Integrate GigaChat Function Calling

GigaChat SDK Functions covers function calling directly on the official gigachat Python SDK: designing a functions schema with explicit, flat fields, choosing a function_call mode, detecting when the model wants to call a tool (finish_reason == "function_call"), executing that tool in your own code, and sending the result back to the model with role function so it can produce a final answer. It also covers GigaChat's built-in functions, such as text2image, alongside custom user-defined ones.

Use it whenever an agent or tool-use flow is being built directly on top of the gigachat SDK - i.e. the model needs to decide to call a function and get its result back before answering. It deliberately does not cover two adjacent integration layers: LangChain-style tool wrappers, which belong to langchain-gigachat, and OpenAI-compatible tool-call mapping, which belongs to gpt2giga. If schema validation fails or a function call looks wrong, the skill's guidance is to check the schema itself before assuming the model is misbehaving.

The workflow starts from an ordinary Chat(messages=[...]) payload with a functions=[...] array added, using flat, explicit schema fields rather than complex nested constructs by default. function_call="auto" is the default mode unless the task specifically needs to force a particular function. When the model responds with finish_reason == "function_call", the calling code executes the corresponding tool, then appends both the assistant's function-call message and a new function-result message before calling the model again - the full two-step loop, not just the first call, is the pattern the skill insists on documenting. For built-in functions like text2image, the skill calls out explicitly when the response is a file reference rather than inline bytes, since that changes how the result must be handled downstream. Deeper patterns for both custom and built-in functions live in references/function-calling.md.

This skill is scoped to raw SDK usage and sits alongside langchain-gigachat (for LangChain-based tool wrappers) and gpt2giga (for OpenAI-compatible tool-call mapping through a proxy) - each handling function/tool calling in its own layer so this one can stay focused on the SDK-level mechanics of functions, function_call, and result round-tripping.

Developers building agents or tool-using applications directly on the gigachat SDK who need the complete request-execute-respond loop for function calling done correctly, including the easy-to-miss step of sending the function's result back to the model.