Leveraging MongoDB Atlas Vector Search With LangChain

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{
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  "fields": [
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    {
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      "type": "vector",
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      "path": "embedding",  
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      "numDimensions": 1536, 
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      "similarity": "cosine"  
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    }
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  ]
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}

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pip3 install pip --upgrade
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pip3 install langchain

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pip3 install pymongo openai pypdf tiktoken

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export OPENAI_API_KEY="xxxxxxxxxxx"
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export ATLAS_CONNECTION_STRING="mongodb+srv://user:passwd@vectorsearch.abc.mongodb.net/?retryWrites=true"

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import os
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from pymongo import MongoClient
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from langchain.document_loaders import PyPDFLoader
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.embeddings import OpenAIEmbeddings
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from langchain_mongodb import MongoDBAtlasVectorSearch
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# Define the URL of the PDF MongoDB Atlas Best Practices document
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pdf_url = "https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4HkJP"
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# Retrieve environment variables for sensitive information
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OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
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if not OPENAI_API_KEY:
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    raise ValueError("The OPENAI_API_KEY environment variable is not set.")
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ATLAS_CONNECTION_STRING = os.getenv('ATLAS_CONNECTION_STRING')
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if not ATLAS_CONNECTION_STRING:
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    raise ValueError("The ATLAS_CONNECTION_STRING environment variable is not set.")
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# Connect to MongoDB Atlas cluster using the connection string
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cluster = MongoClient(ATLAS_CONNECTION_STRING)
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# Define the MongoDB database and collection names
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DB_NAME = "langchain"
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COLLECTION_NAME = "vectorSearch"
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# Connect to the specific collection in the database
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MONGODB_COLLECTION = cluster[DB_NAME][COLLECTION_NAME]
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# Initialize the PDF loader with the defined URL
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loader = PyPDFLoader(pdf_url)
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data = loader.load()
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# Initialize the text splitter
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
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# Split the document into manageable segments
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docs = text_splitter.split_documents(data)
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# Initialize MongoDB Atlas vector search with the document segments
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vector_search = MongoDBAtlasVectorSearch.from_documents(
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    documents=docs,
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    embedding=OpenAIEmbeddings(),
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    collection=MONGODB_COLLECTION,
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    index_name="default"  # Use a predefined index name
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)
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# At this point, 'docs' are split and indexed in MongoDB Atlas, enabling text search capabilities.

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def create_vector_search():
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    """
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    Creates a MongoDBAtlasVectorSearch object using the connection string, database, and collection names, along with the OpenAI embeddings and index configuration.
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    :return: MongoDBAtlasVectorSearch object
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    """
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    vector_search = MongoDBAtlasVectorSearch.from_connection_string(
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        ATLAS_CONNECTION_STRING,
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        f"{DB_NAME}.{COLLECTION_NAME}",
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        OpenAIEmbeddings(),
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        index_name="default"
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    )
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    return vector_search

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def perform_similarity_search(query, top_k=3):
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    """
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    This function performs a similarity search within a MongoDB Atlas collection. It leverages the capabilities of the MongoDB Atlas Search, which under the hood, may use the `$vectorSearch` operator, to find and return the top `k` documents that match the provided query semantically.
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    :param query: The search query string.
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    :param top_k: Number of top matches to return.
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    :return: A list of the top `k` matching documents with their similarity scores.
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    """
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   # Get the MongoDBAtlasVectorSearch object
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    vector_search = create_vector_search()
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    # Execute the similarity search with the given query
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    results = vector_search.similarity_search_with_score(
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        query=query,
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        k=top_k,
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    )
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    return results
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# Example of calling the function directly
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search_results = perform_similarity_search("MongoDB Atlas auditing")