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- Edit this `README.md` markdown file to author your organization card.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Graph RAG
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+
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+ Retrievers providing both **unstructured** (similarity-search on vectors) and
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+ **structured** (traversal of metadata properties).
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+
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+ ## About The Project
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+
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+ Graph RAG provides retrievers combining vector-search (for unstructured similarity) and traversal (for structured relationships in metadata).
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+ These retrievers are implemented using the metadata search functionality of existing vector stores, **allowing you to traverse your existing vector store**!
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+
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+ The core library (`graph-retriever`) supports can be used in generic Python applications, while `langchain-graph-retriever` provides [langchain](https://python.langchain.com/docs/introduction/)-specific functionality.
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+
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+ <!-- GETTING STARTED -->
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+ ## Getting Started with LangChain
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+
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+ 1. Install `langchain-graph-retriever` (or add to your Python dependencies).
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+
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+ ```sh
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+ pip install langchain-graph-retriever
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+ ```
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+
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+ 1. Wrap your existing vector store to enable graph retrieval:
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+
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+ ```python
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+ from langchain_graph_retriever import GraphRetriever
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+
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+ retriever = GraphRetriever(
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+ # Adapt AstraDBVectorStore for use with Graph Retrievers.
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+ # Exposes functionality of the underlying store that is otherwise not available.
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+ store = store,
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+ # Define the relationships to navigate:
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+ # 1. From nodes with a list of `mentions` to the nodes with the corresponding `ids`.
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+ # 2. From nodes with a list of related `entities` to other nodes with the same entities.
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+ edges = [("mentions", "id"), "entities"],
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+ )
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+
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+ retriever.invoke("where is Santa Clara?")
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+ ```