From b8e70be2b72e78bbd9f14d64e4c14c49ee7dd00c Mon Sep 17 00:00:00 2001 From: Shivaya Pandey <143429039+shivayapandey@users.noreply.github.com> Date: Tue, 3 Sep 2024 20:25:35 +0530 Subject: [PATCH] added BeyondLLM cookbooks --- README.md | 3 + ...uating_llamaindex_with_beyondllm (1).ipynb | 1636 +++++ ...f_Mistral_Instruct_using_HuggingFace.ipynb | 224 + ...Tune_Embeddings_using_Mistral_AI_LLM.ipynb | 6208 +++++++++++++++++ 4 files changed, 8071 insertions(+) create mode 100644 third_party/BeyondLLM/Evaluating_llamaindex_with_beyondllm (1).ipynb create mode 100644 third_party/BeyondLLM/Evaluation_of_Mistral_Instruct_using_HuggingFace.ipynb create mode 100644 third_party/BeyondLLM/Fine_Tune_Embeddings_using_Mistral_AI_LLM.ipynb diff --git a/README.md b/README.md index e4200d0b..5023b6af 100644 --- a/README.md +++ b/README.md @@ -75,3 +75,6 @@ Disclaimer: Examples contributed by the community and partners do not represent | [SubQuestionQueryEngine.ipynb](third_party/LlamaIndex/RouterQueryEngine.ipynb) | agent | LLamaIndex | | [LLM Judge: Detecting hallucinations in language models](third_party/wandb/README.md) | fine-tuning, evaluation | Weights & Biases | | [`x mistral`: CLI & TUI APP Module in X-CMD](third_party/x-cmd/README.md) | CLI, TUI APP, Chat | x-cmd | +| [Evaluation of Mistral Instruct using HuggingFace](shivayapandey/cookbook/third_party/BeyondLLM/Evaluation_of_Mistral_Instruct_using_HuggingFace.ipynb) | RAG, evaluation | BeyondLLM | +| [Fine Tune Embeddings Advanced RAG](shivayapandey/cookbook/third_party/BeyondLLM/Fine_Tune_Embeddings_using_Mistral_AI_LLM.ipynb) | fine-tuning, RAG | BeyondLLM | +| [LlamaIndex x Mistral AI x BeyondLLM Evaluation](shivayapandey/cookbook/third_party/BeyondLLM/Evaluating_llamaindex_with_beyondllm(1).ipynb) | RAG, evaluation | BeyondLLM | \ No newline at end of file diff --git a/third_party/BeyondLLM/Evaluating_llamaindex_with_beyondllm (1).ipynb b/third_party/BeyondLLM/Evaluating_llamaindex_with_beyondllm (1).ipynb new file mode 100644 index 00000000..5591d87e --- /dev/null +++ b/third_party/BeyondLLM/Evaluating_llamaindex_with_beyondllm (1).ipynb @@ -0,0 +1,1636 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "hP33L02SwkuN" + }, + "source": [ + "### **LlamaIndex x Mistral AI x BeyondLLM Evaluation**\n", + "\n", + "In this notebook, we evaluate the performance of LlamaIndex, Mistral AI, and BeyondLLM in a unified retrieval and generation pipeline.\n", + "\n", + "- **Objective:** Integrate LlamaIndex, Mistral AI, and BeyondLLM to assess and improve model performance.\n", + "- **Setup:**\n", + " - Load and prepare data using LlamaIndex.\n", + " - Configure the Mistral AI model and retrieval system.\n", + "- **Steps:**\n", + " - Set up data processing and indexing with LlamaIndex.\n", + " - Initialize the Mistral AI model for response generation.\n", + " - Configure the retriever to handle queries effectively.\n", + " - Generate and evaluate responses to determine model performance.\n", + "- **Outcome:** Gain insights into the effectiveness of combining these technologies for enhanced model evaluation and performance assessment.\n", + "\n", + "## Setup and Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "collapsed": true, + "id": "wbL7Xn-_vGhT", + "outputId": "20ac6311-dc60-4e6e-e6c4-cf51ee0173f7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting llama-index-vector-stores-chroma\n", + " Downloading llama_index_vector_stores_chroma-0.2.0-py3-none-any.whl.metadata (704 bytes)\n", + "Collecting chromadb<0.6.0,>=0.4.0 (from llama-index-vector-stores-chroma)\n", + " Downloading chromadb-0.5.5-py3-none-any.whl.metadata (6.8 kB)\n", + "Collecting llama-index-core<0.12.0,>=0.11.0 (from 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sqlalchemy, regex, pyyaml, pysbd, pypdf, pydantic, tiktoken, pandas, nltk, openai, llamaindex-py-client, llama-index-core, llama-parse, llama-index-readers-file, llama-index-llms-openai, llama-index-indices-managed-llama-cloud, llama-index-embeddings-openai, llama-index-readers-llama-parse, llama-index-multi-modal-llms-openai, llama-index-cli, llama-index-agent-openai, google-ai-generativelanguage, llama-index-program-openai, google-generativeai, llama-index-question-gen-openai, llama-index-embeddings-gemini, llama-index, beyondllm\n", + " Attempting uninstall: sqlalchemy\n", + " Found existing installation: SQLAlchemy 2.0.32\n", + " Uninstalling SQLAlchemy-2.0.32:\n", + " Successfully uninstalled SQLAlchemy-2.0.32\n", + " Attempting uninstall: regex\n", + " Found existing installation: regex 2024.5.15\n", + " Uninstalling regex-2024.5.15:\n", + " Successfully uninstalled regex-2024.5.15\n", + " Attempting uninstall: pyyaml\n", + " Found existing installation: PyYAML 6.0.2\n", + " 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1.43.0\n", + " Uninstalling openai-1.43.0:\n", + " Successfully uninstalled openai-1.43.0\n", + " Attempting uninstall: llama-index-core\n", + " Found existing installation: llama-index-core 0.11.3\n", + " Uninstalling llama-index-core-0.11.3:\n", + " Successfully uninstalled llama-index-core-0.11.3\n", + " Attempting uninstall: llama-parse\n", + " Found existing installation: llama-parse 0.5.1\n", + " Uninstalling llama-parse-0.5.1:\n", + " Successfully uninstalled llama-parse-0.5.1\n", + " Attempting uninstall: llama-index-readers-file\n", + " Found existing installation: llama-index-readers-file 0.2.0\n", + " Uninstalling llama-index-readers-file-0.2.0:\n", + " Successfully uninstalled llama-index-readers-file-0.2.0\n", + " Attempting uninstall: llama-index-llms-openai\n", + " Found existing installation: llama-index-llms-openai 0.2.0\n", + " Uninstalling llama-index-llms-openai-0.2.0:\n", + " Successfully uninstalled llama-index-llms-openai-0.2.0\n", + " Attempting uninstall: llama-index-indices-managed-llama-cloud\n", + " Found existing installation: llama-index-indices-managed-llama-cloud 0.3.0\n", + " Uninstalling llama-index-indices-managed-llama-cloud-0.3.0:\n", + " Successfully uninstalled llama-index-indices-managed-llama-cloud-0.3.0\n", + " Attempting uninstall: llama-index-embeddings-openai\n", + " Found existing installation: llama-index-embeddings-openai 0.2.3\n", + " Uninstalling llama-index-embeddings-openai-0.2.3:\n", + " Successfully uninstalled llama-index-embeddings-openai-0.2.3\n", + " Attempting uninstall: llama-index-readers-llama-parse\n", + " Found existing installation: llama-index-readers-llama-parse 0.2.0\n", + " Uninstalling llama-index-readers-llama-parse-0.2.0:\n", + " Successfully uninstalled llama-index-readers-llama-parse-0.2.0\n", + " Attempting uninstall: llama-index-multi-modal-llms-openai\n", + " Found existing installation: llama-index-multi-modal-llms-openai 0.2.0\n", + " Uninstalling llama-index-multi-modal-llms-openai-0.2.0:\n", + " Successfully uninstalled llama-index-multi-modal-llms-openai-0.2.0\n", + " Attempting uninstall: llama-index-cli\n", + " Found existing installation: llama-index-cli 0.3.0\n", + " Uninstalling llama-index-cli-0.3.0:\n", + " Successfully uninstalled llama-index-cli-0.3.0\n", + " Attempting uninstall: llama-index-agent-openai\n", + " Found existing installation: llama-index-agent-openai 0.3.0\n", + " Uninstalling llama-index-agent-openai-0.3.0:\n", + " Successfully uninstalled llama-index-agent-openai-0.3.0\n", + " Attempting uninstall: google-ai-generativelanguage\n", + " Found existing installation: google-ai-generativelanguage 0.6.6\n", + " Uninstalling google-ai-generativelanguage-0.6.6:\n", + " Successfully uninstalled google-ai-generativelanguage-0.6.6\n", + " Attempting uninstall: llama-index-program-openai\n", + " Found existing installation: llama-index-program-openai 0.2.0\n", + " Uninstalling llama-index-program-openai-0.2.0:\n", + " Successfully uninstalled llama-index-program-openai-0.2.0\n", + " Attempting uninstall: google-generativeai\n", + " Found existing installation: google-generativeai 0.7.2\n", + " Uninstalling google-generativeai-0.7.2:\n", + " Successfully uninstalled google-generativeai-0.7.2\n", + " Attempting uninstall: llama-index-question-gen-openai\n", + " Found existing installation: llama-index-question-gen-openai 0.2.0\n", + " Uninstalling llama-index-question-gen-openai-0.2.0:\n", + " Successfully uninstalled llama-index-question-gen-openai-0.2.0\n", + " Attempting uninstall: llama-index\n", + " Found existing installation: llama-index 0.11.3\n", + " Uninstalling llama-index-0.11.3:\n", + " Successfully uninstalled llama-index-0.11.3\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "albumentations 1.4.14 requires pydantic>=2.7.0, but you have pydantic 1.10.18 which is incompatible.\n", + "google-colab 1.0.0 requires pandas==2.1.4, but you have pandas 2.0.3 which is incompatible.\n", + "llama-index-embeddings-fastembed 0.2.0 requires llama-index-core<0.12.0,>=0.11.0, but you have llama-index-core 0.10.68.post1 which is incompatible.\n", + "llama-index-llms-huggingface-api 0.2.0 requires llama-index-core<0.12.0,>=0.11.0, but you have llama-index-core 0.10.68.post1 which is incompatible.\n", + "llama-index-vector-stores-chroma 0.2.0 requires llama-index-core<0.12.0,>=0.11.0, but you have llama-index-core 0.10.68.post1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed beyondllm-0.2.3 google-ai-generativelanguage-0.4.0 google-generativeai-0.4.1 llama-index-0.10.27 llama-index-agent-openai-0.2.9 llama-index-cli-0.1.13 llama-index-core-0.10.68.post1 llama-index-embeddings-gemini-0.1.6 llama-index-embeddings-openai-0.1.11 llama-index-indices-managed-llama-cloud-0.1.6 llama-index-llms-openai-0.1.27 llama-index-multi-modal-llms-openai-0.1.9 llama-index-program-openai-0.1.7 llama-index-question-gen-openai-0.1.3 llama-index-readers-file-0.1.33 llama-index-readers-llama-parse-0.1.6 llama-parse-0.4.9 llamaindex-py-client-0.1.19 nltk-3.8.1 openai-1.20.0 pandas-2.0.3 pydantic-1.10.18 pypdf-4.2.0 pysbd-0.3.4 pyyaml-6.0.1 regex-2024.4.16 sqlalchemy-2.0.29 tiktoken-0.6.0\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.colab-display-data+json": { + "pip_warning": { + "packages": [ + "google" + ] + }, + "id": "13a5686abf9c4537930e03df6f6ca76b" + } + }, + "metadata": {} + } + ], + "source": [ + "!pip install llama-index-vector-stores-chroma\n", + "!pip install llama-index\n", + "!pip install llama-index-embeddings-fastembed\n", + "!pip install llama-index-llms-huggingface-api\n", + "!pip install beyondllm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mZlvYARWY2Xh" + }, + "source": [ + "\n", + "## Set Up Hugging Face API Token" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vjaL2G2fxspm", + "outputId": "fee7b825-d8ae-418f-9235-7e48f954c0f9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "API:··········\n" + ] + } + ], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "HUGGINGFACEHUB_API_TOKEN = getpass(\"API:\")\n", + "\n", + "# Set the API token in the environment variable\n", + "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = HUGGINGFACEHUB_API_TOKEN" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Import Libraries\n", + "Import the necessary libraries to work with the LlamaIndex framework, vector stores, embeddings, and evaluation metrics:" + ], + "metadata": { + "id": "lyLflaxEap1E" + } + }, + { + "cell_type": "code", + "source": [ + "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext\n", + "from llama_index.vector_stores.chroma import ChromaVectorStore\n", + "from llama_index.embeddings.fastembed import FastEmbedEmbedding\n", + "from llama_index.llms.huggingface import HuggingFaceLLM\n", + "from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n", + "import chromadb\n", + "from beyondllm.utils import CONTEXT_RELEVENCE, GROUNDEDNESS, ANSWER_RELEVENCE\n", + "import re\n", + "import numpy as np\n", + "import pysbd" + ], + "metadata": { + "collapsed": true, + "id": "yHNQEwt1GzyH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_e_pzRXAxN7g" + }, + "source": [ + "## Load the Documents\n", + "Here we are using a heart health document that can be downloaded [here](https://www.nhlbi.nih.gov/files/docs/public/heart/healthyheart.pdf).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FTILzji8xSXO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4da680bb-15bd-4dcd-d765-c073853dd3ed" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pypdf/_crypt_providers/_cryptography.py:32: CryptographyDeprecationWarning: ARC4 has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.ARC4 and will be removed from this module in 48.0.0.\n", + " from cryptography.hazmat.primitives.ciphers.algorithms import AES, ARC4\n" + ] + } + ], + "source": [ + "documents = SimpleDirectoryReader(\"/content/sample_data/Data\").load_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DBWBD_tPxeMH" + }, + "source": [ + "## Configure Embeddings and Language Models\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "f39bd29263be4b8a8edfff243fdba7c9", + "5d7381d0bf964fc9981f3651ac719eaf", + "c0d927a9ab584baab2d30b8d14e94641", + "6775bb12195b49a59230a5ba1e1820f8", + "a8917c98052f49598f6948ecf676faf0", + "2cb74127127445bbab525f3d16850301", + "1b9819d551a84695898d2e03cfa28527", + "0c9b735b5e624957a4a808834108f1ce", + "966760fd28664590a2212780dabbe47a", + "829c451917004011b881fe32b4136226", + "57308d3c54fc4275a6385aa559da9997" + ] + }, + "collapsed": true, + "id": "RBh-3yEHbLEs", + "outputId": "a1bbc301-f275-4c46-9ea6-9c87f79804fe" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Fetching 5 files: 0%| | 0/5 [00:00 0 else 0\n", + " return f\"Context Relevancy Score: {round(average_score, 1)}\"\n", + "\n" + ], + "metadata": { + "id": "GjtjdrNEIdp1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Answer Relevancy\n", + "This function assesses how relevant the generated answer is to the query:" + ], + "metadata": { + "id": "08gZlHGdcGVJ" + } + }, + { + "cell_type": "code", + "source": [ + "def get_answer_relevancy(llm, query, response):\n", + " score_response = llm.complete(ANSWER_RELEVENCE.format(question=query, context=response))\n", + " score_str = score_response.text\n", + " return f\"Answer Relevancy Score: {score_str}\"\n", + "\n" + ], + "metadata": { + "id": "W3fQBHiPb5G4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Groundedness\n", + "Evaluate how grounded the response is based on the context provided. This function calculates the average groundedness score for each statement in the response:" + ], + "metadata": { + "id": "pEGmTsGWcJ_B" + } + }, + { + "cell_type": "code", + "source": [ + "def get_groundedness(llm, query, context, response):\n", + " total_score = 0\n", + " score_count = 0\n", + "\n", + " statements = sent_tokenize(response)\n", + "\n", + " for statement in statements:\n", + " score_response = llm.complete(GROUNDEDNESS.format(statement=statement, context=\" \".join(context)))\n", + " score_str = score_response.text\n", + " score = float(extract_number(score_str))\n", + " total_score += score\n", + " score_count += 1\n", + "\n", + " average_groundedness = total_score / score_count if score_count > 0 else 0\n", + " return f\"Groundedness Score: {round(average_groundedness, 1)}\"\n" + ], + "metadata": { + "id": "LwBAnPwwb6yk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Execute Evaluation\n", + "Perform a query using the pipeline and evaluate its performance with the defined functions:" + ], + "metadata": { + "id": "28qsZhrYcQ3s" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XjtTP2B0NNsD", + "outputId": "b5dbf762-722d-4767-b026-2472283c5b3a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Context Relevancy Score: 3.0\n", + "Answer Relevancy Score: 6\n", + "\n", + "The context provides some information about factors that can help protect against heart disease, but it does not provide a definitive list of things that do not cause heart disease. Therefore, the relevance score is 6.\n", + "Groundedness Score: 6.0\n" + ] + } + ], + "source": [ + "query = \"what doesnt cause heart diseases\"\n", + "\n", + "retrieved_documents = query_engine.retrieve(query)\n", + "context = [doc.node.text for doc in retrieved_documents]\n", + "\n", + "response = query_engine.query(query)\n", + "\n", + "print(get_context_relevancy(llm, query, context))\n", + "print(get_answer_relevancy(llm, query, response.response))\n", + "print(get_groundedness(llm, query, context, response.response))" + ] + }, + { + "cell_type": "code", + "source": [ + "query = \"what is the capital of turkey\"\n", + "\n", + "retrieved_documents = query_engine.retrieve(query)\n", + "context = [doc.node.text for doc in retrieved_documents]\n", + "\n", + "response = query_engine.query(query)\n", + "\n", + "print(get_context_relevancy(llm, query, context))\n", + "print(get_answer_relevancy(llm, query, response.response))\n", + "print(get_groundedness(llm, query, context, response.response))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SQwfvuU6c3sy", + "outputId": "e909a4d1-d835-4ed3-eab5-b120947a0aa8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Context Relevancy Score: 0.0\n", + "Answer Relevancy Score: 0\n", + "Groundedness Score: 5.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Conclusion\n", + "\n", + "The evaluation of the LlamaIndex pipeline with BeyondLLM revealed the following results for two example queries:\n", + "\n", + "### Query 1: \"What doesn't cause heart diseases?\"\n", + "\n", + "- **Context Relevancy Score:** 3.0 - The context is partially relevant but doesn’t fully address the query.\n", + "- **Answer Relevancy Score:** 6 - The response provides some relevant information but lacks a definitive list.\n", + "- **Groundedness Score:** 6.0 - The response is reasonably grounded based on the context.\n", + "\n", + "### Query 2: \"What is the capital of Turkey?\"\n", + "\n", + "- **Context Relevancy Score:** 0.0 - The context is not relevant to the query.\n", + "- **Answer Relevancy Score:** 0 - The response does not answer the query.\n", + "- **Groundedness Score:** 5.0 - The response shows some level of groundedness, though it doesn't match the query.\n", + "\n", + "These results highlight how context and response relevance affect evaluation scores. Proper alignment with the query improves these scores, while poor relevancy and incorrect answers lead to lower scores." + ], + "metadata": { + "id": "NR6MvcfddnaO" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vStm7Xwz4NVY" + }, + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "f39bd29263be4b8a8edfff243fdba7c9": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + 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00000000..98f39538 --- /dev/null +++ b/third_party/BeyondLLM/Evaluation_of_Mistral_Instruct_using_HuggingFace.ipynb @@ -0,0 +1,224 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "### **Evaluation of Mistral Instruct using HuggingFace**\n", + "\n", + "In this notebook, we explore the evaluation of the Mistral Instruct model using HuggingFace and BeyondLLM. Evaluation is essential for understanding how well a model performs on various tasks and its ability to generate relevant responses.\n", + "\n", + "- **Objective:** Assess the Mistral Instruct model's performance using context relevancy, answer relevancy, and groundedness metrics.\n", + "- **Setup:**\n", + " - Configure the retrieval pipeline for data processing.\n", + " - Use HuggingFace and BeyondLLM for model integration.\n", + "- **Steps:**\n", + " - Load and preprocess data.\n", + " - Initialize the Mistral Instruct model and retrieval system.\n", + " - Generate responses to sample queries.\n", + " - Evaluate the responses using defined metrics.\n", + "- **Outcome:** Gain insights into the model’s effectiveness and areas for improvement in understanding and generating responses based on the given data.\n", + "\n", + "\n", + "## Setup and Installation\n", + "\n", + "First, let's install the necessary packages:" + ], + "metadata": { + "id": "ZlPqyPD47rG8" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install beyondllm==0.2.2\n", + "!pip install huggingface_hub==0.20.1\n", + "!pip install llama-index-embeddings-fastembed==0.1.3" + ], + "metadata": { + "id": "I1WvikA17uvw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Now, let's import the required libraries and set up our environment:" + ], + "metadata": { + "id": "G7-Gawhp7_1t" + } + }, + { + "cell_type": "code", + "source": [ + "from beyondllm.source import fit\n", + "from beyondllm.embeddings import FastEmbedEmbeddings\n", + "from beyondllm.retrieve import auto_retriever\n", + "from beyondllm.llms import HuggingFaceHubModel\n", + "from beyondllm.generator import Generate\n", + "import os\n", + "from getpass import getpass\n", + "\n", + "# Set up HuggingFace API token\n", + "os.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass(\"Enter your HF API token:\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "827aVCn4798Q", + "outputId": "d45ab4cb-c0bc-4126-c7a2-3aba564fd2d6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package punkt_tab to\n", + "[nltk_data] /usr/local/lib/python3.10/dist-\n", + "[nltk_data] packages/llama_index/core/_static/nltk_cache...\n", + "[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter your HF API token:··········\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Data Loading and Model Configuration\n", + "\n", + "Let's load our data and configure the embedding model:" + ], + "metadata": { + "id": "gNPIqciU8iui" + } + }, + { + "cell_type": "code", + "source": [ + "# Load the PDF data\n", + "data = fit(\"/content/Shivaya Resume.pdf\", dtype=\"pdf\")\n", + "\n", + "# Initialize the embedding model\n", + "embed_model = FastEmbedEmbeddings()\n", + "\n", + "# Set up the retriever\n", + "retriever = auto_retriever(data=data, embed_model=embed_model, type=\"normal\", top_k=3)\n", + "\n", + "# Initialize the Mistral Instruct model\n", + "llm = HuggingFaceHubModel(model=\"mistralai/Mistral-7B-Instruct-v0.2\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fQ2f2WMR8kKN", + "outputId": "113b94fc-c5bf-49b0-e720-4632737417f0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 76.7M/76.7M [00:01<00:00, 68.0MiB/s]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Generate Response and Evaluate\n", + "\n", + "Now, let's generate a response to a question and evaluate the model's performance:\n" + ], + "metadata": { + "id": "TYaXTCGc9Cvw" + } + }, + { + "cell_type": "code", + "source": [ + "# Set up the generation pipeline\n", + "pipeline = Generate(question=\"What are her hobbies?\", llm=llm, retriever=retriever)\n", + "\n", + "# Generate the response\n", + "response = pipeline.call()\n", + "print(\"AI Response:\")\n", + "print(response)\n", + "\n", + "# Get evaluation metrics\n", + "eval_metrics = pipeline.get_rag_triad_evals()\n", + "print(\"\\nEvaluation Metrics:\")\n", + "print(eval_metrics)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sHXRNzQs9FQq", + "outputId": "fa6edd7b-8d2c-4dca-d06a-f4a2c90f3a43" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "AI Response:\n", + "\n", + " ANSWER: Based on the provided context, Shivaya Pandey has shown interest and expertise in various areas related to Computer Science, Data Science, and Artificial Intelligence. Some of the projects mentioned in her portfolio include a Heart Disease Prediction System, Sign Language Detector, Video Text Overlay Tool, and Gold Price Prediction Model. These projects suggest that she enjoys working with Python, machine learning, and developing innovative solutions. Additionally, she has experience in coding in Java, C, and C++. Furthermore, she has published tech articles on Medium and is certified in Python programming. Therefore, her hobbies can be inferred to be related to computer science, data science, artificial intelligence, and writing.\n", + "Executing RAG Triad Evaluations...\n", + "\n", + "Evaluation Metrics:\n", + "Context relevancy Score: 3.5\n", + "This response does not meet the evaluation threshold. Consider refining the structure and content for better clarity and effectiveness.\n", + "Answer relevancy Score: 10.0\n", + "This response meets the evaluation threshold. It demonstrates strong comprehension and coherence.\n", + "Groundness score: 9.5\n", + "This response meets the evaluation threshold. It demonstrates strong comprehension and coherence.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Conclusion\n", + "\n", + "In this notebook, we demonstrated how to evaluate the Mistral Instruct model using HuggingFace and BeyondLLM. We set up a retrieval pipeline, generated a response to a specific question, and obtained evaluation metrics for context relevancy, answer relevancy, and groundedness.\n", + "\n", + "This approach allows us to assess the model's performance in understanding context, providing relevant answers, and ensuring the responses are grounded in the provided information. Such evaluation is crucial for improving and fine-tuning language models for specific tasks or domains.\n", + "\n", + "Feel free to experiment with different questions, datasets, or model parameters to further explore the capabilities of the Mistral Instruct model." + ], + "metadata": { + "id": "SYOIF_pB9bBp" + } + } + ] +} \ No newline at end of file diff --git a/third_party/BeyondLLM/Fine_Tune_Embeddings_using_Mistral_AI_LLM.ipynb b/third_party/BeyondLLM/Fine_Tune_Embeddings_using_Mistral_AI_LLM.ipynb new file mode 100644 index 00000000..16b833d3 --- /dev/null +++ b/third_party/BeyondLLM/Fine_Tune_Embeddings_using_Mistral_AI_LLM.ipynb @@ -0,0 +1,6208 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "02195a79492844499f09ecfde90aa33c": { + "model_module": 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and generation pipeline.\n", + "- **Steps:**\n", + " - Train the embedding model with the provided dataset.\n", + " - Configure the retriever with fine-tuned embeddings.\n", + " - Initialize the Mistral AI LLM and set up the generation pipeline.\n", + " - Generate responses to queries and assess the pipeline’s performance.\n", + "- **Outcome:** Understand the impact of fine-tuning on the RAG system's performance and how it enhances the quality of generated responses.\n", + "\n", + "\n", + "## Setup and Installation\n", + "\n", + "Let's start by installing the necessary packages:" + ], + "metadata": { + "id": "MXL-l5fVIZHw" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8D36AM7bj57P" + }, + "outputs": [], + "source": [ + "!pip install beyondllm\n", + "!pip install llama-index-finetuning==0.1.5 llama-index-embeddings-huggingface==0.2.0" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Next, we'll import the required libraries and set up the environment:" + ], + "metadata": { + "id": "eKpGLJ_zJDZ_" + } + }, + { + "cell_type": "code", + "source": [ + "from beyondllm import source, retrieve, llms, generator\n", + "from beyondllm.embeddings import FineTuneEmbeddings\n", + "import os" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HJaJ83blj9mD", + "outputId": "fca9edd9-c609-40ed-cd81-1273b8d8abf4" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package punkt_tab to\n", + "[nltk_data] /usr/local/lib/python3.10/dist-\n", + "[nltk_data] packages/llama_index/core/_static/nltk_cache...\n", + "[nltk_data] Package punkt_tab is already up-to-date!\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Load the data.\n", + " Here lets use a PDF:\n", + "\n", + "[The Last Question](https://docs.google.com/file/d/0ByoueGSWXluVVUtHYnRJVEg4YnM/edit?resourcekey=0-piVl8D4gdQhTyiaImqjduQ). Download it and upload on Colab or save within your project directory" + ], + "metadata": { + "id": "IR0D2Cdcv-CP" + } + }, + { + "cell_type": "code", + "source": [ + "path = \"/content/TheLastQuestion.pdf\"\n", + "\n", + "data = source.fit(path, dtype=\"pdf\", chunk_size=1024, chunk_overlap=0)\n", + "list_of_files = [path]" + ], + "metadata": { + "id": "TK7tqWxBv2Zf" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Define Open Source LLM" + ], + "metadata": { + "id": "SeunU3zpwf7I" + } + }, + { + "cell_type": "code", + "source": [ + "from beyondllm.llms import HuggingFaceHubModel\n", + "\n", + "from getpass import getpass\n", + "\n", + "HF_TOKEN = getpass(\"Access token:\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ele7WB7wwijm", + "outputId": "67c1b148-38f1-4a5a-cff8-75c356a39ca4" + }, + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Access token:··········\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "llm = HuggingFaceHubModel(\n", + " token = HF_TOKEN,\n", + " model = \"mistralai/Mistral-7B-Instruct-v0.2\",\n", + " model_kwargs = {\"max_new_tokens\": 1024,\n", + " \"temperature\": 0.1,\n", + " \"top_p\": 0.95,\n", + " \"repetition_penalty\": 1.1,\n", + " \"return_full_text\": False\n", + " }\n", + ")" + ], + "metadata": { + "id": "Yr5AOmfowe8k" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Fine tune the Embedding model" + ], + "metadata": { + "id": "dF1ZGAlGwwPs" + } + }, + { + "cell_type": "code", + "source": [ + "embed_model = FineTuneEmbeddings()\n", + "\n", + "fine_tuned_model = embed_model.train(list_of_files, \"BAAI/bge-small-en-v1.5\", llm, \"fintune\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 600, + "referenced_widgets": [ + "02195a79492844499f09ecfde90aa33c", + 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"id": "34t1jKIbxhEx" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "pipeline = generator.Generate(question=prompt,retriever=retriever,llm=llm)" + ], + "metadata": { + "id": "bU26pEY4lm0h" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(pipeline.call())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fh-ZOGFWxjeG", + "outputId": "8d91bfd4-9cee-4a9b-ec0a-d90ee45025c7" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Lupov replied: \"Darn right they will.\"\n", + "\n", + "Context and chat history indicate that Lupov and Adell were discussing the fact that all stars, including the sun, will eventually run out of fuel and die. They were considering the implications of this, including the eventual end of all life in the universe due to the increase of entropy (the amount of disorder in the universe). Lupov acknowledged that other stars will also eventually die, agreeing with Adell's statement.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Conclusion\n", + "In this notebook, we fine-tuned an embedding model using BeyondLLM, configured a retrieval pipeline, and evaluated the Mistral AI model's performance in answering a specific query. This approach allows us to improve model accuracy and tailor responses to the dataset in use.\n", + "\n", + "Feel free to experiment with different parameters, datasets, and models to explore further capabilities." + ], + "metadata": { + "id": "jrx2rjdzLXk2" + } + } + ] +} \ No newline at end of file