From 404d92ded0616f56a06ff90d056d620cdc5c842a Mon Sep 17 00:00:00 2001 From: ChengZi Date: Tue, 28 May 2024 23:24:20 +0800 Subject: [PATCH] milvus: New langchain_milvus package and new milvus features (#21077) New features: - New langchain_milvus package in partner - Milvus collection hybrid search retriever - Zilliz cloud pipeline retriever - Milvus Local guid - Rag-milvus template --------- Signed-off-by: ChengZi Signed-off-by: Jael Gu Co-authored-by: Jael Gu Co-authored-by: Jackson Co-authored-by: Erick Friis Co-authored-by: Erick Friis --- .../retrievers/milvus_hybrid_search.ipynb | 636 ++++ .../retrievers/zilliz_cloud_pipeline.ipynb | 222 ++ .../integrations/vectorstores/milvus.ipynb | 52 +- .../vectorstores/milvus.py | 6 + libs/partners/milvus/.gitignore | 1 + libs/partners/milvus/LICENSE | 21 + libs/partners/milvus/Makefile | 57 + libs/partners/milvus/README.md | 42 + .../milvus/langchain_milvus/__init__.py | 12 + .../partners/milvus/langchain_milvus/py.typed | 0 .../langchain_milvus/retrievers/__init__.py | 8 + .../retrievers/milvus_hybrid_search.py | 160 + .../zilliz_cloud_pipeline_retriever.py | 215 ++ .../milvus/langchain_milvus/utils/__init__.py | 0 .../milvus/langchain_milvus/utils/sparse.py | 54 + .../langchain_milvus/vectorstores/__init__.py | 7 + .../langchain_milvus/vectorstores/milvus.py | 1148 ++++++++ .../langchain_milvus/vectorstores/zilliz.py | 196 ++ libs/partners/milvus/poetry.lock | 1297 ++++++++ libs/partners/milvus/pyproject.toml | 101 + libs/partners/milvus/scripts/check_imports.py | 17 + .../partners/milvus/scripts/check_pydantic.sh | 27 + libs/partners/milvus/scripts/lint_imports.sh | 17 + libs/partners/milvus/tests/__init__.py | 0 .../tests/integration_tests/__init__.py | 0 .../integration_tests/retrievers/__init__.py | 0 .../tests/integration_tests/test_compile.py | 7 + .../milvus/tests/integration_tests/utils.py | 40 + .../vectorstores/__init__.py | 0 .../vectorstores/test_milvus.py | 184 ++ .../milvus/tests/unit_tests/__init__.py | 0 .../tests/unit_tests/retrievers/__init__.py | 0 .../milvus/tests/unit_tests/test_imports.py | 12 + .../tests/unit_tests/vectorstores/__init__.py | 0 .../unit_tests/vectorstores/test_milvus.py | 17 + templates/rag-milvus/.gitignore | 1 + templates/rag-milvus/LICENSE | 21 + templates/rag-milvus/README.md | 68 + templates/rag-milvus/poetry.lock | 2621 +++++++++++++++++ templates/rag-milvus/pyproject.toml | 34 + templates/rag-milvus/rag_milvus/__init__.py | 3 + templates/rag-milvus/rag_milvus/chain.py | 69 + templates/rag-milvus/tests/__init__.py | 0 43 files changed, 7345 insertions(+), 28 deletions(-) create mode 100644 docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb create mode 100644 docs/docs/integrations/retrievers/zilliz_cloud_pipeline.ipynb create mode 100644 libs/partners/milvus/.gitignore create mode 100644 libs/partners/milvus/LICENSE create mode 100644 libs/partners/milvus/Makefile create mode 100644 libs/partners/milvus/README.md create mode 100644 libs/partners/milvus/langchain_milvus/__init__.py create mode 100644 libs/partners/milvus/langchain_milvus/py.typed create mode 100644 libs/partners/milvus/langchain_milvus/retrievers/__init__.py create mode 100644 libs/partners/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py create mode 100644 libs/partners/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py create mode 100644 libs/partners/milvus/langchain_milvus/utils/__init__.py create mode 100644 libs/partners/milvus/langchain_milvus/utils/sparse.py create mode 100644 libs/partners/milvus/langchain_milvus/vectorstores/__init__.py create mode 100644 libs/partners/milvus/langchain_milvus/vectorstores/milvus.py create mode 100644 libs/partners/milvus/langchain_milvus/vectorstores/zilliz.py create mode 100644 libs/partners/milvus/poetry.lock create mode 100644 libs/partners/milvus/pyproject.toml create mode 100644 libs/partners/milvus/scripts/check_imports.py create mode 100755 libs/partners/milvus/scripts/check_pydantic.sh create mode 100755 libs/partners/milvus/scripts/lint_imports.sh create mode 100644 libs/partners/milvus/tests/__init__.py create mode 100644 libs/partners/milvus/tests/integration_tests/__init__.py create mode 100644 libs/partners/milvus/tests/integration_tests/retrievers/__init__.py create mode 100644 libs/partners/milvus/tests/integration_tests/test_compile.py create mode 100644 libs/partners/milvus/tests/integration_tests/utils.py create mode 100644 libs/partners/milvus/tests/integration_tests/vectorstores/__init__.py create mode 100644 libs/partners/milvus/tests/integration_tests/vectorstores/test_milvus.py create mode 100644 libs/partners/milvus/tests/unit_tests/__init__.py create mode 100644 libs/partners/milvus/tests/unit_tests/retrievers/__init__.py create mode 100644 libs/partners/milvus/tests/unit_tests/test_imports.py create mode 100644 libs/partners/milvus/tests/unit_tests/vectorstores/__init__.py create mode 100644 libs/partners/milvus/tests/unit_tests/vectorstores/test_milvus.py create mode 100644 templates/rag-milvus/.gitignore create mode 100644 templates/rag-milvus/LICENSE create mode 100644 templates/rag-milvus/README.md create mode 100644 templates/rag-milvus/poetry.lock create mode 100644 templates/rag-milvus/pyproject.toml create mode 100644 templates/rag-milvus/rag_milvus/__init__.py create mode 100644 templates/rag-milvus/rag_milvus/chain.py create mode 100644 templates/rag-milvus/tests/__init__.py diff --git a/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb b/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb new file mode 100644 index 0000000000000..2aac742475850 --- /dev/null +++ b/docs/docs/integrations/retrievers/milvus_hybrid_search.ipynb @@ -0,0 +1,636 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Milvus Hybrid Search\n", + "\n", + "> [Milvus](https://milvus.io/docs) is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.\n", + "\n", + "This notebook goes over how to use the Milvus Hybrid Search retriever, which combines the strengths of both dense and sparse vector search.\n", + "\n", + "For more reference please go to [Milvus Multi-Vector Search](https://milvus.io/docs/multi-vector-search.md)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Prerequisites\n", + "### Install dependencies\n", + "You need to prepare to install the following dependencies\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet pymilvus[model] langchain-milvus langchain-openai" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Import necessary modules and classes" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from pymilvus import (\n", + " Collection,\n", + " CollectionSchema,\n", + " DataType,\n", + " FieldSchema,\n", + " WeightedRanker,\n", + " connections,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.prompts import PromptTemplate\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_milvus.retrievers import MilvusCollectionHybridSearchRetriever\n", + "from langchain_milvus.utils.sparse import BM25SparseEmbedding\n", + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "### Start the Milvus service\n", + "\n", + "Please refer to the [Milvus documentation](https://milvus.io/docs/install_standalone-docker.md) to start the Milvus service.\n", + "\n", + "After starting milvus, you need to specify your milvus connection URI.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "CONNECTION_URI = \"http://localhost:19530\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "### Prepare OpenAI API Key\n", + "\n", + "Please refer to the [OpenAI documentation](https://platform.openai.com/account/api-keys) to obtain your OpenAI API key, and set it as an environment variable.\n", + "\n", + "```shell\n", + "export OPENAI_API_KEY=\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Prepare data and Load\n", + "### Prepare dense and sparse embedding functions\n", + "\n", + " Let us fictionalize 10 fake descriptions of novels. In actual production, it may be a large amount of text data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "texts = [\n", + " \"In 'The Whispering Walls' by Ava Moreno, a young journalist named Sophia uncovers a decades-old conspiracy hidden within the crumbling walls of an ancient mansion, where the whispers of the past threaten to destroy her own sanity.\",\n", + " \"In 'The Last Refuge' by Ethan Blackwood, a group of survivors must band together to escape a post-apocalyptic wasteland, where the last remnants of humanity cling to life in a desperate bid for survival.\",\n", + " \"In 'The Memory Thief' by Lila Rose, a charismatic thief with the ability to steal and manipulate memories is hired by a mysterious client to pull off a daring heist, but soon finds themselves trapped in a web of deceit and betrayal.\",\n", + " \"In 'The City of Echoes' by Julian Saint Clair, a brilliant detective must navigate a labyrinthine metropolis where time is currency, and the rich can live forever, but at a terrible cost to the poor.\",\n", + " \"In 'The Starlight Serenade' by Ruby Flynn, a shy astronomer discovers a mysterious melody emanating from a distant star, which leads her on a journey to uncover the secrets of the universe and her own heart.\",\n", + " \"In 'The Shadow Weaver' by Piper Redding, a young orphan discovers she has the ability to weave powerful illusions, but soon finds herself at the center of a deadly game of cat and mouse between rival factions vying for control of the mystical arts.\",\n", + " \"In 'The Lost Expedition' by Caspian Grey, a team of explorers ventures into the heart of the Amazon rainforest in search of a lost city, but soon finds themselves hunted by a ruthless treasure hunter and the treacherous jungle itself.\",\n", + " \"In 'The Clockwork Kingdom' by Augusta Wynter, a brilliant inventor discovers a hidden world of clockwork machines and ancient magic, where a rebellion is brewing against the tyrannical ruler of the land.\",\n", + " \"In 'The Phantom Pilgrim' by Rowan Welles, a charismatic smuggler is hired by a mysterious organization to transport a valuable artifact across a war-torn continent, but soon finds themselves pursued by deadly assassins and rival factions.\",\n", + " \"In 'The Dreamwalker's Journey' by Lyra Snow, a young dreamwalker discovers she has the ability to enter people's dreams, but soon finds herself trapped in a surreal world of nightmares and illusions, where the boundaries between reality and fantasy blur.\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the [OpenAI Embedding](https://platform.openai.com/docs/guides/embeddings) to generate dense vectors, and the [BM25 algorithm](https://en.wikipedia.org/wiki/Okapi_BM25) to generate sparse vectors.\n", + "\n", + "Initialize dense embedding function and get dimension" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1536" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dense_embedding_func = OpenAIEmbeddings()\n", + "dense_dim = len(dense_embedding_func.embed_query(texts[1]))\n", + "dense_dim" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize sparse embedding function.\n", + "\n", + "Note that the output of sparse embedding is a set of sparse vectors, which represents the index and weight of the keywords of the input text." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 0.4270424944042204,\n", + " 21: 1.845826690498331,\n", + " 22: 1.845826690498331,\n", + " 23: 1.845826690498331,\n", + " 24: 1.845826690498331,\n", + " 25: 1.845826690498331,\n", + " 26: 1.845826690498331,\n", + " 27: 1.2237754316221157,\n", + " 28: 1.845826690498331,\n", + " 29: 1.845826690498331,\n", + " 30: 1.845826690498331,\n", + " 31: 1.845826690498331,\n", + " 32: 1.845826690498331,\n", + " 33: 1.845826690498331,\n", + " 34: 1.845826690498331,\n", + " 35: 1.845826690498331,\n", + " 36: 1.845826690498331,\n", + " 37: 1.845826690498331,\n", + " 38: 1.845826690498331,\n", + " 39: 1.845826690498331}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sparse_embedding_func = BM25SparseEmbedding(corpus=texts)\n", + "sparse_embedding_func.embed_query(texts[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Milvus Collection and load data\n", + "\n", + "Initialize connection URI and establish connection" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "connections.connect(uri=CONNECTION_URI)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define field names and their data types" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "pk_field = \"doc_id\"\n", + "dense_field = \"dense_vector\"\n", + "sparse_field = \"sparse_vector\"\n", + "text_field = \"text\"\n", + "fields = [\n", + " FieldSchema(\n", + " name=pk_field,\n", + " dtype=DataType.VARCHAR,\n", + " is_primary=True,\n", + " auto_id=True,\n", + " max_length=100,\n", + " ),\n", + " FieldSchema(name=dense_field, dtype=DataType.FLOAT_VECTOR, dim=dense_dim),\n", + " FieldSchema(name=sparse_field, dtype=DataType.SPARSE_FLOAT_VECTOR),\n", + " FieldSchema(name=text_field, dtype=DataType.VARCHAR, max_length=65_535),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a collection with the defined schema" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "schema = CollectionSchema(fields=fields, enable_dynamic_field=False)\n", + "collection = Collection(\n", + " name=\"IntroductionToTheNovels\", schema=schema, consistency_level=\"Strong\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define index for dense and sparse vectors" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "dense_index = {\"index_type\": \"FLAT\", \"metric_type\": \"IP\"}\n", + "collection.create_index(\"dense_vector\", dense_index)\n", + "sparse_index = {\"index_type\": \"SPARSE_INVERTED_INDEX\", \"metric_type\": \"IP\"}\n", + "collection.create_index(\"sparse_vector\", sparse_index)\n", + "collection.flush()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Insert entities into the collection and load the collection" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "entities = []\n", + "for text in texts:\n", + " entity = {\n", + " dense_field: dense_embedding_func.embed_documents([text])[0],\n", + " sparse_field: sparse_embedding_func.embed_documents([text])[0],\n", + " text_field: text,\n", + " }\n", + " entities.append(entity)\n", + "collection.insert(entities)\n", + "collection.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build RAG chain with Retriever\n", + "### Create the Retriever\n", + "\n", + "Define search parameters for sparse and dense fields, and create a retriever" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "sparse_search_params = {\"metric_type\": \"IP\"}\n", + "dense_search_params = {\"metric_type\": \"IP\", \"params\": {}}\n", + "retriever = MilvusCollectionHybridSearchRetriever(\n", + " collection=collection,\n", + " rerank=WeightedRanker(0.5, 0.5),\n", + " anns_fields=[dense_field, sparse_field],\n", + " field_embeddings=[dense_embedding_func, sparse_embedding_func],\n", + " field_search_params=[dense_search_params, sparse_search_params],\n", + " top_k=3,\n", + " text_field=text_field,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "In the input parameters of this Retriever, we use a dense embedding and a sparse embedding to perform hybrid search on the two fields of this Collection, and use WeightedRanker for reranking. Finally, 3 top-K Documents will be returned." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Document(page_content=\"In 'The Lost Expedition' by Caspian Grey, a team of explorers ventures into the heart of the Amazon rainforest in search of a lost city, but soon finds themselves hunted by a ruthless treasure hunter and the treacherous jungle itself.\", metadata={'doc_id': '449281835035545843'}),\n", + " Document(page_content=\"In 'The Phantom Pilgrim' by Rowan Welles, a charismatic smuggler is hired by a mysterious organization to transport a valuable artifact across a war-torn continent, but soon finds themselves pursued by deadly assassins and rival factions.\", metadata={'doc_id': '449281835035545845'}),\n", + " Document(page_content=\"In 'The Dreamwalker's Journey' by Lyra Snow, a young dreamwalker discovers she has the ability to enter people's dreams, but soon finds herself trapped in a surreal world of nightmares and illusions, where the boundaries between reality and fantasy blur.\", metadata={'doc_id': '449281835035545846'})]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "retriever.invoke(\"What are the story about ventures?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build the RAG chain\n", + "\n", + "Initialize ChatOpenAI and define a prompt template" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "llm = ChatOpenAI()\n", + "\n", + "PROMPT_TEMPLATE = \"\"\"\n", + "Human: You are an AI assistant, and provides answers to questions by using fact based and statistical information when possible.\n", + "Use the following pieces of information to provide a concise answer to the question enclosed in tags.\n", + "\n", + "\n", + "{context}\n", + "\n", + "\n", + "\n", + "{question}\n", + "\n", + "\n", + "Assistant:\"\"\"\n", + "\n", + "prompt = PromptTemplate(\n", + " template=PROMPT_TEMPLATE, input_variables=[\"context\", \"question\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Define a function for formatting documents" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "def format_docs(docs):\n", + " return \"\\n\\n\".join(doc.page_content for doc in docs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Define a chain using the retriever and other components" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "rag_chain = (\n", + " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n", + " | prompt\n", + " | llm\n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Perform a query using the defined chain" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Lila Rose has written 'The Memory Thief,' which follows a charismatic thief with the ability to steal and manipulate memories as they navigate a daring heist and a web of deceit and betrayal.\"" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rag_chain.invoke(\"What novels has Lila written and what are their contents?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Drop the collection" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "collection.drop()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/docs/integrations/retrievers/zilliz_cloud_pipeline.ipynb b/docs/docs/integrations/retrievers/zilliz_cloud_pipeline.ipynb new file mode 100644 index 0000000000000..bfbf6ff92340b --- /dev/null +++ b/docs/docs/integrations/retrievers/zilliz_cloud_pipeline.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Zilliz Cloud Pipeline\n", + "\n", + "> [Zilliz Cloud Pipelines](https://docs.zilliz.com/docs/pipelines) transform your unstructured data to a searchable vector collection, chaining up the embedding, ingestion, search, and deletion of your data.\n", + "> \n", + "> Zilliz Cloud Pipelines are available in the Zilliz Cloud Console and via RestFul APIs.\n", + "\n", + "This notebook demonstrates how to prepare Zilliz Cloud Pipelines and use the them via a LangChain Retriever." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare Zilliz Cloud Pipelines\n", + "\n", + "To get pipelines ready for LangChain Retriever, you need to create and configure the services in Zilliz Cloud." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**1. Set up Database**\n", + "\n", + "- [Register with Zilliz Cloud](https://docs.zilliz.com/docs/register-with-zilliz-cloud)\n", + "- [Create a cluster](https://docs.zilliz.com/docs/create-cluster)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2. Create Pipelines**\n", + "\n", + "- [Document ingestion, search, deletion](https://docs.zilliz.com/docs/pipelines-doc-data)\n", + "- [Text ingestion, search, deletion](https://docs.zilliz.com/docs/pipelines-text-data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use LangChain Retriever" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet langchain-milvus" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_milvus import ZillizCloudPipelineRetriever\n", + "\n", + "retriever = ZillizCloudPipelineRetriever(\n", + " pipeline_ids={\n", + " \"ingestion\": \"\", # skip this line if you do NOT need to add documents\n", + " \"search\": \"\", # skip this line if you do NOT need to get relevant documents\n", + " \"deletion\": \"\", # skip this line if you do NOT need to delete documents\n", + " },\n", + " token=\"\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add documents\n", + "\n", + "To add documents, you can use the method `add_texts` or `add_doc_url`, which inserts documents from a list of texts or a presigned/public url with corresponding metadata into the store." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- if using a **text ingestion pipeline**, you can use the method `add_texts`, which inserts a batch of texts with the corresponding metadata into the Zilliz Cloud storage.\n", + "\n", + " **Arguments:**\n", + " - `texts`: A list of text strings.\n", + " - `metadata`: A key-value dictionary of metadata will be inserted as preserved fields required by ingestion pipeline. Defaults to None.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# retriever.add_texts(\n", + "# texts = [\"example text 1e\", \"example text 2\"],\n", + "# metadata={\"\": \"\"} # skip this line if no preserved field is required by the ingestion pipeline\n", + "# )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- if using a **document ingestion pipeline**, you can use the method `add_doc_url`, which inserts a document from url with the corresponding metadata into the Zilliz Cloud storage.\n", + "\n", + " **Arguments:**\n", + " - `doc_url`: A document url.\n", + " - `metadata`: A key-value dictionary of metadata will be inserted as preserved fields required by ingestion pipeline. Defaults to None.\n", + "\n", + "The following example works with a document ingestion pipeline, which requires milvus version as metadata. We will use an [example document](https://publicdataset.zillizcloud.com/milvus_doc.md) describing how to delete entities in Milvus v2.3.x. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'token_usage': 1247, 'doc_name': 'milvus_doc.md', 'num_chunks': 6}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "retriever.add_doc_url(\n", + " doc_url=\"https://publicdataset.zillizcloud.com/milvus_doc.md\",\n", + " metadata={\"version\": \"v2.3.x\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get relevant documents\n", + "\n", + "To query the retriever, you can use the method `get_relevant_documents`, which returns a list of LangChain Document objects.\n", + "\n", + "**Arguments:**\n", + "- `query`: String to find relevant documents for.\n", + "- `top_k`: The number of results. Defaults to 10.\n", + "- `offset`: The number of records to skip in the search result. Defaults to 0.\n", + "- `output_fields`: The extra fields to present in output.\n", + "- `filter`: The Milvus expression to filter search results. Defaults to \"\".\n", + "- `run_manager`: The callbacks handler to use." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Document(page_content='# Delete Entities\\nThis topic describes how to delete entities in Milvus. \\nMilvus supports deleting entities by primary key or complex boolean expressions. Deleting entities by primary key is much faster and lighter than deleting them by complex boolean expressions. This is because Milvus executes queries first when deleting data by complex boolean expressions. \\nDeleted entities can still be retrieved immediately after the deletion if the consistency level is set lower than Strong.\\nEntities deleted beyond the pre-specified span of time for Time Travel cannot be retrieved again.\\nFrequent deletion operations will impact the system performance. \\nBefore deleting entities by comlpex boolean expressions, make sure the collection has been loaded.\\nDeleting entities by complex boolean expressions is not an atomic operation. Therefore, if it fails halfway through, some data may still be deleted.\\nDeleting entities by complex boolean expressions is supported only when the consistency is set to Bounded. For details, see Consistency.\\\\\\n\\\\\\n# Delete Entities\\n## Prepare boolean expression\\nPrepare the boolean expression that filters the entities to delete. \\nMilvus supports deleting entities by primary key or complex boolean expressions. For more information on expression rules and supported operators, see Boolean Expression Rules.', metadata={'id': 448986959321277978, 'distance': 0.7871403694152832}),\n", + " Document(page_content='# Delete Entities\\n## Prepare boolean expression\\n### Simple boolean expression\\nUse a simple expression to filter data with primary key values of 0 and 1: \\n```python\\nexpr = \"book_id in [0,1]\"\\n```\\\\\\n\\\\\\n# Delete Entities\\n## Prepare boolean expression\\n### Complex boolean expression\\nTo filter entities that meet specific conditions, define complex boolean expressions. \\nFilter entities whose word_count is greater than or equal to 11000: \\n```python\\nexpr = \"word_count >= 11000\"\\n``` \\nFilter entities whose book_name is not Unknown: \\n```python\\nexpr = \"book_name != Unknown\"\\n``` \\nFilter entities whose primary key values are greater than 5 and word_count is smaller than or equal to 9999: \\n```python\\nexpr = \"book_id > 5 && word_count <= 9999\"\\n```', metadata={'id': 448986959321277979, 'distance': 0.7775762677192688}),\n", + " Document(page_content='# Delete Entities\\n## Delete entities\\nDelete the entities with the boolean expression you created. Milvus returns the ID list of the deleted entities.\\n```python\\nfrom pymilvus import Collection\\ncollection = Collection(\"book\") # Get an existing collection.\\ncollection.delete(expr)\\n``` \\nParameter\\tDescription\\nexpr\\tBoolean expression that specifies the entities to delete.\\npartition_name (optional)\\tName of the partition to delete entities from.\\\\\\n\\\\\\n# Upsert Entities\\nThis topic describes how to upsert entities in Milvus. \\nUpserting is a combination of insert and delete operations. In the context of a Milvus vector database, an upsert is a data-level operation that will overwrite an existing entity if a specified field already exists in a collection, and insert a new entity if the specified value doesn’t already exist. \\nThe following example upserts 3,000 rows of randomly generated data as the example data. When performing upsert operations, it\\'s important to note that the operation may compromise performance. This is because the operation involves deleting data during execution.', metadata={'id': 448986959321277980, 'distance': 0.680284857749939}),\n", + " Document(page_content='# Upsert Entities\\n## Flush data\\nWhen data is upserted into Milvus it is updated and inserted into segments. Segments have to reach a certain size to be sealed and indexed. Unsealed segments will be searched brute force. In order to avoid this with any remainder data, it is best to call flush(). The flush() call will seal any remaining segments and send them for indexing. It is important to only call this method at the end of an upsert session. Calling it too often will cause fragmented data that will need to be cleaned later on.\\\\\\n\\\\\\n# Upsert Entities\\n## Limits\\nUpdating primary key fields is not supported by upsert().\\nupsert() is not applicable and an error can occur if autoID is set to True for primary key fields.', metadata={'id': 448986959321277983, 'distance': 0.5672488212585449}),\n", + " Document(page_content='# Upsert Entities\\n## Prepare data\\nFirst, prepare the data to upsert. The type of data to upsert must match the schema of the collection, otherwise Milvus will raise an exception. \\nMilvus supports default values for scalar fields, excluding a primary key field. This indicates that some fields can be left empty during data inserts or upserts. For more information, refer to Create a Collection. \\n```python\\n# Generate data to upsert\\n\\nimport random\\nnb = 3000\\ndim = 8\\nvectors = [[random.random() for _ in range(dim)] for _ in range(nb)]\\ndata = [\\n[i for i in range(nb)],\\n[str(i) for i in range(nb)],\\n[i for i in range(10000, 10000+nb)],\\nvectors,\\n[str(\"dy\"*i) for i in range(nb)]\\n]\\n```', metadata={'id': 448986959321277981, 'distance': 0.5107149481773376}),\n", + " Document(page_content='# Upsert Entities\\n## Upsert data\\nUpsert the data to the collection. \\n```python\\nfrom pymilvus import Collection\\ncollection = Collection(\"book\") # Get an existing collection.\\nmr = collection.upsert(data)\\n``` \\nParameter\\tDescription\\ndata\\tData to upsert into Milvus.\\npartition_name (optional)\\tName of the partition to upsert data into.\\ntimeout (optional)\\tAn optional duration of time in seconds to allow for the RPC. If it is set to None, the client keeps waiting until the server responds or error occurs.\\nAfter upserting entities into a collection that has previously been indexed, you do not need to re-index the collection, as Milvus will automatically create an index for the newly upserted data. For more information, refer to Can indexes be created after inserting vectors?', metadata={'id': 448986959321277982, 'distance': 0.4341375529766083})]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "retriever.get_relevant_documents(\n", + " \"Can users delete entities by complex boolean expressions?\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "develop", + "language": "python", + "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": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/docs/docs/integrations/vectorstores/milvus.ipynb b/docs/docs/integrations/vectorstores/milvus.ipynb index 97a7b1d45f6ed..7fb751453c592 100644 --- a/docs/docs/integrations/vectorstores/milvus.ipynb +++ b/docs/docs/integrations/vectorstores/milvus.ipynb @@ -11,9 +11,7 @@ "\n", "This notebook shows how to use functionality related to the Milvus vector database.\n", "\n", - "You'll need to install `langchain-community` with `pip install -qU langchain-community` to use this integration\n", - "\n", - "To run, you should have a [Milvus instance up and running](https://milvus.io/docs/install_standalone-docker.md)." + "You'll need to install `langchain-milvus` with `pip install -qU langchain-milvus` to use this integration\n" ] }, { @@ -28,6 +26,14 @@ "%pip install --upgrade --quiet pymilvus" ] }, + { + "cell_type": "markdown", + "id": "633addc3", + "metadata": {}, + "source": [ + "The latest version of pymilvus comes with a local vector database Milvus Lite, good for prototyping. If you have large scale of data such as more than a million docs, we recommend setting up a more performant Milvus server on [docker or kubernetes](https://milvus.io/docs/install_standalone-docker.md#Start-Milvus)." + ] + }, { "cell_type": "markdown", "id": "7a0f9e02-8eb0-4aef-b11f-8861360472ee", @@ -43,15 +49,7 @@ "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "OpenAI API Key:········\n" - ] - } - ], + "outputs": [], "source": [ "import getpass\n", "import os\n", @@ -83,8 +81,6 @@ }, "outputs": [], "source": [ - "from langchain_community.document_loaders import TextLoader\n", - "\n", "loader = TextLoader(\"../../how_to/state_of_the_union.txt\")\n", "documents = loader.load()\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", @@ -102,10 +98,14 @@ }, "outputs": [], "source": [ + "# The easiest way is to use Milvus Lite where everything is stored in a local file.\n", + "# If you have a Milvus server you can use the server URI such as \"http://localhost:19530\".\n", + "URI = \"./milvus_demo.db\"\n", + "\n", "vector_db = Milvus.from_documents(\n", " docs,\n", " embeddings,\n", - " connection_args={\"host\": \"127.0.0.1\", \"port\": \"19530\"},\n", + " connection_args={\"uri\": URI},\n", ")" ] }, @@ -170,7 +170,7 @@ " docs,\n", " embeddings,\n", " collection_name=\"collection_1\",\n", - " connection_args={\"host\": \"127.0.0.1\", \"port\": \"19530\"},\n", + " connection_args={\"uri\": URI},\n", ")" ] }, @@ -191,7 +191,7 @@ "source": [ "vector_db = Milvus(\n", " embeddings,\n", - " connection_args={\"host\": \"127.0.0.1\", \"port\": \"19530\"},\n", + " connection_args={\"uri\": URI},\n", " collection_name=\"collection_1\",\n", ")" ] @@ -208,7 +208,6 @@ "cell_type": "markdown", "id": "7fb27b941602401d91542211134fc71a", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -218,7 +217,8 @@ "\n", "When building a retrieval app, you often have to build it with multiple users in mind. This means that you may be storing data not just for one user, but for many different users, and they should not be able to see eachother’s data.\n", "\n", - "Milvus recommends using [partition_key](https://milvus.io/docs/multi_tenancy.md#Partition-key-based-multi-tenancy) to implement multi-tenancy, here is an example." + "Milvus recommends using [partition_key](https://milvus.io/docs/multi_tenancy.md#Partition-key-based-multi-tenancy) to implement multi-tenancy, here is an example.\n", + "> The feature of Partition key is now not available in Milvus Lite, if you want to use it, you need to start Milvus server from [docker or kubernetes](https://milvus.io/docs/install_standalone-docker.md#Start-Milvus)." ] }, { @@ -226,7 +226,6 @@ "execution_count": 2, "id": "acae54e37e7d407bbb7b55eff062a284", "metadata": { - "collapsed": false, "pycharm": { "name": "#%%\n" } @@ -242,7 +241,7 @@ "vectorstore = Milvus.from_documents(\n", " docs,\n", " embeddings,\n", - " connection_args={\"host\": \"127.0.0.1\", \"port\": \"19530\"},\n", + " connection_args={\"uri\": URI},\n", " drop_old=True,\n", " partition_key_field=\"namespace\", # Use the \"namespace\" field as the partition key\n", ")" @@ -252,7 +251,6 @@ "cell_type": "markdown", "id": "9a63283cbaf04dbcab1f6479b197f3a8", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -274,7 +272,6 @@ "execution_count": 3, "id": "8dd0d8092fe74a7c96281538738b07e2", "metadata": { - "collapsed": false, "pycharm": { "name": "#%%\n" } @@ -303,7 +300,6 @@ "execution_count": 4, "id": "72eea5119410473aa328ad9291626812", "metadata": { - "collapsed": false, "pycharm": { "name": "#%%\n" } @@ -332,7 +328,7 @@ "id": "89756e9e", "metadata": {}, "source": [ - "**To delete or upsert (update/insert) one or more entities:**" + "### To delete or upsert (update/insert) one or more entities" ] }, { @@ -353,7 +349,7 @@ "vector_db = Milvus.from_documents(\n", " docs,\n", " embeddings,\n", - " connection_args={\"host\": \"127.0.0.1\", \"port\": \"19530\"},\n", + " connection_args={\"uri\": URI},\n", ")\n", "\n", "# Search pks (primary keys) using expression\n", @@ -389,9 +385,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/libs/community/langchain_community/vectorstores/milvus.py b/libs/community/langchain_community/vectorstores/milvus.py index 30a7d4cc40628..6af20a5d4ccc5 100644 --- a/libs/community/langchain_community/vectorstores/milvus.py +++ b/libs/community/langchain_community/vectorstores/milvus.py @@ -5,6 +5,7 @@ from uuid import uuid4 import numpy as np +from langchain_core._api.deprecation import deprecated from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VectorStore @@ -22,6 +23,11 @@ } +@deprecated( + since="0.2.0", + removal="0.3.0", + alternative_import="langchain_milvus.MilvusVectorStore", +) class Milvus(VectorStore): """`Milvus` vector store. diff --git a/libs/partners/milvus/.gitignore b/libs/partners/milvus/.gitignore new file mode 100644 index 0000000000000..bee8a64b79a99 --- /dev/null +++ b/libs/partners/milvus/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/libs/partners/milvus/LICENSE b/libs/partners/milvus/LICENSE new file mode 100644 index 0000000000000..426b65090341f --- /dev/null +++ b/libs/partners/milvus/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/partners/milvus/Makefile b/libs/partners/milvus/Makefile new file mode 100644 index 0000000000000..263896e6e0a0b --- /dev/null +++ b/libs/partners/milvus/Makefile @@ -0,0 +1,57 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ +integration_test integration_tests: TEST_FILE=tests/integration_tests/ + +test tests integration_test integration_tests: + poetry run pytest $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/milvus --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_milvus +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + poetry run ruff format $(PYTHON_FILES) --diff + poetry run ruff --select I $(PYTHON_FILES) + mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_milvus -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/milvus/README.md b/libs/partners/milvus/README.md new file mode 100644 index 0000000000000..2908408102888 --- /dev/null +++ b/libs/partners/milvus/README.md @@ -0,0 +1,42 @@ +# langchain-milvus + +This is a library integration with [Milvus](https://milvus.io/) and [Zilliz Cloud](https://zilliz.com/cloud). + +## Installation + +```bash +pip install -U langchain-milvus +``` + +## Milvus vector database + +See a [usage example](https://python.langchain.com/docs/integrations/vectorstores/milvus/) + +```python +from langchain_milvus import Milvus +``` + +## Milvus hybrid search + +See a [usage example](https://python.langchain.com/docs/integrations/retrievers/milvus_hybrid_search/). + +```python +from langchain_milvus import MilvusCollectionHybridSearchRetriever +``` + + +## Zilliz Cloud vector database + +See a [usage example](https://python.langchain.com/docs/integrations/vectorstores/zilliz/). + +```python +from langchain_milvus import Zilliz +``` + +## Zilliz Cloud Pipeline Retriever + +See a [usage example](https://python.langchain.com/docs/integrations/retrievers/zilliz_cloud_pipeline). + +```python +from langchain_milvus import ZillizCloudPipelineRetriever +``` \ No newline at end of file diff --git a/libs/partners/milvus/langchain_milvus/__init__.py b/libs/partners/milvus/langchain_milvus/__init__.py new file mode 100644 index 0000000000000..b19bc1d7e697a --- /dev/null +++ b/libs/partners/milvus/langchain_milvus/__init__.py @@ -0,0 +1,12 @@ +from langchain_milvus.retrievers import ( + MilvusCollectionHybridSearchRetriever, + ZillizCloudPipelineRetriever, +) +from langchain_milvus.vectorstores import Milvus, Zilliz + +__all__ = [ + "Milvus", + "Zilliz", + "ZillizCloudPipelineRetriever", + "MilvusCollectionHybridSearchRetriever", +] diff --git a/libs/partners/milvus/langchain_milvus/py.typed b/libs/partners/milvus/langchain_milvus/py.typed new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/milvus/langchain_milvus/retrievers/__init__.py b/libs/partners/milvus/langchain_milvus/retrievers/__init__.py new file mode 100644 index 0000000000000..1edac3cb5af73 --- /dev/null +++ b/libs/partners/milvus/langchain_milvus/retrievers/__init__.py @@ -0,0 +1,8 @@ +from langchain_milvus.retrievers.milvus_hybrid_search import ( + MilvusCollectionHybridSearchRetriever, +) +from langchain_milvus.retrievers.zilliz_cloud_pipeline_retriever import ( + ZillizCloudPipelineRetriever, +) + +__all__ = ["ZillizCloudPipelineRetriever", "MilvusCollectionHybridSearchRetriever"] diff --git a/libs/partners/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py b/libs/partners/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py new file mode 100644 index 0000000000000..6b6e692dd2c21 --- /dev/null +++ b/libs/partners/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py @@ -0,0 +1,160 @@ +from typing import Any, Dict, List, Optional, Union + +from langchain_core.callbacks import CallbackManagerForRetrieverRun +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.retrievers import BaseRetriever +from pymilvus import AnnSearchRequest, Collection +from pymilvus.client.abstract import BaseRanker, SearchResult # type: ignore + +from langchain_milvus.utils.sparse import BaseSparseEmbedding + + +class MilvusCollectionHybridSearchRetriever(BaseRetriever): + """This is a hybrid search retriever + that uses Milvus Collection to retrieve documents based on multiple fields. + For more information, please refer to: + https://milvus.io/docs/release_notes.md#Multi-Embedding---Hybrid-Search + """ + + collection: Collection + """Milvus Collection object.""" + rerank: BaseRanker + """Milvus ranker object. Such as WeightedRanker or RRFRanker.""" + anns_fields: List[str] + """The names of vector fields that are used for ANNS search.""" + field_embeddings: List[Union[Embeddings, BaseSparseEmbedding]] + """The embedding functions of each vector fields, + which can be either Embeddings or BaseSparseEmbedding.""" + field_search_params: Optional[List[Dict]] = None + """The search parameters of each vector fields. + If not specified, the default search parameters will be used.""" + field_limits: Optional[List[int]] = None + """Limit number of results for each ANNS field. + If not specified, the default top_k will be used.""" + field_exprs: Optional[List[Optional[str]]] = None + """The boolean expression for filtering the search results.""" + top_k: int = 4 + """Final top-K number of documents to retrieve.""" + text_field: str = "text" + """The text field name, + which will be used as the `page_content` of a `Document` object.""" + output_fields: Optional[List[str]] = None + """Final output fields of the documents. + If not specified, all fields except the vector fields will be used as output fields, + which will be the `metadata` of a `Document` object.""" + + def __init__(self, **kwargs: Any): + super().__init__(**kwargs) + + # If some parameters are not specified, set default values + if self.field_search_params is None: + default_search_params = { + "metric_type": "L2", + "params": {"nprobe": 10}, + } + self.field_search_params = [default_search_params] * len(self.anns_fields) + if self.field_limits is None: + self.field_limits = [self.top_k] * len(self.anns_fields) + if self.field_exprs is None: + self.field_exprs = [None] * len(self.anns_fields) + + # Check the fields + self._validate_fields_num() + self.output_fields = self._get_output_fields() + self._validate_fields_name() + + # Load collection + self.collection.load() + + def _validate_fields_num(self) -> None: + assert ( + len(self.anns_fields) >= 2 + ), "At least two fields are required for hybrid search." + lengths = [len(self.anns_fields)] + if self.field_limits is not None: + lengths.append(len(self.field_limits)) + if self.field_exprs is not None: + lengths.append(len(self.field_exprs)) + + if not all(length == lengths[0] for length in lengths): + raise ValueError("All field-related lists must have the same length.") + + if len(self.field_search_params) != len(self.anns_fields): # type: ignore[arg-type] + raise ValueError( + "field_search_params must have the same length as anns_fields." + ) + + def _validate_fields_name(self) -> None: + collection_fields = [x.name for x in self.collection.schema.fields] + for field in self.anns_fields: + assert ( + field in collection_fields + ), f"{field} is not a valid field in the collection." + assert ( + self.text_field in collection_fields + ), f"{self.text_field} is not a valid field in the collection." + for field in self.output_fields: # type: ignore[union-attr] + assert ( + field in collection_fields + ), f"{field} is not a valid field in the collection." + + def _get_output_fields(self) -> List[str]: + if self.output_fields: + return self.output_fields + output_fields = [x.name for x in self.collection.schema.fields] + for field in self.anns_fields: + if field in output_fields: + output_fields.remove(field) + if self.text_field not in output_fields: + output_fields.append(self.text_field) + return output_fields + + def _build_ann_search_requests(self, query: str) -> List[AnnSearchRequest]: + search_requests = [] + for ann_field, embedding, param, limit, expr in zip( + self.anns_fields, + self.field_embeddings, + self.field_search_params, # type: ignore[arg-type] + self.field_limits, # type: ignore[arg-type] + self.field_exprs, # type: ignore[arg-type] + ): + request = AnnSearchRequest( + data=[embedding.embed_query(query)], + anns_field=ann_field, + param=param, + limit=limit, + expr=expr, + ) + search_requests.append(request) + return search_requests + + def _parse_document(self, data: dict) -> Document: + return Document( + page_content=data.pop(self.text_field), + metadata=data, + ) + + def _process_search_result( + self, search_results: List[SearchResult] + ) -> List[Document]: + documents = [] + for result in search_results[0]: + data = {x: result.entity.get(x) for x in self.output_fields} # type: ignore[union-attr] + doc = self._parse_document(data) + documents.append(doc) + return documents + + def _get_relevant_documents( + self, + query: str, + *, + run_manager: CallbackManagerForRetrieverRun, + **kwargs: Any, + ) -> List[Document]: + requests = self._build_ann_search_requests(query) + search_result = self.collection.hybrid_search( + requests, self.rerank, limit=self.top_k, output_fields=self.output_fields + ) + documents = self._process_search_result(search_result) + return documents diff --git a/libs/partners/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py b/libs/partners/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py new file mode 100644 index 0000000000000..6fbccfa47fa7c --- /dev/null +++ b/libs/partners/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py @@ -0,0 +1,215 @@ +from typing import Any, Dict, List, Optional + +import requests +from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun +from langchain_core.documents import Document +from langchain_core.retrievers import BaseRetriever + + +class ZillizCloudPipelineRetriever(BaseRetriever): + """`Zilliz Cloud Pipeline` retriever + + Args: + pipeline_ids (dict): A dictionary of pipeline ids. + Valid keys: "ingestion", "search", "deletion". + token (str): Zilliz Cloud's token. Defaults to "". + cloud_region (str='gcp-us-west1'): The region of Zilliz Cloud's cluster. + Defaults to 'gcp-us-west1'. + """ + + pipeline_ids: Dict + token: str = "" + cloud_region: str = "gcp-us-west1" + + def _get_relevant_documents( + self, + query: str, + top_k: int = 10, + offset: int = 0, + output_fields: List = [], + filter: str = "", + *, + run_manager: CallbackManagerForRetrieverRun, + ) -> List[Document]: + """ + Get documents relevant to a query. + + Args: + query (str): String to find relevant documents for + top_k (int=10): The number of results. Defaults to 10. + offset (int=0): The number of records to skip in the search result. + Defaults to 0. + output_fields (list=[]): The extra fields to present in output. + filter (str=""): The Milvus expression to filter search results. + Defaults to "". + run_manager (CallBackManagerForRetrieverRun): The callbacks handler to use. + + Returns: + List of relevant documents + """ + if "search" in self.pipeline_ids: + search_pipe_id = self.pipeline_ids.get("search") + else: + raise Exception( + "A search pipeline id must be provided in pipeline_ids to " + "get relevant documents." + ) + domain = ( + f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" + ) + headers = { + "Authorization": f"Bearer {self.token}", + "Accept": "application/json", + "Content-Type": "application/json", + } + url = f"{domain}/{search_pipe_id}/run" + + params = { + "data": {"query_text": query}, + "params": { + "limit": top_k, + "offset": offset, + "outputFields": output_fields, + "filter": filter, + }, + } + + response = requests.post(url, headers=headers, json=params) + if response.status_code != 200: + raise RuntimeError(response.text) + response_dict = response.json() + if response_dict["code"] != 200: + raise RuntimeError(response_dict) + response_data = response_dict["data"] + search_results = response_data["result"] + return [ + Document( + page_content=result.pop("text") + if "text" in result + else result.pop("chunk_text"), + metadata=result, + ) + for result in search_results + ] + + def add_texts( + self, texts: List[str], metadata: Optional[Dict[str, Any]] = None + ) -> Dict: + """ + Add documents to store. + Only supported by a text ingestion pipeline in Zilliz Cloud. + + Args: + texts (List[str]): A list of text strings. + metadata (Dict[str, Any]): A key-value dictionary of metadata will + be inserted as preserved fields required by ingestion pipeline. + Defaults to None. + """ + if "ingestion" in self.pipeline_ids: + ingeset_pipe_id = self.pipeline_ids.get("ingestion") + else: + raise Exception( + "An ingestion pipeline id must be provided in pipeline_ids to" + " add documents." + ) + domain = ( + f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" + ) + headers = { + "Authorization": f"Bearer {self.token}", + "Accept": "application/json", + "Content-Type": "application/json", + } + url = f"{domain}/{ingeset_pipe_id}/run" + + metadata = {} if metadata is None else metadata + params = {"data": {"text_list": texts}} + params["data"].update(metadata) + + response = requests.post(url, headers=headers, json=params) + if response.status_code != 200: + raise Exception(response.text) + response_dict = response.json() + if response_dict["code"] != 200: + raise Exception(response_dict) + response_data = response_dict["data"] + return response_data + + def add_doc_url( + self, doc_url: str, metadata: Optional[Dict[str, Any]] = None + ) -> Dict: + """ + Add a document from url. + Only supported by a document ingestion pipeline in Zilliz Cloud. + + Args: + doc_url: A document url. + metadata (Dict[str, Any]): A key-value dictionary of metadata will + be inserted as preserved fields required by ingestion pipeline. + Defaults to None. + """ + if "ingestion" in self.pipeline_ids: + ingest_pipe_id = self.pipeline_ids.get("ingestion") + else: + raise Exception( + "An ingestion pipeline id must be provided in pipeline_ids to " + "add documents." + ) + domain = ( + f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" + ) + headers = { + "Authorization": f"Bearer {self.token}", + "Accept": "application/json", + "Content-Type": "application/json", + } + url = f"{domain}/{ingest_pipe_id}/run" + + params = {"data": {"doc_url": doc_url}} + metadata = {} if metadata is None else metadata + params["data"].update(metadata) + + response = requests.post(url, headers=headers, json=params) + if response.status_code != 200: + raise Exception(response.text) + response_dict = response.json() + if response_dict["code"] != 200: + raise Exception(response_dict) + response_data = response_dict["data"] + return response_data + + def delete(self, key: str, value: Any) -> Dict: + """ + Delete documents. Only supported by a deletion pipeline in Zilliz Cloud. + + Args: + key: input name to run the deletion pipeline + value: input value to run deletion pipeline + """ + if "deletion" in self.pipeline_ids: + deletion_pipe_id = self.pipeline_ids.get("deletion") + else: + raise Exception( + "A deletion pipeline id must be provided in pipeline_ids to " + "add documents." + ) + domain = ( + f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" + ) + headers = { + "Authorization": f"Bearer {self.token}", + "Accept": "application/json", + "Content-Type": "application/json", + } + url = f"{domain}/{deletion_pipe_id}/run" + + params = {"data": {key: value}} + + response = requests.post(url, headers=headers, json=params) + if response.status_code != 200: + raise Exception(response.text) + response_dict = response.json() + if response_dict["code"] != 200: + raise Exception(response_dict) + response_data = response_dict["data"] + return response_data diff --git a/libs/partners/milvus/langchain_milvus/utils/__init__.py b/libs/partners/milvus/langchain_milvus/utils/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/milvus/langchain_milvus/utils/sparse.py b/libs/partners/milvus/langchain_milvus/utils/sparse.py new file mode 100644 index 0000000000000..027c978c65d75 --- /dev/null +++ b/libs/partners/milvus/langchain_milvus/utils/sparse.py @@ -0,0 +1,54 @@ +from abc import ABC, abstractmethod +from typing import Dict, List + +from scipy.sparse import csr_array # type: ignore + + +class BaseSparseEmbedding(ABC): + """Interface for Sparse embedding models. + You can inherit from it and implement your custom sparse embedding model. + """ + + @abstractmethod + def embed_query(self, query: str) -> Dict[int, float]: + """Embed query text.""" + + @abstractmethod + def embed_documents(self, texts: List[str]) -> List[Dict[int, float]]: + """Embed search docs.""" + + +class BM25SparseEmbedding(BaseSparseEmbedding): + """This is a class that inherits BaseSparseEmbedding + and implements a sparse vector embedding model based on BM25. + This class uses the BM25 model in Milvus model to implement sparse vector embedding. + This model requires pymilvus[model] to be installed. + `pip install pymilvus[model]` + For more information please refer to: + https://milvus.io/docs/embed-with-bm25.md + """ + + def __init__(self, corpus: List[str], language: str = "en"): + from pymilvus.model.sparse import BM25EmbeddingFunction # type: ignore + from pymilvus.model.sparse.bm25.tokenizers import ( # type: ignore + build_default_analyzer, + ) + + self.analyzer = build_default_analyzer(language=language) + self.bm25_ef = BM25EmbeddingFunction(self.analyzer, num_workers=1) + self.bm25_ef.fit(corpus) + + def embed_query(self, text: str) -> Dict[int, float]: + return self._sparse_to_dict(self.bm25_ef.encode_queries([text])) + + def embed_documents(self, texts: List[str]) -> List[Dict[int, float]]: + sparse_arrays = self.bm25_ef.encode_documents(texts) + return [self._sparse_to_dict(sparse_array) for sparse_array in sparse_arrays] + + def _sparse_to_dict(self, sparse_array: csr_array) -> Dict[int, float]: + row_indices, col_indices = sparse_array.nonzero() + non_zero_values = sparse_array.data + result_dict = {} + for col_index, value in zip(col_indices, non_zero_values): + result_dict[col_index] = value + return result_dict diff --git a/libs/partners/milvus/langchain_milvus/vectorstores/__init__.py b/libs/partners/milvus/langchain_milvus/vectorstores/__init__.py new file mode 100644 index 0000000000000..5c6f304db98cb --- /dev/null +++ b/libs/partners/milvus/langchain_milvus/vectorstores/__init__.py @@ -0,0 +1,7 @@ +from langchain_milvus.vectorstores.milvus import Milvus +from langchain_milvus.vectorstores.zilliz import Zilliz + +__all__ = [ + "Milvus", + "Zilliz", +] diff --git a/libs/partners/milvus/langchain_milvus/vectorstores/milvus.py b/libs/partners/milvus/langchain_milvus/vectorstores/milvus.py new file mode 100644 index 0000000000000..712d323ccbf51 --- /dev/null +++ b/libs/partners/milvus/langchain_milvus/vectorstores/milvus.py @@ -0,0 +1,1148 @@ +from __future__ import annotations + +import logging +from typing import Any, Iterable, List, Optional, Tuple, Union +from uuid import uuid4 + +import numpy as np +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.vectorstores import VectorStore + +logger = logging.getLogger(__name__) + +DEFAULT_MILVUS_CONNECTION = { + "uri": "http://localhost:19530", +} + +Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray] + + +def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: + """Row-wise cosine similarity between two equal-width matrices.""" + if len(X) == 0 or len(Y) == 0: + return np.array([]) + + X = np.array(X) + Y = np.array(Y) + if X.shape[1] != Y.shape[1]: + raise ValueError( + f"Number of columns in X and Y must be the same. X has shape {X.shape} " + f"and Y has shape {Y.shape}." + ) + try: + import simsimd as simd # type: ignore + + X = np.array(X, dtype=np.float32) + Y = np.array(Y, dtype=np.float32) + Z = 1 - simd.cdist(X, Y, metric="cosine") + if isinstance(Z, float): + return np.array([Z]) + return np.array(Z) + except ImportError: + logger.debug( + "Unable to import simsimd, defaulting to NumPy implementation. If you want " + "to use simsimd please install with `pip install simsimd`." + ) + X_norm = np.linalg.norm(X, axis=1) + Y_norm = np.linalg.norm(Y, axis=1) + # Ignore divide by zero errors run time warnings as those are handled below. + with np.errstate(divide="ignore", invalid="ignore"): + similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm) + similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0 + return similarity + + +def maximal_marginal_relevance( + query_embedding: np.ndarray, + embedding_list: list, + lambda_mult: float = 0.5, + k: int = 4, +) -> List[int]: + """Calculate maximal marginal relevance.""" + if min(k, len(embedding_list)) <= 0: + return [] + if query_embedding.ndim == 1: + query_embedding = np.expand_dims(query_embedding, axis=0) + similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0] + most_similar = int(np.argmax(similarity_to_query)) + idxs = [most_similar] + selected = np.array([embedding_list[most_similar]]) + while len(idxs) < min(k, len(embedding_list)): + best_score = -np.inf + idx_to_add = -1 + similarity_to_selected = cosine_similarity(embedding_list, selected) + for i, query_score in enumerate(similarity_to_query): + if i in idxs: + continue + redundant_score = max(similarity_to_selected[i]) + equation_score = ( + lambda_mult * query_score - (1 - lambda_mult) * redundant_score + ) + if equation_score > best_score: + best_score = equation_score + idx_to_add = i + idxs.append(idx_to_add) + selected = np.append(selected, [embedding_list[idx_to_add]], axis=0) + return idxs + + +class Milvus(VectorStore): + """`Milvus` vector store. + + You need to install `pymilvus` and run Milvus. + + See the following documentation for how to run a Milvus instance: + https://milvus.io/docs/install_standalone-docker.md + + If looking for a hosted Milvus, take a look at this documentation: + https://zilliz.com/cloud and make use of the Zilliz vectorstore found in + this project. + + IF USING L2/IP metric, IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA. + + Args: + embedding_function (Embeddings): Function used to embed the text. + collection_name (str): Which Milvus collection to use. Defaults to + "LangChainCollection". + collection_description (str): The description of the collection. Defaults to + "". + collection_properties (Optional[dict[str, any]]): The collection properties. + Defaults to None. + If set, will override collection existing properties. + For example: {"collection.ttl.seconds": 60}. + connection_args (Optional[dict[str, any]]): The connection args used for + this class comes in the form of a dict. + consistency_level (str): The consistency level to use for a collection. + Defaults to "Session". + index_params (Optional[dict]): Which index params to use. Defaults to + HNSW/AUTOINDEX depending on service. + search_params (Optional[dict]): Which search params to use. Defaults to + default of index. + drop_old (Optional[bool]): Whether to drop the current collection. Defaults + to False. + auto_id (bool): Whether to enable auto id for primary key. Defaults to False. + If False, you needs to provide text ids (string less than 65535 bytes). + If True, Milvus will generate unique integers as primary keys. + primary_field (str): Name of the primary key field. Defaults to "pk". + text_field (str): Name of the text field. Defaults to "text". + vector_field (str): Name of the vector field. Defaults to "vector". + metadata_field (str): Name of the metadta field. Defaults to None. + When metadata_field is specified, + the document's metadata will store as json. + + The connection args used for this class comes in the form of a dict, + here are a few of the options: + address (str): The actual address of Milvus + instance. Example address: "localhost:19530" + uri (str): The uri of Milvus instance. Example uri: + "http://randomwebsite:19530", + "tcp:foobarsite:19530", + "https://ok.s3.south.com:19530". + or "path/to/local/directory/milvus_demo.db" for Milvus Lite. + host (str): The host of Milvus instance. Default at "localhost", + PyMilvus will fill in the default host if only port is provided. + port (str/int): The port of Milvus instance. Default at 19530, PyMilvus + will fill in the default port if only host is provided. + user (str): Use which user to connect to Milvus instance. If user and + password are provided, we will add related header in every RPC call. + password (str): Required when user is provided. The password + corresponding to the user. + secure (bool): Default is false. If set to true, tls will be enabled. + client_key_path (str): If use tls two-way authentication, need to + write the client.key path. + client_pem_path (str): If use tls two-way authentication, need to + write the client.pem path. + ca_pem_path (str): If use tls two-way authentication, need to write + the ca.pem path. + server_pem_path (str): If use tls one-way authentication, need to + write the server.pem path. + server_name (str): If use tls, need to write the common name. + + Example: + .. code-block:: python + + from langchain_milvus.vectorstores import Milvus + from langchain_openai.embeddings import OpenAIEmbeddings + + embedding = OpenAIEmbeddings() + # Connect to a milvus instance on localhost + milvus_store = Milvus( + embedding_function = Embeddings, + collection_name = "LangChainCollection", + drop_old = True, + auto_id = True + ) + + Raises: + ValueError: If the pymilvus python package is not installed. + """ + + def __init__( + self, + embedding_function: Embeddings, + collection_name: str = "LangChainCollection", + collection_description: str = "", + collection_properties: Optional[dict[str, Any]] = None, + connection_args: Optional[dict[str, Any]] = None, + consistency_level: str = "Session", + index_params: Optional[dict] = None, + search_params: Optional[dict] = None, + drop_old: Optional[bool] = False, + auto_id: bool = False, + *, + primary_field: str = "pk", + text_field: str = "text", + vector_field: str = "vector", + metadata_field: Optional[str] = None, + partition_key_field: Optional[str] = None, + partition_names: Optional[list] = None, + replica_number: int = 1, + timeout: Optional[float] = None, + num_shards: Optional[int] = None, + ): + """Initialize the Milvus vector store.""" + try: + from pymilvus import Collection, utility + except ImportError: + raise ValueError( + "Could not import pymilvus python package. " + "Please install it with `pip install pymilvus`." + ) + + # Default search params when one is not provided. + self.default_search_params = { + "IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}}, + "IVF_SQ8": {"metric_type": "L2", "params": {"nprobe": 10}}, + "IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}}, + "HNSW": {"metric_type": "L2", "params": {"ef": 10}}, + "RHNSW_FLAT": {"metric_type": "L2", "params": {"ef": 10}}, + "RHNSW_SQ": {"metric_type": "L2", "params": {"ef": 10}}, + "RHNSW_PQ": {"metric_type": "L2", "params": {"ef": 10}}, + "IVF_HNSW": {"metric_type": "L2", "params": {"nprobe": 10, "ef": 10}}, + "ANNOY": {"metric_type": "L2", "params": {"search_k": 10}}, + "SCANN": {"metric_type": "L2", "params": {"search_k": 10}}, + "AUTOINDEX": {"metric_type": "L2", "params": {}}, + "GPU_CAGRA": { + "metric_type": "L2", + "params": { + "itopk_size": 128, + "search_width": 4, + "min_iterations": 0, + "max_iterations": 0, + "team_size": 0, + }, + }, + "GPU_IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}}, + "GPU_IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}}, + } + + self.embedding_func = embedding_function + self.collection_name = collection_name + self.collection_description = collection_description + self.collection_properties = collection_properties + self.index_params = index_params + self.search_params = search_params + self.consistency_level = consistency_level + self.auto_id = auto_id + + # In order for a collection to be compatible, pk needs to be varchar + self._primary_field = primary_field + # In order for compatibility, the text field will need to be called "text" + self._text_field = text_field + # In order for compatibility, the vector field needs to be called "vector" + self._vector_field = vector_field + self._metadata_field = metadata_field + self._partition_key_field = partition_key_field + self.fields: list[str] = [] + self.partition_names = partition_names + self.replica_number = replica_number + self.timeout = timeout + self.num_shards = num_shards + + # Create the connection to the server + if connection_args is None: + connection_args = DEFAULT_MILVUS_CONNECTION + self.alias = self._create_connection_alias(connection_args) + self.col: Optional[Collection] = None + + # Grab the existing collection if it exists + if utility.has_collection(self.collection_name, using=self.alias): + self.col = Collection( + self.collection_name, + using=self.alias, + ) + if self.collection_properties is not None: + self.col.set_properties(self.collection_properties) + # If need to drop old, drop it + if drop_old and isinstance(self.col, Collection): + self.col.drop() + self.col = None + + # Initialize the vector store + self._init( + partition_names=partition_names, + replica_number=replica_number, + timeout=timeout, + ) + + @property + def embeddings(self) -> Embeddings: + return self.embedding_func + + def _create_connection_alias(self, connection_args: dict) -> str: + """Create the connection to the Milvus server.""" + from pymilvus import MilvusException, connections + + # Grab the connection arguments that are used for checking existing connection + host: str = connection_args.get("host", None) + port: Union[str, int] = connection_args.get("port", None) + address: str = connection_args.get("address", None) + uri: str = connection_args.get("uri", None) + user = connection_args.get("user", None) + + # Order of use is host/port, uri, address + if host is not None and port is not None: + given_address = str(host) + ":" + str(port) + elif uri is not None: + if uri.startswith("https://"): + given_address = uri.split("https://")[1] + elif uri.startswith("http://"): + given_address = uri.split("http://")[1] + else: + given_address = uri # Milvus lite + elif address is not None: + given_address = address + else: + given_address = None + logger.debug("Missing standard address type for reuse attempt") + + # User defaults to empty string when getting connection info + if user is not None: + tmp_user = user + else: + tmp_user = "" + + # If a valid address was given, then check if a connection exists + if given_address is not None: + for con in connections.list_connections(): + addr = connections.get_connection_addr(con[0]) + if ( + con[1] + and ("address" in addr) + and (addr["address"] == given_address) + and ("user" in addr) + and (addr["user"] == tmp_user) + ): + logger.debug("Using previous connection: %s", con[0]) + return con[0] + + # Generate a new connection if one doesn't exist + alias = uuid4().hex + try: + connections.connect(alias=alias, **connection_args) + logger.debug("Created new connection using: %s", alias) + return alias + except MilvusException as e: + logger.error("Failed to create new connection using: %s", alias) + raise e + + def _init( + self, + embeddings: Optional[list] = None, + metadatas: Optional[list[dict]] = None, + partition_names: Optional[list] = None, + replica_number: int = 1, + timeout: Optional[float] = None, + ) -> None: + if embeddings is not None: + self._create_collection(embeddings, metadatas) + self._extract_fields() + self._create_index() + self._create_search_params() + self._load( + partition_names=partition_names, + replica_number=replica_number, + timeout=timeout, + ) + + def _create_collection( + self, embeddings: list, metadatas: Optional[list[dict]] = None + ) -> None: + from pymilvus import ( + Collection, + CollectionSchema, + DataType, + FieldSchema, + MilvusException, + ) + from pymilvus.orm.types import infer_dtype_bydata # type: ignore + + # Determine embedding dim + dim = len(embeddings[0]) + fields = [] + if self._metadata_field is not None: + fields.append(FieldSchema(self._metadata_field, DataType.JSON)) + else: + # Determine metadata schema + if metadatas: + # Create FieldSchema for each entry in metadata. + for key, value in metadatas[0].items(): + # Infer the corresponding datatype of the metadata + dtype = infer_dtype_bydata(value) + # Datatype isn't compatible + if dtype == DataType.UNKNOWN or dtype == DataType.NONE: + logger.error( + ( + "Failure to create collection, " + "unrecognized dtype for key: %s" + ), + key, + ) + raise ValueError(f"Unrecognized datatype for {key}.") + # Dataype is a string/varchar equivalent + elif dtype == DataType.VARCHAR: + fields.append( + FieldSchema(key, DataType.VARCHAR, max_length=65_535) + ) + else: + fields.append(FieldSchema(key, dtype)) + + # Create the text field + fields.append( + FieldSchema(self._text_field, DataType.VARCHAR, max_length=65_535) + ) + # Create the primary key field + if self.auto_id: + fields.append( + FieldSchema( + self._primary_field, DataType.INT64, is_primary=True, auto_id=True + ) + ) + else: + fields.append( + FieldSchema( + self._primary_field, + DataType.VARCHAR, + is_primary=True, + auto_id=False, + max_length=65_535, + ) + ) + # Create the vector field, supports binary or float vectors + fields.append( + FieldSchema(self._vector_field, infer_dtype_bydata(embeddings[0]), dim=dim) + ) + + # Create the schema for the collection + schema = CollectionSchema( + fields, + description=self.collection_description, + partition_key_field=self._partition_key_field, + ) + + # Create the collection + try: + if self.num_shards is not None: + # Issue with defaults: + # https://github.com/milvus-io/pymilvus/blob/59bf5e811ad56e20946559317fed855330758d9c/pymilvus/client/prepare.py#L82-L85 + self.col = Collection( + name=self.collection_name, + schema=schema, + consistency_level=self.consistency_level, + using=self.alias, + num_shards=self.num_shards, + ) + else: + self.col = Collection( + name=self.collection_name, + schema=schema, + consistency_level=self.consistency_level, + using=self.alias, + ) + # Set the collection properties if they exist + if self.collection_properties is not None: + self.col.set_properties(self.collection_properties) + except MilvusException as e: + logger.error( + "Failed to create collection: %s error: %s", self.collection_name, e + ) + raise e + + def _extract_fields(self) -> None: + """Grab the existing fields from the Collection""" + from pymilvus import Collection + + if isinstance(self.col, Collection): + schema = self.col.schema + for x in schema.fields: + self.fields.append(x.name) + + def _get_index(self) -> Optional[dict[str, Any]]: + """Return the vector index information if it exists""" + from pymilvus import Collection + + if isinstance(self.col, Collection): + for x in self.col.indexes: + if x.field_name == self._vector_field: + return x.to_dict() + return None + + def _create_index(self) -> None: + """Create a index on the collection""" + from pymilvus import Collection, MilvusException + + if isinstance(self.col, Collection) and self._get_index() is None: + try: + # If no index params, use a default HNSW based one + if self.index_params is None: + self.index_params = { + "metric_type": "L2", + "index_type": "HNSW", + "params": {"M": 8, "efConstruction": 64}, + } + + try: + self.col.create_index( + self._vector_field, + index_params=self.index_params, + using=self.alias, + ) + + # If default did not work, most likely on Zilliz Cloud + except MilvusException: + # Use AUTOINDEX based index + self.index_params = { + "metric_type": "L2", + "index_type": "AUTOINDEX", + "params": {}, + } + self.col.create_index( + self._vector_field, + index_params=self.index_params, + using=self.alias, + ) + logger.debug( + "Successfully created an index on collection: %s", + self.collection_name, + ) + + except MilvusException as e: + logger.error( + "Failed to create an index on collection: %s", self.collection_name + ) + raise e + + def _create_search_params(self) -> None: + """Generate search params based on the current index type""" + from pymilvus import Collection + + if isinstance(self.col, Collection) and self.search_params is None: + index = self._get_index() + if index is not None: + index_type: str = index["index_param"]["index_type"] + metric_type: str = index["index_param"]["metric_type"] + self.search_params = self.default_search_params[index_type] + self.search_params["metric_type"] = metric_type + + def _load( + self, + partition_names: Optional[list] = None, + replica_number: int = 1, + timeout: Optional[float] = None, + ) -> None: + """Load the collection if available.""" + from pymilvus import Collection, utility + from pymilvus.client.types import LoadState # type: ignore + + timeout = self.timeout or timeout + if ( + isinstance(self.col, Collection) + and self._get_index() is not None + and utility.load_state(self.collection_name, using=self.alias) + == LoadState.NotLoad + ): + self.col.load( + partition_names=partition_names, + replica_number=replica_number, + timeout=timeout, + ) + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + timeout: Optional[float] = None, + batch_size: int = 1000, + *, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> List[str]: + """Insert text data into Milvus. + + Inserting data when the collection has not be made yet will result + in creating a new Collection. The data of the first entity decides + the schema of the new collection, the dim is extracted from the first + embedding and the columns are decided by the first metadata dict. + Metadata keys will need to be present for all inserted values. At + the moment there is no None equivalent in Milvus. + + Args: + texts (Iterable[str]): The texts to embed, it is assumed + that they all fit in memory. + metadatas (Optional[List[dict]]): Metadata dicts attached to each of + the texts. Defaults to None. + should be less than 65535 bytes. Required and work when auto_id is False. + timeout (Optional[float]): Timeout for each batch insert. Defaults + to None. + batch_size (int, optional): Batch size to use for insertion. + Defaults to 1000. + ids (Optional[List[str]]): List of text ids. The length of each item + + Raises: + MilvusException: Failure to add texts + + Returns: + List[str]: The resulting keys for each inserted element. + """ + from pymilvus import Collection, MilvusException + + texts = list(texts) + if not self.auto_id: + assert isinstance( + ids, list + ), "A list of valid ids are required when auto_id is False." + assert len(set(ids)) == len( + texts + ), "Different lengths of texts and unique ids are provided." + assert all( + len(x.encode()) <= 65_535 for x in ids + ), "Each id should be a string less than 65535 bytes." + + try: + embeddings = self.embedding_func.embed_documents(texts) + except NotImplementedError: + embeddings = [self.embedding_func.embed_query(x) for x in texts] + + if len(embeddings) == 0: + logger.debug("Nothing to insert, skipping.") + return [] + + # If the collection hasn't been initialized yet, perform all steps to do so + if not isinstance(self.col, Collection): + kwargs = {"embeddings": embeddings, "metadatas": metadatas} + if self.partition_names: + kwargs["partition_names"] = self.partition_names + if self.replica_number: + kwargs["replica_number"] = self.replica_number + if self.timeout: + kwargs["timeout"] = self.timeout + self._init(**kwargs) + + # Dict to hold all insert columns + insert_dict: dict[str, list] = { + self._text_field: texts, + self._vector_field: embeddings, + } + + if not self.auto_id: + insert_dict[self._primary_field] = ids # type: ignore[assignment] + + if self._metadata_field is not None: + for d in metadatas: # type: ignore[union-attr] + insert_dict.setdefault(self._metadata_field, []).append(d) + else: + # Collect the metadata into the insert dict. + if metadatas is not None: + for d in metadatas: + for key, value in d.items(): + keys = ( + [x for x in self.fields if x != self._primary_field] + if self.auto_id + else [x for x in self.fields] + ) + if key in keys: + insert_dict.setdefault(key, []).append(value) + + # Total insert count + vectors: list = insert_dict[self._vector_field] + total_count = len(vectors) + + pks: list[str] = [] + + assert isinstance(self.col, Collection) + for i in range(0, total_count, batch_size): + # Grab end index + end = min(i + batch_size, total_count) + # Convert dict to list of lists batch for insertion + insert_list = [ + insert_dict[x][i:end] for x in self.fields if x in insert_dict + ] + # Insert into the collection. + try: + res: Collection + timeout = self.timeout or timeout + res = self.col.insert(insert_list, timeout=timeout, **kwargs) + pks.extend(res.primary_keys) + except MilvusException as e: + logger.error( + "Failed to insert batch starting at entity: %s/%s", i, total_count + ) + raise e + return pks + + def similarity_search( + self, + query: str, + k: int = 4, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Perform a similarity search against the query string. + + Args: + query (str): The text to search. + k (int, optional): How many results to return. Defaults to 4. + param (dict, optional): The search params for the index type. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (int, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + List[Document]: Document results for search. + """ + if self.col is None: + logger.debug("No existing collection to search.") + return [] + timeout = self.timeout or timeout + res = self.similarity_search_with_score( + query=query, k=k, param=param, expr=expr, timeout=timeout, **kwargs + ) + return [doc for doc, _ in res] + + def similarity_search_by_vector( + self, + embedding: List[float], + k: int = 4, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Perform a similarity search against the query string. + + Args: + embedding (List[float]): The embedding vector to search. + k (int, optional): How many results to return. Defaults to 4. + param (dict, optional): The search params for the index type. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (int, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + List[Document]: Document results for search. + """ + if self.col is None: + logger.debug("No existing collection to search.") + return [] + timeout = self.timeout or timeout + res = self.similarity_search_with_score_by_vector( + embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs + ) + return [doc for doc, _ in res] + + def similarity_search_with_score( + self, + query: str, + k: int = 4, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Perform a search on a query string and return results with score. + + For more information about the search parameters, take a look at the pymilvus + documentation found here: + https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md + + Args: + query (str): The text being searched. + k (int, optional): The amount of results to return. Defaults to 4. + param (dict): The search params for the specified index. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (float, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + List[float], List[Tuple[Document, any, any]]: + """ + if self.col is None: + logger.debug("No existing collection to search.") + return [] + + # Embed the query text. + embedding = self.embedding_func.embed_query(query) + timeout = self.timeout or timeout + res = self.similarity_search_with_score_by_vector( + embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs + ) + return res + + def similarity_search_with_score_by_vector( + self, + embedding: List[float], + k: int = 4, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Perform a search on a query string and return results with score. + + For more information about the search parameters, take a look at the pymilvus + documentation found here: + https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md + + Args: + embedding (List[float]): The embedding vector being searched. + k (int, optional): The amount of results to return. Defaults to 4. + param (dict): The search params for the specified index. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (float, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + List[Tuple[Document, float]]: Result doc and score. + """ + if self.col is None: + logger.debug("No existing collection to search.") + return [] + + if param is None: + param = self.search_params + + # Determine result metadata fields with PK. + output_fields = self.fields[:] + output_fields.remove(self._vector_field) + timeout = self.timeout or timeout + # Perform the search. + res = self.col.search( + data=[embedding], + anns_field=self._vector_field, + param=param, + limit=k, + expr=expr, + output_fields=output_fields, + timeout=timeout, + **kwargs, + ) + # Organize results. + ret = [] + for result in res[0]: + data = {x: result.entity.get(x) for x in output_fields} + doc = self._parse_document(data) + pair = (doc, result.score) + ret.append(pair) + + return ret + + def max_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Perform a search and return results that are reordered by MMR. + + Args: + query (str): The text being searched. + k (int, optional): How many results to give. Defaults to 4. + fetch_k (int, optional): Total results to select k from. + Defaults to 20. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5 + param (dict, optional): The search params for the specified index. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (float, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + + Returns: + List[Document]: Document results for search. + """ + if self.col is None: + logger.debug("No existing collection to search.") + return [] + + embedding = self.embedding_func.embed_query(query) + timeout = self.timeout or timeout + return self.max_marginal_relevance_search_by_vector( + embedding=embedding, + k=k, + fetch_k=fetch_k, + lambda_mult=lambda_mult, + param=param, + expr=expr, + timeout=timeout, + **kwargs, + ) + + def max_marginal_relevance_search_by_vector( + self, + embedding: list[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Perform a search and return results that are reordered by MMR. + + Args: + embedding (str): The embedding vector being searched. + k (int, optional): How many results to give. Defaults to 4. + fetch_k (int, optional): Total results to select k from. + Defaults to 20. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5 + param (dict, optional): The search params for the specified index. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (float, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + List[Document]: Document results for search. + """ + if self.col is None: + logger.debug("No existing collection to search.") + return [] + + if param is None: + param = self.search_params + + # Determine result metadata fields. + output_fields = self.fields[:] + output_fields.remove(self._vector_field) + timeout = self.timeout or timeout + # Perform the search. + res = self.col.search( + data=[embedding], + anns_field=self._vector_field, + param=param, + limit=fetch_k, + expr=expr, + output_fields=output_fields, + timeout=timeout, + **kwargs, + ) + # Organize results. + ids = [] + documents = [] + scores = [] + for result in res[0]: + data = {x: result.entity.get(x) for x in output_fields} + doc = self._parse_document(data) + documents.append(doc) + scores.append(result.score) + ids.append(result.id) + + vectors = self.col.query( + expr=f"{self._primary_field} in {ids}", + output_fields=[self._primary_field, self._vector_field], + timeout=timeout, + ) + # Reorganize the results from query to match search order. + vectors = {x[self._primary_field]: x[self._vector_field] for x in vectors} + + ordered_result_embeddings = [vectors[x] for x in ids] + + # Get the new order of results. + new_ordering = maximal_marginal_relevance( + np.array(embedding), ordered_result_embeddings, k=k, lambda_mult=lambda_mult + ) + + # Reorder the values and return. + ret = [] + for x in new_ordering: + # Function can return -1 index + if x == -1: + break + else: + ret.append(documents[x]) + return ret + + def delete( # type: ignore[no-untyped-def] + self, ids: Optional[List[str]] = None, expr: Optional[str] = None, **kwargs: str + ): + """Delete by vector ID or boolean expression. + Refer to [Milvus documentation](https://milvus.io/docs/delete_data.md) + for notes and examples of expressions. + + Args: + ids: List of ids to delete. + expr: Boolean expression that specifies the entities to delete. + kwargs: Other parameters in Milvus delete api. + """ + if isinstance(ids, list) and len(ids) > 0: + if expr is not None: + logger.warning( + "Both ids and expr are provided. " "Ignore expr and delete by ids." + ) + expr = f"{self._primary_field} in {ids}" + else: + assert isinstance( + expr, str + ), "Either ids list or expr string must be provided." + return self.col.delete(expr=expr, **kwargs) # type: ignore[union-attr] + + @classmethod + def from_texts( + cls, + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + collection_name: str = "LangChainCollection", + connection_args: dict[str, Any] = DEFAULT_MILVUS_CONNECTION, + consistency_level: str = "Session", + index_params: Optional[dict] = None, + search_params: Optional[dict] = None, + drop_old: bool = False, + *, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> Milvus: + """Create a Milvus collection, indexes it with HNSW, and insert data. + + Args: + texts (List[str]): Text data. + embedding (Embeddings): Embedding function. + metadatas (Optional[List[dict]]): Metadata for each text if it exists. + Defaults to None. + collection_name (str, optional): Collection name to use. Defaults to + "LangChainCollection". + connection_args (dict[str, Any], optional): Connection args to use. Defaults + to DEFAULT_MILVUS_CONNECTION. + consistency_level (str, optional): Which consistency level to use. Defaults + to "Session". + index_params (Optional[dict], optional): Which index_params to use. Defaults + to None. + search_params (Optional[dict], optional): Which search params to use. + Defaults to None. + drop_old (Optional[bool], optional): Whether to drop the collection with + that name if it exists. Defaults to False. + ids (Optional[List[str]]): List of text ids. Defaults to None. + + Returns: + Milvus: Milvus Vector Store + """ + if isinstance(ids, list) and len(ids) > 0: + auto_id = False + else: + auto_id = True + + vector_db = cls( + embedding_function=embedding, + collection_name=collection_name, + connection_args=connection_args, + consistency_level=consistency_level, + index_params=index_params, + search_params=search_params, + drop_old=drop_old, + auto_id=auto_id, + **kwargs, + ) + vector_db.add_texts(texts=texts, metadatas=metadatas, ids=ids) + return vector_db + + def _parse_document(self, data: dict) -> Document: + return Document( + page_content=data.pop(self._text_field), + metadata=data.pop(self._metadata_field) if self._metadata_field else data, + ) + + def get_pks(self, expr: str, **kwargs: Any) -> List[int] | None: + """Get primary keys with expression + + Args: + expr: Expression - E.g: "id in [1, 2]", or "title LIKE 'Abc%'" + + Returns: + List[int]: List of IDs (Primary Keys) + """ + + from pymilvus import MilvusException + + if self.col is None: + logger.debug("No existing collection to get pk.") + return None + + try: + query_result = self.col.query( + expr=expr, output_fields=[self._primary_field] + ) + except MilvusException as exc: + logger.error("Failed to get ids: %s error: %s", self.collection_name, exc) + raise exc + pks = [item.get(self._primary_field) for item in query_result] + return pks + + def upsert( + self, + ids: Optional[List[str]] = None, + documents: List[Document] | None = None, + **kwargs: Any, + ) -> List[str] | None: + """Update/Insert documents to the vectorstore. + + Args: + ids: IDs to update - Let's call get_pks to get ids with expression \n + documents (List[Document]): Documents to add to the vectorstore. + + Returns: + List[str]: IDs of the added texts. + """ + + from pymilvus import MilvusException + + if documents is None or len(documents) == 0: + logger.debug("No documents to upsert.") + return None + + if ids is not None and len(ids): + try: + self.delete(ids=ids) + except MilvusException: + pass + try: + return self.add_documents(documents=documents, **kwargs) + except MilvusException as exc: + logger.error( + "Failed to upsert entities: %s error: %s", self.collection_name, exc + ) + raise exc diff --git a/libs/partners/milvus/langchain_milvus/vectorstores/zilliz.py b/libs/partners/milvus/langchain_milvus/vectorstores/zilliz.py new file mode 100644 index 0000000000000..02f2ce739ff4c --- /dev/null +++ b/libs/partners/milvus/langchain_milvus/vectorstores/zilliz.py @@ -0,0 +1,196 @@ +from __future__ import annotations + +import logging +from typing import Any, Dict, List, Optional + +from langchain_core.embeddings import Embeddings + +from langchain_milvus.vectorstores.milvus import Milvus + +logger = logging.getLogger(__name__) + + +class Zilliz(Milvus): + """`Zilliz` vector store. + + You need to have `pymilvus` installed and a + running Zilliz database. + + See the following documentation for how to run a Zilliz instance: + https://docs.zilliz.com/docs/create-cluster + + + IF USING L2/IP metric IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA. + + Args: + embedding_function (Embeddings): Function used to embed the text. + collection_name (str): Which Zilliz collection to use. Defaults to + "LangChainCollection". + connection_args (Optional[dict[str, any]]): The connection args used for + this class comes in the form of a dict. + consistency_level (str): The consistency level to use for a collection. + Defaults to "Session". + index_params (Optional[dict]): Which index params to use. Defaults to + HNSW/AUTOINDEX depending on service. + search_params (Optional[dict]): Which search params to use. Defaults to + default of index. + drop_old (Optional[bool]): Whether to drop the current collection. Defaults + to False. + auto_id (bool): Whether to enable auto id for primary key. Defaults to False. + If False, you needs to provide text ids (string less than 65535 bytes). + If True, Milvus will generate unique integers as primary keys. + + The connection args used for this class comes in the form of a dict, + here are a few of the options: + address (str): The actual address of Zilliz + instance. Example address: "localhost:19530" + uri (str): The uri of Zilliz instance. Example uri: + "https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com", + host (str): The host of Zilliz instance. Default at "localhost", + PyMilvus will fill in the default host if only port is provided. + port (str/int): The port of Zilliz instance. Default at 19530, PyMilvus + will fill in the default port if only host is provided. + user (str): Use which user to connect to Zilliz instance. If user and + password are provided, we will add related header in every RPC call. + password (str): Required when user is provided. The password + corresponding to the user. + token (str): API key, for serverless clusters which can be used as + replacements for user and password. + secure (bool): Default is false. If set to true, tls will be enabled. + client_key_path (str): If use tls two-way authentication, need to + write the client.key path. + client_pem_path (str): If use tls two-way authentication, need to + write the client.pem path. + ca_pem_path (str): If use tls two-way authentication, need to write + the ca.pem path. + server_pem_path (str): If use tls one-way authentication, need to + write the server.pem path. + server_name (str): If use tls, need to write the common name. + + Example: + .. code-block:: python + + from langchain_community.vectorstores import Zilliz + from langchain_community.embeddings import OpenAIEmbeddings + + embedding = OpenAIEmbeddings() + # Connect to a Zilliz instance + milvus_store = Milvus( + embedding_function = embedding, + collection_name = "LangChainCollection", + connection_args = { + "uri": "https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com", + "user": "temp", + "password": "temp", + "token": "temp", # API key as replacements for user and password + "secure": True + } + drop_old: True, + ) + + Raises: + ValueError: If the pymilvus python package is not installed. + """ + + def _create_index(self) -> None: + """Create a index on the collection""" + from pymilvus import Collection, MilvusException + + if isinstance(self.col, Collection) and self._get_index() is None: + try: + # If no index params, use a default AutoIndex based one + if self.index_params is None: + self.index_params = { + "metric_type": "L2", + "index_type": "AUTOINDEX", + "params": {}, + } + + try: + self.col.create_index( + self._vector_field, + index_params=self.index_params, + using=self.alias, + ) + + # If default did not work, most likely Milvus self-hosted + except MilvusException: + # Use HNSW based index + self.index_params = { + "metric_type": "L2", + "index_type": "HNSW", + "params": {"M": 8, "efConstruction": 64}, + } + self.col.create_index( + self._vector_field, + index_params=self.index_params, + using=self.alias, + ) + logger.debug( + "Successfully created an index on collection: %s", + self.collection_name, + ) + + except MilvusException as e: + logger.error( + "Failed to create an index on collection: %s", self.collection_name + ) + raise e + + @classmethod + def from_texts( + cls, + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + collection_name: str = "LangChainCollection", + connection_args: Optional[Dict[str, Any]] = None, + consistency_level: str = "Session", + index_params: Optional[dict] = None, + search_params: Optional[dict] = None, + drop_old: bool = False, + *, + ids: Optional[List[str]] = None, + auto_id: bool = False, + **kwargs: Any, + ) -> Zilliz: + """Create a Zilliz collection, indexes it with HNSW, and insert data. + + Args: + texts (List[str]): Text data. + embedding (Embeddings): Embedding function. + metadatas (Optional[List[dict]]): Metadata for each text if it exists. + Defaults to None. + collection_name (str, optional): Collection name to use. Defaults to + "LangChainCollection". + connection_args (dict[str, Any], optional): Connection args to use. Defaults + to DEFAULT_MILVUS_CONNECTION. + consistency_level (str, optional): Which consistency level to use. Defaults + to "Session". + index_params (Optional[dict], optional): Which index_params to use. + Defaults to None. + search_params (Optional[dict], optional): Which search params to use. + Defaults to None. + drop_old (Optional[bool], optional): Whether to drop the collection with + that name if it exists. Defaults to False. + ids (Optional[List[str]]): List of text ids. + auto_id (bool): Whether to enable auto id for primary key. Defaults to + False. If False, you needs to provide text ids (string less than 65535 + bytes). 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"https://github.com/langchain-ai/langchain/tree/master/libs/partners/milvus" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain-core = ">=0.0.12" +pymilvus = "^2.4.3" +scipy = "^1.7" + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.test.dependencies] +pytest = "^7.3.0" +freezegun = "^1.2.2" +pytest-mock = "^3.10.0" +syrupy = "^4.0.2" +pytest-watcher = "^0.3.4" +pytest-asyncio = "^0.21.1" +langchain-core = {path = "../../core", develop = true} + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.0" + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.test_integration.dependencies] + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" + +[tool.poetry.group.typing.dependencies] +mypy = "^0.991" +langchain-core = {path = "../../core", develop = true} +types-requests = "^2" + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +langchain-core = {path = "../../core", develop = true} + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print +] + +[tool.mypy] +disallow_untyped_defs = "True" + +[[tool.mypy.overrides]] +module = ["pymilvus"] +ignore_missing_imports = "True" + +[tool.coverage.run] +omit = [ + "tests/*", +] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +# https://github.com/tophat/syrupy +# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" diff --git a/libs/partners/milvus/scripts/check_imports.py b/libs/partners/milvus/scripts/check_imports.py new file mode 100644 index 0000000000000..365f5fa118da4 --- /dev/null +++ b/libs/partners/milvus/scripts/check_imports.py @@ -0,0 +1,17 @@ +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + SourceFileLoader("x", file).load_module() + except Exception: + has_faillure = True + print(file) # noqa: T201 + traceback.print_exc() + print() # noqa: T201 + + sys.exit(1 if has_failure else 0) diff --git a/libs/partners/milvus/scripts/check_pydantic.sh b/libs/partners/milvus/scripts/check_pydantic.sh new file mode 100755 index 0000000000000..06b5bb81ae236 --- /dev/null +++ b/libs/partners/milvus/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/partners/milvus/scripts/lint_imports.sh b/libs/partners/milvus/scripts/lint_imports.sh new file mode 100755 index 0000000000000..695613c7ba8fd --- /dev/null +++ b/libs/partners/milvus/scripts/lint_imports.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/partners/milvus/tests/__init__.py b/libs/partners/milvus/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/milvus/tests/integration_tests/__init__.py b/libs/partners/milvus/tests/integration_tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/milvus/tests/integration_tests/retrievers/__init__.py b/libs/partners/milvus/tests/integration_tests/retrievers/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/milvus/tests/integration_tests/test_compile.py b/libs/partners/milvus/tests/integration_tests/test_compile.py new file mode 100644 index 0000000000000..33ecccdfa0fbd --- /dev/null +++ b/libs/partners/milvus/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/partners/milvus/tests/integration_tests/utils.py b/libs/partners/milvus/tests/integration_tests/utils.py new file mode 100644 index 0000000000000..f3ef87d2f2acc --- /dev/null +++ b/libs/partners/milvus/tests/integration_tests/utils.py @@ -0,0 +1,40 @@ +from typing import List + +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings + +fake_texts = ["foo", "bar", "baz"] + + +class FakeEmbeddings(Embeddings): + """Fake embeddings functionality for testing.""" + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Return simple embeddings. + Embeddings encode each text as its index.""" + return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))] + + async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + return self.embed_documents(texts) + + def embed_query(self, text: str) -> List[float]: + """Return constant query embeddings. + Embeddings are identical to embed_documents(texts)[0]. + Distance to each text will be that text's index, + as it was passed to embed_documents.""" + return [float(1.0)] * 9 + [float(0.0)] + + async def aembed_query(self, text: str) -> List[float]: + return self.embed_query(text) + + +def assert_docs_equal_without_pk( + docs1: List[Document], docs2: List[Document], pk_field: str = "pk" +) -> None: + """Assert two lists of Documents are equal, ignoring the primary key field.""" + assert len(docs1) == len(docs2) + for doc1, doc2 in zip(docs1, docs2): + assert doc1.page_content == doc2.page_content + doc1.metadata.pop(pk_field, None) + doc2.metadata.pop(pk_field, None) + assert doc1.metadata == doc2.metadata diff --git a/libs/partners/milvus/tests/integration_tests/vectorstores/__init__.py b/libs/partners/milvus/tests/integration_tests/vectorstores/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/milvus/tests/integration_tests/vectorstores/test_milvus.py b/libs/partners/milvus/tests/integration_tests/vectorstores/test_milvus.py new file mode 100644 index 0000000000000..5eaf2abcc904f --- /dev/null +++ b/libs/partners/milvus/tests/integration_tests/vectorstores/test_milvus.py @@ -0,0 +1,184 @@ +"""Test Milvus functionality.""" +from typing import Any, List, Optional + +from langchain_core.documents import Document + +from langchain_milvus.vectorstores import Milvus +from tests.integration_tests.utils import ( + FakeEmbeddings, + assert_docs_equal_without_pk, + fake_texts, +) + +# +# To run this test properly, please start a Milvus server with the following command: +# +# ```shell +# wget https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh +# bash standalone_embed.sh start +# ``` +# +# Here is the reference: +# https://milvus.io/docs/install_standalone-docker.md +# + + +def _milvus_from_texts( + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + drop: bool = True, +) -> Milvus: + return Milvus.from_texts( + fake_texts, + FakeEmbeddings(), + metadatas=metadatas, + ids=ids, + # connection_args={"uri": "http://127.0.0.1:19530"}, + connection_args={"uri": "./milvus_demo.db"}, + drop_old=drop, + ) + + +def _get_pks(expr: str, docsearch: Milvus) -> List[Any]: + return docsearch.get_pks(expr) # type: ignore[return-value] + + +def test_milvus() -> None: + """Test end to end construction and search.""" + docsearch = _milvus_from_texts() + output = docsearch.similarity_search("foo", k=1) + assert_docs_equal_without_pk(output, [Document(page_content="foo")]) + + +def test_milvus_with_metadata() -> None: + """Test with metadata""" + docsearch = _milvus_from_texts(metadatas=[{"label": "test"}] * len(fake_texts)) + output = docsearch.similarity_search("foo", k=1) + assert_docs_equal_without_pk( + output, [Document(page_content="foo", metadata={"label": "test"})] + ) + + +def test_milvus_with_id() -> None: + """Test with ids""" + ids = ["id_" + str(i) for i in range(len(fake_texts))] + docsearch = _milvus_from_texts(ids=ids) + output = docsearch.similarity_search("foo", k=1) + assert_docs_equal_without_pk(output, [Document(page_content="foo")]) + + output = docsearch.delete(ids=ids) + assert output.delete_count == len(fake_texts) # type: ignore[attr-defined] + + try: + ids = ["dup_id" for _ in fake_texts] + _milvus_from_texts(ids=ids) + except Exception as e: + assert isinstance(e, AssertionError) + + +def test_milvus_with_score() -> None: + """Test end to end construction and search with scores and IDs.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas) + output = docsearch.similarity_search_with_score("foo", k=3) + docs = [o[0] for o in output] + scores = [o[1] for o in output] + assert_docs_equal_without_pk( + docs, + [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="bar", metadata={"page": 1}), + Document(page_content="baz", metadata={"page": 2}), + ], + ) + assert scores[0] < scores[1] < scores[2] + + +def test_milvus_max_marginal_relevance_search() -> None: + """Test end to end construction and MRR search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas) + output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3) + assert_docs_equal_without_pk( + output, + [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="baz", metadata={"page": 2}), + ], + ) + + +def test_milvus_add_extra() -> None: + """Test end to end construction and MRR search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas) + + docsearch.add_texts(texts, metadatas) + + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 6 + + +def test_milvus_no_drop() -> None: + """Test end to end construction and MRR search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas) + del docsearch + + docsearch = _milvus_from_texts(metadatas=metadatas, drop=False) + + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 6 + + +def test_milvus_get_pks() -> None: + """Test end to end construction and get pks with expr""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas) + expr = "id in [1,2]" + output = _get_pks(expr, docsearch) + assert len(output) == 2 + + +def test_milvus_delete_entities() -> None: + """Test end to end construction and delete entities""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas) + expr = "id in [1,2]" + pks = _get_pks(expr, docsearch) + result = docsearch.delete(pks) + assert result.delete_count == 2 # type: ignore[attr-defined] + + +def test_milvus_upsert_entities() -> None: + """Test end to end construction and upsert entities""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas) + expr = "id in [1,2]" + pks = _get_pks(expr, docsearch) + documents = [ + Document(page_content="test_1", metadata={"id": 1}), + Document(page_content="test_2", metadata={"id": 3}), + ] + ids = docsearch.upsert(pks, documents) + assert len(ids) == 2 # type: ignore[arg-type] + + +# if __name__ == "__main__": +# test_milvus() +# test_milvus_with_metadata() +# test_milvus_with_id() +# test_milvus_with_score() +# test_milvus_max_marginal_relevance_search() +# test_milvus_add_extra() +# test_milvus_no_drop() +# test_milvus_get_pks() +# test_milvus_delete_entities() +# test_milvus_upsert_entities() diff --git a/libs/partners/milvus/tests/unit_tests/__init__.py b/libs/partners/milvus/tests/unit_tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/milvus/tests/unit_tests/retrievers/__init__.py b/libs/partners/milvus/tests/unit_tests/retrievers/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/milvus/tests/unit_tests/test_imports.py b/libs/partners/milvus/tests/unit_tests/test_imports.py new file mode 100644 index 0000000000000..8be170e36f272 --- /dev/null +++ b/libs/partners/milvus/tests/unit_tests/test_imports.py @@ -0,0 +1,12 @@ +from langchain_milvus import __all__ + +EXPECTED_ALL = [ + "Milvus", + "MilvusCollectionHybridSearchRetriever", + "Zilliz", + "ZillizCloudPipelineRetriever", +] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/milvus/tests/unit_tests/vectorstores/__init__.py b/libs/partners/milvus/tests/unit_tests/vectorstores/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/libs/partners/milvus/tests/unit_tests/vectorstores/test_milvus.py b/libs/partners/milvus/tests/unit_tests/vectorstores/test_milvus.py new file mode 100644 index 0000000000000..1ef2314e79590 --- /dev/null +++ b/libs/partners/milvus/tests/unit_tests/vectorstores/test_milvus.py @@ -0,0 +1,17 @@ +import os +from tempfile import TemporaryDirectory +from unittest.mock import Mock + +from langchain_milvus.vectorstores import Milvus + + +def test_initialization() -> None: + """Test integration milvus initialization.""" + embedding = Mock() + with TemporaryDirectory() as tmp_dir: + Milvus( + embedding_function=embedding, + connection_args={ + "uri": os.path.join(tmp_dir, "milvus.db"), + }, + ) diff --git a/templates/rag-milvus/.gitignore b/templates/rag-milvus/.gitignore new file mode 100644 index 0000000000000..bee8a64b79a99 --- /dev/null +++ b/templates/rag-milvus/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/templates/rag-milvus/LICENSE b/templates/rag-milvus/LICENSE new file mode 100644 index 0000000000000..fc0602feecdd6 --- /dev/null +++ b/templates/rag-milvus/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/rag-milvus/README.md b/templates/rag-milvus/README.md new file mode 100644 index 0000000000000..c5c289817304a --- /dev/null +++ b/templates/rag-milvus/README.md @@ -0,0 +1,68 @@ +# rag-milvus + +This template performs RAG using Milvus and OpenAI. + +## Environment Setup + +Start the milvus server instance, and get the host ip and port. + +Set the `OPENAI_API_KEY` environment variable to access the OpenAI models. + +## Usage + +To use this package, you should first have the LangChain CLI installed: + +```shell +pip install -U langchain-cli +``` + +To create a new LangChain project and install this as the only package, you can do: + +```shell +langchain app new my-app --package rag-milvus +``` + +If you want to add this to an existing project, you can just run: + +```shell +langchain app add rag-milvus +``` + +And add the following code to your `server.py` file: +```python +from rag_milvus import chain as rag_milvus_chain + +add_routes(app, rag_milvus_chain, path="/rag-milvus") +``` + +(Optional) Let's now configure LangSmith. +LangSmith will help us trace, monitor and debug LangChain applications. +You can sign up for LangSmith [here](https://smith.langchain.com/). +If you don't have access, you can skip this section + + +```shell +export LANGCHAIN_TRACING_V2=true +export LANGCHAIN_API_KEY= +export LANGCHAIN_PROJECT= # if not specified, defaults to "default" +``` + +If you are inside this directory, then you can spin up a LangServe instance directly by: + +```shell +langchain serve +``` + +This will start the FastAPI app with a server is running locally at +[http://localhost:8000](http://localhost:8000) + +We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) +We can access the playground at [http://127.0.0.1:8000/rag-milvus/playground](http://127.0.0.1:8000/rag-milvus/playground) + +We can access the template from code with: + +```python +from langserve.client import RemoteRunnable + +runnable = RemoteRunnable("http://localhost:8000/rag-milvus") +``` diff --git a/templates/rag-milvus/poetry.lock b/templates/rag-milvus/poetry.lock new file mode 100644 index 0000000000000..48776d16b44d0 --- /dev/null +++ b/templates/rag-milvus/poetry.lock @@ -0,0 +1,2621 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.9.5" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fcde4c397f673fdec23e6b05ebf8d4751314fa7c24f93334bf1f1364c1c69ac7"}, + {file = 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+1,34 @@ +[tool.poetry] +name = "rag-milvus" +version = "0.1.0" +description = "RAG using Milvus" +authors = [] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = "^0.1" +langchain-core = "^0.1" +langchain-openai = "^0.1" +langchain-community = "^0.0.30" +pymilvus = "^2.4" +scipy = "^1.9" + +[tool.poetry.group.dev.dependencies] +langchain-cli = ">=0.0.4" +fastapi = "^0.104.0" +sse-starlette = "^1.6.5" + +[tool.langserve] +export_module = "rag_milvus" +export_attr = "chain" + +[tool.templates-hub] +use-case = "rag" +author = "LangChain" +integrations = ["OpenAI", "Milvus"] +tags = ["vectordbs"] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-milvus/rag_milvus/__init__.py b/templates/rag-milvus/rag_milvus/__init__.py new file mode 100644 index 0000000000000..cf9e1eac2677f --- /dev/null +++ b/templates/rag-milvus/rag_milvus/__init__.py @@ -0,0 +1,3 @@ +from rag_milvus.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-milvus/rag_milvus/chain.py b/templates/rag-milvus/rag_milvus/chain.py new file mode 100644 index 0000000000000..b48edd6b2cf31 --- /dev/null +++ b/templates/rag-milvus/rag_milvus/chain.py @@ -0,0 +1,69 @@ +from langchain_community.vectorstores import Milvus +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.pydantic_v1 import BaseModel +from langchain_core.runnables import RunnableParallel, RunnablePassthrough +from langchain_openai import ChatOpenAI, OpenAIEmbeddings + +# Example for document loading (from url), splitting, and creating vectorstore + +""" +# Load +from langchain_community.document_loaders import WebBaseLoader + +loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +data = loader.load() + +# Split +from langchain_text_splitters import RecursiveCharacterTextSplitter + +text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +all_splits = text_splitter.split_documents(data) + +# Add to vectorDB +vectorstore = Milvus.from_documents(documents=all_splits, + collection_name="rag_milvus", + embedding=OpenAIEmbeddings(), + drop_old=True, + ) +retriever = vectorstore.as_retriever() +""" + +# Embed a single document as a test +vectorstore = Milvus.from_texts( + ["harrison worked at kensho"], + collection_name="rag_milvus", + embedding=OpenAIEmbeddings(), + drop_old=True, + connection_args={ + "uri": "http://127.0.0.1:19530", + }, +) +retriever = vectorstore.as_retriever() + +# RAG prompt +template = """Answer the question based only on the following context: +{context} + +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# LLM +model = ChatOpenAI() + +# RAG chain +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) + + +# Add typing for input +class Question(BaseModel): + __root__: str + + +chain = chain.with_types(input_type=Question) diff --git a/templates/rag-milvus/tests/__init__.py b/templates/rag-milvus/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d