Create app.py

app.py

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+import os
+import streamlit as st
+from openai import OpenAI
+from llama_index.node_parser import SemanticSplitterNodeParser
+from llama_index.embeddings import OpenAIEmbedding
+from llama_index.ingestion import IngestionPipeline
+from pinecone.grpc import PineconeGRPC
+from pinecone import ServerlessSpec
+from llama_index.vector_stores import PineconeVectorStore
+from llama_index import VectorStoreIndex
+from llama_index.retrievers import VectorIndexRetriever
+from llama_index.query_engine import RetrieverQueryEngine
+
+# Set OpenAI API key from environment variables
+openai_api_key = os.getenv("OPENAI_API_KEY")
+pinecone_api_key = os.getenv("PINECONE_API_KEY")
+index_name = os.getenv("INDEX_NAME")
+
+# Initialize OpenAI client
+client = OpenAI(api_key=openai_api_key)
+
+# Initialize connection to Pinecone
+pc = PineconeGRPC(api_key=pinecone_api_key)
+
+# Initialize your index
+if index_name not in pc.list_indexes():
+    spec = ServerlessSpec(replicas=1, pod_type="p1")
+    pc.create_index(name=index_name, dimension=1536, spec=spec)
+
+pinecone_index = pc.Index(index_name)
+
+# Initialize VectorStore
+vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
+pinecone_index.describe_index_stats()
+
+# Initialize vector index and retriever
+vector_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
+retriever = VectorIndexRetriever(index=vector_index, similarity_top_k=5)
+query_engine = RetrieverQueryEngine(retriever=retriever)
+
+# Set up LlamaIndex embedding model and pipeline
+embed_model = OpenAIEmbedding(api_key=openai_api_key)
+pipeline = IngestionPipeline(
+    transformations=[
+        SemanticSplitterNodeParser(buffer_size=1, breakpoint_percentile_threshold=95, embed_model=embed_model),
+        embed_model,
+    ],
+)
+
+def query_annual_report(query):
+    response = query_engine.query(query)
+    return response.response
+
+# Streamlit app setup
+st.title("ChatGPT-like Clone with Pinecone Integration")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("What is up?"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        response = query_annual_report(prompt)
+        st.markdown(response)
+    st.session_state.messages.append({"role": "assistant", "content": response})