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rag

Skill0 starsChanged 6 months agoMITView on GitHub

Store and retrieve knowledge documents using vector search.

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1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

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    Common

    Most of the 4 edited versions we read keep these.

  • 1

    Distinctive

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  • 0

    Very specific

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  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Delete Knowledge: Remove outdated documents

    line 12

“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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Files
SKILL.md

RAG (Retrieval-Augmented Generation)

Store and retrieve knowledge documents using vector search.

Description

Enables the agent to maintain a persistent knowledge base. Documents are embedded and stored in ChromaDB for semantic search. When answering questions, the agent can search for relevant context to provide more accurate and informed responses.

Capabilities

  • Add Knowledge: Store text documents with metadata (source, tags)
  • Search Knowledge: Find semantically similar content by query
  • List Knowledge: Browse all stored documents
  • Delete Knowledge: Remove outdated documents
  • Context Injection: Automatically injects relevant knowledge when answering user queries

Usage

# Add knowledge
tools.add_knowledge(content="Text content", source="url_or_filename")

# Search
results = tools.search_knowledge(query="What is X?", n_results=5)

# List all
docs = tools.list_knowledge()

Embedded Model

  • ChromaDB default: all-MiniLM-L6-v2 (384-dim, ONNX runtime)
  • Storage: data/chroma/

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