MCP Wikipedia Server

from __future__ import annotations from collections.abc import AsyncIterator, Iterator, Sequence from typing import ( Any, Callable, TypeVar, cast, ) from uuid import UUID, uuid4 from langchain_core.callbacks import BaseCallbackHandler from langchain_core.messages import BaseMessage from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, LLMResult from langgraph._internal._constants import NS_SEP from langgraph.constants import TAG_HIDDEN, TAG_NOSTREAM from langgraph.pregel.protocol import StreamChunk from langgraph.types import Command try: from langchain_core.tracers._streaming import _StreamingCallbackHandler except ImportError: _StreamingCallbackHandler = object # type: ignore T = TypeVar("T") Meta = tuple[tuple[str, ...], dict[str, Any]] class StreamMessagesHandler(BaseCallbackHandler, _StreamingCallbackHandler): """A callback handler that implements stream_mode=messages. Collects messages from (1) chat model stream events and (2) node outputs.""" run_inline = True """We want this callback to run in the main thread, to avoid order/locking issues.""" def __init__(self, stream: Callable[[StreamChunk], None], subgraphs: bool): self.stream = stream self.subgraphs = subgraphs self.metadata: dict[UUID, Meta] = {} self.seen: set[int | str] = set() def _emit(self, meta: Meta, message: BaseMessage, *, dedupe: bool = False) -> None: if dedupe and message.id in self.seen: return else: if message.id is None: message.id = str(uuid4()) self.seen.add(message.id) self.stream((meta[0], "messages", (message, meta[1]))) def _find_and_emit_messages(self, meta: Meta, response: Any) -> None: if isinstance(response, BaseMessage): self._emit(meta, response, dedupe=True) elif isinstance(response, Sequence): for value in response: if isinstance(value, BaseMessage): self._emit(meta, value, dedupe=True) elif isinstance(response, dict): for value in response.values(): if isinstance(value, BaseMessage): self._emit(meta, value, dedupe=True) elif isinstance(value, Sequence): for item in value: if isinstance(item, BaseMessage): self._emit(meta, item, dedupe=True) elif hasattr(response, "__dir__") and callable(response.__dir__): for key in dir(response): try: value = getattr(response, key) if isinstance(value, BaseMessage): self._emit(meta, value, dedupe=True) elif isinstance(value, Sequence): for item in value: if isinstance(item, BaseMessage): self._emit(meta, item, dedupe=True) except AttributeError: pass def tap_output_aiter( self, run_id: UUID, output: AsyncIterator[T] ) -> AsyncIterator[T]: return output def tap_output_iter(self, run_id: UUID, output: Iterator[T]) -> Iterator[T]: return output def on_chat_model_start( self, serialized: dict[str, Any], messages: list[list[BaseMessage]], *, run_id: UUID, parent_run_id: UUID | None = None, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, **kwargs: Any, ) -> Any: if metadata and (not tags or (TAG_NOSTREAM not in tags)): ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))[ :-1 ] if not self.subgraphs and len(ns) > 0: return if tags: if filtered_tags := [t for t in tags if not t.startswith("seq:step")]: metadata["tags"] = filtered_tags self.metadata[run_id] = (ns, metadata) def on_llm_new_token( self, token: str, *, chunk: ChatGenerationChunk | None = None, run_id: UUID, parent_run_id: UUID | None = None, tags: list[str] | None = None, **kwargs: Any, ) -> Any: if not isinstance(chunk, ChatGenerationChunk): return if meta := self.metadata.get(run_id): self._emit(meta, chunk.message) def on_llm_end( self, response: LLMResult, *, run_id: UUID, parent_run_id: UUID | None = None, **kwargs: Any, ) -> Any: if meta := self.metadata.get(run_id): if response.generations and response.generations[0]: gen = response.generations[0][0] if isinstance(gen, ChatGeneration): self._emit(meta, gen.message, dedupe=True) self.metadata.pop(run_id, None) def on_llm_error( self, error: BaseException, *, run_id: UUID, parent_run_id: UUID | None = None, **kwargs: Any, ) -> Any: self.metadata.pop(run_id, None) def on_chain_start( self, serialized: dict[str, Any], inputs: dict[str, Any], *, run_id: UUID, parent_run_id: UUID | None = None, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, **kwargs: Any, ) -> Any: if ( metadata and kwargs.get("name") == metadata.get("langgraph_node") and (not tags or TAG_HIDDEN not in tags) ): ns = tuple(cast(str, metadata["langgraph_checkpoint_ns"]).split(NS_SEP))[ :-1 ] if not self.subgraphs and len(ns) > 0: return self.metadata[run_id] = (ns, metadata) if isinstance(inputs, dict): for key, value in inputs.items(): if isinstance(value, BaseMessage): if value.id is not None: self.seen.add(value.id) elif isinstance(value, Sequence) and not isinstance(value, str): for item in value: if isinstance(item, BaseMessage): if item.id is not None: self.seen.add(item.id) def on_chain_end( self, response: Any, *, run_id: UUID, parent_run_id: UUID | None = None, **kwargs: Any, ) -> Any: if meta := self.metadata.pop(run_id, None): # Handle Command node updates if isinstance(response, Command): self._find_and_emit_messages(meta, response.update) # Handle list of Command updates elif isinstance(response, Sequence) and any( isinstance(value, Command) for value in response ): for value in response: if isinstance(value, Command): self._find_and_emit_messages(meta, value.update) else: self._find_and_emit_messages(meta, value) # Handle basic updates / streaming else: self._find_and_emit_messages(meta, response) def on_chain_error( self, error: BaseException, *, run_id: UUID, parent_run_id: UUID | None = None, **kwargs: Any, ) -> Any: self.metadata.pop(run_id, None)