# # Copyright 2024 The InfiniFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import re from openai.lib.azure import AzureOpenAI from zhipuai import ZhipuAI from dashscope import Generation from abc import ABC from openai import OpenAI import openai from ollama import Client from rag.nlp import is_chinese, is_english from rag.utils import num_tokens_from_string import os import json import requests import asyncio LENGTH_NOTIFICATION_CN = "······\n由于长度的原因，回答被截断了，要继续吗？" LENGTH_NOTIFICATION_EN = "...\nFor the content length reason, it stopped, continue?" class Base(ABC): def __init__(self, key, model_name, base_url): timeout = int(os.environ.get('LM_TIMEOUT_SECONDS', 600)) self.client = OpenAI(api_key=key, base_url=base_url, timeout=timeout) self.model_name = model_name def chat(self, system, history, gen_conf): if system: history.insert(0, {"role": "system", "content": system}) try: response = self.client.chat.completions.create( model=self.model_name, messages=history, **gen_conf) ans = response.choices[0].message.content.strip() if response.choices[0].finish_reason == "length": if is_chinese(ans): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN 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}) 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: continue if not resp.choices[0].delta.content: resp.choices[0].delta.content = "" ans += resp.choices[0].delta.content if not hasattr(resp, "usage") or not resp.usage: total_tokens = ( total_tokens + num_tokens_from_string(resp.choices[0].delta.content) ) elif isinstance(resp.usage, dict): total_tokens = resp.usage.get("total_tokens", total_tokens) else: total_tokens = resp.usage.total_tokens if resp.choices[0].finish_reason == "length": if is_chinese(ans): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN yield ans except openai.APIError as e: yield ans + "\n**ERROR**: " + str(e) yield total_tokens class GptTurbo(Base): def __init__(self, key, model_name="gpt-3.5-turbo", base_url="https://api.openai.com/v1"): if not base_url: base_url = "https://api.openai.com/v1" super().__init__(key, model_name, base_url) class MoonshotChat(Base): def __init__(self, key, model_name="moonshot-v1-8k", base_url="https://api.moonshot.cn/v1"): if not base_url: base_url = "https://api.moonshot.cn/v1" super().__init__(key, model_name, base_url) class XinferenceChat(Base): def __init__(self, key=None, 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") super().__init__(key, model_name, base_url) class HuggingFaceChat(Base): def __init__(self, key=None, 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") super().__init__(key, model_name.split("___")[0], base_url) class DeepSeekChat(Base): def __init__(self, key, model_name="deepseek-chat", base_url="https://api.deepseek.com/v1"): if not base_url: base_url = "https://api.deepseek.com/v1" super().__init__(key, model_name, base_url) class AzureChat(Base): def __init__(self, key, model_name, **kwargs): api_key = json.loads(key).get('api_key', '') api_version = json.loads(key).get('api_version', '2024-02-01') self.client = AzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version) self.model_name = model_name class BaiChuanChat(Base): def __init__(self, key, model_name="Baichuan3-Turbo", base_url="https://api.baichuan-ai.com/v1"): if not base_url: base_url = "https://api.baichuan-ai.com/v1" super().__init__(key, model_name, base_url) @staticmethod def _format_params(params): return { "temperature": params.get("temperature", 0.3), "max_tokens": params.get("max_tokens", 2048), "top_p": params.get("top_p", 0.85), } def chat(self, system, history, gen_conf): if system: history.insert(0, {"role": "system", "content": system}) try: response = self.client.chat.completions.create( model=self.model_name, messages=history, extra_body={ "tools": [{ "type": "web_search", "web_search": { "enable": True, "search_mode": "performance_first" } }] }, **self._format_params(gen_conf)) ans = response.choices[0].message.content.strip() if response.choices[0].finish_reason == "length": if is_chinese([ans]): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN 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}) ans = "" total_tokens = 0 try: response = self.client.chat.completions.create( model=self.model_name, messages=history, extra_body={ "tools": [{ "type": "web_search", "web_search": { "enable": True, "search_mode": "performance_first" } }] }, stream=True, **self._format_params(gen_conf)) for resp in response: if not resp.choices: continue if not resp.choices[0].delta.content: resp.choices[0].delta.content = "" ans += resp.choices[0].delta.content total_tokens = ( ( total_tokens + num_tokens_from_string(resp.choices[0].delta.content) ) if not hasattr(resp, "usage") else resp.usage["total_tokens"] ) if resp.choices[0].finish_reason == "length": if is_chinese([ans]): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN yield ans except Exception as e: yield ans + "\n**ERROR**: " + str(e) yield total_tokens class QWenChat(Base): def __init__(self, key, model_name=Generation.Models.qwen_turbo, **kwargs): import dashscope dashscope.api_key = key self.model_name = model_name def chat(self, system, history, gen_conf): stream_flag = str(os.environ.get('QWEN_CHAT_BY_STREAM', 'true')).lower() == 'true' if not stream_flag: from http import HTTPStatus if system: history.insert(0, {"role": "system", "content": system}) response = Generation.call( self.model_name, messages=history, result_format='message', **gen_conf ) ans = "" tk_count = 0 if response.status_code == HTTPStatus.OK: ans += response.output.choices[0]['message']['content'] tk_count += response.usage.total_tokens if response.output.choices[0].get("finish_reason", "") == "length": if is_chinese([ans]): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN return ans, tk_count return "**ERROR**: " + response.message, tk_count else: g = self._chat_streamly(system, history, gen_conf, incremental_output=True) result_list = list(g) error_msg_list = [item for item in result_list if str(item).find("**ERROR**") >= 0] if len(error_msg_list) > 0: return "**ERROR**: " + "".join(error_msg_list) , 0 else: return "".join(result_list[:-1]), result_list[-1] def _chat_streamly(self, system, history, gen_conf, incremental_output=False): from http import HTTPStatus if system: history.insert(0, {"role": "system", "content": system}) ans = "" tk_count = 0 try: response = Generation.call( self.model_name, messages=history, result_format='message', stream=True, incremental_output=incremental_output, **gen_conf ) for resp in response: if resp.status_code == HTTPStatus.OK: ans = resp.output.choices[0]['message']['content'] tk_count = resp.usage.total_tokens if resp.output.choices[0].get("finish_reason", "") == "length": if is_chinese(ans): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN yield ans else: yield ans + "\n**ERROR**: " + resp.message if not re.search(r" (key|quota)", str(resp.message).lower()) else "Out of credit. Please set the API key in **settings > Model providers.**" except Exception as e: yield ans + "\n**ERROR**: " + str(e) yield tk_count def chat_streamly(self, system, history, gen_conf): return self._chat_streamly(system, history, gen_conf) class ZhipuChat(Base): def __init__(self, key, model_name="glm-3-turbo", **kwargs): self.client = ZhipuAI(api_key=key) self.model_name = model_name def chat(self, system, history, gen_conf): if system: history.insert(0, {"role": "system", "content": system}) try: if "presence_penalty" in gen_conf: del gen_conf["presence_penalty"] if "frequency_penalty" in gen_conf: del gen_conf["frequency_penalty"] response = self.client.chat.completions.create( model=self.model_name, messages=history, **gen_conf ) ans = response.choices[0].message.content.strip() if response.choices[0].finish_reason == "length": if is_chinese(ans): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN 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}) if "presence_penalty" in gen_conf: del gen_conf["presence_penalty"] if "frequency_penalty" in gen_conf: del gen_conf["frequency_penalty"] ans = "" tk_count = 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[0].delta.content: continue delta = resp.choices[0].delta.content ans += delta if resp.choices[0].finish_reason == "length": if is_chinese(ans): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN tk_count = resp.usage.total_tokens if resp.choices[0].finish_reason == "stop": tk_count = resp.usage.total_tokens yield ans except Exception as e: yield ans + "\n**ERROR**: " + str(e) yield tk_count class OllamaChat(Base): def __init__(self, key, model_name, **kwargs): self.client = Client(host=kwargs["base_url"]) self.model_name = model_name def chat(self, system, history, gen_conf): if system: history.insert(0, {"role": "system", "content": system}) try: options = {} if "temperature" in gen_conf: options["temperature"] = gen_conf["temperature"] if "max_tokens" in gen_conf: options["num_predict"] = gen_conf["max_tokens"] if "top_p" in gen_conf: options["top_p"] = gen_conf["top_p"] if "presence_penalty" in gen_conf: options["presence_penalty"] = gen_conf["presence_penalty"] if "frequency_penalty" in gen_conf: options["frequency_penalty"] = gen_conf["frequency_penalty"] response = self.client.chat( model=self.model_name, messages=history, options=options, keep_alive=-1 ) ans = response["message"]["content"].strip() return ans, response.get("eval_count", 0) + response.get("prompt_eval_count", 0) 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}) options = {} if "temperature" in gen_conf: options["temperature"] = gen_conf["temperature"] if "max_tokens" in gen_conf: options["num_predict"] = gen_conf["max_tokens"] if "top_p" in gen_conf: options["top_p"] = gen_conf["top_p"] if "presence_penalty" in gen_conf: options["presence_penalty"] = gen_conf["presence_penalty"] if "frequency_penalty" in gen_conf: options["frequency_penalty"] = gen_conf["frequency_penalty"] ans = "" try: response = self.client.chat( model=self.model_name, messages=history, stream=True, options=options, keep_alive=-1 ) for resp in response: if resp["done"]: yield resp.get("prompt_eval_count", 0) + resp.get("eval_count", 0) ans += resp["message"]["content"] yield ans except Exception as e: yield ans + "\n**ERROR**: " + str(e) yield 0 class LocalAIChat(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="empty", base_url=base_url) self.model_name = model_name.split("___")[0] class LocalLLM(Base): class RPCProxy: def __init__(self, host, port): self.host = host self.port = int(port) self.__conn() def __conn(self): from multiprocessing.connection import Client self._connection = Client( (self.host, self.port), authkey=b"infiniflow-token4kevinhu" ) def __getattr__(self, name): import pickle def do_rpc(*args, **kwargs): for _ in range(3): try: self._connection.send(pickle.dumps((name, args, kwargs))) return pickle.loads(self._connection.recv()) except Exception: self.__conn() raise Exception("RPC connection lost!") return do_rpc def __init__(self, key, model_name): from jina import Client self.client = Client(port=12345, protocol="grpc", asyncio=True) def _prepare_prompt(self, system, history, gen_conf): from rag.svr.jina_server import Prompt if system: history.insert(0, {"role": "system", "content": system}) if "max_tokens" in gen_conf: gen_conf["max_new_tokens"] = gen_conf.pop("max_tokens") return Prompt(message=history, gen_conf=gen_conf) def _stream_response(self, endpoint, prompt): from rag.svr.jina_server import Generation answer = "" try: res = self.client.stream_doc( on=endpoint, inputs=prompt, return_type=Generation ) loop = asyncio.get_event_loop() try: while True: answer = loop.run_until_complete(res.__anext__()).text yield answer except StopAsyncIteration: 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": if is_chinese(ans): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN 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": if is_chinese(ans): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN 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": if is_chinese(ans): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN 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 yield response._chunks[-1].usage_metadata.total_token_count except Exception as e: yield ans + "\n**ERROR**: " + str(e) yield 0 class GroqChat: def __init__(self, key, model_name, base_url=''): from groq import Groq 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": if is_chinese(ans): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN 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": if is_chinese(ans): ans += LENGTH_NOTIFICATION_CN else: ans += LENGTH_NOTIFICATION_EN 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", } version2model = {v: k for k, v in model2version.items()} assert model_name in model2version or model_name in version2model, f"The given model name is not supported yet. Support: {list(model2version.keys())}" if model_name in model2version: model_version = model2version[model_name] else: model_version = 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 if "presence_penalty" in gen_conf: del gen_conf["presence_penalty"] if "frequency_penalty" in gen_conf: del gen_conf["frequency_penalty"] ans = "" try: response = self.client.messages.create( model=self.model_name, messages=history, system=self.system, stream=False, **gen_conf, ).to_dict() 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 if "presence_penalty" in gen_conf: del gen_conf["presence_penalty"] if "frequency_penalty" in gen_conf: del gen_conf["frequency_penalty"] 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(): if res.type == 'content_block_delta': text = res.delta.text ans += text total_tokens += num_tokens_from_string(text) yield ans 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