MongoDB atlas 集成

使用 LangChain Python 与 MongoDB atlas 集成。

MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. It now has support for native Vector Search on the MongoDB document data.

安装和设置

查看详细配置说明.

我们需要安装 langchain-mongodb Python 包.

pip

pip install langchain-mongodb

向量存储

查看使用示例.

from langchain_mongodb import MongoDBAtlasVectorSearch

检索器

Full text search retriever

Hybrid Search Retriever performs full-text searches using Lucene’s standard (BM25) analyzer.

from langchain_mongodb.retrievers import MongoDBAtlasFullTextSearchRetriever

Hybrid search retriever

Hybrid Search Retriever combines vector and full-text searches weighting them the via Reciprocal Rank Fusion (RRF) algorithm.

from langchain_mongodb.retrievers import MongoDBAtlasHybridSearchRetriever

Model caches

MongoDBCache

An abstraction to store a simple cache in MongoDB. This does not use Semantic Caching, nor does it require an index to be made on the collection before generation.

要导入此缓存：

from langchain_mongodb.cache import MongoDBCache

要将此缓存与您的大语言模型一起使用：

from langchain_core.globals import set_llm_cache

# use any embedding provider...
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings

mongodb_atlas_uri = "<YOUR_CONNECTION_STRING>"
COLLECTION_NAME="<YOUR_CACHE_COLLECTION_NAME>"
DATABASE_NAME="<YOUR_DATABASE_NAME>"

set_llm_cache(MongoDBCache(
    connection_string=mongodb_atlas_uri,
    collection_name=COLLECTION_NAME,
    database_name=DATABASE_NAME,
))

MongoDBAtlasSemanticCache

Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends MongoDBAtlas as both a cache and a vectorstore. The MongoDBAtlasSemanticCache inherits from MongoDBAtlasVectorSearch and needs an Atlas Vector Search Index defined to work. Please look at the usage example on how to set up the index.

要导入此缓存：

from langchain_mongodb.cache import MongoDBAtlasSemanticCache

要将此缓存与您的大语言模型一起使用：

from langchain_core.globals import set_llm_cache

# use any embedding provider...
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings

mongodb_atlas_uri = "<YOUR_CONNECTION_STRING>"
COLLECTION_NAME="<YOUR_CACHE_COLLECTION_NAME>"
DATABASE_NAME="<YOUR_DATABASE_NAME>"

set_llm_cache(MongoDBAtlasSemanticCache(
    embedding=FakeEmbeddings(),
    connection_string=mongodb_atlas_uri,
    collection_name=COLLECTION_NAME,
    database_name=DATABASE_NAME,
))

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