feat(mistralai): native content-block streaming events

libs/partners/mistralai/langchain_mistralai/_stream_events.py

@@ -0,0 +1,153 @@
+"""Native content-block streaming-event converter for MistralAI.
+
+Mirrors `ChatMistralAI._stream`: threads `(index, index_type, default_class)`
+through `_convert_chunk_to_message_chunk` (injected to avoid a circular import)
+and feeds each resulting `AIMessageChunk`'s content blocks into the shared
+`BlockStreamTracker`.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any
+
+from langchain_core.language_models.stream_events import (
+    BlockStreamTracker,
+    accumulate_usage,
+    build_message_finish,
+    iter_protocol_blocks,
+)
+from langchain_core.messages import AIMessageChunk
+
+if TYPE_CHECKING:
+    from collections.abc import AsyncIterator, Iterator
+
+    from langchain_core.messages import BaseMessageChunk
+    from langchain_protocol.protocol import (
+        MessageMetadata,
+        MessagesData,
+        MessageStartData,
+    )
+
+# The module-level `_convert_chunk_to_message_chunk`, injected so the converter
+# stays pure and avoids a circular import. It takes a raw chunk plus the running
+# `default_class`, `index`, and `index_type`, returning the built chunk and the
+# updated index/index_type.
+ConvertChunk = Callable[..., "tuple[BaseMessageChunk, int, str]"]
+
+
+def _message_start(message_id: str | None, model: str | None) -> MessageStartData:
+    # Do not use the provider chunk id here: on the v3 path core seeds the
+    # stream with the LangChain run id, and an empty id lets that stand
+    # (matching the compat bridge). Only an explicit `message_id` wins.
+    metadata: MessageMetadata = {"provider": "mistralai"}
+    if model:
+        metadata["model"] = model
+    return {
+        "event": "message-start",
+        "role": "ai",
+        "id": message_id or "",
+        "metadata": metadata,
+    }
+
+
+def convert_mistral_stream(
+    raw: Iterator[Any],
+    convert_chunk: ConvertChunk,
+    *,
+    output_version: str | None = None,
+    message_id: str | None = None,
+) -> Iterator[MessagesData]:
+    """Convert a raw Mistral chat stream to protocol events.
+
+    Args:
+        raw: Raw Mistral chat-completion chunks (OpenAI-shaped dicts).
+        convert_chunk: `_convert_chunk_to_message_chunk`, injected so the
+            converter stays pure and avoids a circular import.
+        output_version: Forwarded to `convert_chunk`; reasoning blocks only
+            surface under `"v1"`.
+        message_id: Overrides the id on `message-start`.
+
+    Yields:
+        Protocol `MessagesData` lifecycle events.
+    """
+    tracker = BlockStreamTracker()
+    started = False
+    index = -1
+    index_type = ""
+    default_class: type[BaseMessageChunk] = AIMessageChunk
+    usage: dict[str, Any] | None = None
+    response_metadata: dict[str, Any] = {"model_provider": "mistralai"}
+    model: str | None = None
+
+    for chunk in raw:
+        if len(chunk.get("choices", [])) == 0:
+            continue
+        if model is None:
+            model = chunk.get("model")
+        new_chunk, index, index_type = convert_chunk(
+            chunk, default_class, index, index_type, output_version
+        )
+        # Make future chunks the same type as the first chunk.
+        default_class = new_chunk.__class__
+        if not started:
+            started = True
+            yield _message_start(message_id, model)
+        if isinstance(new_chunk, AIMessageChunk):
+            for key, block in iter_protocol_blocks(new_chunk):
+                yield from tracker.feed(key, block)
+            if new_chunk.usage_metadata:
+                usage = accumulate_usage(usage, new_chunk.usage_metadata)
+            if new_chunk.response_metadata:
+                response_metadata.update(new_chunk.response_metadata)
+
+    if not started:
+        return
+    yield from tracker.finish_all()
+    yield build_message_finish(usage=usage, response_metadata=response_metadata)
+
+
+async def aconvert_mistral_stream(
+    raw: AsyncIterator[Any],
+    convert_chunk: ConvertChunk,
+    *,
+    output_version: str | None = None,
+    message_id: str | None = None,
+) -> AsyncIterator[MessagesData]:
+    """Async twin of `convert_mistral_stream`. `convert_chunk` is sync."""
+    tracker = BlockStreamTracker()
+    started = False
+    index = -1
+    index_type = ""
+    default_class: type[BaseMessageChunk] = AIMessageChunk
+    usage: dict[str, Any] | None = None
+    response_metadata: dict[str, Any] = {"model_provider": "mistralai"}
+    model: str | None = None
+
+    async for chunk in raw:
+        if len(chunk.get("choices", [])) == 0:
+            continue
+        if model is None:
+            model = chunk.get("model")
+        new_chunk, index, index_type = convert_chunk(
+            chunk, default_class, index, index_type, output_version
+        )
+        # Make future chunks the same type as the first chunk.
+        default_class = new_chunk.__class__
+        if not started:
+            started = True
+            yield _message_start(message_id, model)
+        if isinstance(new_chunk, AIMessageChunk):
+            for key, block in iter_protocol_blocks(new_chunk):
+                for ev in tracker.feed(key, block):
+                    yield ev
+            if new_chunk.usage_metadata:
+                usage = accumulate_usage(usage, new_chunk.usage_metadata)
+            if new_chunk.response_metadata:
+                response_metadata.update(new_chunk.response_metadata)
+
+    if not started:
+        return
+    for ev in tracker.finish_all():
+        yield ev
+    yield build_message_finish(usage=usage, response_metadata=response_metadata)

libs/partners/mistralai/langchain_mistralai/chat_models.py

@@ -85,6 +85,8 @@
     from collections.abc import AsyncIterator, Iterator
     from contextlib import AbstractAsyncContextManager
 
+    from langchain_protocol.protocol import MessagesData
+
 logger = logging.getLogger(__name__)
 
 # Mistral enforces a specific pattern for tool call IDs
@@ -830,6 +832,78 @@ async def _astream(
                 )
             yield gen_chunk
 
+    def _stream_chat_model_events(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        *,
+        message_id: str | None = None,
+        **kwargs: Any,
+    ) -> Iterator[MessagesData]:
+        """Emit Mistral-native content-block protocol events.
+
+        Detected by `langchain-core`'s `_iter_v2_events`; powers
+        `stream_events(version="v3")`. Falls through to the compat bridge
+        only if this method is absent. `message_id` is threaded from the
+        stream so `message-start` matches the bridge's LangChain run id.
+        """
+        # Local import avoids a circular import: `_stream_events` imports
+        # `_convert_chunk_to_message_chunk` from this module.
+        from langchain_mistralai._stream_events import convert_mistral_stream
+
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs, "stream": True}
+        raw = self.completion_with_retry(
+            messages=message_dicts, run_manager=run_manager, **params
+        )
+        for event in convert_mistral_stream(
+            raw,
+            _convert_chunk_to_message_chunk,
+            output_version=self.output_version,
+            message_id=message_id,
+        ):
+            if (
+                run_manager is not None
+                and event["event"] == "content-block-delta"
+                and event["delta"].get("type") == "text-delta"
+            ):
+                run_manager.on_llm_new_token(str(event["delta"].get("text", "")))
+            yield event
+
+    async def _astream_chat_model_events(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: AsyncCallbackManagerForLLMRun | None = None,
+        *,
+        message_id: str | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[MessagesData]:
+        """Async twin of `_stream_chat_model_events`."""
+        # Local import avoids a circular import: `_stream_events` imports
+        # `_convert_chunk_to_message_chunk` from this module.
+        from langchain_mistralai._stream_events import aconvert_mistral_stream
+
+        message_dicts, params = self._create_message_dicts(messages, stop)
+        params = {**params, **kwargs, "stream": True}
+        raw = await acompletion_with_retry(
+            self, messages=message_dicts, run_manager=run_manager, **params
+        )
+        async for event in aconvert_mistral_stream(
+            raw,
+            _convert_chunk_to_message_chunk,
+            output_version=self.output_version,
+            message_id=message_id,
+        ):
+            if (
+                run_manager is not None
+                and event["event"] == "content-block-delta"
+                and event["delta"].get("type") == "text-delta"
+            ):
+                await run_manager.on_llm_new_token(str(event["delta"].get("text", "")))
+            yield event
+
     async def _agenerate(
         self,
         messages: list[BaseMessage],