mirror of
https://github.com/arc53/DocsGPT.git
synced 2025-11-29 08:33:20 +00:00
feat: streaming responses with function call
This commit is contained in:
Siddhant Rai
@@ -104,7 +104,8 @@ class ClassicAgent(BaseAgent):
|
||||
model=self.gpt_model, messages=messages_combine, tools=self.tools
|
||||
)
|
||||
for line in completion:
|
||||
yield {"answer": line}
|
||||
if isinstance(line, str):
|
||||
yield {"answer": line}
|
||||
|
||||
yield {"tool_calls": self.tool_calls.copy()}
|
||||
|
||||
@@ -116,7 +117,7 @@ class ClassicAgent(BaseAgent):
|
||||
return retrieved_data
|
||||
|
||||
def _llm_gen(self, messages_combine, log_context):
|
||||
resp = self.llm.gen(
|
||||
resp = self.llm.gen_stream(
|
||||
model=self.gpt_model, messages=messages_combine, tools=self.tools
|
||||
)
|
||||
if log_context:
|
||||
@@ -131,5 +132,4 @@ class ClassicAgent(BaseAgent):
|
||||
if log_context:
|
||||
data = build_stack_data(self.llm_handler)
|
||||
log_context.stacks.append({"component": "llm_handler", "data": data})
|
||||
|
||||
return resp
|
||||
|
||||
@@ -15,84 +15,221 @@ class LLMHandler(ABC):
|
||||
|
||||
|
||||
class OpenAILLMHandler(LLMHandler):
|
||||
def handle_response(self, agent, resp, tools_dict, messages):
|
||||
while resp.finish_reason == "tool_calls":
|
||||
message = json.loads(resp.model_dump_json())["message"]
|
||||
keys_to_remove = {"audio", "function_call", "refusal"}
|
||||
filtered_data = {
|
||||
k: v for k, v in message.items() if k not in keys_to_remove
|
||||
}
|
||||
messages.append(filtered_data)
|
||||
def handle_response(self, agent, resp, tools_dict, messages, stream: bool = True):
|
||||
if not stream:
|
||||
while hasattr(resp, "finish_reason") and resp.finish_reason == "tool_calls":
|
||||
message = json.loads(resp.model_dump_json())["message"]
|
||||
keys_to_remove = {"audio", "function_call", "refusal"}
|
||||
filtered_data = {
|
||||
k: v for k, v in message.items() if k not in keys_to_remove
|
||||
}
|
||||
messages.append(filtered_data)
|
||||
|
||||
tool_calls = resp.message.tool_calls
|
||||
for call in tool_calls:
|
||||
try:
|
||||
self.tool_calls.append(call)
|
||||
tool_response, call_id = agent._execute_tool_action(
|
||||
tools_dict, call
|
||||
)
|
||||
function_call_dict = {
|
||||
"function_call": {
|
||||
"name": call.function.name,
|
||||
"args": call.function.arguments,
|
||||
"call_id": call_id,
|
||||
tool_calls = resp.message.tool_calls
|
||||
for call in tool_calls:
|
||||
try:
|
||||
self.tool_calls.append(call)
|
||||
tool_response, call_id = agent._execute_tool_action(
|
||||
tools_dict, call
|
||||
)
|
||||
function_call_dict = {
|
||||
"function_call": {
|
||||
"name": call.function.name,
|
||||
"args": call.function.arguments,
|
||||
"call_id": call_id,
|
||||
}
|
||||
}
|
||||
}
|
||||
function_response_dict = {
|
||||
"function_response": {
|
||||
"name": call.function.name,
|
||||
"response": {"result": tool_response},
|
||||
"call_id": call_id,
|
||||
function_response_dict = {
|
||||
"function_response": {
|
||||
"name": call.function.name,
|
||||
"response": {"result": tool_response},
|
||||
"call_id": call_id,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
messages.append(
|
||||
{"role": "assistant", "content": [function_call_dict]}
|
||||
)
|
||||
messages.append(
|
||||
{"role": "tool", "content": [function_response_dict]}
|
||||
)
|
||||
messages.append(
|
||||
{"role": "assistant", "content": [function_call_dict]}
|
||||
)
|
||||
messages.append(
|
||||
{"role": "tool", "content": [function_response_dict]}
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
messages.append(
|
||||
{
|
||||
"role": "tool",
|
||||
"content": f"Error executing tool: {str(e)}",
|
||||
"tool_call_id": call_id,
|
||||
}
|
||||
)
|
||||
resp = agent.llm.gen(
|
||||
model=agent.gpt_model, messages=messages, tools=agent.tools
|
||||
)
|
||||
self.llm_calls.append(build_stack_data(agent.llm))
|
||||
return resp
|
||||
except Exception as e:
|
||||
messages.append(
|
||||
{
|
||||
"role": "tool",
|
||||
"content": f"Error executing tool: {str(e)}",
|
||||
"tool_call_id": call_id,
|
||||
}
|
||||
)
|
||||
resp = agent.llm.gen_stream(
|
||||
model=agent.gpt_model, messages=messages, tools=agent.tools
|
||||
)
|
||||
self.llm_calls.append(build_stack_data(agent.llm))
|
||||
return resp
|
||||
|
||||
else:
|
||||
while True:
|
||||
tool_calls = {}
|
||||
for chunk in resp:
|
||||
if isinstance(chunk, str):
|
||||
return
|
||||
else:
|
||||
chunk_delta = chunk.delta
|
||||
|
||||
if (
|
||||
hasattr(chunk_delta, "tool_calls")
|
||||
and chunk_delta.tool_calls is not None
|
||||
):
|
||||
for tool_call in chunk_delta.tool_calls:
|
||||
index = tool_call.index
|
||||
if index not in tool_calls:
|
||||
tool_calls[index] = {
|
||||
"id": "",
|
||||
"function": {"name": "", "arguments": ""},
|
||||
}
|
||||
|
||||
current = tool_calls[index]
|
||||
if tool_call.id:
|
||||
current["id"] = tool_call.id
|
||||
if tool_call.function.name:
|
||||
current["function"][
|
||||
"name"
|
||||
] = tool_call.function.name
|
||||
if tool_call.function.arguments:
|
||||
current["function"][
|
||||
"arguments"
|
||||
] += tool_call.function.arguments
|
||||
tool_calls[index] = current
|
||||
|
||||
if (
|
||||
hasattr(chunk, "finish_reason")
|
||||
and chunk.finish_reason == "tool_calls"
|
||||
):
|
||||
for index in sorted(tool_calls.keys()):
|
||||
call = tool_calls[index]
|
||||
try:
|
||||
self.tool_calls.append(call)
|
||||
tool_response, call_id = agent._execute_tool_action(
|
||||
tools_dict, call
|
||||
)
|
||||
|
||||
function_call_dict = {
|
||||
"function_call": {
|
||||
"name": call["function"]["name"],
|
||||
"args": call["function"]["arguments"],
|
||||
"call_id": call["id"],
|
||||
}
|
||||
}
|
||||
function_response_dict = {
|
||||
"function_response": {
|
||||
"name": call["function"]["name"],
|
||||
"response": {"result": tool_response},
|
||||
"call_id": call["id"],
|
||||
}
|
||||
}
|
||||
|
||||
messages.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": [function_call_dict],
|
||||
}
|
||||
)
|
||||
messages.append(
|
||||
{
|
||||
"role": "tool",
|
||||
"content": [function_response_dict],
|
||||
}
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
messages.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": f"Error executing tool: {str(e)}",
|
||||
}
|
||||
)
|
||||
tool_calls = {}
|
||||
|
||||
if (
|
||||
hasattr(chunk, "finish_reason")
|
||||
and chunk.finish_reason == "stop"
|
||||
):
|
||||
return
|
||||
|
||||
resp = agent.llm.gen_stream(
|
||||
model=agent.gpt_model, messages=messages, tools=agent.tools
|
||||
)
|
||||
self.llm_calls.append(build_stack_data(agent.llm))
|
||||
|
||||
|
||||
class GoogleLLMHandler(LLMHandler):
|
||||
def handle_response(self, agent, resp, tools_dict, messages):
|
||||
def handle_response(self, agent, resp, tools_dict, messages, stream: bool = True):
|
||||
from google.genai import types
|
||||
|
||||
while True:
|
||||
response = agent.llm.gen(
|
||||
model=agent.gpt_model, messages=messages, tools=agent.tools
|
||||
)
|
||||
self.llm_calls.append(build_stack_data(agent.llm))
|
||||
if response.candidates and response.candidates[0].content.parts:
|
||||
if not stream:
|
||||
response = agent.llm.gen(
|
||||
model=agent.gpt_model, messages=messages, tools=agent.tools
|
||||
)
|
||||
self.llm_calls.append(build_stack_data(agent.llm))
|
||||
if response.candidates and response.candidates[0].content.parts:
|
||||
tool_call_found = False
|
||||
for part in response.candidates[0].content.parts:
|
||||
if part.function_call:
|
||||
tool_call_found = True
|
||||
self.tool_calls.append(part.function_call)
|
||||
tool_response, call_id = agent._execute_tool_action(
|
||||
tools_dict, part.function_call
|
||||
)
|
||||
function_response_part = types.Part.from_function_response(
|
||||
name=part.function_call.name,
|
||||
response={"result": tool_response},
|
||||
)
|
||||
|
||||
messages.append(
|
||||
{"role": "model", "content": [part.to_json_dict()]}
|
||||
)
|
||||
messages.append(
|
||||
{
|
||||
"role": "tool",
|
||||
"content": [function_response_part.to_json_dict()],
|
||||
}
|
||||
)
|
||||
|
||||
if (
|
||||
not tool_call_found
|
||||
and response.candidates[0].content.parts
|
||||
and response.candidates[0].content.parts[0].text
|
||||
):
|
||||
return response.candidates[0].content.parts[0].text
|
||||
elif not tool_call_found:
|
||||
return response.candidates[0].content.parts
|
||||
|
||||
else:
|
||||
return response
|
||||
|
||||
else:
|
||||
response = agent.llm.gen_stream(
|
||||
model=agent.gpt_model, messages=messages, tools=agent.tools
|
||||
)
|
||||
self.llm_calls.append(build_stack_data(agent.llm))
|
||||
|
||||
tool_call_found = False
|
||||
for part in response.candidates[0].content.parts:
|
||||
if part.function_call:
|
||||
for result in response:
|
||||
if hasattr(result, "function_call"):
|
||||
tool_call_found = True
|
||||
self.tool_calls.append(part.function_call)
|
||||
self.tool_calls.append(result.function_call)
|
||||
tool_response, call_id = agent._execute_tool_action(
|
||||
tools_dict, part.function_call
|
||||
tools_dict, result.function_call
|
||||
)
|
||||
function_response_part = types.Part.from_function_response(
|
||||
name=part.function_call.name,
|
||||
name=result.function_call.name,
|
||||
response={"result": tool_response},
|
||||
)
|
||||
|
||||
messages.append(
|
||||
{"role": "model", "content": [part.to_json_dict()]}
|
||||
{"role": "model", "content": [result.to_json_dict()]}
|
||||
)
|
||||
messages.append(
|
||||
{
|
||||
@@ -101,17 +238,8 @@ class GoogleLLMHandler(LLMHandler):
|
||||
}
|
||||
)
|
||||
|
||||
if (
|
||||
not tool_call_found
|
||||
and response.candidates[0].content.parts
|
||||
and response.candidates[0].content.parts[0].text
|
||||
):
|
||||
return response.candidates[0].content.parts[0].text
|
||||
elif not tool_call_found:
|
||||
return response.candidates[0].content.parts
|
||||
|
||||
else:
|
||||
return response
|
||||
if not tool_call_found:
|
||||
return response
|
||||
|
||||
|
||||
def get_llm_handler(llm_type):
|
||||
|
||||
@@ -31,7 +31,6 @@ class CryptoPriceTool(Tool):
|
||||
response = requests.get(url)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
# data will be like {"USD": <price>} if the call is successful
|
||||
if currency.upper() in data:
|
||||
return {
|
||||
"status_code": response.status_code,
|
||||
|
||||
@@ -14,9 +14,20 @@ class ToolActionParser:
|
||||
return parser(call)
|
||||
|
||||
def _parse_openai_llm(self, call):
|
||||
call_args = json.loads(call.function.arguments)
|
||||
tool_id = call.function.name.split("_")[-1]
|
||||
action_name = call.function.name.rsplit("_", 1)[0]
|
||||
if isinstance(call, dict):
|
||||
try:
|
||||
call_args = json.loads(call["function"]["arguments"])
|
||||
tool_id = call["function"]["name"].split("_")[-1]
|
||||
action_name = call["function"]["name"].rsplit("_", 1)[0]
|
||||
except (KeyError, TypeError) as e:
|
||||
return None, None, None
|
||||
else:
|
||||
try:
|
||||
call_args = json.loads(call.function.arguments)
|
||||
tool_id = call.function.name.split("_")[-1]
|
||||
action_name = call.function.name.rsplit("_", 1)[0]
|
||||
except (AttributeError, TypeError) as e:
|
||||
return None, None, None
|
||||
return tool_id, action_name, call_args
|
||||
|
||||
def _parse_google_llm(self, call):
|
||||
|
||||
Reference in New Issue
Block a user