where` clause." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eb6419b", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "from llama_index.core.vector_stores import (\n", + " MetadataFilters,\n", + " FilterOperator,\n", + " FilterCondition,\n", + " MetadataFilter,\n", + ")\n", + "\n", + "date = datetime.today().strftime('%Y-%m-%d')\n", + "query_filters = MetadataFilters(\n", + " filters=[\n", + " MetadataFilter(\n", + " key=\"creation_date\", operator=FilterOperator.EQ, value=date #using current date as the latest data is scraped\n", + " ),\n", + " MetadataFilter(\n", + " key=\"file_size\", value=75040, operator=FilterOperator.GT\n", + " ),\n", + " ],\n", + " condition=FilterCondition.AND,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Viaweb charged $100 a month for a small store and $300 a month for a big one.\n", + "metadata - ..." + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query_engine = index.as_query_engine(\n", + " filters=query_filters,\n", + ")\n", + "\n", + "response = query_engine.query(\"How much did Viaweb charge per month?\")\n", + "print(response)\n", + "print(\"metadata -\", response.metadata)" + ] + }, + { + "cell_type": "markdown", + "id": "0c1c6c73", + "metadata": {}, + "source": [ + "Let's use LanceDB filters(SQL like) directly via the `where` clause :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a2bcc07", + "metadata": {}, + "outputs": [], + "source": [ + "lance_filter = \"metadata.file_name = 'paul_graham_essay.txt' \"\n", + "retriever = index.as_retriever(vector_store_kwargs={\"where\": lance_filter})\n", + "response = retriever.retrieve(\"What did the author do growing up?\")\n", + "print(response[0].get_content())\n", + "print(\"metadata -\", response[0].metadata)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sZOUxfqzXr1m" + }, + "source": [ + "### Append data to the index \n", + "You can also add data to an existing index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "069fc099", + "metadata": {}, + "outputs": [], + "source": [ + "del index\n", + "\n", + "index = VectorStoreIndex.from_documents(\n", + " [Document(text=\"The sky is purple in Portland, Maine\")],\n", + " uri=\"/tmp/new_dataset\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5cffcfe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Portland, Maine\n" + ] + } + ], + "source": [ + "query_engine = index.as_query_engine()\n", + "response = query_engine.query(\"Where is the sky purple?\")\n", + "print(textwrap.fill(str(response), 100))" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/LlamaIndex-demo/lancedb_cloud/main.py b/examples/LlamaIndex-demo/lancedb_cloud/main.py new file mode 100644 index 0000000..ef4e168 --- /dev/null +++ b/examples/LlamaIndex-demo/lancedb_cloud/main.py @@ -0,0 +1,89 @@ +import os +import textwrap +from datetime import datetime + +import openai +import requests +from llama_index.core import ( + Document, + SimpleDirectoryReader, + StorageContext, + VectorStoreIndex, +) +from llama_index.vector_stores.lancedb import LanceDBVectorStore + +if __name__ == "__main__": + if "OPENAI_API_KEY" not in os.environ: + raise ValueError("OPENAI_API_KEY environment variable not set. Please set it") + else: + openai.api_key = os.environ["OPENAI_API_KEY"] + + # Download the document + data_path = r"data/paul_graham/" + if not os.path.exists(data_path): + os.makedirs(data_path) + url = "https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt" + r = requests.get(url) + with open(data_path + "/paul_graham_essay.txt", "wb") as f: + f.write(r.content) + + # Load the document + documents = SimpleDirectoryReader(data_path).load_data() + print("Document ID:", documents[0].doc_id, "Document Hash:", documents[0].hash) + + # Create a LanceDBVectorStore and create an index + vector_store = LanceDBVectorStore( + uri="db://your-project-slug", # your remote DB URI + api_key="sk_...", # lancedb cloud api key + region="us-east-1", # the region you configured + ) + + storage_context = StorageContext.from_defaults(vector_store=vector_store) + + index = VectorStoreIndex.from_documents(documents, storage_context=storage_context) + + # Query via MetadataFilters + from llama_index.core.vector_stores import ( + FilterCondition, + FilterOperator, + MetadataFilter, + MetadataFilters, + ) + + date = datetime.today().strftime("%Y-%m-%d") + query_filters = MetadataFilters( + filters=[ + MetadataFilter(key="creation_date", operator=FilterOperator.EQ, value=date), + MetadataFilter(key="file_size", value=75040, operator=FilterOperator.GT), + ], + condition=FilterCondition.AND, + ) + + query_engine = index.as_query_engine( + filters=query_filters, + ) + + response = query_engine.query("How much did Viaweb charge per month?") + print("==== query via MetadataFilters") + print(response) + print("metadata -", response.metadata) + + # Query via LanceDB where clause + lance_filter = "metadata.file_name = 'paul_graham_essay.txt' " + retriever = index.as_retriever(vector_store_kwargs={"where": lance_filter}) + response = retriever.retrieve("What did the author do growing up?") + print("==== query via LanceDB where clause") + print(response[0].get_content()) + print("metadata -", response[0].metadata) + + # add data to an existing index and query with the new data + del index + + index = VectorStoreIndex.from_documents( + [Document(text="The sky is purple in Portland, Maine")], + uri="/tmp/new_dataset", + ) + query_engine = index.as_query_engine() + response = query_engine.query("Where is the sky purple?") + print("==== query with new data") + print(textwrap.fill(str(response), 100)) diff --git a/examples/LlamaIndex-demo/lancedb_cloud/requirements.txt b/examples/LlamaIndex-demo/lancedb_cloud/requirements.txt new file mode 100644 index 0000000..8272f87 --- /dev/null +++ b/examples/LlamaIndex-demo/lancedb_cloud/requirements.txt @@ -0,0 +1,5 @@ +llama-index-vector-stores-lancedb +llama-index-readers-file +llama-index-embeddings-openai +llama-index-llms-openai +lancedb \ No newline at end of file From 5af6248171f9b054790cab56dd87a0ea960a505b Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Fri, 7 Jun 2024 18:43:21 +0530 Subject: [PATCH 13/16] restructured README (#202) --- README.md | 131 +++++++++++------- .../better-rag-FLAIR/README.md | 0 .../better-rag-FLAIR/app.py | 0 .../better-rag-FLAIR/main.ipynb | 0 .../better-rag-FLAIR/requirements.txt | 0 5 files changed, 82 insertions(+), 49 deletions(-) rename {tutorials => examples}/better-rag-FLAIR/README.md (100%) rename {tutorials => examples}/better-rag-FLAIR/app.py (100%) rename {tutorials => examples}/better-rag-FLAIR/main.ipynb (100%) rename {tutorials => examples}/better-rag-FLAIR/requirements.txt (100%) diff --git a/README.md b/README.md index 6df6a08..3eabee6 100644 --- a/README.md +++ b/README.md @@ -15,10 +15,10 @@ Join our community for support - Discord --- -This repository is divided into 3 sections: +This repository is divided into 2 sections: - [Examples](#examples) - Get right into the code with minimal introduction, aimed at getting you from an idea to PoC within minutes! - [Applications](#projects--applications) - Ready to use Python and web apps using applied LLMs, VectorDB and GenAI tools -- [Tutorials](#tutorials) - A curated list of tutorials, blogs, Colabs and courses to get you started with GenAI in greater depth. + ## Examples Applied examples that get right into the code with minimal introduction, aimed at getting you from an idea to PoC within minutes! @@ -27,48 +27,102 @@ Examples are available as: * **Python scripts** - for cases where you'd like directly to use the file or snippets to integrate in your application * **JS/TS scripts** - Some examples are written using lancedb's native js library! These script/snippets can also be directly integrated in your web applications. -If you're looking for in-depth tutorial-like examples, checkout the [tutorials](#tutorials) section! +The following examples are organized into different tables to make similar types of examples easily accessible. -| Example   | Notebook & Scripts   | Read The Blog!       | -|-------- | ------------- | ------------- | -| | | | -| [Youtube transcript search bot](/examples/Youtube-Search-QA-Bot/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Youtube-Search-QA-Bot/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/Youtube-Search-QA-Bot/index.js) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|| -| [Langchain: Code Docs QA bot](/examples/Code-Documentation-QA-Bot/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Code-Documentation-QA-Bot/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/Code-Documentation-QA-Bot/index.js) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|| -| [Databricks DBRX Website Bot](./examples/databricks_DBRX_website_bot/) | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/databricks_DBRX_website_bot/main.py) [![Databricks LLM](https://img.shields.io/badge/databricks-api-red)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| -| [CLI-based SDK Manual Chatbot with Phidata](/examples/CLI-SDK-Manual-Chatbot-Locally/) | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/CLI-SDK-Manual-Chatbot-Locally/assistant.py) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| -| [TransformersJS Embedding example](./examples/js-transformers/) |[![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/js-transformers/index.js) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| | -| [Inbuilt Hybrid Search](/examples/Inbuilt-Hybrid-Search) |Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|| -| [Audio Search](./examples/audio_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/audio_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | -| [Multi-lingual search](/examples/multi-lingual-wiki-qa) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multi-lingual-wiki-qa/main.py) [![LLM](https://img.shields.io/badge/cohere-api-pink)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | -| [Hybrid search BM25 & lancedb ](./examples/Hybrid_search_bm25_lancedb/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6)| -| [Search Within Images](/examples/search-within-images-with-sam-and-clip/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/search-within-an-image-331b54e4285e)| -| [Accelerate Vector Search Applications Using OpenVINO](/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-lancedb/)| +| Build from Scratch    | Interactive Notebook & Scripts   | +|-------- | -------------: | +||| +| [Build RAG from Scratch](./tutorials/RAG-from-Scratch) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | +| [Local RAG from Scratch with Llama3](./tutorials/Local-RAG-from-Scratch) | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./tutorials/Local-RAG-from-Scratch/rag.py) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | +|||| + + +| Multimodal    | Interactive Notebook & Scripts   | Blog | +| --------- | -------------------------- | ----------- | +|||| | [Multimodal CLIP: DiffusionDB](/examples/multimodal_clip_diffusiondb/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_clip_diffusiondb/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)| | [Multimodal CLIP: Youtube videos](/examples/multimodal_video_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_video_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)| | [Multimodal Image + Text Search](/examples/multimodal_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)| -| [Movie Recommender](/examples/movie-recommender/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/movie-recommender/main.py) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | -| [Movie Recommender with Genre](./examples/movie-recommendation-with-genres/) | Open In Colab [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/movie-recommendation-system-using-lancedb-and-doc2vec/)| -| [Product Recommender](./examples/product-recommender/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/product-recommender/main.py)[![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| | -| [Arxiv paper recommender](/examples/arxiv-recommender) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/arxiv-recommender/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | +|||| + +| RAG    | Interactive Notebook & Scripts   | Blog | +| --------- | -------------------------- | ----------- | +|||| | [Improve RAG with Re-ranking](/examples/RAG_Reranking/) | Open In Colab [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)| -| [Improve RAG with FLARE](/examples/Advanced-RAG-with-FLARE) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Advanced-RAG-with-FLARE/app.py) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)| +| [Instruct-Multitask](./examples/instruct-multitask) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543)| | [Improve RAG with HyDE](/examples/Advance-RAG-with-HyDE/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb)| | [Improve RAG with LOTR ](/examples/Advance_RAG_LOTR/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35)| | [Advanced RAG: Parent Document Retriever](/examples/parent_document_retriever/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6)| +| [Corrective RAG with Langgraph](./tutorials/Corrective-RAG-with_Langgraph/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/implementing-corrective-rag-in-the-easiest-way-2/)| +| [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301/) | +| [Improve RAG with FLARE](./examples/better-rag-FLAIR) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/better-rag-FLAIR/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/) | | [Query Expansion and Reranker ](/examples/QueryExpansion&Reranker/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/improving-rag-with-query-expansion-reranking-models/)| | [RAG Fusion](/examples/RAG_Fusion/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| -| [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301/) | -| [Instruct-Multitask](./examples/instruct-multitask) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543)| +|||| + + +| Vector Search    | Interactive Notebook & Scripts   | Blog | +| --------- | -------------------------- | ----------- | +|||| +| [Inbuilt Hybrid Search](/examples/Inbuilt-Hybrid-Search) |Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|| +| [Hybrid search BM25 & lancedb ](./examples/Hybrid_search_bm25_lancedb/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6)| +| [NER powered Semantic Search](./tutorials/NER-powered-Semantic-Search) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493) | +| [Audio Search](./examples/audio_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/audio_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | +| [Multi-lingual search](/examples/multi-lingual-wiki-qa) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multi-lingual-wiki-qa/main.py) [![LLM](https://img.shields.io/badge/cohere-api-pink)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | +| [Facial Recognition](./examples/facial_recognition) | Open In Colab [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| +[Sentiment Analysis : Analysing Hotel Reviews](/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6)| +| [Imagebind demo app](./examples/imagebind_demo/) | hf spaces [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| +| [Search Within Images](/examples/search-within-images-with-sam-and-clip/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/search-within-an-image-331b54e4285e)| +| [Vector Search with TransformersJS](./examples/js-transformers/) |[![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/js-transformers/index.js) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| | +| [Accelerate Vector Search Applications Using OpenVINO](/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-lancedb/)| +|||| + +| Chatbot    | Interactive Notebook & Scripts   | Blog  | +| --------- | -------------------------- | ----------- | +|||| +| [Databricks DBRX Website Bot](./examples/databricks_DBRX_website_bot/) | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/databricks_DBRX_website_bot/main.py) [![Databricks LLM](https://img.shields.io/badge/databricks-api-red)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| +| [CLI-based SDK Manual Chatbot with Phidata](/examples/CLI-SDK-Manual-Chatbot-Locally/) | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/CLI-SDK-Manual-Chatbot-Locally/assistant.py) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| +| [Youtube transcript search bot](/examples/Youtube-Search-QA-Bot/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Youtube-Search-QA-Bot/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/Youtube-Search-QA-Bot/index.js) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|| +| [Langchain: Code Docs QA bot](/examples/Code-Documentation-QA-Bot/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Code-Documentation-QA-Bot/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/Code-Documentation-QA-Bot/index.js) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|| +| [Context-Aware Chatbot using Llama 2 & LanceDB](./tutorials/chatbot_using_Llama2_&_lanceDB) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755) | +|||| + +| Evaluation    | Interactive Notebook & Scripts   | Blog | +| --------- | -------------------------- | ----------- | +|||| | [Evaluating Prompts with Prompttools](/examples/prompttools-eval-prompts/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| | | [Evaluating RAG with RAGAs](./examples/Evaluating_RAG_with_RAGAs/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| | +|||| + +| AI Agents    | Interactive Notebook & Scripts   | Blog | +| --------- | -------------------------- | ----------- | +|||| | [AI Agents: Reducing Hallucination](/examples/reducing_hallucinations_ai_agents/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/reducing_hallucinations_ai_agents/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/reducing_hallucinations_ai_agents/index.js) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f/)| | [AI Trends Searcher with CrewAI](./examples/AI-Trends-with-CrewAI/) |Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/track-ai-trends-crewai-agents-rag/)| | [SuperAgent Autogen](/examples/SuperAgent_Autogen) |Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)|| -[Sentiment Analysis : Analysing Hotel Reviews](/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6)| -| [Facial Recognition](./examples/facial_recognition) | Open In Colab [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| -| [Imagebind demo app](./examples/imagebind_demo/) | hf spaces [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| +|||| +| Recommender Systems    | Interactive Notebook & Scripts   | Blog | +| --------- | -------------------------- | ----------- | +|||| +| [Movie Recommender](/examples/movie-recommender/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/movie-recommender/main.py) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | +| [Movie Recommender with Genre](./examples/movie-recommendation-with-genres/) | Open In Colab [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/movie-recommendation-system-using-lancedb-and-doc2vec/)| +| [Product Recommender](./examples/product-recommender/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/product-recommender/main.py)[![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| | +| [Arxiv paper recommender](/examples/arxiv-recommender) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/arxiv-recommender/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | +|||| + +| Concepts | Interactive Notebook | Blog Link | +| --------- | -------------------------- | ----------- | +| | | | +| [A Primer on Text Chunking and its Types](./tutorials/different-types-text-chunking-in-RAG) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/different-types-text-chunking-in-RAG/Text_Chunking_on_RAG_application_with_LanceDB.ipynb) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/a-primer-on-text-chunking-and-its-types-a420efc96a13) | +| [Langchain LlamaIndex Chunking](./tutorials/Langchain-LlamaIndex-Chunking) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Langchain-LlamaIndex-Chunking/Langchain_Llamaindex_chunking.ipynb) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/chunking-techniques-with-langchain-and-llamaindex/) | +| [Comparing Cohere Rerankers with LanceDB](./tutorials/cohere-reranker) | [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/benchmarking-cohere-reranker-with-lancedb/) | +| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/benchmarking-lancedb-92b01032874a-2/) |[![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/benchmarking-lancedb-92b01032874a-2/) | +| [LLMs, RAG, & the missing storage layer for AI](https://blog.lancedb.com/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984) | [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984/) | +| [Fine-Tuning LLM using PEFT & QLoRA](./tutorials/fine-tuning_LLM_with_PEFT_QLoRA) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/fine-tuning_LLM_with_PEFT_QLoRA/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/optimizing-llms-a-step-by-step-guide-to-fine-tuning-with-peft-and-qlora-22eddd13d25b) | +| [Extracting Complex tables-text from PDFs using LlamaParse ](./tutorials/Advace_RAG_LlamaParser) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Advace_RAG_LlamaParser/main.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![LlamaCloud](https://img.shields.io/badge/Llama-api-pink)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | +|||| ## Projects & Applications These are ready to use applications built using LanceDB serverless vector database. You can explore these open source projects, use parts of them in your projects or build your applications on top of these. @@ -91,28 +145,7 @@ These are ready to use applications built using LanceDB serverless vector databa | [ Fastapi RAG template ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Chatbot_RAG_with_FASTAPI) | FastAPI based RAG template with Websocket support | ![image](./assets/chatbot_fastapi.png)| | [ GTE MLX RAG ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/GTE_mlx_RAG) | mlx based RAG model using lancedb api support | ![image](./assets/rag-mlx.png)| | [ Healthcare Chatbot ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Healthcare_chatbot/) | Healthcare chatbot using domain specific LLM & Embedding model | ![image](./assets/chatbot_medical.png)| - - - -## Tutorials -Looking to get started with LLMs, vectorDBs, and the world of Generative AI? These in-depth tutorials and courses cover these concepts with practical follow along colabs where possible. -| Tutorial | Interactive Environment | Blog Link | -| --------- | -------------------------- | ----------- | -| | | | -| [Build RAG from Scratch](./tutorials/RAG-from-Scratch) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | -| [Local RAG from Scratch with Llama3](./tutorials/Local-RAG-from-Scratch) | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./tutorials/Local-RAG-from-Scratch/rag.py) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | -| [A Primer on Text Chunking and its Types](./tutorials/different-types-text-chunking-in-RAG) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/different-types-text-chunking-in-RAG/Text_Chunking_on_RAG_application_with_LanceDB.ipynb) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/a-primer-on-text-chunking-and-its-types-a420efc96a13) | -| [Langchain LlamaIndex Chunking](./tutorials/Langchain-LlamaIndex-Chunking) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Langchain-LlamaIndex-Chunking/Langchain_Llamaindex_chunking.ipynb) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/chunking-techniques-with-langchain-and-llamaindex/) | -| [Comparing Cohere Rerankers with LanceDB](./tutorials/cohere-reranker) | [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/benchmarking-cohere-reranker-with-lancedb/) | -| [NER powered Semantic Search](./tutorials/NER-powered-Semantic-Search) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493) | -| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/benchmarking-lancedb-92b01032874a-2/) |[![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/benchmarking-lancedb-92b01032874a-2/) | -| [Corrective RAG with Langgraph](./tutorials/Corrective-RAG-with_Langgraph/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/implementing-corrective-rag-in-the-easiest-way-2/)| -| [LLMs, RAG, & the missing storage layer for AI](https://blog.lancedb.com/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984) | [![intermediate](https://img.shields.io/badge/intermediate-FFDA33)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984/) | -| [Fine-Tuning LLM using PEFT & QLoRA](./tutorials/fine-tuning_LLM_with_PEFT_QLoRA) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/fine-tuning_LLM_with_PEFT_QLoRA/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/optimizing-llms-a-step-by-step-guide-to-fine-tuning-with-peft-and-qlora-22eddd13d25b) | -| [Context-Aware Chatbot using Llama 2 & LanceDB](./tutorials/chatbot_using_Llama2_&_lanceDB) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755) | -| [Better RAG with FLARE](./tutorials/better-rag-FLAIR) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/better-rag-FLAIR/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/) | -| [Extracting Complex tables-text from PDFs using LlamaParse ](./tutorials/Advace_RAG_LlamaParser) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Advace_RAG_LlamaParser/main.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![LlamaCloud](https://img.shields.io/badge/Llama-api-pink)](#) [![beginner](https://img.shields.io/badge/beginner-B5FF33)](#)| | - +|||| **🌟 New! 🌟 Applied GenAI and VectorDB course on Udacity** diff --git a/tutorials/better-rag-FLAIR/README.md b/examples/better-rag-FLAIR/README.md similarity index 100% rename from tutorials/better-rag-FLAIR/README.md rename to examples/better-rag-FLAIR/README.md diff --git a/tutorials/better-rag-FLAIR/app.py b/examples/better-rag-FLAIR/app.py similarity index 100% rename from tutorials/better-rag-FLAIR/app.py rename to examples/better-rag-FLAIR/app.py diff --git a/tutorials/better-rag-FLAIR/main.ipynb b/examples/better-rag-FLAIR/main.ipynb similarity index 100% rename from tutorials/better-rag-FLAIR/main.ipynb rename to examples/better-rag-FLAIR/main.ipynb diff --git a/tutorials/better-rag-FLAIR/requirements.txt b/examples/better-rag-FLAIR/requirements.txt similarity index 100% rename from tutorials/better-rag-FLAIR/requirements.txt rename to examples/better-rag-FLAIR/requirements.txt From d62902aecc2be3ff120cd23c6d24378c4d0ecc1a Mon Sep 17 00:00:00 2001 From: Michael Becker Date: Fri, 7 Jun 2024 09:39:31 -0400 Subject: [PATCH 14/16] Update README.md (#203) --- examples/QueryExpansion&Reranker/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/QueryExpansion&Reranker/README.md b/examples/QueryExpansion&Reranker/README.md index b89b35d..3daf63c 100644 --- a/examples/QueryExpansion&Reranker/README.md +++ b/examples/QueryExpansion&Reranker/README.md @@ -19,4 +19,4 @@ Our focus is on improving the precision and recall of document retrieval process For a detailed exploration of the concepts and methodologies discussed in this project, visit our blog -[Read the Blog Post](https://blog.lancedb.com/improving-rag-with-query-expansion-reranking-models/) +[Read the Blog Post](https://aksdesai1998.medium.com/improving-rag-with-query-expansion-reranking-models-31d252856580) From 08734eac68b6c14a7b0114d25742bb2f5a8aa78e Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Sun, 9 Jun 2024 23:17:36 +0530 Subject: [PATCH 15/16] Tutorial/Agentic rag (#205) * updated README.md * Add files via upload * Update README.md * Update README.md * Update README for agentic rag * Update README.md * Update README.md * Update README.md * fix path * Update and rename tutorials/Agentic_RAG/main.ipynb to tutorials/Agentic_RAG/data/info.txt * Add files via upload * added medium blog link * Add files via upload * remove ghost blog . added personal medium blog link in readme --- README.md | 3 +- tutorials/Agentic_RAG/README.md | 28 ++ tutorials/Agentic_RAG/data/CIEP.pdf | Bin 0 -> 1120467 bytes tutorials/Agentic_RAG/data/GAE.pdf | Bin 0 -> 1208745 bytes tutorials/Agentic_RAG/data/info.txt | 1 + tutorials/Agentic_RAG/main.ipynb | 379 ++++++++++++++++++++++++++++ 6 files changed, 410 insertions(+), 1 deletion(-) create mode 100644 tutorials/Agentic_RAG/README.md create mode 100644 tutorials/Agentic_RAG/data/CIEP.pdf create mode 100644 tutorials/Agentic_RAG/data/GAE.pdf create mode 100644 tutorials/Agentic_RAG/data/info.txt create mode 100644 tutorials/Agentic_RAG/main.ipynb diff --git a/README.md b/README.md index 3eabee6..0e7594b 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Dive into building GenAI applications! This repository contains examples, applications, starter code, & tutorials to help you kickstart your GenAI projects. - These are built using LanceDB, a free, open-source, serverless vectorDB that **requires no setup**. -- It **integrates into python data ecosystem** so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc. +- It **integrates into Python data ecosystem** so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc. - LanceDB has **native Typescript SDK** using which you can **run vector search** in serverless functions! @@ -58,6 +58,7 @@ The following examples are organized into different tables to make similar types | [Improve RAG with FLARE](./examples/better-rag-FLAIR) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/better-rag-FLAIR/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/) | | [Query Expansion and Reranker ](/examples/QueryExpansion&Reranker/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/improving-rag-with-query-expansion-reranking-models/)| | [RAG Fusion](/examples/RAG_Fusion/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| +| [Agentic RAG ](/tutorials/Agentic_RAG/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![advanced](https://img.shields.io/badge/advanced-FF3333)](#)| |||| diff --git a/tutorials/Agentic_RAG/README.md b/tutorials/Agentic_RAG/README.md new file mode 100644 index 0000000..ca60dad --- /dev/null +++ b/tutorials/Agentic_RAG/README.md @@ -0,0 +1,28 @@ +## Agentic RAG +Agentic RAG is an advanced framework designed to handle complex information retrieval tasks using a network of intelligent agents. +These agents collaborate to perform nuanced tasks such as synthesizing information from multiple documents, +summarizing content, and comparing data points across various sources. Agentic RAG infuses autonomy and +intelligence into traditional retrieval systems, enabling them to act as proactive entities that +understand context, evaluate data quality, and make informed decisions. + + +## Implementation Example with Langraph and LanceDB + + +Install necessary packages: +``` +!pip install langchain-community tiktoken langchain-openai langchainhub lancedb langchain langgraph langchain-text-splitters langchain_openai gradio + +``` + +## Google Colab Walkthrough +For a detailed, interactive walkthrough of this implementation, you can explore the Google Colab notebook provided below. +This notebook includes support for ***Gradio***, making it easier to create UIs for your machine-learning models, +ensuring a more interactive and user-friendly experience. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG/main.ipynb) + + +## Blog + +For a 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