#
# 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.
#
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_english
from rag.utils import num_tokens_from_string
from groq import Groq
import os
import json
import requests
import asyncio
class Base(ABC):
def __init__(self, key, model_name, base_url):
self.client = OpenAI(api_key=key, base_url=base_url)
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":
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})
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
total_tokens = (
(
total_tokens
+ num_tokens_from_string(resp.choices[0].delta.content)
)
if not hasattr(resp, "usage") or not resp.usage
else resp.usage.get("total_tokens",total_tokens)
)
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 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 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):
self.client = AzureOpenAI(api_key=key, azure_endpoint=kwargs["base_url"], api_version="2024-02-01")
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":
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})
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":
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 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):
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":
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, tk_count
return "**ERROR**: " + response.message, tk_count
def chat_streamly(self, system, history, gen_conf):
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,
**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":
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
else:
yield ans + "\n**ERROR**: " + resp.message if str(resp.message).find("Access")<0 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
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":
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})
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":
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？"
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_k"] = 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["eval_count"] + 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_k"] = 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 as e:
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,Generation
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 Prompt,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":
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 None,
)
# 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 None,
)
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
)
# 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 '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 ans + "\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