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Build a self-hosted RAG pipeline for code search, findings, and CI integration.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ragkit" yet — see the docs or source repo.
Use ragkit to index this repository and scan the code, configs, and docs for high-priority issues. Focus on security risks, deprecated dependencies, misconfigurations, and clear stability concerns, then return prioritized findings with remediation advice.
A prioritized list of findings with evidence locations, risk explanations, and remediation suggestions.
Explain how to integrate ragkit into our CI workflow so it updates indexes and runs code scans on every commit and pull request, and fails the build when high-risk findings are detected. Include recommended steps and output formatting.
A CI integration plan covering triggers, execution steps, failure conditions, and result presentation.
Use ragkit to build a searchable knowledge base from our repositories, technical docs, and configuration files, and provide example queries for AI agents, such as locating authentication logic, finding database connection settings, and tracing service dependencies.
A retrieval setup for AI agents plus several ready-to-use example queries.
Index repositories for symbols, call graphs, history, and repo memory.
Build RAG workflows with document ingestion, hybrid search, and agentic answers.
Connect AI to documents, embeddings, and semantic search through PostgREST APIs.
Set up a local RAG server for private knowledge search and QA.
Retrieve relevant context and metadata from Qdrant using natural language queries.
Access GraphRAG research, implementation patterns, and best practices for building RAG systems.