Rag Agent

Purpose

Stores and retrieves project artifacts with semantic search capabilities

When to Use This Skill

1. Context Retrieval - Get relevant info for LLMs
2. Documentation Search - Find relevant docs
3. Prompt Management - Store/retrieve prompts
4. Code Examples - Find similar implementations

Responsibilities

1. Store artifacts - (prompts, code, docs) with embeddings
2. Semantic search - across project knowledge
3. Context retrieval - for LLM queries
4. Version management - for artifacts
5. Integration with - Knowledge Graph

Integration with Pipeline

Communication

Receives:

• Artifacts to store (prompts, docs, code)
• Search queries from other agents
• Context retrieval requests for LLMs

Sends:

• Relevant artifacts based on semantic similarity
• Context for LLM queries
• Search results with relevance scores

Usage Examples

Standalone Usage

python3 rag_agent.py \
  --operation store \
  --content-file prompt.txt \
  --collection prompts \
  --metadata '{"type": "developer_prompt", "version": "1.0"}'

Programmatic Usage

from rag_agent import RAGAgent

rag = RAGAgent(persist_directory="./rag_data")

# Store artifact
rag.store_artifact(
    content=prompt_text,
    collection_name="prompts",
    metadata={"type": "developer_prompt"}
)

# Retrieve context
results = rag.query(
    query_text="How to implement authentication?",
    collection_name="documentation",
    top_k=5
)

for doc in results:
    print(f"Relevance: {doc['score']:.2f}")
    print(f"Content: {doc['content'][:200]}...")

Configuration

Environment Variables

# Agent-specific configuration
ARTEMIS_RAG_AGENT_ENABLED=true
ARTEMIS_LLM_PROVIDER=openai
ARTEMIS_LLM_MODEL=gpt-4o

Hydra Configuration (if applicable)

rag_agent:
  enabled: true
  llm:
    provider: openai
    model: gpt-4o

Best Practices

1. Organize Collections - Separate prompts, docs, code
2. Rich Metadata - Tag artifacts for better filtering
3. Regular Cleanup - Archive old/unused artifacts
4. Monitor Size - ChromaDB can grow large
5. Backup Regularly - Persist directory is critical

Cost Considerations

Typical cost: $0.05-0.20 per operation depending on complexity

Limitations

• Depends on LLM quality
• Context window limits
• May require multiple iterations

References

• Artemis Documentation
• Agent Pattern

Version: 1.0.0

Maintained By: Artemis Pipeline Team

Last Updated: October 24, 2025