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update rest and other parts of the code to work with arch fc 1.1

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Adil Hafeez 2025-03-28 03:04:12 -07:00
parent e5949c584f
commit b31a7a569a
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GPG key ID: 9B18EF7691369645
8 changed files with 196 additions and 47 deletions
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14
model_server/src/core/function_calling.py
View file

@ -197,12 +197,12 @@ class ArchFunctionHandler(ArchBaseHandler):
response_dict["response"] = model_response.get("response", "")
response_dict["required_functions"] = model_response.get(
"required_functions", ""
"required_functions", []
)
response_dict["clarification"] = model_response.get("clarification", "")
for tool_call in model_response.get("tool_calls", []):
response_dict["tool_call"].append(
response_dict["tool_calls"].append(
{
"id": f"call_{random.randint(1000, 10000)}",
"type": "function",
@ -448,6 +448,7 @@ class ArchFunctionHandler(ArchBaseHandler):
if len(chunk.choices) > 0 and chunk.choices[0].delta.content:
model_response += chunk.choices[0].delta.content
logger.info(f"[arch-fc]: raw model response: {model_response}")
# Extract tool calls from model response
response_dict = self._parse_model_resonse(model_response)
@ -499,10 +500,15 @@ class ArchFunctionHandler(ArchBaseHandler):
model_message = Message(content="", tool_calls=[])
chat_completion_response = ChatCompletionResponse(
choices=[Choice(message=model_message)], model=self.model_name
choices=[Choice(message=model_message)],
model=self.model_name,
metadata={"x-arch-fc-model-response": model_response},
role="assistant",
)
logger.info(f"[response]: {json.dumps(chat_completion_response.model_dump())}")
logger.info(
f"[response arch-fc]: {json.dumps(chat_completion_response.model_dump())}"
)
return chat_completion_response

16
model_server/src/core/utils/model_utils.py
View file

@ -142,7 +142,7 @@ class ArchBaseHandler:
{"role": "system", "content": self._format_system_prompt(tools)}
)
for message in messages:
for idx, message in enumerate(messages):
role, content, tool_calls = (
message.role,
message.content,
@ -158,9 +158,17 @@ class ArchBaseHandler:
if metadata.get("optimize_context_window", "false").lower() == "true":
content = f"<tool_response>\n\n</tool_response>"
else:
content = (
f"<tool_response>\n{json.dumps(content)}\n</tool_response>"
)
# sample response below
# "content": "<tool_response>\n{'name': 'get_stock_price', 'result': '$196.66'}\n</tool_response>"
# msg[idx-1] contains tool call = '{"tool_calls": [{"name": "currency_exchange", "arguments": {"currency_symbol": "NZD"}}]}'
func_name = json.loads(messages[idx - 1].content)["tool_calls"][
0
].get("name", "no_name")
tool_response = {
"name": func_name,
"result": content,
}
content = f"<tool_response>\n{json.dumps(tool_response)}\n</tool_response>"
processed_messages.append({"role": role, "content": content})

15
model_server/src/main.py
View file

@ -87,16 +87,15 @@ async def function_calling(req: ChatMessage, res: Response):
final_response = await model_handler.chat_completion(req)
latency = time.perf_counter() - start_time
if not final_response.metadata:
final_response.metadata = {}
# Parameter gathering for detected intents
if final_response.choices[0].message.content:
final_response.metadata = {
"function_latency": str(round(latency * 1000, 3)),
}
final_response.metadata["function_latency"] = str(round(latency * 1000, 3))
# Function Calling
elif final_response.choices[0].message.tool_calls:
final_response.metadata = {
"function_latency": str(round(latency * 1000, 3)),
}
final_response.metadata["function_latency"] = str(round(latency * 1000, 3))
# *********************************************************************************************
# TODO: Put the following code back when hallucination check is ready
@ -107,9 +106,7 @@ async def function_calling(req: ChatMessage, res: Response):
# )
# No intent detected
else:
final_response.metadata = {
"intent_latency": str(round(latency * 1000, 3)),
}
final_response.metadata["intent_latency"] = str(round(latency * 1000, 3))
if not use_agent_orchestrator:
final_response.metadata["intent_latency"] = str(round(latency * 1000, 3))