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import chainlit as cl |
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import logging |
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import sys |
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logging.basicConfig(stream=sys.stdout, level=logging.INFO) |
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_logger = logging.getLogger("lang-chat") |
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_logger.addHandler(logging.StreamHandler(stream=sys.stdout)) |
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from langchain_core.prompts import ChatPromptTemplate |
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from langchain_core.vectorstores import VectorStore |
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from globals import ( |
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DEFAULT_QUESTION1, |
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DEFAULT_QUESTION2, |
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gpt35_model, |
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gpt4_model |
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) |
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from semantic import ( |
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SemanticStoreFactory, |
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SemanticRAGChainFactory |
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) |
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_semantic_rag_chain = SemanticRAGChainFactory.get_semantic_rag_chain() |
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@cl.on_message |
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async def main(message: cl.Message): |
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content = "> " |
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try: |
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response = _semantic_rag_chain.invoke({"question": message.content}) |
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content += response["response"].content |
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except Exception as e: |
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_logger.error(f"chat error: {e}") |
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await cl.Message( |
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content=f"{content}", |
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).send() |
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@cl.on_chat_start |
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async def start(): |
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await cl.Avatar( |
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name="Chatbot", |
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url="https://cdn-icons-png.flaticon.com/512/8649/8649595.png" |
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).send() |
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await cl.Avatar( |
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name="User", |
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url="https://media.architecturaldigest.com/photos/5f241de2c850b2a36b415024/master/w_1600%2Cc_limit/Luke-logo.png" |
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).send() |
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content = "" |
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if _semantic_rag_chain is not None: |
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try: |
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response1 = _semantic_rag_chain.invoke({"question": DEFAULT_QUESTION1}) |
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response2 = _semantic_rag_chain.invoke({"question": DEFAULT_QUESTION2}) |
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content = ( |
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f"**Question**: {DEFAULT_QUESTION1}\n\n" |
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f"{response1['response'].content}\n\n" |
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f"**Question**: {DEFAULT_QUESTION2}\n\n" |
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f"{response2['response'].content}\n\n" |
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) |
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except Exception as e: |
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_logger.error(f"init error: {e}") |
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cl.user_session.set("message_history", [{"role": "system", "content": "Welcome to the chat!"}]) |
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await cl.Message( |
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content=content + "\nHow can I help you with Meta's 2023 10K?" |
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).send() |
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