mirror of
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style: introduce ruff as linter and formatter (#1356)
* style: remove necessary imports * style: fix F841 * style: fix F401 * style: fix F811 * style: fix E402 * style: fix E721 * style: fix E722 * style: fix E722 * style: fix F541 * style: ruff format * style: all passed * style: add ruff in deps * style: more ignores in ruff.toml * style: add pre-commit
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commit
209f16af76
@@ -2,16 +2,12 @@ from __future__ import annotations
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import typing
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import json
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import traceback
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import base64
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import anthropic
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import httpx
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from ....core import app
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from .. import entities, errors, requester
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from .. import errors, requester
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from .. import entities, errors
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from ....core import entities as core_entities
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from ... import entities as llm_entities
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from ...tools import entities as tools_entities
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@@ -29,7 +25,6 @@ class AnthropicMessages(requester.LLMAPIRequester):
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}
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async def initialize(self):
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httpx_client = anthropic._base_client.AsyncHttpxClientWrapper(
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base_url=self.requester_cfg['base_url'],
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# cast to a valid type because mypy doesn't understand our type narrowing
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@@ -40,7 +35,7 @@ class AnthropicMessages(requester.LLMAPIRequester):
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)
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self.client = anthropic.AsyncAnthropic(
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api_key="",
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api_key='',
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http_client=httpx_client,
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)
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@@ -55,7 +50,7 @@ class AnthropicMessages(requester.LLMAPIRequester):
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self.client.api_key = model.token_mgr.get_token()
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args = extra_args.copy()
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args["model"] = model.model_entity.name
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args['model'] = model.model_entity.name
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# 处理消息
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@@ -63,14 +58,15 @@ class AnthropicMessages(requester.LLMAPIRequester):
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system_role_message = None
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for i, m in enumerate(messages):
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if m.role == "system":
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if m.role == 'system':
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system_role_message = m
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messages.pop(i)
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break
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if isinstance(system_role_message, llm_entities.Message) \
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and isinstance(system_role_message.content, str):
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if isinstance(system_role_message, llm_entities.Message) and isinstance(
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system_role_message.content, str
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):
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args['system'] = system_role_message.content
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req_messages = []
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@@ -79,67 +75,64 @@ class AnthropicMessages(requester.LLMAPIRequester):
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if m.role == 'tool':
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tool_call_id = m.tool_call_id
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req_messages.append({
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"role": "user",
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"content": [
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{
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"type": "tool_result",
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"tool_use_id": tool_call_id,
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"content": m.content
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}
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]
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})
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req_messages.append(
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{
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'role': 'user',
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'content': [
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{
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'type': 'tool_result',
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'tool_use_id': tool_call_id,
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'content': m.content,
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}
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],
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}
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)
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continue
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msg_dict = m.dict(exclude_none=True)
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if isinstance(m.content, str) and m.content.strip() != "":
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msg_dict["content"] = [
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{
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"type": "text",
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"text": m.content
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}
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]
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if isinstance(m.content, str) and m.content.strip() != '':
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msg_dict['content'] = [{'type': 'text', 'text': m.content}]
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elif isinstance(m.content, list):
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for i, ce in enumerate(m.content):
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if ce.type == "image_base64":
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image_b64, image_format = await image.extract_b64_and_format(ce.image_base64)
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if ce.type == 'image_base64':
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image_b64, image_format = await image.extract_b64_and_format(
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ce.image_base64
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)
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alter_image_ele = {
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": f"image/{image_format}",
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"data": image_b64
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}
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'type': 'image',
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'source': {
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'type': 'base64',
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'media_type': f'image/{image_format}',
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'data': image_b64,
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},
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}
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msg_dict["content"][i] = alter_image_ele
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msg_dict['content'][i] = alter_image_ele
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if m.tool_calls:
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for tool_call in m.tool_calls:
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msg_dict["content"].append({
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"type": "tool_use",
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"id": tool_call.id,
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"name": tool_call.function.name,
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"input": json.loads(tool_call.function.arguments)
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})
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msg_dict['content'].append(
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{
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'type': 'tool_use',
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'id': tool_call.id,
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'name': tool_call.function.name,
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'input': json.loads(tool_call.function.arguments),
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}
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)
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del msg_dict["tool_calls"]
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del msg_dict['tool_calls']
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req_messages.append(msg_dict)
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args["messages"] = req_messages
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args['messages'] = req_messages
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if funcs:
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tools = await self.ap.tool_mgr.generate_tools_for_anthropic(funcs)
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if tools:
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args["tools"] = tools
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args['tools'] = tools
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try:
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# print(json.dumps(args, indent=4, ensure_ascii=False))
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@@ -149,23 +142,24 @@ class AnthropicMessages(requester.LLMAPIRequester):
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'content': '',
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'role': resp.role,
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}
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assert type(resp) is anthropic.types.message.Message
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for block in resp.content:
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if block.type == 'thinking':
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args['content'] = '<think>' + block.thinking + '</think>\n' + args['content']
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args['content'] = (
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'<think>' + block.thinking + '</think>\n' + args['content']
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)
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elif block.type == 'text':
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args['content'] += block.text
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elif block.type == 'tool_use':
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assert type(block) is anthropic.types.tool_use_block.ToolUseBlock
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tool_call = llm_entities.ToolCall(
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id=block.id,
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type="function",
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type='function',
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function=llm_entities.FunctionCall(
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name=block.name,
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arguments=json.dumps(block.input)
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)
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name=block.name, arguments=json.dumps(block.input)
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),
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)
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if 'tool_calls' not in args:
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args['tool_calls'] = []
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@@ -4,8 +4,6 @@ import typing
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import openai
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from . import chatcmpl
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from .. import requester
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from ....core import app
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class BailianChatCompletions(chatcmpl.OpenAIChatCompletions):
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@@ -2,22 +2,15 @@ from __future__ import annotations
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import asyncio
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import typing
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import json
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import base64
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from typing import AsyncGenerator
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import openai
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import openai.types.chat.chat_completion as chat_completion
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import openai.types.chat.chat_completion_message_tool_call as chat_completion_message_tool_call
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import httpx
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import aiohttp
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import async_lru
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from .. import entities, errors, requester
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from ....core import entities as core_entities, app
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from .. import errors, requester
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from ....core import entities as core_entities
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from ... import entities as llm_entities
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from ...tools import entities as tools_entities
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from ....utils import image
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class OpenAIChatCompletions(requester.LLMAPIRequester):
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@@ -26,18 +19,17 @@ class OpenAIChatCompletions(requester.LLMAPIRequester):
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client: openai.AsyncClient
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default_config: dict[str, typing.Any] = {
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"base_url": "https://api.openai.com/v1",
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"timeout": 120,
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'base_url': 'https://api.openai.com/v1',
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'timeout': 120,
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}
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async def initialize(self):
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self.client = openai.AsyncClient(
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api_key="",
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base_url=self.requester_cfg["base_url"],
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timeout=self.requester_cfg["timeout"],
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api_key='',
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base_url=self.requester_cfg['base_url'],
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timeout=self.requester_cfg['timeout'],
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http_client=httpx.AsyncClient(
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trust_env=True, timeout=self.requester_cfg["timeout"]
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trust_env=True, timeout=self.requester_cfg['timeout']
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),
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)
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@@ -54,8 +46,8 @@ class OpenAIChatCompletions(requester.LLMAPIRequester):
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chatcmpl_message = chat_completion.choices[0].message.model_dump()
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# 确保 role 字段存在且不为 None
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if "role" not in chatcmpl_message or chatcmpl_message["role"] is None:
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chatcmpl_message["role"] = "assistant"
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if 'role' not in chatcmpl_message or chatcmpl_message['role'] is None:
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chatcmpl_message['role'] = 'assistant'
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message = llm_entities.Message(**chatcmpl_message)
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@@ -72,27 +64,27 @@ class OpenAIChatCompletions(requester.LLMAPIRequester):
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self.client.api_key = use_model.token_mgr.get_token()
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args = extra_args.copy()
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args["model"] = use_model.model_entity.name
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args['model'] = use_model.model_entity.name
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if use_funcs:
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tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
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if tools:
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args["tools"] = tools
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args['tools'] = tools
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# 设置此次请求中的messages
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messages = req_messages.copy()
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# 检查vision
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for msg in messages:
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if "content" in msg and isinstance(msg["content"], list):
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for me in msg["content"]:
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if me["type"] == "image_base64":
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me["image_url"] = {"url": me["image_base64"]}
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me["type"] = "image_url"
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del me["image_base64"]
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if 'content' in msg and isinstance(msg['content'], list):
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for me in msg['content']:
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if me['type'] == 'image_base64':
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me['image_url'] = {'url': me['image_base64']}
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me['type'] = 'image_url'
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del me['image_base64']
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args["messages"] = messages
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args['messages'] = messages
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# 发送请求
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resp = await self._req(args)
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@@ -113,15 +105,15 @@ class OpenAIChatCompletions(requester.LLMAPIRequester):
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req_messages = [] # req_messages 仅用于类内,外部同步由 query.messages 进行
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for m in messages:
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msg_dict = m.dict(exclude_none=True)
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content = msg_dict.get("content")
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content = msg_dict.get('content')
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if isinstance(content, list):
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# 检查 content 列表中是否每个部分都是文本
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if all(
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isinstance(part, dict) and part.get("type") == "text"
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isinstance(part, dict) and part.get('type') == 'text'
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for part in content
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):
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# 将所有文本部分合并为一个字符串
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msg_dict["content"] = "\n".join(part["text"] for part in content)
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msg_dict['content'] = '\n'.join(part['text'] for part in content)
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req_messages.append(msg_dict)
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try:
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@@ -133,17 +125,17 @@ class OpenAIChatCompletions(requester.LLMAPIRequester):
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extra_args=extra_args,
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)
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except asyncio.TimeoutError:
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raise errors.RequesterError("请求超时")
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raise errors.RequesterError('请求超时')
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except openai.BadRequestError as e:
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if "context_length_exceeded" in e.message:
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raise errors.RequesterError(f"上文过长,请重置会话: {e.message}")
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if 'context_length_exceeded' in e.message:
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raise errors.RequesterError(f'上文过长,请重置会话: {e.message}')
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else:
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raise errors.RequesterError(f"请求参数错误: {e.message}")
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raise errors.RequesterError(f'请求参数错误: {e.message}')
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except openai.AuthenticationError as e:
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raise errors.RequesterError(f"无效的 api-key: {e.message}")
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raise errors.RequesterError(f'无效的 api-key: {e.message}')
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except openai.NotFoundError as e:
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raise errors.RequesterError(f"请求路径错误: {e.message}")
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raise errors.RequesterError(f'请求路径错误: {e.message}')
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except openai.RateLimitError as e:
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raise errors.RequesterError(f"请求过于频繁或余额不足: {e.message}")
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raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
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except openai.APIError as e:
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raise errors.RequesterError(f"请求错误: {e.message}")
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raise errors.RequesterError(f'请求错误: {e.message}')
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@@ -3,8 +3,8 @@ from __future__ import annotations
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import typing
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from . import chatcmpl
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from .. import entities, errors, requester
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from ....core import entities as core_entities, app
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from .. import errors, requester
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from ....core import entities as core_entities
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from ... import entities as llm_entities
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from ...tools import entities as tools_entities
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@@ -28,23 +28,23 @@ class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
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self.client.api_key = use_model.token_mgr.get_token()
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args = extra_args.copy()
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args["model"] = use_model.model_entity.name
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args['model'] = use_model.model_entity.name
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if use_funcs:
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tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
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if tools:
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args["tools"] = tools
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args['tools'] = tools
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# 设置此次请求中的messages
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messages = req_messages
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# deepseek 不支持多模态,把content都转换成纯文字
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for m in messages:
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if 'content' in m and isinstance(m["content"], list):
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m["content"] = " ".join([c["text"] for c in m["content"]])
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if 'content' in m and isinstance(m['content'], list):
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m['content'] = ' '.join([c['text'] for c in m['content']])
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args["messages"] = messages
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args['messages'] = messages
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# 发送请求
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resp = await self._req(args)
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@@ -55,4 +55,4 @@ class DeepseekChatCompletions(chatcmpl.OpenAIChatCompletions):
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# 处理请求结果
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message = await self._make_msg(resp)
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return message
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return message
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@@ -1,17 +1,13 @@
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from __future__ import annotations
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import json
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import asyncio
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import aiohttp
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import typing
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from . import chatcmpl
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from .. import entities, errors, requester
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from ....core import app, entities as core_entities
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from .. import requester
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from ....core import entities as core_entities
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from ... import entities as llm_entities
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from ...tools import entities as tools_entities
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from .. import entities as modelmgr_entities
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class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
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@@ -33,20 +29,20 @@ class GiteeAIChatCompletions(chatcmpl.OpenAIChatCompletions):
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self.client.api_key = use_model.token_mgr.get_token()
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args = extra_args.copy()
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args["model"] = use_model.model_entity.name
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args['model'] = use_model.model_entity.name
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if use_funcs:
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tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
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if tools:
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args["tools"] = tools
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args['tools'] = tools
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# gitee 不支持多模态,把content都转换成纯文字
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for m in req_messages:
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if 'content' in m and isinstance(m["content"], list):
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m["content"] = " ".join([c["text"] for c in m["content"]])
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if 'content' in m and isinstance(m['content'], list):
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m['content'] = ' '.join([c['text'] for c in m['content']])
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args["messages"] = req_messages
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args['messages'] = req_messages
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resp = await self._req(args)
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@@ -4,8 +4,6 @@ import typing
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import openai
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from . import chatcmpl
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from .. import requester
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from ....core import app
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class LmStudioChatCompletions(chatcmpl.OpenAIChatCompletions):
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@@ -2,11 +2,10 @@ from __future__ import annotations
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import typing
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from ....core import app
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|
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from . import chatcmpl
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from .. import entities, errors, requester
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from ....core import entities as core_entities, app
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from .. import requester
|
||||
from ....core import entities as core_entities
|
||||
from ... import entities as llm_entities
|
||||
from ...tools import entities as tools_entities
|
||||
|
||||
@@ -30,26 +29,26 @@ class MoonshotChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
self.client.api_key = use_model.token_mgr.get_token()
|
||||
|
||||
args = extra_args.copy()
|
||||
args["model"] = use_model.model_entity.name
|
||||
args['model'] = use_model.model_entity.name
|
||||
|
||||
if use_funcs:
|
||||
tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
|
||||
|
||||
if tools:
|
||||
args["tools"] = tools
|
||||
args['tools'] = tools
|
||||
|
||||
# 设置此次请求中的messages
|
||||
messages = req_messages
|
||||
|
||||
# deepseek 不支持多模态,把content都转换成纯文字
|
||||
for m in messages:
|
||||
if 'content' in m and isinstance(m["content"], list):
|
||||
m["content"] = " ".join([c["text"] for c in m["content"]])
|
||||
if 'content' in m and isinstance(m['content'], list):
|
||||
m['content'] = ' '.join([c['text'] for c in m['content']])
|
||||
|
||||
# 删除空的
|
||||
messages = [m for m in messages if m["content"].strip() != ""]
|
||||
messages = [m for m in messages if m['content'].strip() != '']
|
||||
|
||||
args["messages"] = messages
|
||||
args['messages'] = messages
|
||||
|
||||
# 发送请求
|
||||
resp = await self._req(args)
|
||||
@@ -57,4 +56,4 @@ class MoonshotChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
# 处理请求结果
|
||||
message = await self._make_msg(resp)
|
||||
|
||||
return message
|
||||
return message
|
||||
|
||||
@@ -6,18 +6,15 @@ import typing
|
||||
from typing import Union, Mapping, Any, AsyncIterator
|
||||
import uuid
|
||||
import json
|
||||
import base64
|
||||
|
||||
import async_lru
|
||||
import ollama
|
||||
|
||||
from .. import entities, errors, requester
|
||||
from .. import errors, requester
|
||||
from ... import entities as llm_entities
|
||||
from ...tools import entities as tools_entities
|
||||
from ....core import app, entities as core_entities
|
||||
from ....utils import image
|
||||
from ....core import entities as core_entities
|
||||
|
||||
REQUESTER_NAME: str = "ollama-chat"
|
||||
REQUESTER_NAME: str = 'ollama-chat'
|
||||
|
||||
|
||||
class OllamaChatCompletions(requester.LLMAPIRequester):
|
||||
@@ -26,13 +23,13 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
|
||||
client: ollama.AsyncClient
|
||||
|
||||
default_config: dict[str, typing.Any] = {
|
||||
"base_url": "http://127.0.0.1:11434",
|
||||
"timeout": 120,
|
||||
'base_url': 'http://127.0.0.1:11434',
|
||||
'timeout': 120,
|
||||
}
|
||||
|
||||
async def initialize(self):
|
||||
os.environ["OLLAMA_HOST"] = self.requester_cfg["base_url"]
|
||||
self.client = ollama.AsyncClient(timeout=self.requester_cfg["timeout"])
|
||||
os.environ['OLLAMA_HOST'] = self.requester_cfg['base_url']
|
||||
self.client = ollama.AsyncClient(timeout=self.requester_cfg['timeout'])
|
||||
|
||||
async def _req(
|
||||
self,
|
||||
@@ -49,35 +46,35 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
|
||||
extra_args: dict[str, typing.Any] = {},
|
||||
) -> llm_entities.Message:
|
||||
args = extra_args.copy()
|
||||
args["model"] = use_model.model_entity.name
|
||||
args['model'] = use_model.model_entity.name
|
||||
|
||||
messages: list[dict] = req_messages.copy()
|
||||
for msg in messages:
|
||||
if "content" in msg and isinstance(msg["content"], list):
|
||||
if 'content' in msg and isinstance(msg['content'], list):
|
||||
text_content: list = []
|
||||
image_urls: list = []
|
||||
for me in msg["content"]:
|
||||
if me["type"] == "text":
|
||||
text_content.append(me["text"])
|
||||
elif me["type"] == "image_base64":
|
||||
image_urls.append(me["image_base64"])
|
||||
for me in msg['content']:
|
||||
if me['type'] == 'text':
|
||||
text_content.append(me['text'])
|
||||
elif me['type'] == 'image_base64':
|
||||
image_urls.append(me['image_base64'])
|
||||
|
||||
msg["content"] = "\n".join(text_content)
|
||||
msg["images"] = [url.split(",")[1] for url in image_urls]
|
||||
msg['content'] = '\n'.join(text_content)
|
||||
msg['images'] = [url.split(',')[1] for url in image_urls]
|
||||
if (
|
||||
"tool_calls" in msg
|
||||
'tool_calls' in msg
|
||||
): # LangBot 内部以 str 存储 tool_calls 的参数,这里需要转换为 dict
|
||||
for tool_call in msg["tool_calls"]:
|
||||
tool_call["function"]["arguments"] = json.loads(
|
||||
tool_call["function"]["arguments"]
|
||||
for tool_call in msg['tool_calls']:
|
||||
tool_call['function']['arguments'] = json.loads(
|
||||
tool_call['function']['arguments']
|
||||
)
|
||||
args["messages"] = messages
|
||||
args['messages'] = messages
|
||||
|
||||
args["tools"] = []
|
||||
args['tools'] = []
|
||||
if user_funcs:
|
||||
tools = await self.ap.tool_mgr.generate_tools_for_openai(user_funcs)
|
||||
if tools:
|
||||
args["tools"] = tools
|
||||
args['tools'] = tools
|
||||
|
||||
resp = await self._req(args)
|
||||
message: llm_entities.Message = await self._make_msg(resp)
|
||||
@@ -93,7 +90,7 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
|
||||
ret_msg: llm_entities.Message = None
|
||||
|
||||
if message.content is not None:
|
||||
ret_msg = llm_entities.Message(role="assistant", content=message.content)
|
||||
ret_msg = llm_entities.Message(role='assistant', content=message.content)
|
||||
if message.tool_calls is not None and len(message.tool_calls) > 0:
|
||||
tool_calls: list[llm_entities.ToolCall] = []
|
||||
|
||||
@@ -101,7 +98,7 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
|
||||
tool_calls.append(
|
||||
llm_entities.ToolCall(
|
||||
id=uuid.uuid4().hex,
|
||||
type="function",
|
||||
type='function',
|
||||
function=llm_entities.FunctionCall(
|
||||
name=tool_call.function.name,
|
||||
arguments=json.dumps(tool_call.function.arguments),
|
||||
@@ -123,13 +120,13 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
|
||||
req_messages: list = []
|
||||
for m in messages:
|
||||
msg_dict: dict = m.dict(exclude_none=True)
|
||||
content: Any = msg_dict.get("content")
|
||||
content: Any = msg_dict.get('content')
|
||||
if isinstance(content, list):
|
||||
if all(
|
||||
isinstance(part, dict) and part.get("type") == "text"
|
||||
isinstance(part, dict) and part.get('type') == 'text'
|
||||
for part in content
|
||||
):
|
||||
msg_dict["content"] = "\n".join(part["text"] for part in content)
|
||||
msg_dict['content'] = '\n'.join(part['text'] for part in content)
|
||||
req_messages.append(msg_dict)
|
||||
try:
|
||||
return await self._closure(
|
||||
@@ -140,4 +137,4 @@ class OllamaChatCompletions(requester.LLMAPIRequester):
|
||||
extra_args=extra_args,
|
||||
)
|
||||
except asyncio.TimeoutError:
|
||||
raise errors.RequesterError("请求超时")
|
||||
raise errors.RequesterError('请求超时')
|
||||
|
||||
@@ -4,8 +4,6 @@ import typing
|
||||
import openai
|
||||
|
||||
from . import chatcmpl
|
||||
from .. import requester
|
||||
from ....core import app
|
||||
|
||||
|
||||
class SiliconFlowChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
|
||||
@@ -4,8 +4,6 @@ import typing
|
||||
import openai
|
||||
|
||||
from . import chatcmpl
|
||||
from .. import requester
|
||||
from ....core import app
|
||||
|
||||
|
||||
class VolcArkChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
|
||||
@@ -4,8 +4,6 @@ import typing
|
||||
import openai
|
||||
|
||||
from . import chatcmpl
|
||||
from .. import requester
|
||||
from ....core import app
|
||||
|
||||
|
||||
class XaiChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
|
||||
@@ -3,9 +3,7 @@ from __future__ import annotations
|
||||
import typing
|
||||
import openai
|
||||
|
||||
from ....core import app
|
||||
from . import chatcmpl
|
||||
from .. import requester
|
||||
|
||||
|
||||
class ZhipuAIChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
|
||||
Reference in New Issue
Block a user