138 lines
4.2 KiB
Python
138 lines
4.2 KiB
Python
"""
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Query the knowledge base using index-guided retrieval (no RAG).
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The LLM reads the index, picks relevant articles, and synthesizes an answer.
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No vector database, no embeddings, no chunking - just structured markdown
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and an index the LLM can reason over.
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Usage:
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uv run python query.py "How should I handle auth redirects?"
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uv run python query.py "What patterns do I use for API design?" --file-back
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"""
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from __future__ import annotations
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import argparse
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import asyncio
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from pathlib import Path
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from config import KNOWLEDGE_DIR, QA_DIR, now_iso
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from utils import load_state, read_all_wiki_content, save_state
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ROOT_DIR = Path(__file__).resolve().parent.parent
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async def run_query(question: str, file_back: bool = False) -> str:
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"""Query the knowledge base and optionally file the answer back."""
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from claude_agent_sdk import (
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AssistantMessage,
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ClaudeAgentOptions,
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ResultMessage,
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TextBlock,
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query,
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)
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wiki_content = read_all_wiki_content()
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tools = ["Read", "Glob", "Grep"]
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if file_back:
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tools.extend(["Write", "Edit"])
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file_back_instructions = ""
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if file_back:
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timestamp = now_iso()
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file_back_instructions = f"""
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## File Back Instructions
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After answering, do the following:
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1. Create a Q&A article at {QA_DIR}/ with the filename being a slugified version
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of the question (e.g., knowledge/qa/how-to-handle-auth-redirects.md)
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2. Use the Q&A article format from the schema (frontmatter with title, question,
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consulted articles, filed date)
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3. Update {KNOWLEDGE_DIR / 'index.md'} with a new row for this Q&A article
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4. Append to {KNOWLEDGE_DIR / 'log.md'}:
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## [{timestamp}] query (filed) | question summary
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- Question: {question}
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- Consulted: [[list of articles read]]
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- Filed to: [[qa/article-name]]
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"""
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prompt = f"""You are a knowledge base query engine. Answer the user's question by
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consulting the knowledge base below.
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## How to Answer
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1. Read the INDEX section first - it lists every article with a one-line summary
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2. Identify 3-10 articles that are relevant to the question
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3. Read those articles carefully (they're included below)
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4. Synthesize a clear, thorough answer
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5. Cite your sources using [[wikilinks]] (e.g., [[concepts/supabase-auth]])
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6. If the knowledge base doesn't contain relevant information, say so honestly
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## Knowledge Base
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{wiki_content}
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## Question
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{question}
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{file_back_instructions}"""
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answer = ""
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cost = 0.0
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try:
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async for message in query(
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prompt=prompt,
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options=ClaudeAgentOptions(
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cwd=str(ROOT_DIR),
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system_prompt={"type": "preset", "preset": "claude_code"},
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allowed_tools=tools,
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permission_mode="acceptEdits",
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max_turns=15,
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),
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):
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if isinstance(message, AssistantMessage):
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for block in message.content:
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if isinstance(block, TextBlock):
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answer += block.text
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elif isinstance(message, ResultMessage):
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cost = message.total_cost_usd or 0.0
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except Exception as e:
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answer = f"Error querying knowledge base: {e}"
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# Update state
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state = load_state()
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state["query_count"] = state.get("query_count", 0) + 1
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state["total_cost"] = state.get("total_cost", 0.0) + cost
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save_state(state)
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return answer
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def main():
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parser = argparse.ArgumentParser(description="Query the personal knowledge base")
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parser.add_argument("question", help="The question to ask")
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parser.add_argument(
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"--file-back",
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action="store_true",
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help="File the answer back into the knowledge base as a Q&A article",
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)
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args = parser.parse_args()
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print(f"Question: {args.question}")
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print(f"File back: {'yes' if args.file_back else 'no'}")
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print("-" * 60)
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answer = asyncio.run(run_query(args.question, file_back=args.file_back))
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print(answer)
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if args.file_back:
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print("\n" + "-" * 60)
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qa_count = len(list(QA_DIR.glob("*.md"))) if QA_DIR.exists() else 0
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print(f"Answer filed to knowledge/qa/ ({qa_count} Q&A articles total)")
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if __name__ == "__main__":
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main()
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