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‎libs/langchain-mongodb-retrievers/.gitignore‎

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__pycache__

‎libs/langchain-mongodb-retrievers/CHANGELOG.md‎

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# Changelog
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---
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## Changes in version 1.0 (2025/XX/XX)
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- Initial release, supporting LangChain 1.0.

‎libs/langchain-mongodb-retrievers/LICENSE‎

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MIT License
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Copyright (c) 2024 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎libs/langchain-mongodb-retrievers/README.md‎

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from libs.community.tests.unit_tests.chains.test_pebblo_retrieval import retriever
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# langchain-mongodb-retriever
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# Installation
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```
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pip install -U langchain-mongodb-retriever
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```
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# Usage
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- [Integrate Atlas Vector Search with LangChain](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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import os
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from langchain_mongodb import MongoDBAtlasVectorSearch
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from langchain_openai import OpenAIEmbeddings
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# Pull MongoDB Atlas URI from environment variables
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MONGODB_ATLAS_CONNECTION_STRING = os.environ["MONGODB_CONNECTION_STRING"]
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DB_NAME = "langchain_db"
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COLLECTION_NAME = "test"
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VECTOR_SEARCH_INDEX_NAME = "index_name"
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MODEL_NAME = "text-embedding-3-large"
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OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
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vectorstore = MongoDBAtlasVectorSearch.from_connection_string(
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connection_string=MONGODB_ATLAS_CONNECTION_STRING,
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namespace=DB_NAME + "." + COLLECTION_NAME,
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embedding=OpenAIEmbeddings(model=MODEL_NAME),
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index_name=VECTOR_SEARCH_INDEX_NAME,
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)
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retrieved_docs = vectorstore.similarity_search(
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"How do I deploy MongoDBAtlasVectorSearch in our production environment?")
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```

‎libs/langchain-mongodb-retrievers/justfile‎

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set shell := ["bash", "-c"]
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set dotenv-load
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set dotenv-filename := "../../.local_atlas_uri"
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# Default target executed when no arguments are given.
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[private]
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default:
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@just --list
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install:
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uv sync --frozen
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[group('test')]
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integration_tests *args="":
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uv run pytest tests/integration_tests/ {{args}}
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[group('test')]
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unit_tests *args="":
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uv run pytest tests/unit_tests {{args}}
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[group('test')]
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tests *args="":
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uv run pytest {{args}}
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[group('test')]
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test_watch filename:
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uv run ptw --snapshot-update --now . -- -vv {{filename}}
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[group('lint')]
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lint:
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git ls-files -- '*.py' | xargs uv run pre-commit run ruff --files
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git ls-files -- '*.py' | xargs uv run pre-commit run ruff-format --files
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[group('lint')]
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typing:
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uv run mypy --install-types --non-interactive .
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[group('lint')]
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codespell:
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git ls-files -- '*.py' | xargs uv run pre-commit run --hook-stage manual codespell --files

libs/langchain-mongodb/langchain_mongodb/retrievers/__init__.py renamed to libs/langchain-mongodb-retrievers/langchain_mongodb_retrievers/__init__.py

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to create MongoDB's core Vector Search Retriever.
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"""
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from langchain_mongodb.retrievers.full_text_search import (
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from langchain_mongodb_retrievers.full_text_search import (
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MongoDBAtlasFullTextSearchRetriever,
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)
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from langchain_mongodb.retrievers.graphrag import MongoDBGraphRAGRetriever
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from langchain_mongodb.retrievers.hybrid_search import MongoDBAtlasHybridSearchRetriever
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from langchain_mongodb.retrievers.parent_document import (
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from langchain_mongodb_retrievers.hybrid_search import MongoDBAtlasHybridSearchRetriever
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from langchain_mongodb_retrievers.parent_document import (
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MongoDBAtlasParentDocumentRetriever,
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)
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from langchain_mongodb.retrievers.self_querying import MongoDBAtlasSelfQueryRetriever
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from langchain_mongodb_retrievers.self_querying import MongoDBAtlasSelfQueryRetriever
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__all__ = [
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"MongoDBAtlasHybridSearchRetriever",
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"MongoDBAtlasFullTextSearchRetriever",
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"MongoDBAtlasParentDocumentRetriever",
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"MongoDBGraphRAGRetriever",
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"MongoDBAtlasSelfQueryRetriever",
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]

libs/langchain-mongodb/langchain_mongodb/retrievers/full_text_search.py renamed to libs/langchain-mongodb-retrievers/langchain_mongodb_retrievers/full_text_search.py

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libs/langchain-mongodb/langchain_mongodb/retrievers/hybrid_search.py renamed to libs/langchain-mongodb-retrievers/langchain_mongodb_retrievers/hybrid_search.py

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libs/langchain-mongodb/langchain_mongodb/retrievers/parent_document.py renamed to libs/langchain-mongodb-retrievers/langchain_mongodb_retrievers/parent_document.py

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from typing import Any, List, Optional
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import pymongo
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from langchain.retrievers.parent_document_retriever import ParentDocumentRetriever
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from langchain_classic.retrievers.parent_document_retriever import ParentDocumentRetriever
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from langchain_core.callbacks import (
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AsyncCallbackManagerForRetrieverRun,
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CallbackManagerForRetrieverRun,
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by the same MongoDB Collection.
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For more details, see superclasses
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:class:`~langchain.retrievers.parent_document_retriever.ParentDocumentRetriever`
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and :class:`~langchain.retrievers.MultiVectorRetriever`.
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:class:`~langchain_classic.retrievers.parent_document_retriever.ParentDocumentRetriever`
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and :class:`~langchain_classic.retrievers.MultiVectorRetriever`.
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Examples:
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>>> from langchain_mongodb.retrievers.parent_document import (
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"""Construct Retriever using one Collection for VectorStore and one for DocStore
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See parent classes
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:class:`~langchain.retrievers.parent_document_retriever.ParentDocumentRetriever`
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and :class:`~langchain.retrievers.MultiVectorRetriever` for further details.
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:class:`~langchain_classic.retrievers.parent_document_retriever.ParentDocumentRetriever`
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and :class:`~langchain_classic.retrievers.MultiVectorRetriever` for further details.
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Args:
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connection_string: A valid MongoDB Atlas connection URI.

libs/langchain-mongodb/langchain_mongodb/retrievers/self_querying.py renamed to libs/langchain-mongodb-retrievers/langchain_mongodb_retrievers/self_querying.py

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from typing import Any, Dict, Sequence, Tuple, Union
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from langchain.chains.query_constructor.schema import AttributeInfo
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from langchain.retrievers.self_query.base import SelfQueryRetriever
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from langchain_classic.chains.query_constructor.schema import AttributeInfo
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from langchain_classic.retrievers.self_query.base import SelfQueryRetriever
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.runnables import Runnable
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from langchain_core.structured_query import (

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