RAG & Vector Search
OfficialBoost responses with retrieval-augmented context.
Software Engineering#embeddings#langchain#rag#electron#vector-search#retrieval-augmented-generation#document-search
Authorjhl-labs
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Retrieves and augments LLM responses with relevant document context using RAG and vector search, reducing hallucinations and increasing accuracy.
Core Features & Use Cases
- Embeddings-based retrieval and vector storage for fast, scalable context.
- Document search and semantic retrieval across knowledge bases and corpora.
- Use Case: Enhance customer support or research assistants by referencing pertinent documents during conversations.
Quick Start
Provide a retrieval-augmented answer by embedding the query, retrieving top relevant documents, and augmenting the prompt for the LLM.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: RAG & Vector Search Download link: https://github.com/jhl-labs/sepilot_desktop/archive/main.zip#rag-vector-search Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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