{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/16"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"Combines vector similarity search with graph traversal over metadata to retrieve documents from existing vector stores, enabling both unstructured and structured retrieval in a single query.","skillfed_tags":["rag","graph-traversal","vector-retrieval"],"use_cases":["Retrieve documents by following entity mentions and relationships in knowledge graphs alongside semantic similarity.","Build RAG systems that navigate document metadata hierarchies to surface contextually related content.","Combine vector search with structured metadata properties to refine retrieval results in domain-specific applications.","Extend existing vector stores with graph traversal without replacing the underlying store infrastructure."],"what_it_does":"Graph Retriever is a Python library that extends vector stores with graph-based retrieval capabilities. It wraps existing vector stores to enable traversal of metadata relationships\u2014such as following mentions or entity links\u2014alongside traditional similarity search. This hybrid approach lets you retrieve documents based on both semantic similarity and structured connections in your metadata.\n\nThe library is designed for Retrieval-Augmented Generation (RAG) applications where you want to combine the strengths of vector search (finding semantically similar content) with graph traversal (following explicit relationships between documents). It supports Python 3.10 and later and depends on immutabledict, numpy, pytest, and typing-extensions. A separate LangChain integration package is available for applications already using LangChain.","worth_installing":"Yes, if you need hybrid retrieval combining vector similarity with metadata graph traversal. The low install friction, permissive license, and active repository make it a reasonable choice for RAG applications. However, the package is in Beta with limited recent activity (last release April 2025), so verify that your vector store is supported and test thoroughly before production use."},"id":"graph-retriever","links":{"html":"https://skillfed.io/packages/graph-retriever","md":"https://skillfed.io/packages/graph-retriever.md","pypi":"https://pypi.org/project/graph-retriever/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-04-04","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"graph-retriever","python_support":"supports_current","summary":"Retriever combining unstructured similarity and structured document traversal."},"popularity":{"monthly_downloads":187078,"position":9971,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.0"}