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add doc about reusing MongoDBAtlasVectorSearch (#5805)

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DOC: add doc about reusing MongoDBAtlasVectorSearch

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Anyone authorized.
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Sun bin 2023-06-07 13:10:36 +08:00 committed by GitHub
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docs/modules/indexes/vectorstores/examples/mongodb_atlas_vector_search.ipynb
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@ -43,7 +43,7 @@
},
{
"cell_type": "markdown",
"id": "320af802-9271-46ee-948f-d2453933d44b",
"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."
@ -143,6 +143,47 @@
"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": {