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import os
from getpass import getpass
import gradio as gr
import random
import time
pinecone_api_key = os.getenv("PINECONE_API_KEY") or getpass("Enter your Pinecone API Key: ")
openai_api_key = os.getenv("OPENAI_API_KEY") or getpass("Enter your OpenAI API Key: ")
from llama_index.node_parser import SemanticSplitterNodeParser
from llama_index.embeddings import OpenAIEmbedding
from llama_index.ingestion import IngestionPipeline
# This will be the model we use both for Node parsing and for vectorization
embed_model = OpenAIEmbedding(api_key=openai_api_key)
# Define the initial pipeline
pipeline = IngestionPipeline(
transformations=[
SemanticSplitterNodeParser(
buffer_size=1,
breakpoint_percentile_threshold=95,
embed_model=embed_model,
),
embed_model,
],
)
from pinecone.grpc import PineconeGRPC
from pinecone import ServerlessSpec
from llama_index.vector_stores import PineconeVectorStore
# Initialize connection to Pinecone
pc = PineconeGRPC(api_key=pinecone_api_key)
index_name = "anualreport"
# Initialize your index
pinecone_index = pc.Index(index_name)
# Initialize VectorStore
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
pinecone_index.describe_index_stats()
from llama_index import VectorStoreIndex
from llama_index.retrievers import VectorIndexRetriever
# Set the OpenAI API key if not already set
if not os.getenv('OPENAI_API_KEY'):
os.environ['OPENAI_API_KEY'] = openai_api_key
# Instantiate VectorStoreIndex object from our vector_store object
vector_index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
# Grab 5 search results
retriever = VectorIndexRetriever(index=vector_index, similarity_top_k=5)
from llama_index.query_engine import RetrieverQueryEngine
# Pass in your retriever from above, which is configured to return the top 5 results
query_engine = RetrieverQueryEngine(retriever=retriever)
def query_anual_report(query):
response = query_engine.query(query)
return response.response
# Define the chat functions
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history):
bot_message = query_anual_report(history[-1][0])
history[-1][1] = ""
for character in bot_message:
history[-1][1] += character
time.sleep(0.01) # Reduced sleep time to make response appear faster
yield history
# Define Gradio Blocks interface
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, chatbot, chatbot
)
clear.click(lambda: None, None, chatbot, queue=False)
if __name__ == "__main__":
demo.launch()
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