core, community: propagate context between threads (#15171)

While using `chain.batch`, the default implementation uses a
`ThreadPoolExecutor` and run the chains in separate threads. An issue
with this approach is that that [the token counting
callback](https://python.langchain.com/docs/modules/callbacks/token_counting)
fails to work as a consequence of the context not being propagated
between threads. This PR adds context propagation to the new threads and
adds some thread synchronization in the OpenAI callback. With this
change, the token counting callback works as intended.

Having the context propagation change would be highly beneficial for
those implementing custom callbacks for similar functionalities as well.

---------

Co-authored-by: Nuno Campos <nuno@langchain.dev>

libs/community/langchain_community/callbacks/openai_info.py

@@ -1,4 +1,5 @@
 """Callback Handler that prints to std out."""
+import threading
 from typing import Any, Dict, List
 
 from langchain_core.callbacks import BaseCallbackHandler
@@ -154,6 +155,10 @@ class OpenAICallbackHandler(BaseCallbackHandler):
     successful_requests: int = 0
     total_cost: float = 0.0
 
+    def __init__(self) -> None:
+        super().__init__()
+        self._lock = threading.Lock()
+
     def __repr__(self) -> str:
         return (
             f"Tokens Used: {self.total_tokens}\n"
@@ -182,9 +187,13 @@ class OpenAICallbackHandler(BaseCallbackHandler):
         """Collect token usage."""
         if response.llm_output is None:
             return None
-        self.successful_requests += 1
+
         if "token_usage" not in response.llm_output:
+            with self._lock:
+                self.successful_requests += 1
             return None
+
+        # compute tokens and cost for this request
         token_usage = response.llm_output["token_usage"]
         completion_tokens = token_usage.get("completion_tokens", 0)
         prompt_tokens = token_usage.get("prompt_tokens", 0)
@@ -194,10 +203,17 @@ class OpenAICallbackHandler(BaseCallbackHandler):
                 model_name, completion_tokens, is_completion=True
             )
             prompt_cost = get_openai_token_cost_for_model(model_name, prompt_tokens)
+        else:
+            completion_cost = 0
+            prompt_cost = 0
+
+        # update shared state behind lock
+        with self._lock:
             self.total_cost += prompt_cost + completion_cost
-        self.total_tokens += token_usage.get("total_tokens", 0)
-        self.prompt_tokens += prompt_tokens
-        self.completion_tokens += completion_tokens
+            self.total_tokens += token_usage.get("total_tokens", 0)
+            self.prompt_tokens += prompt_tokens
+            self.completion_tokens += completion_tokens
+            self.successful_requests += 1
 
     def __copy__(self) -> "OpenAICallbackHandler":
         """Return a copy of the callback handler."""

libs/core/langchain_core/callbacks/manager.py

@@ -6,6 +6,7 @@ import logging
 import uuid
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import asynccontextmanager, contextmanager
+from contextvars import Context, copy_context
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -271,12 +272,25 @@ def handle_event(
                 # we end up in a deadlock, as we'd have gotten here from a
                 # running coroutine, which we cannot interrupt to run this one.
                 # The solution is to create a new loop in a new thread.
-                with ThreadPoolExecutor(1) as executor:
+                with _executor_w_context(1) as executor:
                     executor.submit(_run_coros, coros).result()
             else:
                 _run_coros(coros)
 
 
+def _set_context(context: Context) -> None:
+    for var, value in context.items():
+        var.set(value)
+
+
+def _executor_w_context(max_workers: Optional[int] = None) -> ThreadPoolExecutor:
+    return ThreadPoolExecutor(
+        max_workers=max_workers,
+        initializer=_set_context,
+        initargs=(copy_context(),),
+    )
+
+
 def _run_coros(coros: List[Coroutine[Any, Any, Any]]) -> None:
     if hasattr(asyncio, "Runner"):
         # Python 3.11+
@@ -301,6 +315,7 @@ def _run_coros(coros: List[Coroutine[Any, Any, Any]]) -> None:
 
 
 async def _ahandle_event_for_handler(
+    executor: ThreadPoolExecutor,
     handler: BaseCallbackHandler,
     event_name: str,
     ignore_condition_name: Optional[str],
@@ -317,12 +332,13 @@ async def _ahandle_event_for_handler(
                     event(*args, **kwargs)
                 else:
                     await asyncio.get_event_loop().run_in_executor(
-                        None, functools.partial(event, *args, **kwargs)
+                        executor, functools.partial(event, *args, **kwargs)
                     )
     except NotImplementedError as e:
         if event_name == "on_chat_model_start":
             message_strings = [get_buffer_string(m) for m in args[1]]
             await _ahandle_event_for_handler(
+                executor,
                 handler,
                 "on_llm_start",
                 "ignore_llm",
@@ -364,19 +380,25 @@ async def ahandle_event(
         *args: The arguments to pass to the event handler
         **kwargs: The keyword arguments to pass to the event handler
     """
-    for handler in [h for h in handlers if h.run_inline]:
-        await _ahandle_event_for_handler(
-            handler, event_name, ignore_condition_name, *args, **kwargs
-        )
-    await asyncio.gather(
-        *(
-            _ahandle_event_for_handler(
-                handler, event_name, ignore_condition_name, *args, **kwargs
+    with _executor_w_context() as executor:
+        for handler in [h for h in handlers if h.run_inline]:
+            await _ahandle_event_for_handler(
+                executor, handler, event_name, ignore_condition_name, *args, **kwargs
+            )
+        await asyncio.gather(
+            *(
+                _ahandle_event_for_handler(
+                    executor,
+                    handler,
+                    event_name,
+                    ignore_condition_name,
+                    *args,
+                    **kwargs,
+                )
+                for handler in handlers
+                if not handler.run_inline
             )
-            for handler in handlers
-            if not handler.run_inline
         )
-    )
 
 
 BRM = TypeVar("BRM", bound="BaseRunManager")

libs/core/langchain_core/language_models/chat_models.py

@@ -260,7 +260,8 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         if type(self)._astream == BaseChatModel._astream:
             # model doesn't implement streaming, so use default implementation
             yield cast(
-                BaseMessageChunk, self.invoke(input, config=config, stop=stop, **kwargs)
+                BaseMessageChunk,
+                await self.ainvoke(input, config=config, stop=stop, **kwargs),
             )
         else:
             config = config or {}

libs/core/langchain_core/runnables/base.py

@@ -472,9 +472,10 @@ class Runnable(Generic[Input, Output], ABC):
 
         Subclasses should override this method if they can run asynchronously.
         """
-        return await asyncio.get_running_loop().run_in_executor(
-            None, partial(self.invoke, **kwargs), input, config
-        )
+        with get_executor_for_config(config) as executor:
+            return await asyncio.get_running_loop().run_in_executor(
+                executor, partial(self.invoke, **kwargs), input, config
+            )
 
     def batch(
         self,
@@ -2882,9 +2883,10 @@ class RunnableLambda(Runnable[Input, Output]):
 
             @wraps(self.func)
             async def f(*args, **kwargs):  # type: ignore[no-untyped-def]
-                return await asyncio.get_running_loop().run_in_executor(
-                    None, partial(self.func, **kwargs), *args
-                )
+                with get_executor_for_config(config) as executor:
+                    return await asyncio.get_running_loop().run_in_executor(
+                        executor, partial(self.func, **kwargs), *args
+                    )
 
             afunc = f
 

libs/core/langchain_core/runnables/config.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 from concurrent.futures import Executor, ThreadPoolExecutor
 from contextlib import contextmanager
+from contextvars import Context, copy_context
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -387,8 +388,15 @@ def get_async_callback_manager_for_config(
     )
 
 
+def _set_context(context: Context) -> None:
+    for var, value in context.items():
+        var.set(value)
+
+
 @contextmanager
-def get_executor_for_config(config: RunnableConfig) -> Generator[Executor, None, None]:
+def get_executor_for_config(
+    config: Optional[RunnableConfig]
+) -> Generator[Executor, None, None]:
     """Get an executor for a config.
 
     Args:
@@ -397,5 +405,10 @@ def get_executor_for_config(config: RunnableConfig) -> Generator[Executor, None,
     Yields:
         Generator[Executor, None, None]: The executor.
     """
-    with ThreadPoolExecutor(max_workers=config.get("max_concurrency")) as executor:
+    config = config or {}
+    with ThreadPoolExecutor(
+        max_workers=config.get("max_concurrency"),
+        initializer=_set_context,
+        initargs=(copy_context(),),
+    ) as executor:
         yield executor