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**Description:** Adds options for configuring MongoDBChatMessageHistory (no breaking changes): - session_id_key: name of the field that stores the session id - history_key: name of the field that stores the chat history - create_index: whether to create an index on the session id field - index_kwargs: additional keyword arguments to pass to the index creation **Discussion:** https://github.com/langchain-ai/langchain/discussions/22918 **Twitter handle:** @userlerueda --------- Co-authored-by: Jib <Jibzade@gmail.com> Co-authored-by: Eugene Yurtsev <eugene@langchain.dev> |
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| .. | ||
| langchain_mongodb | ||
| scripts | ||
| tests | ||
| .gitignore | ||
| LICENSE | ||
| Makefile | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
langchain-mongodb
Installation
pip install -U langchain-mongodb
Usage
- See integrations doc for more in-depth usage instructions.
- See Getting Started with the LangChain Integration for a walkthrough on using your first LangChain implementation with MongoDB Atlas.
Using MongoDBAtlasVectorSearch
from langchain_mongodb import MongoDBAtlasVectorSearch
# Pull MongoDB Atlas URI from environment variables
MONGODB_ATLAS_CLUSTER_URI = os.environ.get("MONGODB_ATLAS_CLUSTER_URI")
DB_NAME = "langchain_db"
COLLECTION_NAME = "test"
ATLAS_VECTOR_SEARCH_INDEX_NAME = "index_name"
MONGODB_COLLECTION = client[DB_NAME][COLLECITON_NAME]
# Create the vector search via `from_connection_string`
vector_search = MongoDBAtlasVectorSearch.from_connection_string(
MONGODB_ATLAS_CLUSTER_URI,
DB_NAME + "." + COLLECTION_NAME,
OpenAIEmbeddings(disallowed_special=()),
index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
)
# Initialize MongoDB python client
client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
# Create the vector search via instantiation
vector_search_2 = MongoDBAtlasVectorSearch(
collection=MONGODB_COLLECTION,
embeddings=OpenAIEmbeddings(disallowed_special=()),
index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
)