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import os |
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from logger import log_response |
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from custom_agent import CustomHfAgent |
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from langchain.vectorstores import FAISS |
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from langchain.memory import ConversationBufferMemory |
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from langchain.chains import ConversationalRetrievalChain, ConversationChain |
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from langchain.llms import HuggingFaceHub |
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image = [] |
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def handle_submission(user_message, selected_tools, url_endpoint, document, image, context): |
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log_response("User input \n {}".format(user_message)) |
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log_response("selected_tools \n {}".format(selected_tools)) |
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log_response("url_endpoint \n {}".format(url_endpoint)) |
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log_response("document \n {}".format(document)) |
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log_response("image \n {}".format(image)) |
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log_response("context \n {}".format(context)) |
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agent = CustomHfAgent( |
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url_endpoint=url_endpoint, |
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token=os.environ['HF_token'], |
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additional_tools=selected_tools, |
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input_params={"max_new_tokens": 192}, |
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) |
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response = agent.chat(user_message,document=document,image=image, context = context) |
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log_response("Agent Response\n {}".format(response)) |
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return response |
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def handle_submission_chat(user_message, response): |
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agent_chat_bot = get_conversation_chain() |
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if response is not None: |
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text = agent_chat_bot.predict(input=user_message + response) |
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else: |
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text = agent_chat_bot.predict(input=user_message) |
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print(text) |
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return text |
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def get_conversation_chain( ): |
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""" |
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Create a conversational retrieval chain and a language model. |
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""" |
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llm = HuggingFaceHub( |
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repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1", |
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model_kwargs={"temperature": 0.5, "max_length": 1048}, |
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) |
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memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) |
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conversation_chain = ConversationChain( |
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llm=llm, verbose=True, memory=ConversationBufferMemory() |
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) |
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return conversation_chain |
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