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import os |
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from getpass import getpass |
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import streamlit as st |
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from dotenv import load_dotenv |
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from openai.embeddings_utils import OpenAIEmbeddings |
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from openai import OpenAI |
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from pinecone import PineconeClient, VectorStore |
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from faiss import IndexFlatL2 |
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from llama_index import VectorStoreIndex, VectorIndexRetriever |
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from llama_index.node_parser import SemanticSplitterNodeParser |
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from llama_index.embeddings import OpenAIEmbedding |
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from llama_index.ingestion import IngestionPipeline |
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from llama_index.query_engine import RetrieverQueryEngine |
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from llama_index.memory import ConversationBufferMemory |
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from llama_index.chains import ConversationalRetrievalChain |
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from llama_index.prompts import user_template, bot_template, css |
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load_dotenv() |
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pinecone_api_key = os.getenv("PINECONE_API_KEY") |
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openai_api_key = os.getenv("OPENAI_API_KEY") |
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index_name = os.getenv("INDEX_NAME") |
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openai.api_key = openai_api_key |
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pinecone_client = PineconeClient(api_key=pinecone_api_key) |
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pinecone_index = pinecone_client.Index(index_name) |
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vector_store = VectorStore(pinecone_index=pinecone_index) |
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embed_model = OpenAIEmbedding(api_key=openai_api_key) |
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pipeline = IngestionPipeline( |
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transformations=[ |
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SemanticSplitterNodeParser( |
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buffer_size=1, |
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breakpoint_percentile_threshold=95, |
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embed_model=embed_model, |
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), |
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embed_model, |
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], |
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) |
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vector_index = VectorStoreIndex.from_vector_store(vector_store=vector_store) |
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retriever = VectorIndexRetriever(index=vector_index, similarity_top_k=5) |
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query_engine = RetrieverQueryEngine(retriever=retriever) |
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def get_vectorstore(text_chunks): |
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embeddings = OpenAIEmbeddings() |
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vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings) |
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return vectorstore |
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def get_conversation_chain(vectorstore): |
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llm = OpenAI() |
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memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) |
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conversation_chain = ConversationalRetrievalChain.from_llm( |
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llm=llm, |
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retriever=vectorstore.as_retriever(), |
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memory=memory |
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) |
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return conversation_chain |
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def handle_userinput(user_question): |
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response = st.session_state.conversation({'question': user_question}) |
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st.session_state.chat_history = response['chat_history'] |
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for i, message in enumerate(st.session_state.chat_history): |
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if i % 2 == 0: |
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st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True) |
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else: |
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st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True) |
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def main(): |
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load_dotenv() |
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st.set_page_config(page_title="Chat with Annual Reports", page_icon=":books:") |
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st.write(css, unsafe_allow_html=True) |
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if "conversation" not in st.session_state: |
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st.session_state.conversation = None |
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if "chat_history" not in st.session_state: |
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st.session_state.chat_history = None |
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st.header("Chat with Annual Report Documents") |
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user_question = st.text_input("Ask a question about your documents:") |
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if user_question: |
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handle_userinput(user_question) |
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if __name__ == "__main__": |
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main() |
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