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517eeaf4b9
- Local servers (LM Studio, vLLM) reject the object-form tool_choice langchain sends for function calling. The generic openai_compatible provider now binds the schema as a tool without forcing tool_choice. - A structured call can return no parsed result (a thinking model answering in plain text); fall back to free text with a clear reason instead of an opaque render error.
80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
"""Shared helpers for invoking an agent with structured output and a graceful fallback.
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The Portfolio Manager, Trader, and Research Manager all follow the same
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canonical pattern:
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1. At agent creation, wrap the LLM with ``with_structured_output(Schema)``
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so the model returns a typed Pydantic instance. If the provider does
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not support structured output (rare; mostly older Ollama models), the
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wrap is skipped and the agent uses free-text generation instead.
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2. At invocation, run the structured call and render the result back to
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markdown. If the structured call itself fails for any reason
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(malformed JSON from a weak model, transient provider issue), fall
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back to a plain ``llm.invoke`` so the pipeline never blocks.
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Centralising the pattern here keeps the agent factories small and ensures
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all three agents log the same warnings when fallback fires.
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"""
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from __future__ import annotations
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import logging
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from collections.abc import Callable
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from typing import Any, TypeVar
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from pydantic import BaseModel
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logger = logging.getLogger(__name__)
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T = TypeVar("T", bound=BaseModel)
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def bind_structured(llm: Any, schema: type[T], agent_name: str) -> Any | None:
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"""Return ``llm.with_structured_output(schema)`` or ``None`` if unsupported.
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Logs a warning when the binding fails so the user understands the agent
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will use free-text generation for every call instead of one-shot fallback.
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"""
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try:
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return llm.with_structured_output(schema)
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except (NotImplementedError, AttributeError) as exc:
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logger.warning(
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"%s: provider does not support with_structured_output (%s); "
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"falling back to free-text generation",
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agent_name, exc,
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)
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return None
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def invoke_structured_or_freetext(
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structured_llm: Any | None,
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plain_llm: Any,
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prompt: Any,
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render: Callable[[T], str],
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agent_name: str,
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) -> str:
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"""Run the structured call and render to markdown; fall back to free-text on any failure.
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``prompt`` is whatever the underlying LLM accepts (a string for chat
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invocations, a list of message dicts for chat models that take that
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shape). The same value is forwarded to the free-text path so the
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fallback sees the same input the structured call did.
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"""
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if structured_llm is not None:
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try:
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result = structured_llm.invoke(prompt)
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if result is None:
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# A thinking model can answer in plain text instead of calling
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# the tool, leaving the parser with nothing to return. Treat it
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# as a structured miss and fall back, with a clear reason.
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raise ValueError("structured output returned no parsed result")
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return render(result)
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except Exception as exc:
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logger.warning(
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"%s: structured-output invocation failed (%s); retrying once as free text",
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agent_name, exc,
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)
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response = plain_llm.invoke(prompt)
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return response.content
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