You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
langchain/docs/modules/indexes/vectorstores/examples/mongodb_atlas_vector_search...

211 lines
6.7 KiB
Plaintext

{
"cells": [
{
"cell_type": "markdown",
"id": "683953b3",
"metadata": {},
"source": [
"# MongoDB Atlas Vector Search\n",
"\n",
">[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",
"\n",
"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",
"\n",
"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",
"\n",
"To use MongoDB Atlas, you must have first deployed a cluster. Free clusters are available. \n",
"Here is the MongoDB Atlas [quick start](https://www.mongodb.com/docs/atlas/getting-started/)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4c41cad-08ef-4f72-a545-2151e4598efe",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"!pip install pymongo"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1e38361-c1fe-4ac6-86e9-c90ebaf7ae87",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"MONGODB_ATLAS_URI = os.environ['MONGODB_ATLAS_URI']"
]
},
{
"cell_type": "markdown",
"id": "457ace44-1d95-4001-9dd5-78811ab208ad",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "markdown",
"id": "1f3ecc42",
"metadata": {},
"source": [
"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",
"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",
"\n",
"```json\n",
"{\n",
" \"mappings\": {\n",
" \"dynamic\": true,\n",
" \"fields\": {\n",
" \"embedding\": {\n",
" \"dimensions\": 1536,\n",
" \"similarity\": \"cosine\",\n",
" \"type\": \"knnVector\"\n",
" }\n",
" }\n",
" }\n",
"}\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "aac9563e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.vectorstores import MongoDBAtlasVectorSearch\n",
"from langchain.document_loaders import TextLoader"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a3c3999a",
"metadata": {},
"outputs": [],
"source": [
"from langchain.document_loaders import TextLoader\n",
"loader = TextLoader('../../../state_of_the_union.txt')\n",
"documents = loader.load()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"docs = text_splitter.split_documents(documents)\n",
"\n",
"embeddings = OpenAIEmbeddings()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e104aee",
"metadata": {},
"outputs": [],
"source": [
"from pymongo import MongoClient\n",
"\n",
"# initialize MongoDB python client\n",
"client = MongoClient(MONGODB_ATLAS_CONNECTION_STRING)\n",
"\n",
"db_name = \"lanchain_db\"\n",
"collection_name = \"langchain_col\"\n",
"collection = client[db_name][collection_name]\n",
"index_name = \"langchain_demo\"\n",
"\n",
"# insert the documents in MongoDB Atlas with their embedding\n",
"docsearch = MongoDBAtlasVectorSearch.from_documents(\n",
" docs,\n",
" embeddings,\n",
" collection=collection,\n",
" index_name=index_name\n",
")\n",
"\n",
"# perform a similarity search between the embedding of the query and the embeddings of the documents\n",
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
"docs = docsearch.similarity_search(query)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9c608226",
"metadata": {},
"outputs": [],
"source": [
"print(docs[0].page_content)"
]
},
{
"cell_type": "markdown",
"id": "851a2ec9-9390-49a4-8412-3e132c9f789d",
"metadata": {},
"source": [
"You can reuse vector index you created before, make sure environment variable `OPENAI_API_KEY` is set up, then create another file."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6336fe79-3e73-48be-b20a-0ff1bb6a4399",
"metadata": {},
"outputs": [],
"source": [
"from pymongo import MongoClient\n",
"from langchain.vectorstores import MongoDBAtlasVectorSearch\n",
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"import os\n",
"\n",
"MONGODB_ATLAS_URI = os.environ['MONGODB_ATLAS_URI']\n",
"\n",
"# initialize MongoDB python client\n",
"client = MongoClient(MONGODB_ATLAS_URI)\n",
"\n",
"db_name = \"langchain_db\"\n",
"collection_name = \"langchain_col\"\n",
"collection = client[db_name][collection_name]\n",
"index_name = \"langchain_index\"\n",
"\n",
"# initialize vector store\n",
"vectorStore = MongoDBAtlasVectorSearch(\n",
" collection, OpenAIEmbeddings(), index_name=index_name)\n",
"\n",
"# perform a similarity search between the embedding of the query and the embeddings of the documents\n",
"query = \"What did the president say about Ketanji Brown Jackson\"\n",
"docs = vectorStore.similarity_search(query)\n",
"\n",
"print(docs[0].page_content)"
]
}
],
"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": "3.11.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}