File size: 1,764 Bytes
1375112 57ab06a 1375112 be0f180 57ab06a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
---
title: README
emoji: 🏆
colorFrom: indigo
colorTo: blue
sdk: static
pinned: false
license: apache-2.0
short_description: Smarter vector search with metadata-aware traversal
---
# Graph RAG
Retrievers providing both **unstructured** (similarity-search on vectors) and
**structured** (traversal of metadata properties).
## About The Project
Graph RAG provides retrievers combining vector-search (for unstructured similarity) and traversal (for structured relationships in metadata).
These retrievers are implemented using the metadata search functionality of existing vector stores, **allowing you to traverse your existing vector store**!
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.
<!-- GETTING STARTED -->
## Getting Started with LangChain
1. Install `langchain-graph-retriever` (or add to your Python dependencies).
```sh
pip install langchain-graph-retriever
```
1. Wrap your existing vector store to enable graph retrieval:
```python
from langchain_graph_retriever import GraphRetriever
retriever = GraphRetriever(
# Adapt AstraDBVectorStore for use with Graph Retrievers.
# Exposes functionality of the underlying store that is otherwise not available.
store = store,
# Define the relationships to navigate:
# 1. From nodes with a list of `mentions` to the nodes with the corresponding `ids`.
# 2. From nodes with a list of related `entities` to other nodes with the same entities.
edges = [("mentions", "id"), "entities"],
)
retriever.invoke("where is Santa Clara?")
``` |