Initial commit: OpenClaw RAG Knowledge System

- 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)

launch_rag_agent.sh

@@ -0,0 +1,43 @@
+#!/bin/bash
+# RAG Agent Launcher - Spawns an agent with automatic knowledge base access
+
+# This spawns a sub-agent that has RAG automatically integrated
+# The agent will query your knowledge base before responding to questions
+
+SESSION_SPAWN_COMMAND='python3 -c "
+import sys
+sys.path.insert(0, \"/home/william/.openclaw/workspace/rag\")
+
+# Add RAG context to system prompt
+ORIGINAL_TASK=\"$@\"
+
+# Search for relevant context
+from rag_system import RAGSystem
+rag = RAGSystem()
+
+# Find similar past conversations
+results = rag.search(ORIGINAL_TASK, n_results=3)
+
+if results:
+    context = \"\\n=== RELEVANT CONTEXT FROM KNOWLEDGE BASE ===\\n\"
+    for i, r in enumerate(results, 1):
+        meta = r.get(\"metadata\", {})
+        text = r.get(\"text\", \"\")[:500]
+        doc_type = meta.get(\"type\", \"unknown\")
+        source = meta.get(\"source\", \"unknown\")
+        context += f\"\\n[{doc_type.upper()} - {source}]\\n{text}\\n\"
+else:
+    context = \"\"
+
+# Respond with context-aware task
+print(f\"\"\"{context}
+
+=== CURRENT TASK ===
+{ORIGINAL_TASK}
+
+Use the context above if relevant to help answer the question.\"
+
+\"\")"
+
+# Spawn the agent with RAG context
+/home/william/.local/bin/openclaw sessions spawn "$SESSION_SPAWN_COMMAND"