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b2524eec49
### What problem does this PR solve? #1363 ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
1447 lines
55 KiB
Python
1447 lines
55 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from openai.lib.azure import AzureOpenAI
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from zhipuai import ZhipuAI
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from dashscope import Generation
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from abc import ABC
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from openai import OpenAI
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import openai
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from ollama import Client
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from rag.nlp import is_english
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from rag.utils import num_tokens_from_string
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from groq import Groq
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import os
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import json
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import requests
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import asyncio
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class Base(ABC):
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def __init__(self, key, model_name, base_url):
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timeout = int(os.environ.get('LM_TIMEOUT_SECONDS', 600))
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self.client = OpenAI(api_key=key, base_url=base_url, timeout=timeout)
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self.model_name = model_name
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def chat(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=history,
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**gen_conf)
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ans = response.choices[0].message.content.strip()
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if response.choices[0].finish_reason == "length":
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ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
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[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
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return ans, response.usage.total_tokens
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except openai.APIError as e:
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return "**ERROR**: " + str(e), 0
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def chat_streamly(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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ans = ""
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total_tokens = 0
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=history,
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stream=True,
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**gen_conf)
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for resp in response:
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if not resp.choices: continue
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if not resp.choices[0].delta.content:
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resp.choices[0].delta.content = ""
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ans += resp.choices[0].delta.content
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total_tokens += 1
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if not hasattr(resp, "usage") or not resp.usage:
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total_tokens = (
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total_tokens
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+ num_tokens_from_string(resp.choices[0].delta.content)
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)
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elif isinstance(resp.usage, dict):
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total_tokens = resp.usage.get("total_tokens", total_tokens)
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else: total_tokens = resp.usage.total_tokens
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if resp.choices[0].finish_reason == "length":
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ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
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[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
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yield ans
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except openai.APIError as e:
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yield ans + "\n**ERROR**: " + str(e)
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yield total_tokens
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class GptTurbo(Base):
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def __init__(self, key, model_name="gpt-3.5-turbo", base_url="https://api.openai.com/v1"):
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if not base_url: base_url = "https://api.openai.com/v1"
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super().__init__(key, model_name, base_url)
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class MoonshotChat(Base):
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def __init__(self, key, model_name="moonshot-v1-8k", base_url="https://api.moonshot.cn/v1"):
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if not base_url: base_url = "https://api.moonshot.cn/v1"
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super().__init__(key, model_name, base_url)
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class XinferenceChat(Base):
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def __init__(self, key=None, model_name="", base_url=""):
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if not base_url:
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raise ValueError("Local llm url cannot be None")
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if base_url.split("/")[-1] != "v1":
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base_url = os.path.join(base_url, "v1")
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super().__init__(key, model_name, base_url)
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class HuggingFaceChat(Base):
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def __init__(self, key=None, model_name="", base_url=""):
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if not base_url:
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raise ValueError("Local llm url cannot be None")
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if base_url.split("/")[-1] != "v1":
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base_url = os.path.join(base_url, "v1")
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super().__init__(key, model_name, base_url)
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class DeepSeekChat(Base):
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def __init__(self, key, model_name="deepseek-chat", base_url="https://api.deepseek.com/v1"):
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if not base_url: base_url = "https://api.deepseek.com/v1"
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super().__init__(key, model_name, base_url)
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class AzureChat(Base):
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def __init__(self, key, model_name, **kwargs):
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api_key = json.loads(key).get('api_key', '')
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api_version = json.loads(key).get('api_version', '2024-02-01')
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self.client = AzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version)
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self.model_name = model_name
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class BaiChuanChat(Base):
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def __init__(self, key, model_name="Baichuan3-Turbo", base_url="https://api.baichuan-ai.com/v1"):
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if not base_url:
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base_url = "https://api.baichuan-ai.com/v1"
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super().__init__(key, model_name, base_url)
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@staticmethod
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def _format_params(params):
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return {
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"temperature": params.get("temperature", 0.3),
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"max_tokens": params.get("max_tokens", 2048),
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"top_p": params.get("top_p", 0.85),
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}
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def chat(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=history,
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extra_body={
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"tools": [{
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"type": "web_search",
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"web_search": {
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"enable": True,
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"search_mode": "performance_first"
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}
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}]
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},
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**self._format_params(gen_conf))
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ans = response.choices[0].message.content.strip()
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if response.choices[0].finish_reason == "length":
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ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
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[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
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return ans, response.usage.total_tokens
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except openai.APIError as e:
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return "**ERROR**: " + str(e), 0
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def chat_streamly(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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ans = ""
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total_tokens = 0
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=history,
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extra_body={
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"tools": [{
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"type": "web_search",
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"web_search": {
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"enable": True,
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"search_mode": "performance_first"
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}
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}]
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},
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stream=True,
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**self._format_params(gen_conf))
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for resp in response:
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if not resp.choices: continue
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if not resp.choices[0].delta.content:
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resp.choices[0].delta.content = ""
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ans += resp.choices[0].delta.content
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total_tokens = (
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(
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total_tokens
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+ num_tokens_from_string(resp.choices[0].delta.content)
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)
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if not hasattr(resp, "usage")
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else resp.usage["total_tokens"]
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)
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if resp.choices[0].finish_reason == "length":
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ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
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[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
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yield ans
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except Exception as e:
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yield ans + "\n**ERROR**: " + str(e)
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yield total_tokens
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class QWenChat(Base):
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def __init__(self, key, model_name=Generation.Models.qwen_turbo, **kwargs):
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import dashscope
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dashscope.api_key = key
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self.model_name = model_name
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def chat(self, system, history, gen_conf):
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stream_flag = str(os.environ.get('QWEN_CHAT_BY_STREAM', 'true')).lower() == 'true'
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if not stream_flag:
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from http import HTTPStatus
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if system:
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history.insert(0, {"role": "system", "content": system})
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response = Generation.call(
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self.model_name,
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messages=history,
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result_format='message',
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**gen_conf
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)
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ans = ""
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tk_count = 0
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if response.status_code == HTTPStatus.OK:
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ans += response.output.choices[0]['message']['content']
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tk_count += response.usage.total_tokens
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if response.output.choices[0].get("finish_reason", "") == "length":
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ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
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[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
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return ans, tk_count
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return "**ERROR**: " + response.message, tk_count
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else:
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g = self._chat_streamly(system, history, gen_conf, incremental_output=True)
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result_list = list(g)
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error_msg_list = [item for item in result_list if str(item).find("**ERROR**") >= 0]
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if len(error_msg_list) > 0:
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return "**ERROR**: " + "".join(error_msg_list) , 0
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else:
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return "".join(result_list[:-1]), result_list[-1]
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def _chat_streamly(self, system, history, gen_conf, incremental_output=False):
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from http import HTTPStatus
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if system:
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history.insert(0, {"role": "system", "content": system})
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ans = ""
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tk_count = 0
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try:
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response = Generation.call(
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self.model_name,
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messages=history,
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result_format='message',
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stream=True,
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incremental_output=incremental_output,
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**gen_conf
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)
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for resp in response:
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if resp.status_code == HTTPStatus.OK:
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ans = resp.output.choices[0]['message']['content']
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tk_count = resp.usage.total_tokens
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if resp.output.choices[0].get("finish_reason", "") == "length":
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ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
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[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
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yield ans
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else:
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yield ans + "\n**ERROR**: " + resp.message if str(resp.message).find(
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"Access") < 0 else "Out of credit. Please set the API key in **settings > Model providers.**"
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except Exception as e:
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yield ans + "\n**ERROR**: " + str(e)
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yield tk_count
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def chat_streamly(self, system, history, gen_conf):
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return self._chat_streamly(system, history, gen_conf)
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class ZhipuChat(Base):
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def __init__(self, key, model_name="glm-3-turbo", **kwargs):
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self.client = ZhipuAI(api_key=key)
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self.model_name = model_name
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def chat(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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try:
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if "presence_penalty" in gen_conf: del gen_conf["presence_penalty"]
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if "frequency_penalty" in gen_conf: del gen_conf["frequency_penalty"]
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=history,
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**gen_conf
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)
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ans = response.choices[0].message.content.strip()
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if response.choices[0].finish_reason == "length":
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ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
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[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
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return ans, response.usage.total_tokens
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except Exception as e:
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return "**ERROR**: " + str(e), 0
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def chat_streamly(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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if "presence_penalty" in gen_conf: del gen_conf["presence_penalty"]
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if "frequency_penalty" in gen_conf: del gen_conf["frequency_penalty"]
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ans = ""
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tk_count = 0
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=history,
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stream=True,
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**gen_conf
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)
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for resp in response:
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if not resp.choices[0].delta.content: continue
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delta = resp.choices[0].delta.content
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ans += delta
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if resp.choices[0].finish_reason == "length":
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ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
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[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
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tk_count = resp.usage.total_tokens
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if resp.choices[0].finish_reason == "stop": tk_count = resp.usage.total_tokens
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yield ans
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except Exception as e:
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yield ans + "\n**ERROR**: " + str(e)
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yield tk_count
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class OllamaChat(Base):
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def __init__(self, key, model_name, **kwargs):
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self.client = Client(host=kwargs["base_url"])
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self.model_name = model_name
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def chat(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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try:
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options = {}
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if "temperature" in gen_conf: options["temperature"] = gen_conf["temperature"]
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if "max_tokens" in gen_conf: options["num_predict"] = gen_conf["max_tokens"]
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if "top_p" in gen_conf: options["top_k"] = gen_conf["top_p"]
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if "presence_penalty" in gen_conf: options["presence_penalty"] = gen_conf["presence_penalty"]
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if "frequency_penalty" in gen_conf: options["frequency_penalty"] = gen_conf["frequency_penalty"]
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response = self.client.chat(
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model=self.model_name,
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messages=history,
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options=options,
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keep_alive=-1
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)
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ans = response["message"]["content"].strip()
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return ans, response["eval_count"] + response.get("prompt_eval_count", 0)
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except Exception as e:
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return "**ERROR**: " + str(e), 0
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def chat_streamly(self, system, history, gen_conf):
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if system:
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history.insert(0, {"role": "system", "content": system})
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options = {}
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if "temperature" in gen_conf: options["temperature"] = gen_conf["temperature"]
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if "max_tokens" in gen_conf: options["num_predict"] = gen_conf["max_tokens"]
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if "top_p" in gen_conf: options["top_k"] = gen_conf["top_p"]
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if "presence_penalty" in gen_conf: options["presence_penalty"] = gen_conf["presence_penalty"]
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if "frequency_penalty" in gen_conf: options["frequency_penalty"] = gen_conf["frequency_penalty"]
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ans = ""
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try:
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response = self.client.chat(
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model=self.model_name,
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messages=history,
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stream=True,
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options=options,
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keep_alive=-1
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)
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for resp in response:
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if resp["done"]:
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yield resp.get("prompt_eval_count", 0) + resp.get("eval_count", 0)
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ans += resp["message"]["content"]
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yield ans
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except Exception as e:
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yield ans + "\n**ERROR**: " + str(e)
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yield 0
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|
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class LocalAIChat(Base):
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def __init__(self, key, model_name, base_url):
|
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if not base_url:
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raise ValueError("Local llm url cannot be None")
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if base_url.split("/")[-1] != "v1":
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base_url = os.path.join(base_url, "v1")
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self.client = OpenAI(api_key="empty", base_url=base_url)
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self.model_name = model_name.split("___")[0]
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class LocalLLM(Base):
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class RPCProxy:
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def __init__(self, host, port):
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self.host = host
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self.port = int(port)
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self.__conn()
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def __conn(self):
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from multiprocessing.connection import Client
|
|
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self._connection = Client(
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(self.host, self.port), authkey=b"infiniflow-token4kevinhu"
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)
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def __getattr__(self, name):
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import pickle
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|
|
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def do_rpc(*args, **kwargs):
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for _ in range(3):
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|
try:
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self._connection.send(pickle.dumps((name, args, kwargs)))
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return pickle.loads(self._connection.recv())
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except Exception as e:
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self.__conn()
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raise Exception("RPC connection lost!")
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return do_rpc
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|
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def __init__(self, key, model_name):
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from jina import Client
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self.client = Client(port=12345, protocol="grpc", asyncio=True)
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|
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def _prepare_prompt(self, system, history, gen_conf):
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from rag.svr.jina_server import Prompt, Generation
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if system:
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history.insert(0, {"role": "system", "content": system})
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if "max_tokens" in gen_conf:
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gen_conf["max_new_tokens"] = gen_conf.pop("max_tokens")
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return Prompt(message=history, gen_conf=gen_conf)
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|
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def _stream_response(self, endpoint, prompt):
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from rag.svr.jina_server import Prompt, Generation
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answer = ""
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try:
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res = self.client.stream_doc(
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on=endpoint, inputs=prompt, return_type=Generation
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)
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loop = asyncio.get_event_loop()
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try:
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while True:
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answer = loop.run_until_complete(res.__anext__()).text
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yield answer
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except StopAsyncIteration:
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pass
|
|
except Exception as e:
|
|
yield answer + "\n**ERROR**: " + str(e)
|
|
yield num_tokens_from_string(answer)
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
prompt = self._prepare_prompt(system, history, gen_conf)
|
|
chat_gen = self._stream_response("/chat", prompt)
|
|
ans = next(chat_gen)
|
|
total_tokens = next(chat_gen)
|
|
return ans, total_tokens
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
prompt = self._prepare_prompt(system, history, gen_conf)
|
|
return self._stream_response("/stream", prompt)
|
|
|
|
|
|
class VolcEngineChat(Base):
|
|
def __init__(self, key, model_name, base_url='https://ark.cn-beijing.volces.com/api/v3'):
|
|
"""
|
|
Since do not want to modify the original database fields, and the VolcEngine authentication method is quite special,
|
|
Assemble ark_api_key, ep_id into api_key, store it as a dictionary type, and parse it for use
|
|
model_name is for display only
|
|
"""
|
|
base_url = base_url if base_url else 'https://ark.cn-beijing.volces.com/api/v3'
|
|
ark_api_key = json.loads(key).get('ark_api_key', '')
|
|
model_name = json.loads(key).get('ep_id', '') + json.loads(key).get('endpoint_id', '')
|
|
super().__init__(ark_api_key, model_name, base_url)
|
|
|
|
|
|
class MiniMaxChat(Base):
|
|
def __init__(
|
|
self,
|
|
key,
|
|
model_name,
|
|
base_url="https://api.minimax.chat/v1/text/chatcompletion_v2",
|
|
):
|
|
if not base_url:
|
|
base_url = "https://api.minimax.chat/v1/text/chatcompletion_v2"
|
|
self.base_url = base_url
|
|
self.model_name = model_name
|
|
self.api_key = key
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
if system:
|
|
history.insert(0, {"role": "system", "content": system})
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_tokens"]:
|
|
del gen_conf[k]
|
|
headers = {
|
|
"Authorization": f"Bearer {self.api_key}",
|
|
"Content-Type": "application/json",
|
|
}
|
|
payload = json.dumps(
|
|
{"model": self.model_name, "messages": history, **gen_conf}
|
|
)
|
|
try:
|
|
response = requests.request(
|
|
"POST", url=self.base_url, headers=headers, data=payload
|
|
)
|
|
response = response.json()
|
|
ans = response["choices"][0]["message"]["content"].strip()
|
|
if response["choices"][0]["finish_reason"] == "length":
|
|
ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
|
|
[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
|
|
return ans, response["usage"]["total_tokens"]
|
|
except Exception as e:
|
|
return "**ERROR**: " + str(e), 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
if system:
|
|
history.insert(0, {"role": "system", "content": system})
|
|
ans = ""
|
|
total_tokens = 0
|
|
try:
|
|
headers = {
|
|
"Authorization": f"Bearer {self.api_key}",
|
|
"Content-Type": "application/json",
|
|
}
|
|
payload = json.dumps(
|
|
{
|
|
"model": self.model_name,
|
|
"messages": history,
|
|
"stream": True,
|
|
**gen_conf,
|
|
}
|
|
)
|
|
response = requests.request(
|
|
"POST",
|
|
url=self.base_url,
|
|
headers=headers,
|
|
data=payload,
|
|
)
|
|
for resp in response.text.split("\n\n")[:-1]:
|
|
resp = json.loads(resp[6:])
|
|
text = ""
|
|
if "choices" in resp and "delta" in resp["choices"][0]:
|
|
text = resp["choices"][0]["delta"]["content"]
|
|
ans += text
|
|
total_tokens = (
|
|
total_tokens + num_tokens_from_string(text)
|
|
if "usage" not in resp
|
|
else resp["usage"]["total_tokens"]
|
|
)
|
|
yield ans
|
|
|
|
except Exception as e:
|
|
yield ans + "\n**ERROR**: " + str(e)
|
|
|
|
yield total_tokens
|
|
|
|
|
|
class MistralChat(Base):
|
|
|
|
def __init__(self, key, model_name, base_url=None):
|
|
from mistralai.client import MistralClient
|
|
self.client = MistralClient(api_key=key)
|
|
self.model_name = model_name
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
if system:
|
|
history.insert(0, {"role": "system", "content": system})
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_tokens"]:
|
|
del gen_conf[k]
|
|
try:
|
|
response = self.client.chat(
|
|
model=self.model_name,
|
|
messages=history,
|
|
**gen_conf)
|
|
ans = response.choices[0].message.content
|
|
if response.choices[0].finish_reason == "length":
|
|
ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
|
|
[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
|
|
return ans, response.usage.total_tokens
|
|
except openai.APIError as e:
|
|
return "**ERROR**: " + str(e), 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
if system:
|
|
history.insert(0, {"role": "system", "content": system})
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_tokens"]:
|
|
del gen_conf[k]
|
|
ans = ""
|
|
total_tokens = 0
|
|
try:
|
|
response = self.client.chat_stream(
|
|
model=self.model_name,
|
|
messages=history,
|
|
**gen_conf)
|
|
for resp in response:
|
|
if not resp.choices or not resp.choices[0].delta.content: continue
|
|
ans += resp.choices[0].delta.content
|
|
total_tokens += 1
|
|
if resp.choices[0].finish_reason == "length":
|
|
ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
|
|
[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
|
|
yield ans
|
|
|
|
except openai.APIError as e:
|
|
yield ans + "\n**ERROR**: " + str(e)
|
|
|
|
yield total_tokens
|
|
|
|
|
|
class BedrockChat(Base):
|
|
|
|
def __init__(self, key, model_name, **kwargs):
|
|
import boto3
|
|
self.bedrock_ak = json.loads(key).get('bedrock_ak', '')
|
|
self.bedrock_sk = json.loads(key).get('bedrock_sk', '')
|
|
self.bedrock_region = json.loads(key).get('bedrock_region', '')
|
|
self.model_name = model_name
|
|
self.client = boto3.client(service_name='bedrock-runtime', region_name=self.bedrock_region,
|
|
aws_access_key_id=self.bedrock_ak, aws_secret_access_key=self.bedrock_sk)
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
from botocore.exceptions import ClientError
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_tokens"]:
|
|
del gen_conf[k]
|
|
if "max_tokens" in gen_conf:
|
|
gen_conf["maxTokens"] = gen_conf["max_tokens"]
|
|
_ = gen_conf.pop("max_tokens")
|
|
if "top_p" in gen_conf:
|
|
gen_conf["topP"] = gen_conf["top_p"]
|
|
_ = gen_conf.pop("top_p")
|
|
for item in history:
|
|
if not isinstance(item["content"], list) and not isinstance(item["content"], tuple):
|
|
item["content"] = [{"text": item["content"]}]
|
|
|
|
try:
|
|
# Send the message to the model, using a basic inference configuration.
|
|
response = self.client.converse(
|
|
modelId=self.model_name,
|
|
messages=history,
|
|
inferenceConfig=gen_conf,
|
|
system=[{"text": (system if system else "Answer the user's message.")}],
|
|
)
|
|
|
|
# Extract and print the response text.
|
|
ans = response["output"]["message"]["content"][0]["text"]
|
|
return ans, num_tokens_from_string(ans)
|
|
|
|
except (ClientError, Exception) as e:
|
|
return f"ERROR: Can't invoke '{self.model_name}'. Reason: {e}", 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
from botocore.exceptions import ClientError
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_tokens"]:
|
|
del gen_conf[k]
|
|
if "max_tokens" in gen_conf:
|
|
gen_conf["maxTokens"] = gen_conf["max_tokens"]
|
|
_ = gen_conf.pop("max_tokens")
|
|
if "top_p" in gen_conf:
|
|
gen_conf["topP"] = gen_conf["top_p"]
|
|
_ = gen_conf.pop("top_p")
|
|
for item in history:
|
|
if not isinstance(item["content"], list) and not isinstance(item["content"], tuple):
|
|
item["content"] = [{"text": item["content"]}]
|
|
|
|
if self.model_name.split('.')[0] == 'ai21':
|
|
try:
|
|
response = self.client.converse(
|
|
modelId=self.model_name,
|
|
messages=history,
|
|
inferenceConfig=gen_conf,
|
|
system=[{"text": (system if system else "Answer the user's message.")}]
|
|
)
|
|
ans = response["output"]["message"]["content"][0]["text"]
|
|
return ans, num_tokens_from_string(ans)
|
|
|
|
except (ClientError, Exception) as e:
|
|
return f"ERROR: Can't invoke '{self.model_name}'. Reason: {e}", 0
|
|
|
|
ans = ""
|
|
try:
|
|
# Send the message to the model, using a basic inference configuration.
|
|
streaming_response = self.client.converse_stream(
|
|
modelId=self.model_name,
|
|
messages=history,
|
|
inferenceConfig=gen_conf,
|
|
system=[{"text": (system if system else "Answer the user's message.")}]
|
|
)
|
|
|
|
# Extract and print the streamed response text in real-time.
|
|
for resp in streaming_response["stream"]:
|
|
if "contentBlockDelta" in resp:
|
|
ans += resp["contentBlockDelta"]["delta"]["text"]
|
|
yield ans
|
|
|
|
except (ClientError, Exception) as e:
|
|
yield ans + f"ERROR: Can't invoke '{self.model_name}'. Reason: {e}"
|
|
|
|
yield num_tokens_from_string(ans)
|
|
|
|
|
|
class GeminiChat(Base):
|
|
|
|
def __init__(self, key, model_name, base_url=None):
|
|
from google.generativeai import client, GenerativeModel
|
|
|
|
client.configure(api_key=key)
|
|
_client = client.get_default_generative_client()
|
|
self.model_name = 'models/' + model_name
|
|
self.model = GenerativeModel(model_name=self.model_name)
|
|
self.model._client = _client
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
from google.generativeai.types import content_types
|
|
|
|
if system:
|
|
self.model._system_instruction = content_types.to_content(system)
|
|
|
|
if 'max_tokens' in gen_conf:
|
|
gen_conf['max_output_tokens'] = gen_conf['max_tokens']
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_output_tokens"]:
|
|
del gen_conf[k]
|
|
for item in history:
|
|
if 'role' in item and item['role'] == 'assistant':
|
|
item['role'] = 'model'
|
|
if 'role' in item and item['role'] == 'system':
|
|
item['role'] = 'user'
|
|
if 'content' in item:
|
|
item['parts'] = item.pop('content')
|
|
|
|
try:
|
|
response = self.model.generate_content(
|
|
history,
|
|
generation_config=gen_conf)
|
|
ans = response.text
|
|
return ans, response.usage_metadata.total_token_count
|
|
except Exception as e:
|
|
return "**ERROR**: " + str(e), 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
from google.generativeai.types import content_types
|
|
|
|
if system:
|
|
self.model._system_instruction = content_types.to_content(system)
|
|
if 'max_tokens' in gen_conf:
|
|
gen_conf['max_output_tokens'] = gen_conf['max_tokens']
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_output_tokens"]:
|
|
del gen_conf[k]
|
|
for item in history:
|
|
if 'role' in item and item['role'] == 'assistant':
|
|
item['role'] = 'model'
|
|
if 'content' in item:
|
|
item['parts'] = item.pop('content')
|
|
ans = ""
|
|
try:
|
|
response = self.model.generate_content(
|
|
history,
|
|
generation_config=gen_conf, stream=True)
|
|
for resp in response:
|
|
ans += resp.text
|
|
yield ans
|
|
|
|
except Exception as e:
|
|
yield ans + "\n**ERROR**: " + str(e)
|
|
|
|
yield response._chunks[-1].usage_metadata.total_token_count
|
|
|
|
|
|
class GroqChat:
|
|
def __init__(self, key, model_name, base_url=''):
|
|
self.client = Groq(api_key=key)
|
|
self.model_name = model_name
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
if system:
|
|
history.insert(0, {"role": "system", "content": system})
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_tokens"]:
|
|
del gen_conf[k]
|
|
ans = ""
|
|
try:
|
|
response = self.client.chat.completions.create(
|
|
model=self.model_name,
|
|
messages=history,
|
|
**gen_conf
|
|
)
|
|
ans = response.choices[0].message.content
|
|
if response.choices[0].finish_reason == "length":
|
|
ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
|
|
[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
|
|
return ans, response.usage.total_tokens
|
|
except Exception as e:
|
|
return ans + "\n**ERROR**: " + str(e), 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
if system:
|
|
history.insert(0, {"role": "system", "content": system})
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_tokens"]:
|
|
del gen_conf[k]
|
|
ans = ""
|
|
total_tokens = 0
|
|
try:
|
|
response = self.client.chat.completions.create(
|
|
model=self.model_name,
|
|
messages=history,
|
|
stream=True,
|
|
**gen_conf
|
|
)
|
|
for resp in response:
|
|
if not resp.choices or not resp.choices[0].delta.content:
|
|
continue
|
|
ans += resp.choices[0].delta.content
|
|
total_tokens += 1
|
|
if resp.choices[0].finish_reason == "length":
|
|
ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
|
|
[ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
|
|
yield ans
|
|
|
|
except Exception as e:
|
|
yield ans + "\n**ERROR**: " + str(e)
|
|
|
|
yield total_tokens
|
|
|
|
|
|
## openrouter
|
|
class OpenRouterChat(Base):
|
|
def __init__(self, key, model_name, base_url="https://openrouter.ai/api/v1"):
|
|
if not base_url:
|
|
base_url = "https://openrouter.ai/api/v1"
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class StepFunChat(Base):
|
|
def __init__(self, key, model_name, base_url="https://api.stepfun.com/v1"):
|
|
if not base_url:
|
|
base_url = "https://api.stepfun.com/v1"
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class NvidiaChat(Base):
|
|
def __init__(self, key, model_name, base_url="https://integrate.api.nvidia.com/v1"):
|
|
if not base_url:
|
|
base_url = "https://integrate.api.nvidia.com/v1"
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class LmStudioChat(Base):
|
|
def __init__(self, key, model_name, base_url):
|
|
if not base_url:
|
|
raise ValueError("Local llm url cannot be None")
|
|
if base_url.split("/")[-1] != "v1":
|
|
base_url = os.path.join(base_url, "v1")
|
|
self.client = OpenAI(api_key="lm-studio", base_url=base_url)
|
|
self.model_name = model_name
|
|
|
|
|
|
class OpenAI_APIChat(Base):
|
|
def __init__(self, key, model_name, base_url):
|
|
if not base_url:
|
|
raise ValueError("url cannot be None")
|
|
if base_url.split("/")[-1] != "v1":
|
|
base_url = os.path.join(base_url, "v1")
|
|
model_name = model_name.split("___")[0]
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class CoHereChat(Base):
|
|
def __init__(self, key, model_name, base_url=""):
|
|
from cohere import Client
|
|
|
|
self.client = Client(api_key=key)
|
|
self.model_name = model_name
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
if system:
|
|
history.insert(0, {"role": "system", "content": system})
|
|
if "top_p" in gen_conf:
|
|
gen_conf["p"] = gen_conf.pop("top_p")
|
|
if "frequency_penalty" in gen_conf and "presence_penalty" in gen_conf:
|
|
gen_conf.pop("presence_penalty")
|
|
for item in history:
|
|
if "role" in item and item["role"] == "user":
|
|
item["role"] = "USER"
|
|
if "role" in item and item["role"] == "assistant":
|
|
item["role"] = "CHATBOT"
|
|
if "content" in item:
|
|
item["message"] = item.pop("content")
|
|
mes = history.pop()["message"]
|
|
ans = ""
|
|
try:
|
|
response = self.client.chat(
|
|
model=self.model_name, chat_history=history, message=mes, **gen_conf
|
|
)
|
|
ans = response.text
|
|
if response.finish_reason == "MAX_TOKENS":
|
|
ans += (
|
|
"...\nFor the content length reason, it stopped, continue?"
|
|
if is_english([ans])
|
|
else "······\n由于长度的原因，回答被截断了，要继续吗？"
|
|
)
|
|
return (
|
|
ans,
|
|
response.meta.tokens.input_tokens + response.meta.tokens.output_tokens,
|
|
)
|
|
except Exception as e:
|
|
return ans + "\n**ERROR**: " + str(e), 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
if system:
|
|
history.insert(0, {"role": "system", "content": system})
|
|
if "top_p" in gen_conf:
|
|
gen_conf["p"] = gen_conf.pop("top_p")
|
|
if "frequency_penalty" in gen_conf and "presence_penalty" in gen_conf:
|
|
gen_conf.pop("presence_penalty")
|
|
for item in history:
|
|
if "role" in item and item["role"] == "user":
|
|
item["role"] = "USER"
|
|
if "role" in item and item["role"] == "assistant":
|
|
item["role"] = "CHATBOT"
|
|
if "content" in item:
|
|
item["message"] = item.pop("content")
|
|
mes = history.pop()["message"]
|
|
ans = ""
|
|
total_tokens = 0
|
|
try:
|
|
response = self.client.chat_stream(
|
|
model=self.model_name, chat_history=history, message=mes, **gen_conf
|
|
)
|
|
for resp in response:
|
|
if resp.event_type == "text-generation":
|
|
ans += resp.text
|
|
total_tokens += num_tokens_from_string(resp.text)
|
|
elif resp.event_type == "stream-end":
|
|
if resp.finish_reason == "MAX_TOKENS":
|
|
ans += (
|
|
"...\nFor the content length reason, it stopped, continue?"
|
|
if is_english([ans])
|
|
else "······\n由于长度的原因，回答被截断了，要继续吗？"
|
|
)
|
|
yield ans
|
|
|
|
except Exception as e:
|
|
yield ans + "\n**ERROR**: " + str(e)
|
|
|
|
yield total_tokens
|
|
|
|
|
|
class LeptonAIChat(Base):
|
|
def __init__(self, key, model_name, base_url=None):
|
|
if not base_url:
|
|
base_url = os.path.join("https://" + model_name + ".lepton.run", "api", "v1")
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class TogetherAIChat(Base):
|
|
def __init__(self, key, model_name, base_url="https://api.together.xyz/v1"):
|
|
if not base_url:
|
|
base_url = "https://api.together.xyz/v1"
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class PerfXCloudChat(Base):
|
|
def __init__(self, key, model_name, base_url="https://cloud.perfxlab.cn/v1"):
|
|
if not base_url:
|
|
base_url = "https://cloud.perfxlab.cn/v1"
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class UpstageChat(Base):
|
|
def __init__(self, key, model_name, base_url="https://api.upstage.ai/v1/solar"):
|
|
if not base_url:
|
|
base_url = "https://api.upstage.ai/v1/solar"
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class NovitaAIChat(Base):
|
|
def __init__(self, key, model_name, base_url="https://api.novita.ai/v3/openai"):
|
|
if not base_url:
|
|
base_url = "https://api.novita.ai/v3/openai"
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class SILICONFLOWChat(Base):
|
|
def __init__(self, key, model_name, base_url="https://api.siliconflow.cn/v1"):
|
|
if not base_url:
|
|
base_url = "https://api.siliconflow.cn/v1"
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class YiChat(Base):
|
|
def __init__(self, key, model_name, base_url="https://api.lingyiwanwu.com/v1"):
|
|
if not base_url:
|
|
base_url = "https://api.lingyiwanwu.com/v1"
|
|
super().__init__(key, model_name, base_url)
|
|
|
|
|
|
class ReplicateChat(Base):
|
|
def __init__(self, key, model_name, base_url=None):
|
|
from replicate.client import Client
|
|
|
|
self.model_name = model_name
|
|
self.client = Client(api_token=key)
|
|
self.system = ""
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
if "max_tokens" in gen_conf:
|
|
gen_conf["max_new_tokens"] = gen_conf.pop("max_tokens")
|
|
if system:
|
|
self.system = system
|
|
prompt = "\n".join(
|
|
[item["role"] + ":" + item["content"] for item in history[-5:]]
|
|
)
|
|
ans = ""
|
|
try:
|
|
response = self.client.run(
|
|
self.model_name,
|
|
input={"system_prompt": self.system, "prompt": prompt, **gen_conf},
|
|
)
|
|
ans = "".join(response)
|
|
return ans, num_tokens_from_string(ans)
|
|
except Exception as e:
|
|
return ans + "\n**ERROR**: " + str(e), 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
if "max_tokens" in gen_conf:
|
|
gen_conf["max_new_tokens"] = gen_conf.pop("max_tokens")
|
|
if system:
|
|
self.system = system
|
|
prompt = "\n".join(
|
|
[item["role"] + ":" + item["content"] for item in history[-5:]]
|
|
)
|
|
ans = ""
|
|
try:
|
|
response = self.client.run(
|
|
self.model_name,
|
|
input={"system_prompt": self.system, "prompt": prompt, **gen_conf},
|
|
)
|
|
for resp in response:
|
|
ans += resp
|
|
yield ans
|
|
|
|
except Exception as e:
|
|
yield ans + "\n**ERROR**: " + str(e)
|
|
|
|
yield num_tokens_from_string(ans)
|
|
|
|
|
|
class HunyuanChat(Base):
|
|
def __init__(self, key, model_name, base_url=None):
|
|
from tencentcloud.common import credential
|
|
from tencentcloud.hunyuan.v20230901 import hunyuan_client
|
|
|
|
key = json.loads(key)
|
|
sid = key.get("hunyuan_sid", "")
|
|
sk = key.get("hunyuan_sk", "")
|
|
cred = credential.Credential(sid, sk)
|
|
self.model_name = model_name
|
|
self.client = hunyuan_client.HunyuanClient(cred, "")
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
from tencentcloud.hunyuan.v20230901 import models
|
|
from tencentcloud.common.exception.tencent_cloud_sdk_exception import (
|
|
TencentCloudSDKException,
|
|
)
|
|
|
|
_gen_conf = {}
|
|
_history = [{k.capitalize(): v for k, v in item.items()} for item in history]
|
|
if system:
|
|
_history.insert(0, {"Role": "system", "Content": system})
|
|
if "temperature" in gen_conf:
|
|
_gen_conf["Temperature"] = gen_conf["temperature"]
|
|
if "top_p" in gen_conf:
|
|
_gen_conf["TopP"] = gen_conf["top_p"]
|
|
|
|
req = models.ChatCompletionsRequest()
|
|
params = {"Model": self.model_name, "Messages": _history, **_gen_conf}
|
|
req.from_json_string(json.dumps(params))
|
|
ans = ""
|
|
try:
|
|
response = self.client.ChatCompletions(req)
|
|
ans = response.Choices[0].Message.Content
|
|
return ans, response.Usage.TotalTokens
|
|
except TencentCloudSDKException as e:
|
|
return ans + "\n**ERROR**: " + str(e), 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
from tencentcloud.hunyuan.v20230901 import models
|
|
from tencentcloud.common.exception.tencent_cloud_sdk_exception import (
|
|
TencentCloudSDKException,
|
|
)
|
|
|
|
_gen_conf = {}
|
|
_history = [{k.capitalize(): v for k, v in item.items()} for item in history]
|
|
if system:
|
|
_history.insert(0, {"Role": "system", "Content": system})
|
|
|
|
if "temperature" in gen_conf:
|
|
_gen_conf["Temperature"] = gen_conf["temperature"]
|
|
if "top_p" in gen_conf:
|
|
_gen_conf["TopP"] = gen_conf["top_p"]
|
|
req = models.ChatCompletionsRequest()
|
|
params = {
|
|
"Model": self.model_name,
|
|
"Messages": _history,
|
|
"Stream": True,
|
|
**_gen_conf,
|
|
}
|
|
req.from_json_string(json.dumps(params))
|
|
ans = ""
|
|
total_tokens = 0
|
|
try:
|
|
response = self.client.ChatCompletions(req)
|
|
for resp in response:
|
|
resp = json.loads(resp["data"])
|
|
if not resp["Choices"] or not resp["Choices"][0]["Delta"]["Content"]:
|
|
continue
|
|
ans += resp["Choices"][0]["Delta"]["Content"]
|
|
total_tokens += 1
|
|
|
|
yield ans
|
|
|
|
except TencentCloudSDKException as e:
|
|
yield ans + "\n**ERROR**: " + str(e)
|
|
|
|
yield total_tokens
|
|
|
|
|
|
class SparkChat(Base):
|
|
def __init__(
|
|
self, key, model_name, base_url="https://spark-api-open.xf-yun.com/v1"
|
|
):
|
|
if not base_url:
|
|
base_url = "https://spark-api-open.xf-yun.com/v1"
|
|
model2version = {
|
|
"Spark-Max": "generalv3.5",
|
|
"Spark-Lite": "general",
|
|
"Spark-Pro": "generalv3",
|
|
"Spark-Pro-128K": "pro-128k",
|
|
"Spark-4.0-Ultra": "4.0Ultra",
|
|
}
|
|
model_version = model2version[model_name]
|
|
super().__init__(key, model_version, base_url)
|
|
|
|
|
|
class BaiduYiyanChat(Base):
|
|
def __init__(self, key, model_name, base_url=None):
|
|
import qianfan
|
|
|
|
key = json.loads(key)
|
|
ak = key.get("yiyan_ak", "")
|
|
sk = key.get("yiyan_sk", "")
|
|
self.client = qianfan.ChatCompletion(ak=ak, sk=sk)
|
|
self.model_name = model_name.lower()
|
|
self.system = ""
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
if system:
|
|
self.system = system
|
|
gen_conf["penalty_score"] = (
|
|
(gen_conf.get("presence_penalty", 0) + gen_conf.get("frequency_penalty",
|
|
0)) / 2
|
|
) + 1
|
|
if "max_tokens" in gen_conf:
|
|
gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
|
|
ans = ""
|
|
|
|
try:
|
|
response = self.client.do(
|
|
model=self.model_name,
|
|
messages=history,
|
|
system=self.system,
|
|
**gen_conf
|
|
).body
|
|
ans = response['result']
|
|
return ans, response["usage"]["total_tokens"]
|
|
|
|
except Exception as e:
|
|
return ans + "\n**ERROR**: " + str(e), 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
if system:
|
|
self.system = system
|
|
gen_conf["penalty_score"] = (
|
|
(gen_conf.get("presence_penalty", 0) + gen_conf.get("frequency_penalty",
|
|
0)) / 2
|
|
) + 1
|
|
if "max_tokens" in gen_conf:
|
|
gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
|
|
ans = ""
|
|
total_tokens = 0
|
|
|
|
try:
|
|
response = self.client.do(
|
|
model=self.model_name,
|
|
messages=history,
|
|
system=self.system,
|
|
stream=True,
|
|
**gen_conf
|
|
)
|
|
for resp in response:
|
|
resp = resp.body
|
|
ans += resp['result']
|
|
total_tokens = resp["usage"]["total_tokens"]
|
|
|
|
yield ans
|
|
|
|
except Exception as e:
|
|
return ans + "\n**ERROR**: " + str(e), 0
|
|
|
|
yield total_tokens
|
|
|
|
|
|
class AnthropicChat(Base):
|
|
def __init__(self, key, model_name, base_url=None):
|
|
import anthropic
|
|
|
|
self.client = anthropic.Anthropic(api_key=key)
|
|
self.model_name = model_name
|
|
self.system = ""
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
if system:
|
|
self.system = system
|
|
if "max_tokens" not in gen_conf:
|
|
gen_conf["max_tokens"] = 4096
|
|
|
|
try:
|
|
response = self.client.messages.create(
|
|
model=self.model_name,
|
|
messages=history,
|
|
system=self.system,
|
|
stream=False,
|
|
**gen_conf,
|
|
).json()
|
|
ans = response["content"][0]["text"]
|
|
if response["stop_reason"] == "max_tokens":
|
|
ans += (
|
|
"...\nFor the content length reason, it stopped, continue?"
|
|
if is_english([ans])
|
|
else "······\n由于长度的原因，回答被截断了，要继续吗？"
|
|
)
|
|
return (
|
|
ans,
|
|
response["usage"]["input_tokens"] + response["usage"]["output_tokens"],
|
|
)
|
|
except Exception as e:
|
|
return ans + "\n**ERROR**: " + str(e), 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
if system:
|
|
self.system = system
|
|
if "max_tokens" not in gen_conf:
|
|
gen_conf["max_tokens"] = 4096
|
|
|
|
ans = ""
|
|
total_tokens = 0
|
|
try:
|
|
response = self.client.messages.create(
|
|
model=self.model_name,
|
|
messages=history,
|
|
system=self.system,
|
|
stream=True,
|
|
**gen_conf,
|
|
)
|
|
for res in response.iter_lines():
|
|
res = res.decode("utf-8")
|
|
if "content_block_delta" in res and "data" in res:
|
|
text = json.loads(res[6:])["delta"]["text"]
|
|
ans += text
|
|
total_tokens += num_tokens_from_string(text)
|
|
except Exception as e:
|
|
yield ans + "\n**ERROR**: " + str(e)
|
|
|
|
yield total_tokens
|
|
|
|
|
|
class GoogleChat(Base):
|
|
def __init__(self, key, model_name, base_url=None):
|
|
from google.oauth2 import service_account
|
|
import base64
|
|
|
|
key = json.load(key)
|
|
access_token = json.loads(
|
|
base64.b64decode(key.get("google_service_account_key", ""))
|
|
)
|
|
project_id = key.get("google_project_id", "")
|
|
region = key.get("google_region", "")
|
|
|
|
scopes = ["https://www.googleapis.com/auth/cloud-platform"]
|
|
self.model_name = model_name
|
|
self.system = ""
|
|
|
|
if "claude" in self.model_name:
|
|
from anthropic import AnthropicVertex
|
|
from google.auth.transport.requests import Request
|
|
|
|
if access_token:
|
|
credits = service_account.Credentials.from_service_account_info(
|
|
access_token, scopes=scopes
|
|
)
|
|
request = Request()
|
|
credits.refresh(request)
|
|
token = credits.token
|
|
self.client = AnthropicVertex(
|
|
region=region, project_id=project_id, access_token=token
|
|
)
|
|
else:
|
|
self.client = AnthropicVertex(region=region, project_id=project_id)
|
|
else:
|
|
from google.cloud import aiplatform
|
|
import vertexai.generative_models as glm
|
|
|
|
if access_token:
|
|
credits = service_account.Credentials.from_service_account_info(
|
|
access_token
|
|
)
|
|
aiplatform.init(
|
|
credentials=credits, project=project_id, location=region
|
|
)
|
|
else:
|
|
aiplatform.init(project=project_id, location=region)
|
|
self.client = glm.GenerativeModel(model_name=self.model_name)
|
|
|
|
def chat(self, system, history, gen_conf):
|
|
if system:
|
|
self.system = system
|
|
|
|
if "claude" in self.model_name:
|
|
if "max_tokens" not in gen_conf:
|
|
gen_conf["max_tokens"] = 4096
|
|
try:
|
|
response = self.client.messages.create(
|
|
model=self.model_name,
|
|
messages=history,
|
|
system=self.system,
|
|
stream=False,
|
|
**gen_conf,
|
|
).json()
|
|
ans = response["content"][0]["text"]
|
|
if response["stop_reason"] == "max_tokens":
|
|
ans += (
|
|
"...\nFor the content length reason, it stopped, continue?"
|
|
if is_english([ans])
|
|
else "······\n由于长度的原因，回答被截断了，要继续吗？"
|
|
)
|
|
return (
|
|
ans,
|
|
response["usage"]["input_tokens"]
|
|
+ response["usage"]["output_tokens"],
|
|
)
|
|
except Exception as e:
|
|
return "\n**ERROR**: " + str(e), 0
|
|
else:
|
|
self.client._system_instruction = self.system
|
|
if "max_tokens" in gen_conf:
|
|
gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_output_tokens"]:
|
|
del gen_conf[k]
|
|
for item in history:
|
|
if "role" in item and item["role"] == "assistant":
|
|
item["role"] = "model"
|
|
if "content" in item:
|
|
item["parts"] = item.pop("content")
|
|
try:
|
|
response = self.client.generate_content(
|
|
history, generation_config=gen_conf
|
|
)
|
|
ans = response.text
|
|
return ans, response.usage_metadata.total_token_count
|
|
except Exception as e:
|
|
return "**ERROR**: " + str(e), 0
|
|
|
|
def chat_streamly(self, system, history, gen_conf):
|
|
if system:
|
|
self.system = system
|
|
|
|
if "claude" in self.model_name:
|
|
if "max_tokens" not in gen_conf:
|
|
gen_conf["max_tokens"] = 4096
|
|
ans = ""
|
|
total_tokens = 0
|
|
try:
|
|
response = self.client.messages.create(
|
|
model=self.model_name,
|
|
messages=history,
|
|
system=self.system,
|
|
stream=True,
|
|
**gen_conf,
|
|
)
|
|
for res in response.iter_lines():
|
|
res = res.decode("utf-8")
|
|
if "content_block_delta" in res and "data" in res:
|
|
text = json.loads(res[6:])["delta"]["text"]
|
|
ans += text
|
|
total_tokens += num_tokens_from_string(text)
|
|
except Exception as e:
|
|
yield ans + "\n**ERROR**: " + str(e)
|
|
|
|
yield total_tokens
|
|
else:
|
|
self.client._system_instruction = self.system
|
|
if "max_tokens" in gen_conf:
|
|
gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
|
|
for k in list(gen_conf.keys()):
|
|
if k not in ["temperature", "top_p", "max_output_tokens"]:
|
|
del gen_conf[k]
|
|
for item in history:
|
|
if "role" in item and item["role"] == "assistant":
|
|
item["role"] = "model"
|
|
if "content" in item:
|
|
item["parts"] = item.pop("content")
|
|
ans = ""
|
|
try:
|
|
response = self.model.generate_content(
|
|
history, generation_config=gen_conf, stream=True
|
|
)
|
|
for resp in response:
|
|
ans += resp.text
|
|
yield ans
|
|
|
|
except Exception as e:
|
|
yield ans + "\n**ERROR**: " + str(e)
|
|
|
|
yield response._chunks[-1].usage_metadata.total_token_count
|