awesome-rag
github.com/danielskry/awesome-rag âđ Awesome list of Retrieval-Augmented Generation (RAG) applications in Generative AI.
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âšī¸ General Information on RAGđ¯ Advanced Approaches𧰠Frameworks that Facilitate RAGđ ī¸ Techniquesđ Metrics & Evaluationđž Databases
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me chunking resources from awesome-rag"
Installation instructions âWhat's inside
đ ī¸ Techniques
- Adaptive ChunkingChunking
- Adaptive RetrievalRetrieval
- Agentic ChunkingChunking
- Chain of Thought (CoT)Prompting
- Chain of Verification (CoVe)Prompting
- CharacterTextSplitterChunking
đ¯ Advanced Approaches
- Agentic RAG
- A-RAG
- Cache-Augmented Generation (CAG)
- Code-Graph-RAG
- Corrective RAG
- FLARE
An approach that incorporates active retrieval-augmented generation to improve response quality.
đ Metrics & Evaluation
- Annotation queuesResponse Evaluation Metrics
- BLEUResponse Evaluation Metrics
- Cosine SimilaritySimilarity Metrics for Embeddings
- curse of dimensionalitySimilarity Metrics for Embeddings
- Dot ProductSimilarity Metrics for Embeddings
- Euclidean DistanceSimilarity Metrics for Embeddings
đž Databases
- Apache CassandraDistributed Data Processing and Serving Engines:
- Azure Cosmos DBOther Database Systems:
- Chroma DBVector Databases:
- CouchbaseOther Database Systems:
- ElasticsearchSearch Engines with Vector Capabilities:
- FAISSVector Search Libraries and Tools:
âšī¸ General Information on RAG
- Haystack RAG PipelineImplementation Resources
- LangChain Production GuideImplementation Resources
- LangChain RAG TutorialImplementation Resources
- LlamaIndex RAG TutorialImplementation Resources
- Python Async Best PracticesImplementation Resources
- RAG implementation in PythonImplementation Resources
Showing a sample of 116 resources. View the full list on GitHub â