Nova AI
b272748209
- Full RAG system for OpenClaw agents - Semantic search across chat history, code, docs, skills - ChromaDB integration (all-MiniLM-L6-v2 embeddings) - Automatic AI context retrieval - Ingest pipelines for sessions, workspace, skills - Python API and CLI interfaces - Document management (add, delete, stats, reset)
43 lines
1.2 KiB
Bash
43 lines
1.2 KiB
Bash
#!/bin/bash
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# RAG Agent Launcher - Spawns an agent with automatic knowledge base access
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# This spawns a sub-agent that has RAG automatically integrated
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# The agent will query your knowledge base before responding to questions
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SESSION_SPAWN_COMMAND='python3 -c "
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import sys
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sys.path.insert(0, \"/home/william/.openclaw/workspace/rag\")
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# Add RAG context to system prompt
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ORIGINAL_TASK=\"$@\"
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# Search for relevant context
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from rag_system import RAGSystem
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rag = RAGSystem()
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# Find similar past conversations
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results = rag.search(ORIGINAL_TASK, n_results=3)
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if results:
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context = \"\\n=== RELEVANT CONTEXT FROM KNOWLEDGE BASE ===\\n\"
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for i, r in enumerate(results, 1):
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meta = r.get(\"metadata\", {})
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text = r.get(\"text\", \"\")[:500]
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doc_type = meta.get(\"type\", \"unknown\")
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source = meta.get(\"source\", \"unknown\")
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context += f\"\\n[{doc_type.upper()} - {source}]\\n{text}\\n\"
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else:
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context = \"\"
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# Respond with context-aware task
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print(f\"\"\"{context}
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=== CURRENT TASK ===
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{ORIGINAL_TASK}
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Use the context above if relevant to help answer the question.\"
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\"\")"
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# Spawn the agent with RAG context
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/home/william/.local/bin/openclaw sessions spawn "$SESSION_SPAWN_COMMAND" |