fix(fireworks): translate canonical multimodal content blocks for chat completions (#37090)

## Summary

`langchain_fireworks._convert_message_to_dict` ships LangChain canonical
v0/v1 multimodal content blocks (e.g. `{"type": "image", "base64": ...,
"mime_type": ...}`) on the wire unchanged. Fireworks' OpenAI-compatible
chat completions API rejects the unknown `base64`/`mime_type` keys and
the list shape on roles that expect a string, returning HTTP 422 — so
any image upload, including via tools that return image content blocks,
fails for Kimi K2.6 and other Fireworks vision models.

This change mirrors
`langchain_openai.chat_models.base._format_message_content`:

- Walk `content` blocks.
- Drop block types the chat-completions wire doesn't carry (`tool_use`,
`thinking`, `reasoning_content`, `function_call`,
`code_interpreter_call`).
- Detect v0/v1 multimodal data blocks via
`langchain_core.messages.is_data_content_block`, and translate them via
`convert_to_openai_data_block(..., api="chat/completions")`.
- Strings and non-list content pass through unchanged.

Applied in the `ChatMessage`, `HumanMessage`, `SystemMessage`, and
`ToolMessage` paths of `_convert_message_to_dict`. `AIMessage` already
routes through `_convert_from_v1_to_chat_completions` for v1 output and
assistant content is text-only on the way out, so it is left untouched.

## Why this approach

Fireworks is OpenAI-compatible. The canonical → OpenAI translator
already exists in `langchain_core.messages.block_translators.openai` and
is the same one `langchain-openai` uses. Reusing it (rather than
inventing a Fireworks-specific translator) gives:

- v0 (`source_type`-based) and v1 (`base64`/`url`-based) data block
coverage for free.
- Consistent behavior with `langchain-openai` for image, file, and any
future canonical data block.
- A small, focused diff (≈30 lines of new code, plus tests).

## Test plan

- [x] `make test` passes (64/64 unit tests, including 9 new ones for the
new helper and translation paths).
- [x] `make lint` passes (ruff check, ruff format, mypy, lint_imports).
- [ ] End-to-end: image upload to a Kimi K2.6 (Fireworks) agent
translates to `{"type": "image_url", "image_url": {"url":
"data:image/png;base64,..."}}` on the wire and the model returns a
coherent description (validated locally against
`langchain-fireworks==1.0.0` site-packages with the same patch).

---------

Co-authored-by: murugand23 <murugand23@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>

libs/partners/fireworks/langchain_fireworks/chat_models.py

@@ -57,6 +57,10 @@ from langchain_core.messages import (
     ToolCall,
     ToolMessage,
     ToolMessageChunk,
+    is_data_content_block,
+)
+from langchain_core.messages.block_translators.openai import (
+    convert_to_openai_data_block,
 )
 from langchain_core.messages.tool import (
     ToolCallChunk,
@@ -166,6 +170,70 @@ def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
     return ChatMessage(content=_dict.get("content", ""), role=role or "")
 
 
+def _format_message_content(content: Any) -> Any:
+    """Format message content for the Fireworks chat completions wire format.
+
+    Adapted from `langchain_openai.chat_models.base._format_message_content`,
+    scoped to the chat completions API: drops content block types the wire
+    format does not carry, translates canonical v0/v1 multimodal data blocks
+    via `convert_to_openai_data_block(block, api="chat/completions")`, and
+    converts legacy Anthropic-shape image blocks (`{"type": "image",
+    "source": {...}}`) to OpenAI `image_url` blocks. String and non-list
+    content are returned unchanged.
+
+    Args:
+        content: The message content. Strings and non-list values are
+            returned as-is; lists are walked block by block.
+
+    Returns:
+        The formatted content, ready to be placed on the chat completions
+        wire. List inputs return a new list with translations applied; other
+        inputs are returned unchanged.
+    """
+    if not isinstance(content, list):
+        return content
+    formatted: list[Any] = []
+    for block in content:
+        if isinstance(block, dict) and "type" in block:
+            btype = block["type"]
+            if btype in (
+                "tool_use",
+                "thinking",
+                "reasoning_content",
+                "function_call",
+                "code_interpreter_call",
+            ):
+                continue
+            if is_data_content_block(block):
+                formatted.append(
+                    convert_to_openai_data_block(block, api="chat/completions")
+                )
+                continue
+            if (
+                btype == "image"
+                and (source := block.get("source"))
+                and isinstance(source, dict)
+            ):
+                if (
+                    source.get("type") == "base64"
+                    and (media_type := source.get("media_type"))
+                    and (data := source.get("data"))
+                ):
+                    formatted.append(
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": f"data:{media_type};base64,{data}"},
+                        }
+                    )
+                    continue
+                if source.get("type") == "url" and (url := source.get("url")):
+                    formatted.append({"type": "image_url", "image_url": {"url": url}})
+                    continue
+                continue
+        formatted.append(block)
+    return formatted
+
+
 def _convert_message_to_dict(message: BaseMessage) -> dict:
     """Convert a LangChain message to a dictionary.
 
@@ -178,14 +246,23 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
     """
     message_dict: dict[str, Any]
     if isinstance(message, ChatMessage):
-        message_dict = {"role": message.role, "content": message.content}
+        message_dict = {
+            "role": message.role,
+            "content": _format_message_content(message.content),
+        }
     elif isinstance(message, HumanMessage):
-        message_dict = {"role": "user", "content": message.content}
+        message_dict = {
+            "role": "user",
+            "content": _format_message_content(message.content),
+        }
     elif isinstance(message, AIMessage):
         # Translate v1 content
         if message.response_metadata.get("output_version") == "v1":
             message = _convert_from_v1_to_chat_completions(message)
-        message_dict = {"role": "assistant", "content": message.content}
+        message_dict = {
+            "role": "assistant",
+            "content": _format_message_content(message.content),
+        }
         if "function_call" in message.additional_kwargs:
             message_dict["function_call"] = message.additional_kwargs["function_call"]
             # If function call only, content is None not empty string
@@ -206,7 +283,10 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
         else:
             pass
     elif isinstance(message, SystemMessage):
-        message_dict = {"role": "system", "content": message.content}
+        message_dict = {
+            "role": "system",
+            "content": _format_message_content(message.content),
+        }
     elif isinstance(message, FunctionMessage):
         message_dict = {
             "role": "function",
@@ -216,7 +296,7 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
     elif isinstance(message, ToolMessage):
         message_dict = {
             "role": "tool",
-            "content": message.content,
+            "content": _format_message_content(message.content),
             "tool_call_id": message.tool_call_id,
         }
     else: