mirror of
https://github.com/hwchase17/langchain
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40 lines
1.4 KiB
Markdown
40 lines
1.4 KiB
Markdown
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# langchain-mongodb
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# Installation
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```
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pip install -U langchain-mongodb
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```
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# Usage
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- See [integrations doc](../../../docs/docs/integrations/vectorstores/mongodb.ipynb) for more in-depth usage instructions.
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- See [Getting Started with the LangChain Integration](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/#get-started-with-the-langchain-integration) for a walkthrough on using your first LangChain implementation with MongoDB Atlas.
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## Using MongoDBAtlasVectorSearch
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```python
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from langchain_mongodb import MongoDBAtlasVectorSearch
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# Pull MongoDB Atlas URI from environment variables
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MONGODB_ATLAS_CLUSTER_URI = os.environ.get("MONGODB_ATLAS_CLUSTER_URI")
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DB_NAME = "langchain_db"
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COLLECTION_NAME = "test"
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ATLAS_VECTOR_SEARCH_INDEX_NAME = "index_name"
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MONGODB_COLLECTION = client[DB_NAME][COLLECITON_NAME]
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# Create the vector search via `from_connection_string`
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vector_search = MongoDBAtlasVectorSearch.from_connection_string(
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MONGODB_ATLAS_CLUSTER_URI,
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DB_NAME + "." + COLLECTION_NAME,
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OpenAIEmbeddings(disallowed_special=()),
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index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
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)
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# Initialize MongoDB python client
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client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
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# Create the vector search via instantiation
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vector_search_2 = MongoDBAtlasVectorSearch(
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collection=MONGODB_COLLECTION,
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embeddings=OpenAIEmbeddings(disallowed_special=()),
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index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
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)
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```
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