Studio: format search_knowledge_base hits as fenced <chunk> blocks with score/page/tokens
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86b52503dd
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04d4909ed6
2 changed files with 89 additions and 26 deletions
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@ -21,11 +21,11 @@ SEARCH_KNOWLEDGE_BASE_TOOL = {
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"function": {
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"name": "search_knowledge_base",
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"description": (
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"Search the user's attached documents for information relevant to "
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"the user's question. Call this when the user references content "
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"from their docs, asks fact-heavy questions, or needs grounded "
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"citations. Returns numbered chunks with source filenames; cite "
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"them in your reply as [1], [2], etc."
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"Search the user's attached documents. Call this when the user "
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"references content from their docs, asks fact-heavy questions, "
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"or needs grounded citations. Returns chunks wrapped in "
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'<chunk id="N" source="..." page="..." score="...">...</chunk> '
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"tags; cite them in your reply as [1], [2], etc."
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),
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"parameters": {
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"type": "object",
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@ -53,22 +53,51 @@ SEARCH_KNOWLEDGE_BASE_TOOL = {
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}
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def _format_hits_for_llm(hits: list[Any]) -> str:
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"""Render hits as numbered Markdown citations; empty results return a message, not ''."""
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def _xml_attr(value: Any) -> str:
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return (
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str(value)
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.replace("&", "&")
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.replace('"', """)
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.replace("<", "<")
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.replace(">", ">")
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)
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def _format_hits_for_llm(hits: list[dict]) -> str:
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"""Render hits as fenced <chunk> blocks with metadata."""
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if not hits:
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return (
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"No matching chunks were found in the attached documents. "
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"Either nothing in this scope is relevant, or no documents "
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"have been ingested yet."
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)
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lines: list[str] = []
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blocks: list[str] = []
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for index, hit in enumerate(hits, start = 1):
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name = hit.get("filename") or "unknown source"
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attrs = [
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f'id="{index}"',
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f'source="{_xml_attr(hit.get("filename") or "unknown")}"',
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]
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page = hit.get("page_number")
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suffix = f" (page {page})" if page is not None else ""
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if page is not None:
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attrs.append(f'page="{page}"')
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score = hit.get("score")
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if score is not None:
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attrs.append(f'score="{float(score):.3f}"')
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dense = hit.get("dense_score")
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if dense is not None and dense != score:
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attrs.append(f'dense_score="{float(dense):.3f}"')
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chunk_index = hit.get("chunk_index")
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if chunk_index is not None:
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attrs.append(f'chunk_index="{chunk_index}"')
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tokens = hit.get("token_count")
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if tokens:
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attrs.append(f'tokens="{tokens}"')
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kind = hit.get("kind")
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if kind and kind != "text":
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attrs.append(f'kind="{_xml_attr(kind)}"')
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text = (hit.get("text") or "").strip()
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lines.append(f"[{index}] {name}{suffix}: {text}")
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return "\n\n".join(lines)
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blocks.append(f"<chunk {' '.join(attrs)}>\n{text}\n</chunk>")
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return "\n\n".join(blocks)
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def search_knowledge_base(
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@ -166,7 +195,7 @@ def search_knowledge_base(
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rows = conn.execute(
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f"""
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SELECT c.id AS chunk_id, c.text, c.page_number,
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c.kind, d.filename
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c.token_count, c.kind, d.filename
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FROM rag_chunks c
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JOIN rag_documents d ON d.id = c.document_id
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WHERE c.id IN ({placeholders})
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@ -198,9 +227,19 @@ def search_knowledge_base(
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hits = hits[:k]
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# Skip image-kind hits; the paired caption surfaces separately.
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formatted = [
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lookup[hit.chunk_id]
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for hit in hits
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if hit.chunk_id in lookup and lookup[hit.chunk_id].get("kind") != "image"
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]
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# Merge Hit-side metadata (score, dense_score, chunk_index) into the
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# sqlite-side row so the formatter sees one flat dict per chunk.
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formatted: list[dict] = []
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for hit in hits:
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row = lookup.get(hit.chunk_id)
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if row is None or row.get("kind") == "image":
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continue
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formatted.append(
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{
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**row,
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"score": hit.score,
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"dense_score": hit.dense_score,
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"chunk_index": hit.chunk_index,
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}
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)
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return _format_hits_for_llm(formatted)
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