将MongoDB与 LangChain 集成

from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
# Instantiate the vector store
vector_store = MongoDBAtlasVectorSearch.from_connection_string(
   # Vector store arguments...
)
# Use the vector store as a retriever
retriever = vector_store.as_retriever()
# Define your query
query = "some search query"
# Print results
documents = retriever.invoke(query)
for doc in documents:
   print(doc)

from langchain_mongodb.retrievers.full_text_search import MongoDBAtlasFullTextSearchRetriever
# Connect to your Atlas cluster
client = MongoClient("<connection-string>")
collection = client["<database-name>"]["<collection-name>"]
# Initialize the retriever
retriever = MongoDBAtlasFullTextSearchRetriever(
   collection = collection,           # MongoDB Collection in Atlas
   search_field = "<field-name>",     # Name of the field to search
   search_index_name = "<index-name>" # Name of the search index
)
# Define your query
query = "some search query"
# Print results
documents = retriever.invoke(query)
for doc in documents:
   print(doc)

from langchain_mongodb.retrievers.hybrid_search import MongoDBAtlasHybridSearchRetriever
# Initialize the retriever
retriever = MongoDBAtlasHybridSearchRetriever(
   vectorstore = <vector-store>,        # Vector store instance
   search_index_name = "<index-name>",  # Name of the Atlas Search index
   top_k = 5,                           # Number of documents to return
   fulltext_penalty = 60.0,             # Penalty for full-text search
   vector_penalty = 60.0                # Penalty for vector search
)
# Define your query
query = "some search query"
# Print results
documents = retriever.invoke(query)
for doc in documents:
   print(doc)

from langchain_mongodb.retrievers.parent_document import ParentDocumentRetriever
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_voyageai import VoyageAIEmbeddings
retriever = MongoDBAtlasParentDocumentRetriever.from_connection_string(
   connection_string = <connection-string>,           # Atlas cluster or local deployment URI
   embedding_model = VoyageAIEmbeddings(),            # Embedding model to use
   child_splitter = RecursiveCharacterTextSplitter(), # Text splitter to use
   database_name = <database-name>,                   # Database to store the collection
   collection_name = <collection-name>,               # Collection to store the collection
   # Additional vector store or parent class arguments...
)
# Define your query
query = "some search query"
# Print results
documents = retriever.invoke(query)
for doc in documents:
   print(doc)

from langchain_mongodb.retrievers import MongoDBAtlasSelfQueryRetriever
from langchain_mongodb import MongoDBAtlasVectorSearch
# Given an existing vector store with movies data, define metadata describing the data
metadata_field_info = [
    AttributeInfo(
        name="genre",
        description="The genre of the movie. One of ['science fiction', 'comedy', 'drama', 'thriller', 'romance', 'animated']",
        type="string",
    ),
    AttributeInfo(
        name="year",
        description="The year the movie was released",
        type="integer",
    ),
    AttributeInfo(
        name="rating", description="A 1-10 rating for the movie", type="float"
    ),
]
# Create the retriever from the VectorStore, an LLM and info about the documents
retriever = MongoDBAtlasSelfQueryRetriever.from_llm(
    llm=llm,
    vectorstore=vector_store,
    metadata_field_info=metadata_field_info,
    document_contents="Descriptions of movies",
    enable_limit=True
)
# This example results in the following composite filter sent to $vectorSearch:
# {'filter': {'$and': [{'year': {'$lt': 1960}}, {'rating': {'$gt': 8}}]}}
print(retriever.invoke("Movies made before 1960 that are rated higher than 8"))

from langchain_mongodb import MongoDBCache
from langchain_core.globals import set_llm_cache
set_llm_cache(MongoDBCache(
   connection_string = "<connection-string>", # Atlas cluster or local deployment URI
   database_name = "<database-name>",         # Database to store the cache
   collection_name = "<collection-name>"      # Collection to store the cache
))

from langchain_mongodb import MongoDBAtlasSemanticCache
from langchain_core.globals import set_llm_cache
from langchain_voyageai import VoyageAIEmbeddings
set_llm_cache(MongoDBAtlasSemanticCache(
   embedding = VoyageAIEmbeddings(),          # Embedding model to use
   connection_string = "<connection-string>", # Atlas cluster or local deployment URI
   database_name = "<database-name>",         # Database to store the cache
   collection_name = "<collection-name>"      # Collection to store the cache
))

from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from langchain_mongodb.agent_toolkit import (
    MONGODB_AGENT_SYSTEM_PROMPT,
    MongoDBDatabase,
    MongoDBDatabaseToolkit,
)
db_wrapper = MongoDBDatabase.from_connection_string(
CONNECTION_STRING, database=DB_NAME
)
llm = ChatOpenAI(model="gpt-4o-mini", timeout=60)
toolkit = MongoDBDatabaseToolkit(db=db_wrapper, llm=llm)
system_message = MONGODB_AGENT_SYSTEM_PROMPT.format(top_k=5)
test_query = "Which country's customers spent the most?"
agent = create_react_agent(llm, toolkit.get_tools(), state_modifier=system_message)
agent.step_timeout = 60
events = agent.stream(
   {"messages": [("user", test_query)]},
    stream_mode="values",
)
messages = []

from pymongo import MongoClient
from langchain_mongodb.docstores import MongoDBDocStore
# Replace with your MongoDB connection string and namespace
connection_string = "<connection-string>"
namespace = "<database-name>.<collection-name>"
# Initialize the MongoDBDocStore
docstore = MongoDBDocStore.from_connection_string(connection_string, namespace)

from langchain.storage import MongoDBByteStore
# Instantiate the MongoDBByteStore
mongodb_store = MongoDBByteStore(
   connection_string = "<connection-string>",  # Atlas cluster or local deployment URI
   db_name = "<database-name>",                # Name of the database
   collection_name = "<collection-name>"       # Name of the collection
)
# Set values for keys
mongodb_store.mset([("key1", b"hello"), ("key2", b"world")])
# Get values for keys
values = mongodb_store.mget(["key1", "key2"])
print(values)  # Output: [b'hello', b'world']
# Iterate over keys
for key in mongodb_store.yield_keys():
   print(key)  # Output: key1, key2
# Delete keys
mongodb_store.mdelete(["key1", "key2"])