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72bfc1d3db
This PR migrates the existing MongoDBAtlasVectorSearch abstraction from
the `langchain_community` section to the partners package section of the
codebase.
- [x] Run the partner package script as advised in the partner-packages
documentation.
- [x] Add Unit Tests
- [x] Migrate Integration Tests
- [x] Refactor `MongoDBAtlasVectorStore` (autogenerated) to
`MongoDBAtlasVectorSearch`
- [x] ~Remove~ deprecate the old `langchain_community` VectorStore
references.
## Additional Callouts
- Implemented the `delete` method
- Included any missing async function implementations
- `amax_marginal_relevance_search_by_vector`
- `adelete`
- Added new Unit Tests that test for functionality of
`MongoDBVectorSearch` methods
- Removed [`del
res[self._embedding_key]`](e0c81e1cb0/libs/community/langchain_community/vectorstores/mongodb_atlas.py (L218))
in `_similarity_search_with_score` function as it would make the
`maximal_marginal_relevance` function fail otherwise. The `Document`
needs to store the embedding key in metadata to work.
Checklist:
- [x] PR title: Please title your PR "package: description", where
"package" is whichever of langchain, community, core, experimental, etc.
is being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
- Example: "community: add foobar LLM"
- [x] PR message
- [x] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [x] Add tests and docs: If you're adding a new integration, please
include
1. Existing tests supplied in docs/docs do not change. Updated
docstrings for new functions like `delete`
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory. (This already exists)
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Steven Silvester <steven.silvester@ieee.org>
Co-authored-by: Erick Friis <erick@langchain.dev>
40 lines
1.4 KiB
Markdown
40 lines
1.4 KiB
Markdown
# 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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``` |