start on openai

libs/core/langchain_core/messages/utils.py

@@ -307,7 +307,7 @@ def _convert_from_v1_message(message: MessageV1) -> BaseMessage:
             id=message.id,
             name=message.name,
             tool_calls=message.tool_calls,
-            response_metadata=cast(dict, message.response_metadata),
+            response_metadata=cast("dict", message.response_metadata),
         )
     if isinstance(message, AIMessageChunkV1):
         return AIMessageChunk(
@@ -315,7 +315,7 @@ def _convert_from_v1_message(message: MessageV1) -> BaseMessage:
             id=message.id,
             name=message.name,
             tool_call_chunks=message.tool_call_chunks,
-            response_metadata=cast(dict, message.response_metadata),
+            response_metadata=cast("dict", message.response_metadata),
         )
     if isinstance(message, HumanMessageV1):
         return HumanMessage(

libs/core/langchain_core/messages/v1.py

@@ -5,6 +5,8 @@ import uuid
 from dataclasses import dataclass, field
 from typing import Any, Literal, Optional, TypedDict, Union, cast, get_args
 
+from pydantic import BaseModel
+
 import langchain_core.messages.content_blocks as types
 from langchain_core.messages.ai import _LC_ID_PREFIX, UsageMetadata, add_usage
 from langchain_core.messages.base import merge_content
@@ -32,20 +34,20 @@ def _ensure_id(id_val: Optional[str]) -> str:
     return id_val or str(uuid.uuid4())
 
 
-class Provider(TypedDict):
+class ResponseMetadata(TypedDict, total=False):
-    """Information about the provider that generated the message.
+    """Metadata about the response from the AI provider.
 
-    Contains metadata about the AI provider and model used to generate content.
+    Contains additional information returned by the provider, such as
+    response headers, service tiers, log probabilities, system fingerprints, etc.
 
-    Attributes:
+    Extra keys are permitted from what is typed here.
-        name: Name and version of the provider that created the content block.
-        model_name: Name of the model that generated the content block.
     """
 
-    name: str
+    model_provider: str
-    """Name and version of the provider that created the content block."""
+    """Name and version of the provider that created the message (e.g., openai)."""
+
     model_name: str
-    """Name of the model that generated the content block."""
+    """Name of the model that generated the message."""
 
 
 @dataclass
@@ -91,21 +93,29 @@ class AIMessage:
     usage_metadata: Optional[UsageMetadata] = None
     """If provided, usage metadata for a message, such as token counts."""
 
-    response_metadata: dict = field(default_factory=dict)
+    response_metadata: ResponseMetadata = field(
+        default_factory=lambda: ResponseMetadata()
+    )
     """Metadata about the response.
 
     This field should include non-standard data returned by the provider, such as
     response headers, service tiers, or log probabilities.
     """
 
+    parsed: Optional[Union[dict[str, Any], BaseModel]] = None
+    """Auto-parsed message contents, if applicable."""
+
     def __init__(
         self,
         content: Union[str, list[types.ContentBlock]],
         id: Optional[str] = None,
         name: Optional[str] = None,
         lc_version: str = "v1",
-        response_metadata: Optional[dict] = None,
+        response_metadata: Optional[ResponseMetadata] = None,
         usage_metadata: Optional[UsageMetadata] = None,
+        tool_calls: Optional[list[types.ToolCall]] = None,
+        invalid_tool_calls: Optional[list[types.InvalidToolCall]] = None,
+        parsed: Optional[Union[dict[str, Any], BaseModel]] = None,
     ):
         """Initialize an AI message.
 
@@ -116,6 +126,11 @@ class AIMessage:
             lc_version: Encoding version for the message.
             response_metadata: Optional metadata about the response.
             usage_metadata: Optional metadata about token usage.
+            tool_calls: Optional list of tool calls made by the AI. Tool calls should
+                generally be included in message content. If passed on init, they will
+                be added to the content list.
+            invalid_tool_calls: Optional list of tool calls that failed validation.
+            parsed: Optional auto-parsed message contents, if applicable.
         """
         if isinstance(content, str):
             self.content = [{"type": "text", "text": content}]
@@ -126,13 +141,27 @@ class AIMessage:
         self.name = name
         self.lc_version = lc_version
         self.usage_metadata = usage_metadata
+        self.parsed = parsed
         if response_metadata is None:
             self.response_metadata = {}
         else:
             self.response_metadata = response_metadata
 
-        self._tool_calls: list[types.ToolCall] = []
+        # Add tool calls to content if provided on init
-        self._invalid_tool_calls: list[types.InvalidToolCall] = []
+        if tool_calls:
+            content_tool_calls = {
+                block["id"]
+                for block in self.content
+                if block["type"] == "tool_call" and "id" in block
+            }
+            for tool_call in tool_calls:
+                if "id" in tool_call and tool_call["id"] in content_tool_calls:
+                    continue
+                self.content.append(tool_call)
+        self._tool_calls = [
+            block for block in self.content if block["type"] == "tool_call"
+        ]
+        self.invalid_tool_calls = invalid_tool_calls or []
 
     @property
     def text(self) -> Optional[str]:
@@ -150,7 +179,7 @@ class AIMessage:
         tool_calls = [block for block in self.content if block["type"] == "tool_call"]
         if tool_calls:
             self._tool_calls = tool_calls
-        return self._tool_calls
+        return [block for block in self.content if block["type"] == "tool_call"]
 
     @tool_calls.setter
     def tool_calls(self, value: list[types.ToolCall]) -> None:
@@ -202,13 +231,16 @@ class AIMessageChunk:
     These data represent incremental usage statistics, as opposed to a running total.
     """
 
-    response_metadata: dict = field(init=False)
+    response_metadata: ResponseMetadata = field(init=False)
     """Metadata about the response chunk.
 
     This field should include non-standard data returned by the provider, such as
     response headers, service tiers, or log probabilities.
     """
 
+    parsed: Optional[Union[dict[str, Any], BaseModel]] = None
+    """Auto-parsed message contents, if applicable."""
+
     tool_call_chunks: list[types.ToolCallChunk] = field(init=False)
 
     def __init__(
@@ -217,9 +249,10 @@ class AIMessageChunk:
         id: Optional[str] = None,
         name: Optional[str] = None,
         lc_version: str = "v1",
-        response_metadata: Optional[dict] = None,
+        response_metadata: Optional[ResponseMetadata] = None,
         usage_metadata: Optional[UsageMetadata] = None,
         tool_call_chunks: Optional[list[types.ToolCallChunk]] = None,
+        parsed: Optional[Union[dict[str, Any], BaseModel]] = None,
     ):
         """Initialize an AI message.
 
@@ -231,6 +264,7 @@ class AIMessageChunk:
             response_metadata: Optional metadata about the response.
             usage_metadata: Optional metadata about token usage.
             tool_call_chunks: Optional list of partial tool call data.
+            parsed: Optional auto-parsed message contents, if applicable.
         """
         if isinstance(content, str):
             self.content = [{"type": "text", "text": content, "index": 0}]
@@ -241,6 +275,7 @@ class AIMessageChunk:
         self.name = name
         self.lc_version = lc_version
         self.usage_metadata = usage_metadata
+        self.parsed = parsed
         if response_metadata is None:
             self.response_metadata = {}
         else:
@@ -251,7 +286,7 @@ class AIMessageChunk:
             self.tool_call_chunks = tool_call_chunks
 
         self._tool_calls: list[types.ToolCall] = []
-        self._invalid_tool_calls: list[types.InvalidToolCall] = []
+        self.invalid_tool_calls: list[types.InvalidToolCall] = []
         self._init_tool_calls()
 
     def _init_tool_calls(self) -> None:
@@ -264,7 +299,7 @@ class AIMessageChunk:
             ValueError: If the tool call chunks are malformed.
         """
         self._tool_calls = []
-        self._invalid_tool_calls = []
+        self.invalid_tool_calls = []
         if not self.tool_call_chunks:
             if self._tool_calls:
                 self.tool_call_chunks = [
@@ -276,14 +311,14 @@ class AIMessageChunk:
                     )
                     for tc in self._tool_calls
                 ]
-            if self._invalid_tool_calls:
+            if self.invalid_tool_calls:
                 tool_call_chunks = self.tool_call_chunks
                 tool_call_chunks.extend(
                     [
                         create_tool_call_chunk(
                             name=tc["name"], args=tc["args"], id=tc["id"], index=None
                         )
-                        for tc in self._invalid_tool_calls
+                        for tc in self.invalid_tool_calls
                     ]
                 )
                 self.tool_call_chunks = tool_call_chunks
@@ -317,7 +352,7 @@ class AIMessageChunk:
             except Exception:
                 add_chunk_to_invalid_tool_calls(chunk)
         self._tool_calls = tool_calls
-        self._invalid_tool_calls = invalid_tool_calls
+        self.invalid_tool_calls = invalid_tool_calls
 
     @property
     def text(self) -> Optional[str]:
@@ -361,6 +396,20 @@ class AIMessageChunk:
         error_msg = "Can only add AIMessageChunk or sequence of AIMessageChunk."
         raise NotImplementedError(error_msg)
 
+    def to_message(self) -> "AIMessage":
+        """Convert this AIMessageChunk to an AIMessage."""
+        return AIMessage(
+            content=self.content,
+            id=self.id,
+            name=self.name,
+            lc_version=self.lc_version,
+            response_metadata=self.response_metadata,
+            usage_metadata=self.usage_metadata,
+            tool_calls=self.tool_calls,
+            invalid_tool_calls=self.invalid_tool_calls,
+            parsed=self.parsed,
+        )
+
 
 def add_ai_message_chunks(
     left: AIMessageChunk, *others: AIMessageChunk
@@ -371,7 +420,8 @@ def add_ai_message_chunks(
         *(cast("list[str | dict[Any, Any]]", o.content) for o in others),
     )
     response_metadata = merge_dicts(
-        left.response_metadata, *(o.response_metadata for o in others)
+        cast("dict", left.response_metadata),
+        *(cast("dict", o.response_metadata) for o in others),
     )
 
     # Merge tool call chunks
@@ -398,6 +448,15 @@ def add_ai_message_chunks(
     else:
         usage_metadata = None
 
+    # Parsed
+    # 'parsed' always represents an aggregation not an incremental value, so the last
+    # non-null value is kept.
+    parsed = None
+    for m in reversed([left, *others]):
+        if m.parsed is not None:
+            parsed = m.parsed
+            break
+
     chunk_id = None
     candidates = [left.id] + [o.id for o in others]
     # first pass: pick the first non-run-* id
@@ -415,8 +474,9 @@ def add_ai_message_chunks(
     return left.__class__(
         content=cast("list[types.ContentBlock]", content),
         tool_call_chunks=tool_call_chunks,
-        response_metadata=response_metadata,
+        response_metadata=cast("ResponseMetadata", response_metadata),
         usage_metadata=usage_metadata,
+        parsed=parsed,
         id=chunk_id,
     )
 
@@ -453,19 +513,25 @@ class HumanMessage:
     """
 
     def __init__(
-        self, content: Union[str, list[types.ContentBlock]], id: Optional[str] = None
+        self,
+        content: Union[str, list[types.ContentBlock]],
+        *,
+        id: Optional[str] = None,
+        name: Optional[str] = None,
     ):
         """Initialize a human message.
 
         Args:
             content: Message content as string or list of content blocks.
             id: Optional unique identifier for the message.
+            name: Optional human-readable name for the message.
         """
         self.id = _ensure_id(id)
         if isinstance(content, str):
             self.content = [{"type": "text", "text": content}]
         else:
             self.content = content
+        self.name = name
 
     def text(self) -> str:
         """Extract all text content from the message.
@@ -495,20 +561,47 @@ class SystemMessage:
     content: list[types.ContentBlock]
     type: Literal["system"] = "system"
 
+    name: Optional[str] = None
+    """An optional name for the message.
+
+    This can be used to provide a human-readable name for the message.
+
+    Usage of this field is optional, and whether it's used or not is up to the
+    model implementation.
+    """
+
+    custom_role: Optional[str] = None
+    """If provided, a custom role for the system message.
+
+    Example: ``"developer"``.
+
+    Integration packages may use this field to assign the system message role if it
+    contains a recognized value.
+    """
+
     def __init__(
-        self, content: Union[str, list[types.ContentBlock]], *, id: Optional[str] = None
+        self,
+        content: Union[str, list[types.ContentBlock]],
+        *,
+        id: Optional[str] = None,
+        custom_role: Optional[str] = None,
+        name: Optional[str] = None,
     ):
-        """Initialize a system message.
+        """Initialize a human message.
 
         Args:
-            content: System instructions as string or list of content blocks.
+            content: Message content as string or list of content blocks.
             id: Optional unique identifier for the message.
+            custom_role: If provided, a custom role for the system message.
+            name: Optional human-readable name for the message.
         """
         self.id = _ensure_id(id)
         if isinstance(content, str):
             self.content = [{"type": "text", "text": content}]
         else:
             self.content = content
+        self.custom_role = custom_role
+        self.name = name
 
     def text(self) -> str:
         """Extract all text content from the system message."""
@@ -535,11 +628,51 @@ class ToolMessage:
 
     id: str
     tool_call_id: str
-    content: list[dict[str, Any]]
+    content: list[types.ContentBlock]
     artifact: Optional[Any] = None  # App-side payload not for the model
+
+    name: Optional[str] = None
+    """An optional name for the message.
+
+    This can be used to provide a human-readable name for the message.
+
+    Usage of this field is optional, and whether it's used or not is up to the
+    model implementation.
+    """
+
     status: Literal["success", "error"] = "success"
     type: Literal["tool"] = "tool"
 
+    def __init__(
+        self,
+        content: Union[str, list[types.ContentBlock]],
+        tool_call_id: str,
+        *,
+        id: Optional[str] = None,
+        name: Optional[str] = None,
+        artifact: Optional[Any] = None,
+        status: Literal["success", "error"] = "success",
+    ):
+        """Initialize a human message.
+
+        Args:
+            content: Message content as string or list of content blocks.
+            tool_call_id: ID of the tool call this message responds to.
+            id: Optional unique identifier for the message.
+            name: Optional human-readable name for the message.
+            artifact: Optional app-side payload not intended for the model.
+            status: Execution status ("success" or "error").
+        """
+        self.id = _ensure_id(id)
+        self.tool_call_id = tool_call_id
+        if isinstance(content, str):
+            self.content = [{"type": "text", "text": content}]
+        else:
+            self.content = content
+        self.name = name
+        self.artifact = artifact
+        self.status = status
+
     @property
     def text(self) -> str:
         """Extract all text content from the tool message."""

libs/core/langchain_core/prompt_values.py

@@ -14,16 +14,18 @@ from typing_extensions import TypedDict, overload
 
 from langchain_core.load.serializable import Serializable
 from langchain_core.messages import (
+    AIMessage,
     AnyMessage,
     BaseMessage,
     HumanMessage,
+    SystemMessage,
+    ToolMessage,
     get_buffer_string,
 )
 from langchain_core.messages import content_blocks as types
-from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
 from langchain_core.messages.v1 import AIMessage as AIMessageV1
 from langchain_core.messages.v1 import HumanMessage as HumanMessageV1
-from langchain_core.messages.v1 import MessageV1
+from langchain_core.messages.v1 import MessageV1, ResponseMetadata
 from langchain_core.messages.v1 import SystemMessage as SystemMessageV1
 from langchain_core.messages.v1 import ToolMessage as ToolMessageV1
 
@@ -40,7 +42,7 @@ def _convert_to_v1(message: BaseMessage) -> MessageV1:
             if isinstance(block, str):
                 content.append({"type": "text", "text": block})
             elif isinstance(block, dict):
-                content.append(block)
+                content.append(cast("types.ContentBlock", block))
             else:
                 pass
 
@@ -52,7 +54,7 @@ def _convert_to_v1(message: BaseMessage) -> MessageV1:
         return AIMessageV1(
             content=content,
             usage_metadata=message.usage_metadata,
-            response_metadata=message.response_metadata,
+            response_metadata=cast("ResponseMetadata", message.response_metadata),
             tool_calls=message.tool_calls,
         )
     if isinstance(message, SystemMessage):
@@ -92,8 +94,18 @@ class PromptValue(Serializable, ABC):
     def to_string(self) -> str:
         """Return prompt value as string."""
 
+    @overload
+    def to_messages(
+        self, output_version: Literal["v0"] = "v0"
+    ) -> list[BaseMessage]: ...
+
+    @overload
+    def to_messages(self, output_version: Literal["v1"]) -> list[MessageV1]: ...
+
     @abstractmethod
-    def to_messages(self) -> list[BaseMessage]:
+    def to_messages(
+        self, output_version: Literal["v0", "v1"] = "v0"
+    ) -> Union[Sequence[BaseMessage], Sequence[MessageV1]]:
         """Return prompt as a list of Messages."""
 
 
@@ -117,10 +129,6 @@ class StringPromptValue(PromptValue):
         """Return prompt as string."""
         return self.text
 
-    def to_messages(self) -> list[BaseMessage]:
-        """Return prompt as messages."""
-        return [HumanMessage(content=self.text)]
-
     @overload
     def to_messages(
         self, output_version: Literal["v0"] = "v0"
@@ -131,12 +139,8 @@ class StringPromptValue(PromptValue):
 
     def to_messages(
         self, output_version: Literal["v0", "v1"] = "v0"
-    ) -> Union[list[BaseMessage], list[MessageV1]]:
+    ) -> Union[Sequence[BaseMessage], Sequence[MessageV1]]:
-        """Return prompt as a list of messages.
+        """Return prompt as messages."""
-
-        Args:
-            output_version: The output version, either "v0" (default) or "v1".
-        """
         if output_version == "v1":
             return [HumanMessageV1(content=self.text)]
         return [HumanMessage(content=self.text)]
@@ -165,7 +169,7 @@ class ChatPromptValue(PromptValue):
 
     def to_messages(
         self, output_version: Literal["v0", "v1"] = "v0"
-    ) -> Union[list[BaseMessage], list[MessageV1]]:
+    ) -> Union[Sequence[BaseMessage], Sequence[MessageV1]]:
         """Return prompt as a list of messages.
 
         Args:
@@ -207,8 +211,26 @@ class ImagePromptValue(PromptValue):
         """Return prompt (image URL) as string."""
         return self.image_url["url"]
 
-    def to_messages(self) -> list[BaseMessage]:
+    @overload
+    def to_messages(
+        self, output_version: Literal["v0"] = "v0"
+    ) -> list[BaseMessage]: ...
+
+    @overload
+    def to_messages(self, output_version: Literal["v1"]) -> list[MessageV1]: ...
+
+    def to_messages(
+        self, output_version: Literal["v0", "v1"] = "v0"
+    ) -> Union[Sequence[BaseMessage], Sequence[MessageV1]]:
         """Return prompt (image URL) as messages."""
+        if output_version == "v1":
+            block: types.ImageContentBlock = {
+                "type": "image",
+                "url": self.image_url["url"],
+            }
+            if "detail" in self.image_url:
+                block["detail"] = self.image_url["detail"]
+            return [HumanMessageV1(content=[block])]
         return [HumanMessage(content=[cast("dict", self.image_url)])]
 
 

libs/core/pyproject.toml

@@ -67,6 +67,7 @@ langchain-text-splitters = { path = "../text-splitters" }
 strict = "True"
 strict_bytes = "True"
 enable_error_code = "deprecated"
+disable_error_code = ["typeddict-unknown-key"]
 
 # TODO: activate for 'strict' checking
 disallow_any_generics = "False"

libs/partners/openai/langchain_openai/chat_models/_compat.py

@@ -66,11 +66,14 @@ For backwards compatibility, this module provides functions to convert between t
 formats. The functions are used internally by ChatOpenAI.
 """  # noqa: E501
 
+import copy
 import json
 from collections.abc import Iterable, Iterator
 from typing import Any, Literal, Union, cast
 
 from langchain_core.messages import AIMessage, AIMessageChunk, is_data_content_block
+from langchain_core.messages import content_blocks as types
+from langchain_core.messages.v1 import AIMessage as AIMessageV1
 
 _FUNCTION_CALL_IDS_MAP_KEY = "__openai_function_call_ids__"
 
@@ -289,25 +292,21 @@ def _convert_to_v1_from_chat_completions_chunk(chunk: AIMessageChunk) -> AIMessa
     return cast(AIMessageChunk, result)
 
 
-def _convert_from_v1_to_chat_completions(message: AIMessage) -> AIMessage:
+def _convert_from_v1_to_chat_completions(message: AIMessageV1) -> AIMessageV1:
     """Convert a v1 message to the Chat Completions format."""
-    if isinstance(message.content, list):
+    new_content: list[types.ContentBlock] = []
-        new_content: list = []
     for block in message.content:
-            if isinstance(block, dict):
+        if block["type"] == "text":
-                block_type = block.get("type")
-                if block_type == "text":
             # Strip annotations
             new_content.append({"type": "text", "text": block["text"]})
-                elif block_type in ("reasoning", "tool_call"):
+        elif block["type"] in ("reasoning", "tool_call"):
             pass
         else:
             new_content.append(block)
-            else:
+    new_message = copy.copy(message)
-                new_content.append(block)
+    new_message.content = new_content
-        return message.model_copy(update={"content": new_content})
 
-    return message
+    return new_message
 
 
 # v1 / Responses
@@ -367,13 +366,13 @@ def _explode_reasoning(block: dict[str, Any]) -> Iterable[dict[str, Any]]:
         yield new_block
 
 
-def _convert_to_v1_from_responses(message: AIMessage) -> AIMessage:
+def _convert_to_v1_from_responses(
+    content: list[dict[str, Any]],
+) -> list[types.ContentBlock]:
     """Mutate a Responses message to v1 format."""
-    if not isinstance(message.content, list):
-        return message
 
     def _iter_blocks() -> Iterable[dict[str, Any]]:
-        for block in message.content:
+        for block in content:
             if not isinstance(block, dict):
                 continue
             block_type = block.get("type")
@@ -485,16 +484,14 @@ def _convert_to_v1_from_responses(message: AIMessage) -> AIMessage:
                     new_block["index"] = new_block["value"].pop("index")
                 yield new_block
 
-    # Replace the list with the fully converted one
+    return list(_iter_blocks())
-    message.content = list(_iter_blocks())
-
-    return message
 
 
-def _convert_annotation_from_v1(annotation: dict[str, Any]) -> dict[str, Any]:
+def _convert_annotation_from_v1(annotation: types.Annotation) -> dict[str, Any]:
-    annotation_type = annotation.get("type")
+    if annotation["type"] == "citation":
+        if "url" in annotation:
+            return dict(annotation)
 
-    if annotation_type == "document_citation":
         new_ann: dict[str, Any] = {"type": "file_citation"}
 
         if "title" in annotation:
@@ -502,11 +499,11 @@ def _convert_annotation_from_v1(annotation: dict[str, Any]) -> dict[str, Any]:
 
         for fld in ("file_id", "index"):
             if fld in annotation:
-                new_ann[fld] = annotation[fld]
+                new_ann[fld] = annotation[fld]  # type: ignore[typeddict-item]
 
         return new_ann
 
-    elif annotation_type == "non_standard_annotation":
+    elif annotation["type"] == "non_standard_annotation":
         return annotation["value"]
 
     else:
@@ -621,53 +618,50 @@ def _consolidate_calls(
             yield nxt
 
 
-def _convert_from_v1_to_responses(message: AIMessage) -> AIMessage:
+def _convert_from_v1_to_responses(
-    if not isinstance(message.content, list):
+    content: list[types.ContentBlock], tool_calls: list[types.ToolCall]
-        return message
+) -> list[dict[str, Any]]:
-
     new_content: list = []
-    for block in message.content:
+    for block in content:
-        if isinstance(block, dict):
+        if block["type"] == "text" and "annotations" in block:
-            block_type = block.get("type")
-            if block_type == "text" and "annotations" in block:
             # Need a copy because we’re changing the annotations list
             new_block = dict(block)
             new_block["annotations"] = [
                 _convert_annotation_from_v1(a) for a in block["annotations"]
             ]
             new_content.append(new_block)
-            elif block_type == "tool_call":
+        elif block["type"] == "tool_call":
             new_block = {"type": "function_call", "call_id": block["id"]}
             if "item_id" in block:
-                    new_block["id"] = block["item_id"]
+                new_block["id"] = block["item_id"]  # type: ignore[typeddict-item]
             if "name" in block and "arguments" in block:
                 new_block["name"] = block["name"]
-                    new_block["arguments"] = block["arguments"]
+                new_block["arguments"] = block["arguments"]  # type: ignore[typeddict-item]
             else:
-                    tool_call = next(
+                matching_tool_calls = [
-                        call for call in message.tool_calls if call["id"] == block["id"]
+                    call for call in tool_calls if call["id"] == block["id"]
-                    )
+                ]
+                if matching_tool_calls:
+                    tool_call = matching_tool_calls[0]
                     if "name" not in block:
                         new_block["name"] = tool_call["name"]
                     if "arguments" not in block:
                         new_block["arguments"] = json.dumps(tool_call["args"])
             new_content.append(new_block)
         elif (
-                is_data_content_block(block)
+            is_data_content_block(cast(dict, block))
             and block["type"] == "image"
             and "base64" in block
         ):
             new_block = {"type": "image_generation_call", "result": block["base64"]}
             for extra_key in ("id", "status"):
                 if extra_key in block:
-                        new_block[extra_key] = block[extra_key]
+                    new_block[extra_key] = block[extra_key]  # type: ignore[typeddict-item]
             new_content.append(new_block)
-            elif block_type == "non_standard" and "value" in block:
+        elif block["type"] == "non_standard" and "value" in block:
             new_content.append(block["value"])
         else:
             new_content.append(block)
-        else:
-            new_content.append(block)
 
     new_content = list(_implode_reasoning_blocks(new_content))
     new_content = list(
@@ -679,4 +673,4 @@ def _convert_from_v1_to_responses(message: AIMessage) -> AIMessage:
         )
     )
 
-    return message.model_copy(update={"content": new_content})
+    return new_content

libs/partners/openai/pyproject.toml

@@ -56,6 +56,8 @@ langchain-tests = { path = "../../standard-tests", editable = true }
 
 [tool.mypy]
 disallow_untyped_defs = "True"
+disable_error_code = ["typeddict-unknown-key"]
+
 [[tool.mypy.overrides]]
 module = "transformers"
 ignore_missing_imports = true