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LangChain Runnable adapter for BaseLLM.
Wraps the custom BaseLLM so LangGraph's astream_events(version="v2") can
capture per-token deltas from the generate_answer node.
Providers that implement real stream() (Ollama, OpenAI/DeepSeek) yield genuine
tokens. vLLM falls back to yielding the full response as a single chunk.
"""
from __future__ import annotations
import asyncio
import logging
import os
from typing import Any, AsyncIterator, Iterator, Optional
from langchain_core.runnables import Runnable
from langchain_core.runnables.config import RunnableConfig
logger = logging.getLogger(__name__)
class BaseLLMRunnable(Runnable):
"""Expose BaseLLM as a LangChain Runnable for astream_events token capture.
LangGraph's astream_events(version="v2") wraps our stream() generator
automatically and emits on_chain_stream events for each yielded token —
no manual callback management needed.
"""
def __init__(self, llm: Any, **defaults: Any) -> None:
self._llm = llm
self._defaults = defaults
# ── Sync paths ──────────────────────────────────────────────────────────
def invoke(
self,
input: str,
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> str:
params = {**self._defaults, **kwargs}
return self._llm.generate(user_prompt=input, **params)
def stream(
self,
input: str,
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> Iterator[str]:
params = {**self._defaults, **kwargs}
yield from self._llm.stream(user_prompt=input, **params)
# ── Async paths ─────────────────────────────────────────────────────────
async def ainvoke(
self,
input: str,
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> str:
params = {**self._defaults, **kwargs}
return await asyncio.to_thread(self._llm.generate, user_prompt=input, **params)
async def astream(
self,
input: str,
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> AsyncIterator[str]:
def _collect():
return list(self.stream(input, config, **kwargs))
tokens = await asyncio.get_event_loop().run_in_executor(None, _collect)
for token in tokens:
yield token
def get_chat_model(llm_client: Any) -> Any:
"""
Return a LangChain BaseChatModel supporting .bind_tools() for ReAct use.
Feature-detects provider from llm_client.get_model_info() and instantiates
the appropriate LangChain chat model with temperature=0. Returns None if the
provider has no LangChain adapter or if the import fails — callers fall back
to the legacy hardcoded retrieve node in that case.
"""
if llm_client is None:
return None
try:
cfg = llm_client.get_model_info()
except Exception:
cfg = {}
provider = (cfg.get("provider") or "").lower()
model = cfg.get("model_name") or ""
base_url = cfg.get("base_url") or os.environ.get("LLM_BASE_URL", "")
api_key = cfg.get("api_key") or os.environ.get("LLM_API_KEY") or os.environ.get("OPENAI_API_KEY", "")
try:
if provider == "anthropic":
from langchain_anthropic import ChatAnthropic
return ChatAnthropic(model=model, temperature=0, api_key=api_key or None)
if provider == "openai":
from langchain_openai import ChatOpenAI
kwargs: dict = {"model": model, "temperature": 0}
if api_key:
kwargs["api_key"] = api_key
if base_url:
kwargs["base_url"] = base_url
# DeepSeek v4 thinking mode requires reasoning_content forwarded in every
# follow-up turn — create_react_agent doesn't do that, causing 400 errors.
# Use extra_body to disable thinking at the HTTP request level.
if "deepseek" in base_url.lower() or "deepseek" in model.lower():
kwargs["extra_body"] = {"thinking": {"type": "disabled"}}
return ChatOpenAI(**kwargs)
except ImportError as e:
logger.warning("LangChain chat-model adapter unavailable for provider=%r: %s", provider, e)
except Exception as e:
logger.warning("Failed to build chat model for provider=%r: %s", provider, e)
return None
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