773 lines
27 KiB
Python
773 lines
27 KiB
Python
import asyncio
|
|
import json
|
|
from typing import List, Optional
|
|
from fastapi import HTTPException
|
|
from openai import AsyncOpenAI
|
|
from openai.types.chat.chat_completion_message import ChatCompletionMessageToolCall
|
|
from google import genai
|
|
from google.genai.types import GenerateContentConfig
|
|
from anthropic import AsyncAnthropic
|
|
from anthropic.types import Message as AnthropicMessage
|
|
from anthropic import MessageStreamEvent as AnthropicMessageStreamEvent
|
|
from enums.llm_provider import LLMProvider
|
|
from models.llm_message import (
|
|
LLMAssistantMessage,
|
|
LLMMessage,
|
|
LLMSystemMessage,
|
|
LLMUserMessage,
|
|
)
|
|
from models.llm_tool_call import LLMToolCall
|
|
from models.llm_tools import LLMDynamicTool, LLMTool
|
|
from services.llm_tool_calls_handler import LLMToolCallsHandler
|
|
from utils.async_iterator import iterator_to_async
|
|
from utils.get_env import (
|
|
get_anthropic_api_key_env,
|
|
get_custom_llm_api_key_env,
|
|
get_custom_llm_url_env,
|
|
get_disable_thinking_env,
|
|
get_google_api_key_env,
|
|
get_ollama_url_env,
|
|
get_openai_api_key_env,
|
|
get_tool_calls_env,
|
|
)
|
|
from utils.llm_provider import get_llm_provider
|
|
from utils.parsers import parse_bool_or_none
|
|
from utils.randomizers import get_random_uuid
|
|
from utils.schema_utils import ensure_strict_json_schema
|
|
|
|
|
|
class LLMClient:
|
|
def __init__(self):
|
|
self.llm_provider = get_llm_provider()
|
|
self._client = self._get_client()
|
|
self.tool_calls_handler = LLMToolCallsHandler(self)
|
|
|
|
# ? Use tool calls
|
|
def use_tool_calls(self) -> bool:
|
|
if self.llm_provider != LLMProvider.CUSTOM:
|
|
return False
|
|
return parse_bool_or_none(get_tool_calls_env()) or False
|
|
|
|
# ? Disable thinking
|
|
def disable_thinking(self) -> bool:
|
|
return parse_bool_or_none(get_disable_thinking_env()) or False
|
|
|
|
# ? Clients
|
|
def _get_client(self):
|
|
match self.llm_provider:
|
|
case LLMProvider.OPENAI:
|
|
return self._get_openai_client()
|
|
case LLMProvider.GOOGLE:
|
|
return self._get_google_client()
|
|
case LLMProvider.ANTHROPIC:
|
|
return self._get_anthropic_client()
|
|
case LLMProvider.OLLAMA:
|
|
return self._get_ollama_client()
|
|
case LLMProvider.CUSTOM:
|
|
return self._get_custom_client()
|
|
case _:
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail="LLM Provider must be either openai, google, anthropic, ollama, or custom",
|
|
)
|
|
|
|
def _get_openai_client(self):
|
|
if not get_openai_api_key_env():
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail="OpenAI API Key is not set",
|
|
)
|
|
return AsyncOpenAI()
|
|
|
|
def _get_google_client(self):
|
|
if not get_google_api_key_env():
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail="Google API Key is not set",
|
|
)
|
|
return genai.Client()
|
|
|
|
def _get_anthropic_client(self):
|
|
if not get_anthropic_api_key_env():
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail="Anthropic API Key is not set",
|
|
)
|
|
return AsyncAnthropic()
|
|
|
|
def _get_ollama_client(self):
|
|
return AsyncOpenAI(
|
|
base_url=(get_ollama_url_env() or "http://localhost:11434") + "/v1",
|
|
api_key="ollama",
|
|
)
|
|
|
|
def _get_custom_client(self):
|
|
if not get_custom_llm_url_env():
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail="Custom LLM URL is not set",
|
|
)
|
|
return AsyncOpenAI(
|
|
base_url=get_custom_llm_url_env(),
|
|
api_key=get_custom_llm_api_key_env() or "null",
|
|
)
|
|
|
|
# ? Prompts
|
|
def _get_system_prompt(self, messages: List[LLMMessage]) -> str:
|
|
for message in messages:
|
|
if isinstance(message, LLMSystemMessage):
|
|
return message.content
|
|
return ""
|
|
|
|
def _get_user_prompts(self, messages: List[LLMMessage]) -> List[str]:
|
|
return [
|
|
message.content
|
|
for message in messages
|
|
if isinstance(message, LLMUserMessage)
|
|
]
|
|
|
|
def _get_user_llm_messages(self, messages: List[LLMMessage]) -> List[LLMMessage]:
|
|
return [message for message in messages if isinstance(message, LLMUserMessage)]
|
|
|
|
# ? Generate Unstructured Content
|
|
async def _generate_openai(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
max_tokens: Optional[int] = None,
|
|
tools: Optional[List[dict]] = None,
|
|
extra_body: Optional[dict] = None,
|
|
):
|
|
client: AsyncOpenAI = self._client
|
|
response = await client.chat.completions.create(
|
|
model=model,
|
|
messages=[message.model_dump() for message in messages],
|
|
max_completion_tokens=max_tokens,
|
|
tools=tools,
|
|
extra_body=extra_body,
|
|
)
|
|
tool_calls = response.choices[0].message.tool_calls
|
|
if tool_calls:
|
|
tool_call_messages = await self.tool_calls_handler.handle_tool_calls_openai(
|
|
[
|
|
LLMToolCall(
|
|
id=tool_call.id,
|
|
name=tool_call.function.name,
|
|
arguments=tool_call.function.arguments,
|
|
)
|
|
for tool_call in tool_calls
|
|
]
|
|
)
|
|
new_messages = [
|
|
*messages,
|
|
LLMAssistantMessage(
|
|
role="assistant",
|
|
content=response.choices[0].message.content,
|
|
tool_calls=[
|
|
tool_call.model_dump() for tool_call in tool_call_messages
|
|
],
|
|
),
|
|
*tool_call_messages,
|
|
]
|
|
return await self._generate_openai(
|
|
model, new_messages, max_tokens, tools, extra_body
|
|
)
|
|
|
|
return response.choices[0].message.content
|
|
|
|
async def _generate_google(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
client: genai.Client = self._client
|
|
response = await asyncio.to_thread(
|
|
client.models.generate_content,
|
|
model=model,
|
|
contents=self._get_user_prompts(messages),
|
|
config=GenerateContentConfig(
|
|
system_instruction=self._get_system_prompt(messages),
|
|
response_mime_type="text/plain",
|
|
max_output_tokens=max_tokens,
|
|
),
|
|
)
|
|
return response.text
|
|
|
|
async def _generate_anthropic(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
client: AsyncAnthropic = self._client
|
|
response: AnthropicMessage = await client.messages.create(
|
|
model=model,
|
|
system=self._get_system_prompt(messages),
|
|
messages=[
|
|
message.model_dump()
|
|
for message in self._get_user_llm_messages(messages)
|
|
],
|
|
max_tokens=max_tokens or 4000,
|
|
)
|
|
text = ""
|
|
for content in response.content:
|
|
if content.type == "text" and isinstance(content.text, str):
|
|
text += content.text
|
|
if text == "":
|
|
return None
|
|
return text
|
|
|
|
async def _generate_ollama(
|
|
self, model: str, messages: List[LLMMessage], max_tokens: Optional[int] = None
|
|
):
|
|
return await self._generate_openai(model, messages, max_tokens)
|
|
|
|
async def _generate_custom(
|
|
self, model: str, messages: List[LLMMessage], max_tokens: Optional[int] = None
|
|
):
|
|
extra_body = {"enable_thinking": not self.disable_thinking()}
|
|
return await self._generate_openai(
|
|
model, messages, max_tokens, extra_body=extra_body
|
|
)
|
|
|
|
async def generate(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
max_tokens: Optional[int] = None,
|
|
tools: Optional[List[type[LLMTool] | LLMDynamicTool]] = None,
|
|
):
|
|
parsed_tools = self.get_tools(tools)
|
|
|
|
content = None
|
|
match self.llm_provider:
|
|
case LLMProvider.OPENAI:
|
|
content = await self._generate_openai(model, messages, max_tokens)
|
|
case LLMProvider.GOOGLE:
|
|
content = await self._generate_google(model, messages, max_tokens)
|
|
case LLMProvider.ANTHROPIC:
|
|
content = await self._generate_anthropic(model, messages, max_tokens)
|
|
case LLMProvider.OLLAMA:
|
|
content = await self._generate_ollama(model, messages, max_tokens)
|
|
case LLMProvider.CUSTOM:
|
|
content = await self._generate_custom(model, messages, max_tokens)
|
|
if content is None:
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail="LLM did not return any content",
|
|
)
|
|
return content
|
|
|
|
# ? Generate Structured Content
|
|
async def _generate_openai_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
strict: bool = False,
|
|
max_tokens: Optional[int] = None,
|
|
tools: Optional[List[dict]] = None,
|
|
extra_body: Optional[dict] = None,
|
|
):
|
|
client: AsyncOpenAI = self._client
|
|
use_tool_calls = self.use_tool_calls()
|
|
response_schema = response_format
|
|
if strict:
|
|
response_schema = ensure_strict_json_schema(
|
|
response_schema,
|
|
path=(),
|
|
root=response_schema,
|
|
)
|
|
response = await client.chat.completions.create(
|
|
model=model,
|
|
messages=[message.model_dump() for message in messages],
|
|
response_format={
|
|
"type": "json_schema",
|
|
"json_schema": (
|
|
{
|
|
"name": "ResponseSchema",
|
|
"strict": strict,
|
|
"schema": response_schema,
|
|
}
|
|
),
|
|
},
|
|
max_completion_tokens=max_tokens,
|
|
extra_body=extra_body,
|
|
)
|
|
tool_calls = response.choices[0].message.tool_calls
|
|
if tool_calls:
|
|
tool_call_messages = await self.tool_calls_handler.handle_tool_calls_openai(
|
|
[
|
|
LLMToolCall(
|
|
id=tool_call.id,
|
|
name=tool_call.function.name,
|
|
arguments=tool_call.function.arguments,
|
|
)
|
|
for tool_call in tool_calls
|
|
]
|
|
)
|
|
new_messages = [
|
|
*messages,
|
|
LLMAssistantMessage(
|
|
role="assistant",
|
|
content=response.choices[0].message.content,
|
|
tool_calls=[each.model_dump() for each in tool_calls],
|
|
),
|
|
*tool_call_messages,
|
|
]
|
|
return await self._generate_openai_structured(
|
|
model,
|
|
new_messages,
|
|
response_format,
|
|
strict,
|
|
max_tokens,
|
|
tools,
|
|
extra_body,
|
|
)
|
|
content = response.choices[0].message.content
|
|
# else:
|
|
# response = await client.chat.completions.create(
|
|
# model=model,
|
|
# messages=[message.model_dump() for message in messages],
|
|
# tools=[
|
|
# {
|
|
# "type": "function",
|
|
# "function": {
|
|
# "name": "ResponseSchema",
|
|
# "description": "A response to the user's message",
|
|
# "strict": strict,
|
|
# "parameters": response_format,
|
|
# },
|
|
# }
|
|
# ],
|
|
# tool_choice="required",
|
|
# max_completion_tokens=max_tokens,
|
|
# extra_body=extra_body,
|
|
# )
|
|
# tool_calls = response.choices[0].message.tool_calls
|
|
# if tool_calls:
|
|
# content = tool_calls[0].function.arguments
|
|
|
|
if content:
|
|
return json.loads(content)
|
|
return None
|
|
|
|
async def _generate_google_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
client: genai.Client = self._client
|
|
response = await asyncio.to_thread(
|
|
client.models.generate_content,
|
|
model=model,
|
|
contents=self._get_user_prompts(messages),
|
|
config=GenerateContentConfig(
|
|
system_instruction=self._get_system_prompt(messages),
|
|
response_mime_type="application/json",
|
|
response_json_schema=response_format,
|
|
max_output_tokens=max_tokens,
|
|
),
|
|
)
|
|
content = None
|
|
if response.text:
|
|
content = json.loads(response.text)
|
|
|
|
return content
|
|
|
|
async def _generate_anthropic_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
client: AsyncAnthropic = self._client
|
|
response: AnthropicMessage = await client.messages.create(
|
|
model=model,
|
|
system=self._get_system_prompt(messages),
|
|
messages=[
|
|
message.model_dump()
|
|
for message in self._get_user_llm_messages(messages)
|
|
],
|
|
max_tokens=max_tokens or 4000,
|
|
tools=[
|
|
{
|
|
"name": "ResponseSchema",
|
|
"description": "A response to the user's message",
|
|
"input_schema": response_format,
|
|
}
|
|
],
|
|
tool_choice={
|
|
"type": "tool",
|
|
"name": "ResponseSchema",
|
|
},
|
|
)
|
|
content: dict | None = None
|
|
for content_block in response.content:
|
|
if content_block.type == "tool_use":
|
|
content = content_block.input
|
|
|
|
return content
|
|
|
|
async def _generate_ollama_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
strict: bool = False,
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
return await self._generate_openai_structured(
|
|
model, messages, response_format, strict, max_tokens
|
|
)
|
|
|
|
async def _generate_custom_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
strict: bool = False,
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
extra_body = {"enable_thinking": not self.disable_thinking()}
|
|
return await self._generate_openai_structured(
|
|
model, messages, response_format, strict, max_tokens, extra_body
|
|
)
|
|
|
|
async def generate_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
strict: bool = False,
|
|
max_tokens: Optional[int] = None,
|
|
) -> dict:
|
|
content = None
|
|
match self.llm_provider:
|
|
case LLMProvider.OPENAI:
|
|
content = await self._generate_openai_structured(
|
|
model, messages, response_format, strict, max_tokens
|
|
)
|
|
case LLMProvider.GOOGLE:
|
|
content = await self._generate_google_structured(
|
|
model, messages, response_format, max_tokens
|
|
)
|
|
case LLMProvider.ANTHROPIC:
|
|
content = await self._generate_anthropic_structured(
|
|
model, messages, response_format, max_tokens
|
|
)
|
|
case LLMProvider.OLLAMA:
|
|
content = await self._generate_ollama_structured(
|
|
model, messages, response_format, strict, max_tokens
|
|
)
|
|
case LLMProvider.CUSTOM:
|
|
content = await self._generate_custom_structured(
|
|
model, messages, response_format, strict, max_tokens
|
|
)
|
|
if content is None:
|
|
raise HTTPException(
|
|
status_code=400,
|
|
detail="LLM did not return any content",
|
|
)
|
|
return content
|
|
|
|
# ? Stream Unstructured Content
|
|
async def _stream_openai(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
max_tokens: Optional[int] = None,
|
|
tools: Optional[List[dict]] = None,
|
|
extra_body: Optional[dict] = None,
|
|
):
|
|
client: AsyncOpenAI = self._client
|
|
async with client.chat.completions.stream(
|
|
model=model,
|
|
messages=[message.model_dump() for message in messages],
|
|
max_completion_tokens=max_tokens,
|
|
extra_body=extra_body,
|
|
) as stream:
|
|
tool_calls = []
|
|
async for event in stream:
|
|
if (
|
|
event.type == "tool_calls.function.arguments.delta"
|
|
and event.name == "ResponseSchema"
|
|
):
|
|
yield event.arguments_delta
|
|
elif event.type == "content.delta":
|
|
yield event.delta
|
|
elif (
|
|
event.type == "tool_calls.function.arguments.done"
|
|
and event.name != "ResponseSchema"
|
|
):
|
|
tool_calls.append(
|
|
LLMToolCall(
|
|
id=get_random_uuid(),
|
|
name=event.name,
|
|
arguments=event.arguments,
|
|
)
|
|
)
|
|
if tool_calls:
|
|
tool_call_messages = (
|
|
await self.tool_calls_handler.handle_tool_calls_openai(tool_calls)
|
|
)
|
|
|
|
new_messages = [
|
|
*messages,
|
|
*tool_call_messages,
|
|
]
|
|
|
|
async def _stream_google(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
client: genai.Client = self._client
|
|
async for event in iterator_to_async(client.models.generate_content_stream)(
|
|
model=model,
|
|
contents=self._get_user_prompts(messages),
|
|
config=GenerateContentConfig(
|
|
system_instruction=self._get_system_prompt(messages),
|
|
response_mime_type="text/plain",
|
|
max_output_tokens=max_tokens,
|
|
),
|
|
):
|
|
if event.text:
|
|
yield event.text
|
|
|
|
async def _stream_anthropic(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
client: AsyncAnthropic = self._client
|
|
async with client.messages.stream(
|
|
model=model,
|
|
system=self._get_system_prompt(messages),
|
|
messages=[
|
|
message.model_dump()
|
|
for message in self._get_user_llm_messages(messages)
|
|
],
|
|
max_tokens=max_tokens or 4000,
|
|
) as stream:
|
|
async for event in stream:
|
|
event: AnthropicMessageStreamEvent = event
|
|
if event.type == "text" and isinstance(event.text, str):
|
|
yield event.text
|
|
|
|
def _stream_ollama(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
return self._stream_openai(model, messages, max_tokens)
|
|
|
|
def _stream_custom(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
extra_body = {"enable_thinking": not self.disable_thinking()}
|
|
return self._stream_openai(model, messages, max_tokens, extra_body)
|
|
|
|
def stream(
|
|
self, model: str, messages: List[LLMMessage], max_tokens: Optional[int] = None
|
|
):
|
|
match self.llm_provider:
|
|
case LLMProvider.OPENAI:
|
|
return self._stream_openai(model, messages, max_tokens)
|
|
case LLMProvider.GOOGLE:
|
|
return self._stream_google(model, messages, max_tokens)
|
|
case LLMProvider.ANTHROPIC:
|
|
return self._stream_anthropic(model, messages, max_tokens)
|
|
case LLMProvider.OLLAMA:
|
|
return self._stream_ollama(model, messages, max_tokens)
|
|
case LLMProvider.CUSTOM:
|
|
return self._stream_custom(model, messages, max_tokens)
|
|
|
|
# ? Stream Structured Content
|
|
async def _stream_openai_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
strict: bool = False,
|
|
max_tokens: Optional[int] = None,
|
|
tools: Optional[List[dict]] = None,
|
|
extra_body: Optional[dict] = None,
|
|
):
|
|
client: AsyncOpenAI = self._client
|
|
use_tool_calls = self.use_tool_calls()
|
|
response_schema = response_format
|
|
if strict:
|
|
response_schema = ensure_strict_json_schema(
|
|
response_schema,
|
|
path=(),
|
|
root=response_schema,
|
|
)
|
|
if not use_tool_calls:
|
|
async with client.chat.completions.stream(
|
|
model=model,
|
|
messages=[message.model_dump() for message in messages],
|
|
max_completion_tokens=max_tokens,
|
|
response_format=(
|
|
{
|
|
"type": "json_schema",
|
|
"json_schema": {
|
|
"name": "ResponseSchema",
|
|
"strict": strict,
|
|
"schema": response_schema,
|
|
},
|
|
}
|
|
),
|
|
extra_body=extra_body,
|
|
) as stream:
|
|
async for event in stream:
|
|
if event.type == "content.delta":
|
|
yield event.delta
|
|
else:
|
|
async with client.chat.completions.stream(
|
|
model=model,
|
|
messages=[message.model_dump() for message in messages],
|
|
max_completion_tokens=max_tokens,
|
|
tools=[
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "ResponseSchema",
|
|
"description": "A response to the user's message",
|
|
"strict": strict,
|
|
"parameters": response_format,
|
|
},
|
|
}
|
|
],
|
|
tool_choice="required",
|
|
extra_body=extra_body,
|
|
) as stream:
|
|
async for event in stream:
|
|
if event.type == "tool_calls.function.arguments.delta":
|
|
yield event.arguments_delta
|
|
|
|
async def _stream_google_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
client: genai.Client = self._client
|
|
async for event in iterator_to_async(client.models.generate_content_stream)(
|
|
model=model,
|
|
contents=self._get_user_prompts(messages),
|
|
config=GenerateContentConfig(
|
|
system_instruction=self._get_system_prompt(messages),
|
|
response_mime_type="application/json",
|
|
response_json_schema=response_format,
|
|
max_output_tokens=max_tokens,
|
|
),
|
|
):
|
|
if event.text:
|
|
yield event.text
|
|
|
|
async def _stream_anthropic_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
client: AsyncAnthropic = self._client
|
|
async with client.messages.stream(
|
|
model=model,
|
|
system=self._get_system_prompt(messages),
|
|
messages=[
|
|
message.model_dump()
|
|
for message in self._get_user_llm_messages(messages)
|
|
],
|
|
max_tokens=max_tokens or 4000,
|
|
tools=[
|
|
{
|
|
"name": "ResponseSchema",
|
|
"description": "A response to the user's message",
|
|
"input_schema": response_format,
|
|
}
|
|
],
|
|
tool_choice={
|
|
"type": "tool",
|
|
"name": "ResponseSchema",
|
|
},
|
|
) as stream:
|
|
async for event in stream:
|
|
event: AnthropicMessageStreamEvent = event
|
|
if event.type == "input_json" and isinstance(event.partial_json, str):
|
|
yield event.partial_json
|
|
|
|
def _stream_ollama_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
strict: bool = False,
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
return self._stream_openai_structured(
|
|
model, messages, response_format, strict, max_tokens
|
|
)
|
|
|
|
def _stream_custom_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
strict: bool = False,
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
extra_body = {"enable_thinking": not self.disable_thinking()}
|
|
return self._stream_openai_structured(
|
|
model, messages, response_format, strict, max_tokens, extra_body
|
|
)
|
|
|
|
def stream_structured(
|
|
self,
|
|
model: str,
|
|
messages: List[LLMMessage],
|
|
response_format: dict,
|
|
strict: bool = False,
|
|
max_tokens: Optional[int] = None,
|
|
):
|
|
match self.llm_provider:
|
|
case LLMProvider.OPENAI:
|
|
return self._stream_openai_structured(
|
|
model, messages, response_format, strict, max_tokens
|
|
)
|
|
case LLMProvider.GOOGLE:
|
|
return self._stream_google_structured(
|
|
model, messages, response_format, max_tokens
|
|
)
|
|
case LLMProvider.ANTHROPIC:
|
|
return self._stream_anthropic_structured(
|
|
model, messages, response_format, max_tokens
|
|
)
|
|
case LLMProvider.OLLAMA:
|
|
return self._stream_ollama_structured(
|
|
model, messages, response_format, strict, max_tokens
|
|
)
|
|
case LLMProvider.CUSTOM:
|
|
return self._stream_custom_structured(
|
|
model, messages, response_format, strict, max_tokens
|
|
)
|
|
|
|
# ? Tool call handling
|
|
def get_tools(self, tools: Optional[List[type[LLMTool] | LLMDynamicTool]] = None):
|
|
if tools is None:
|
|
return None
|
|
parsed_tools = map(self.tool_calls_handler.parse_tool, tools)
|
|
return list(parsed_tools)
|