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arc53-machine
0bbbac0eb2
With TRACES_ENABLED off, LLM calls record no GenAI metrics, and a streamed answer is joined into a preview only when a span keeps it. Agent runs and continuations build their invoke_agent span in one place, which now reads the model from model_id. The trace panel shows a stream's model time next to how long it was open. Adds missing type hints.
99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
"""Spans for RAG retrieval: the search itself, the query embedding, each source."""
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from __future__ import annotations
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from typing import Any, Dict, Iterable, List, Optional
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from docsgpt.tracing import core
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#: Characters of each retrieved chunk kept in the stored preview.
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_SNIPPET_CHARS = 240
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#: Chunks listed in the preview; the count attribute still reports all of them.
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_MAX_PREVIEW_CHUNKS = 20
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def start_retrieval_span(
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name: str, *, sources: Optional[Iterable[Any]] = None, **attributes: Any
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) -> Any:
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"""Open a ``retrieval`` span (a container: embeddings and searches nest under it)."""
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source_ids = [str(s) for s in (sources or []) if s]
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base = {
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"gen_ai.operation.name": "retrieval",
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"docsgpt.source_ids": source_ids or None,
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"gen_ai.data_source.id": source_ids[0] if len(source_ids) == 1 else None,
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}
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base.update(attributes)
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return core.start_span(
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core.KIND_RETRIEVAL,
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name,
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attributes={k: v for k, v in base.items() if v is not None},
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)
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def describe_documents(span: Any, docs: Optional[List[Dict[str, Any]]], *, query: Optional[str] = None) -> None:
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"""Record what a retrieval returned: counts, sources, top scores and a chunk preview."""
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if not span:
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return
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docs = docs or []
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scores = [d.get("score") for d in docs if isinstance(d, dict) and isinstance(d.get("score"), (int, float))]
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score_kinds = {d.get("score_kind") for d in docs if isinstance(d, dict) and d.get("score_kind")}
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span.set(
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**{
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"docsgpt.chunk_count": len(docs),
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"docsgpt.top_score": max(scores) if scores else None,
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"docsgpt.score_kind": score_kinds.pop() if len(score_kinds) == 1 else None,
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}
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)
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if query:
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span.preview("query", query)
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if docs:
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span.preview(
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"chunks",
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[
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{
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k: v
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for k, v in {
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"title": d.get("title") or d.get("filename"),
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"source": d.get("source"),
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"score": d.get("score"),
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"text": str(d.get("text") or "")[:_SNIPPET_CHARS],
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}.items()
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if v not in (None, "")
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}
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for d in docs[:_MAX_PREVIEW_CHUNKS]
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if isinstance(d, dict)
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],
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)
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def start_embedding_span(model: Optional[str], *, inputs: int = 1, **attributes: Any) -> Any:
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"""Open an ``embeddings`` span for a query embedding (leaf)."""
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base = {
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"gen_ai.operation.name": "embeddings",
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"gen_ai.request.model": model,
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"docsgpt.input_count": inputs,
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}
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base.update(attributes)
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return core.start_span(
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core.KIND_EMBEDDING,
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f"embeddings {model}" if model else "embeddings",
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attributes={k: v for k, v in base.items() if v is not None},
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)
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def start_source_search_span(
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source_id: Any, *, top_k: Optional[int] = None, **attributes: Any
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) -> Any:
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"""Open a per-source vector ``search`` span (leaf)."""
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base = {
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"gen_ai.operation.name": "search",
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"gen_ai.data_source.id": str(source_id) if source_id else None,
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"docsgpt.top_k": top_k,
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}
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base.update(attributes)
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return core.start_span(
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core.KIND_SEARCH,
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f"search {source_id}" if source_id else "search",
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attributes={k: v for k, v in base.items() if v is not None},
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)
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