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Update app.py
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app.py
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import
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from llama_index import
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from llama_index
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from llama_index.data_structs.node import Node, DocumentRelationship
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from llama_index import VectorStoreIndex
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from llama_index import LLMPredictor, VectorStoreIndex, ServiceContext
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from langchain.llms import AzureOpenAI
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from langchain.embeddings.openai import OpenAIEmbeddings
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import logging
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import sys
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) # logging.DEBUG for more verbose output
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logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
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def main() -> None:
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documents = SimpleDirectoryReader("./data").load_data()
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# index = VectorStoreIndex.from_documents(documents)
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# parser = SimpleNodeParser()
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# nodes = parser.get_nodes_from_documents(documents)
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# index = VectorStoreIndex(nodes)
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# define embedding
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embedding = LangchainEmbedding(OpenAIEmbeddings(client=None, chunk_size=1))
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# define LLM
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llm_predictor = LLMPredictor(
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llm=AzureOpenAI(
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client=None,
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deployment_name="text-davinci-003",
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model="text-davinci-003",
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)
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)
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service_context = ServiceContext.from_defaults(
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llm_predictor=llm_predictor, embed_model=embedding
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)
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storage_context = StorageContext.from_defaults(persist_dir="./dataset")
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index = load_index_from_storage(
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storage_context=storage_context, service_context=service_context
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)
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if __name__ == "__main__":
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from llama_hub.github_repo import GithubRepositoryReader, GithubClient
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from llama_index import download_loader, GPTVectorStoreIndex
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from llama_index import LLMPredictor, ServiceContext, LangchainEmbedding
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from langchain.llms import AzureOpenAI
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from langchain.embeddings.openai import OpenAIEmbeddings
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import os
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import pickle
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import streamlit as st
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import logging
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import sys
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) # logging.DEBUG for more verbose output
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logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
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# Sidebar contents
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with st.sidebar:
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st.title("🤗💬 LLM Chat App")
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st.markdown(
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"""
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## About
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This app is an LLM-powered chatbot built using:
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- [Streamlit](https://streamlit.io/)
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- [LangChain](https://python.langchain.com/)
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- [X-Pipe](https://github.com/ctripcorp/x-pipe)
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"""
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)
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# add_vertical_space(5)
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st.write("Made by Nick")
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def main() -> None:
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st.header("X-Pipe Wiki 机器人 💬")
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# define embedding
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embedding = LangchainEmbedding(OpenAIEmbeddings(client=None, chunk_size=1))
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# define LLM
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llm_predictor = LLMPredictor(
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llm=AzureOpenAI(
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deployment_name="text-davinci-003",
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model="text-davinci-003",
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client=None,
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)
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)
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service_context = ServiceContext.from_defaults(
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llm_predictor=llm_predictor, embed_model=embedding
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)
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download_loader("GithubRepositoryReader")
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docs = None
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if os.path.exists("docs/docs.pkl"):
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with open("docs/docs.pkl", "rb") as f:
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docs = pickle.load(f)
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if docs is None:
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github_client = GithubClient(os.getenv("GITHUB_TOKEN"))
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loader = GithubRepositoryReader(
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github_client,
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owner="ctripcorp",
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repo="x-pipe",
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filter_directories=(
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[".", "doc"],
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GithubRepositoryReader.FilterType.INCLUDE,
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),
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filter_file_extensions=([".md"], GithubRepositoryReader.FilterType.INCLUDE),
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verbose=True,
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concurrent_requests=10,
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)
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docs = loader.load_data(branch="master")
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with open("docs/docs.pkl", "wb") as f:
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pickle.dump(docs, f)
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index = GPTVectorStoreIndex.from_documents(docs, service_context=service_context)
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query_engine = index.as_query_engine(service_context=service_context)
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query = st.text_input("X-Pipe Wiki 问题:")
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if query:
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index = GPTVectorStoreIndex.from_documents(
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docs, service_context=service_context
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)
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query_engine = index.as_query_engine(service_context=service_context)
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response = query_engine.query(query)
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st.write(response)
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if __name__ == "__main__":
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