78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
import os
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from langchain.chat_models import ChatOpenAI
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from .llm_config import LLMConfig
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# from configs.model_config import (llm_model_dict, LLM_MODEL)
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def getChatModel(callBack: AsyncIteratorCallbackHandler = None, temperature=0.3, stop=None):
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if callBack is None:
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model = ChatOpenAI(
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streaming=True,
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verbose=True,
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openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
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openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
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model_name=LLM_MODEL,
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temperature=temperature,
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stop=stop
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)
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else:
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model = ChatOpenAI(
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streaming=True,
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verbose=True,
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callBack=[callBack],
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openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
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openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
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model_name=LLM_MODEL,
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temperature=temperature,
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stop=stop
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)
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return model
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def getChatModelFromConfig(llm_config: LLMConfig, callBack: AsyncIteratorCallbackHandler = None, ):
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if callBack is None:
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model = ChatOpenAI(
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streaming=True,
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verbose=True,
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openai_api_key=llm_config.api_key,
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openai_api_base=llm_config.api_base_url,
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model_name=llm_config.model_name,
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temperature=llm_config.temperature,
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stop=llm_config.stop
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)
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else:
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model = ChatOpenAI(
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streaming=True,
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verbose=True,
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callBack=[callBack],
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openai_api_key=llm_config.api_key,
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openai_api_base=llm_config.api_base_url,
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model_name=llm_config.model_name,
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temperature=llm_config.temperature,
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stop=llm_config.stop
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)
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return model
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import json, requests
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def getExtraModel():
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return TestModel()
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class TestModel:
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def predict(self, request_body):
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headers = {"Content-Type":"application/json;charset=UTF-8",
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"codegpt_user":"",
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"codegpt_token":""
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}
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xxx = requests.post(
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'https://codegencore.alipay.com/api/chat/CODE_LLAMA_INT4/completion',
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data=json.dumps(request_body,ensure_ascii=False).encode('utf-8'),
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headers=headers)
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return xxx.json()["data"] |