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Python: follow on work on OpenAI (#169)

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* updated openai, fcc works, with sample

* reduced files in openai
This commit is contained in:
Eduard van Valkenburg
2025-07-12 07:15:51 +02:00
committed by GitHub
Unverified
parent 80c1e2ee0a
commit 407ed6de70
32 changed files with 693 additions and 668 deletions
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python/packages/main/agent_framework/_clients.py
+46 -24
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@@ -108,7 +108,10 @@ def _prepare_tools_and_tool_choice(chat_options: ChatOptions) -> None:
chat_options.tools = [
(_tool_to_json_schema_spec(t) if isinstance(t, AITool) else t) for t in chat_options.tools or []
]
chat_options.tool_choice = chat_tool_mode.mode
if not chat_options.tools:
chat_options.tool_choice = ChatToolMode.NONE.mode
else:
chat_options.tool_choice = chat_tool_mode.mode
def _tool_call_non_streaming(func: TInnerGetResponse) -> TInnerGetResponse:
@@ -208,8 +211,9 @@ def _tool_call_streaming(func: TInnerGetStreamingResponse) -> TInnerGetStreaming
# the full completion depending on the prompt, the message may contain both function call
# content and others
response: ChatResponse = ChatResponse.from_chat_response_updates(all_messages)
function_calls = [item for item in response.messages[0].contents if isinstance(item, FunctionCallContent)]
# add the single assistant response message to the history
messages.append(response.messages[0])
function_calls = [item for item in response.messages[0].contents if isinstance(item, FunctionCallContent)]
if function_calls:
# Run all function calls concurrently
@@ -224,8 +228,9 @@ def _tool_call_streaming(func: TInnerGetStreamingResponse) -> TInnerGetStreaming
for seq_idx, function_call in enumerate(function_calls)
])
yield ChatResponseUpdate(contents=results, role="tool")
response.messages.append(ChatMessage(role="tool", contents=results))
messages.extend(response.messages)
function_result_msg = ChatMessage(role="tool", contents=results)
response.messages.append(function_result_msg)
messages.append(function_result_msg)
continue
# Failsafe: give up on tools, ask model for plain answer
@@ -238,11 +243,17 @@ def _tool_call_streaming(func: TInnerGetStreamingResponse) -> TInnerGetStreaming
def use_tool_calling(cls: type[TChatClientBase]) -> type[TChatClientBase]:
inner_response = getattr(cls, "_inner_get_response", None)
if inner_response is not None:
"""Class decorator that enables tool calling for a chat client.
Remarks:
This only works on classes that derive from ChatClientBase
and have the _tool_map attribute as well as the _inner_get_response
and _inner_get_streaming_response methods.
"""
if inner_response := getattr(cls, "_inner_get_response", None):
cls._inner_get_response = _tool_call_non_streaming(inner_response) # type: ignore
inner_streaming_response = getattr(cls, "_inner_get_streaming_response", None)
if inner_streaming_response is not None:
if inner_streaming_response := getattr(cls, "_inner_get_streaming_response", None):
cls._inner_get_streaming_response = _tool_call_streaming(inner_streaming_response) # type: ignore
return cls
@@ -303,6 +314,17 @@ class ChatClientBase(AFBaseModel, ABC):
maximum_iterations_per_request: int = 10
_tool_map: dict[str, AIFunction[BaseModel, Any]] = PrivateAttr(default_factory=dict) # type: ignore
def _prepare_messages(self, messages: str | ChatMessage | list[str | ChatMessage]) -> MutableSequence[ChatMessage]:
"""Turn the allowed input into a list of chat messages."""
if isinstance(messages, str):
messages = [ChatMessage(role="user", text=messages)]
if isinstance(messages, ChatMessage):
messages = [messages]
for i, msg in enumerate(messages):
if isinstance(msg, str):
messages[i] = ChatMessage(role="user", text=msg)
return messages # type: ignore[return-value]
# region Internal methods to be implemented by the derived classes
@abstractmethod
@@ -355,14 +377,14 @@ class ChatClientBase(AFBaseModel, ABC):
async def get_response(
self,
messages: str | ChatMessage | list[ChatMessage],
messages: str | ChatMessage | list[str | ChatMessage],
*,
model: str | None = None,
max_tokens: int | None = None,
temperature: float | None = None,
top_p: float | None = None,
tool_choice: ChatToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None = None,
tools: Sequence[AITool] | None = None,
tool_choice: ChatToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto",
tools: AITool | Sequence[AITool] | None = None,
response_format: type[BaseModel] | None = None,
user: str | None = None,
stop: str | Sequence[str] | None = None,
@@ -402,6 +424,8 @@ class ChatClientBase(AFBaseModel, ABC):
A chat response from the model.
"""
if tools is not None:
if not isinstance(tools, Sequence):
tools = [tools]
self._tool_map = {tool.name: tool for tool in tools if isinstance(tool, AIFunction)}
chat_options = ChatOptions(
ai_model_id=model,
@@ -421,23 +445,20 @@ class ChatClientBase(AFBaseModel, ABC):
metadata=metadata,
additional_properties=additional_properties or {},
)
if isinstance(messages, str):
messages = [ChatMessage(role="user", text=messages)]
if isinstance(messages, ChatMessage):
messages = [messages]
prepped_messages = self._prepare_messages(messages)
_prepare_tools_and_tool_choice(chat_options=chat_options)
return await self._inner_get_response(messages=messages, chat_options=chat_options, **kwargs)
return await self._inner_get_response(messages=prepped_messages, chat_options=chat_options, **kwargs)
async def get_streaming_response(
self,
messages: str | ChatMessage | list[ChatMessage],
messages: str | ChatMessage | list[str | ChatMessage],
*,
model: str | None = None,
max_tokens: int | None = None,
temperature: float | None = None,
top_p: float | None = None,
tool_choice: ChatToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None = None,
tools: Sequence[AITool] | None = None,
tool_choice: ChatToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto",
tools: AITool | Sequence[AITool] | None = None,
response_format: type[BaseModel] | None = None,
user: str | None = None,
stop: str | Sequence[str] | None = None,
@@ -476,6 +497,8 @@ class ChatClientBase(AFBaseModel, ABC):
A stream representing the response(s) from the LLM.
"""
if tools is not None:
if not isinstance(tools, Sequence):
tools = [tools]
self._tool_map = {tool.name: tool for tool in tools if isinstance(tool, AIFunction)}
chat_options = ChatOptions(
ai_model_id=model,
@@ -496,12 +519,11 @@ class ChatClientBase(AFBaseModel, ABC):
additional_properties=additional_properties or {},
**kwargs,
)
if isinstance(messages, str):
messages = [ChatMessage(role="user", text=messages)]
if isinstance(messages, ChatMessage):
messages = [messages]
prepped_messages = self._prepare_messages(messages)
_prepare_tools_and_tool_choice(chat_options=chat_options)
async for update in self._inner_get_streaming_response(messages=messages, chat_options=chat_options, **kwargs):
async for update in self._inner_get_streaming_response(
messages=prepped_messages, chat_options=chat_options, **kwargs
):
yield update