RAG Implementation
Builds retrieval pipelines that ground answers in real documents.
Install
/plugin marketplace add wshobson/agents; /plugin install llm-application-dev
When an LLM needs to answer from a document set, or an existing RAG pipeline returns weak matches.
- Author
- wshobson
- License
- MIT
Every stage of a retrieval-augmented pipeline gets a decision rather than a default: how documents are split, which embedding model fits the language and the budget, and which vector store to run — Pinecone, Weaviate, Qdrant, Chroma or pgvector, compared on what separates them in practice. Retrieval is treated as more than nearest-neighbour lookup, combining dense and keyword search and reranking the result before it reaches the model. A LangGraph example shows retrieval and generation as separate nodes so each can be measured on its own.
Similar skills
LLM Application Dev
SkillBundles the RAG, vector-index and prompting skills an LLM app needs, rebuilt around LangGraph.
SAST Configuration
SkillSets up Semgrep, CodeQL and SonarQube so the pipeline blocks real findings.
Sourcegraph
SkillCross-repository code search: trace references, assess refactor impact, investigate via commit history.
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