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A file for helper functions that deal with OpenAI API compatible helpers.

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Functions

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FUNC extract_model_tool_requests 

extract_model_tool_requests(tools: dict[str, AbstractMelleaTool], response: dict[str, Any]) -> dict[str, ModelToolCall] | None
Extract tool calls from the dict representation of an OpenAI-like chat response object. Args:
  • tools: Mapping of tool name to AbstractMelleaTool for lookup.
  • response: Dict representation of an OpenAI-compatible chat completion message (must contain a "message" key).
Returns:
  • Mapping of tool name to [ModelToolCall](../core/base#class-modeltoolcall) for each requested tool call, or
  • None if no tool calls were found.

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FUNC chat_completion_delta_merge 

chat_completion_delta_merge(chunks: list[dict], force_all_tool_calls_separate: bool = False) -> dict
Merge a list of deltas from ChatCompletionChunks into a single dict representing the ChatCompletion choice. Args:
  • chunks: The list of dicts that represent the message deltas.
  • force_all_tool_calls_separate: If True, tool calls in separate message deltas will not be merged even if their index values are the same. Use when providers do not return the correct index value for tool calls; all tool calls must then be fully populated in a single delta.
Returns:
  • A single merged dict representing the assembled ChatCompletion choice,
  • with finish_reason, index, and a message sub-dict containing
  • content, role, and tool_calls.

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FUNC message_to_openai_message 

message_to_openai_message(msg: Message)
Serialise a Mellea Message to the format required by OpenAI-compatible API providers. Args:
  • msg: The Message object to serialise.
Returns:
  • A dict with "role" and "content" fields. When the message carries
  • images, "content" is a list of text and image-URL dicts; otherwise it
  • is a plain string.

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FUNC messages_to_docs 

messages_to_docs(msgs: list[Message]) -> list[dict[str, str]]
Extract all Document objects from a list of Message objects. Args:
  • msgs: List of Message objects whose _docs attributes are inspected.
Returns:
  • A list of dicts, each with a "text" key and optional "title" and
  • "doc_id" keys, suitable for passing to an OpenAI-compatible RAG API.