mirror of https://github.com/hwchase17/langchain
Add back in clickhouse mongo vecstore notebooks (#6949)
Davis Chase
1 year ago
committed by
GitHub
parent
73831ef3d8
commit
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "683953b3",
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"metadata": {},
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"source": [
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"# MongoDB Atlas Vector Search\n",
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"\n",
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">[MongoDB Atlas](https://www.mongodb.com/docs/atlas/) is a document database managed in the cloud. It also enables Lucene and its vector search feature.\n",
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"\n",
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"This notebook shows how to use the functionality related to the `MongoDB Atlas Vector Search` feature where you can store your embeddings in MongoDB documents and create a Lucene vector index to perform a KNN search.\n",
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"\n",
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"It uses the [knnBeta Operator](https://www.mongodb.com/docs/atlas/atlas-search/knn-beta) available in MongoDB Atlas Search. This feature is in early access and available only for evaluation purposes, to validate functionality, and to gather feedback from a small closed group of early access users. It is not recommended for production deployments as we may introduce breaking changes.\n",
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"\n",
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"To use MongoDB Atlas, you must have first deployed a cluster. Free clusters are available. \n",
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"Here is the MongoDB Atlas [quick start](https://www.mongodb.com/docs/atlas/getting-started/)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b4c41cad-08ef-4f72-a545-2151e4598efe",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install pymongo"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c1e38361-c1fe-4ac6-86e9-c90ebaf7ae87",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"MONGODB_ATLAS_URI = os.environ['MONGODB_ATLAS_URI']"
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]
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},
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{
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"cell_type": "markdown",
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"id": "320af802-9271-46ee-948f-d2453933d44b",
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"metadata": {},
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"source": [
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"We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key. Make sure the environment variable `OPENAI_API_KEY` is set up before proceeding."
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]
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},
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{
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"cell_type": "markdown",
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"id": "1f3ecc42",
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"metadata": {},
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"source": [
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"Now, let's create a Lucene vector index on your cluster. In the below example, `embedding` is the name of the field that contains the embedding vector. Please refer to the [documentation](https://www.mongodb.com/docs/atlas/atlas-search/define-field-mappings-for-vector-search) to get more details on how to define an Atlas Search index.\n",
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"You can name the index `langchain_demo` and create the index on the namespace `lanchain_db.langchain_col`. Finally, write the following definition in the JSON editor:\n",
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"\n",
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"```json\n",
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"{\n",
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" \"mappings\": {\n",
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" \"dynamic\": true,\n",
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" \"fields\": {\n",
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" \"embedding\": {\n",
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" \"dimensions\": 1536,\n",
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" \"similarity\": \"cosine\",\n",
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" \"type\": \"knnVector\"\n",
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" }\n",
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" }\n",
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" }\n",
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"}\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "aac9563e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import MongoDBAtlasVectorSearch\n",
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"from langchain.document_loaders import TextLoader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a3c3999a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"loader = TextLoader('../../../state_of_the_union.txt')\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6e104aee",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pymongo import MongoClient\n",
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"\n",
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"# initialize MongoDB python client\n",
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"client = MongoClient(MONGODB_ATLAS_CONNECTION_STRING)\n",
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"\n",
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"db_name = \"lanchain_db\"\n",
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"collection_name = \"langchain_col\"\n",
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"collection = client[db_name][collection_name]\n",
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"index_name = \"langchain_demo\"\n",
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"\n",
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"# insert the documents in MongoDB Atlas with their embedding\n",
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"docsearch = MongoDBAtlasVectorSearch.from_documents(\n",
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" docs,\n",
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" embeddings,\n",
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" collection=collection,\n",
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" index_name=index_name\n",
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")\n",
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"\n",
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"# perform a similarity search between the embedding of the query and the embeddings of the documents\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = docsearch.similarity_search(query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9c608226",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(docs[0].page_content)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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