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import streamlit as st
import os
from streamlit_chat import message
import numpy as np
import pandas as pd
# import json
# st.config(PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION="python")
# from datasets import load_dataset
# dataset = load_dataset("wikipedia", "20220301.en", split="train[240000:250000]")
# wikidata = []
# for record in dataset:
# wikidata.append(record["text"])
# wikidata = list(set(wikidata))
# # print("\n".join(wikidata[:5]))
# # print(len(wikidata))
from sentence_transformers import SentenceTransformer
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if device != 'cuda':
st.text(f"you are using {device}. This is much slower than using "
"a CUDA-enabled GPU. If on colab you can chnage this by "
"clicking Runtime > change runtime type > GPU.")
model = SentenceTransformer("all-MiniLM-L6-v2", device=device)
st.divider()
# Creating a Index(Pinecone Vector Database)
import os
# import pinecone
from pinecone.grpc import PineconeGRPC
PINECONE_API_KEY=os.getenv("PINECONE_API_KEY")
PINECONE_ENV=os.getenv("PINECONE_ENV")
PINECONE_ENVIRONMENT=os.getenv("PINECONE_ENVIRONMENT")
# pc = PineconeGRPC( api_key=os.environ.get("PINECONE_API_KEY") ) # Now do stuff if 'my_index' not in pc.list_indexes().names(): pc.create_index( name='my_index', dimension=1536, metric='euclidean', spec=ServerlessSpec( cloud='aws', region='us-west-2' ) )
def connect_pinecone():
pinecone = PineconeGRPC(api_key=PINECONE_API_KEY, environment=PINECONE_ENV)
st.code(pinecone)
st.divider()
st.text(pinecone.list_indexes().names())
st.divider()
st.text(f"Succesfully connected to the pinecone")
return pinecone
def get_pinecone_semantic_index(pinecone):
index_name = "sematic-search"
# only create if it deosnot exists
if index_name not in pinecone.list_indexes().names():
pinecone.create_index(
name=index_name,
description="Semantic search",
dimension=model.get_sentence_embedding_dimension(),
metric="cosine",
spec=ServerlessSpec( cloud='gcp', region='us-central1' )
)
# now connect to index
index = pinecone.Index(index_name)
st.text(f"Succesfully connected to the pinecone index")
return index
def chat_actions():
pinecone = connect_pinecone()
index = get_pinecone_semantic_index(pinecone)
st.session_state["chat_history"].append(
{"role": "user", "content": st.session_state["chat_input"]},
)
query_embedding = model.encode(st.session_state["chat_input"])
# create the query vector
query_vector = query_embedding.tolist()
# now query vector database
result = index.query(query_vector, top_k=5, include_metadata=True) # xc is a list of tuples
with st.sidebar:
st.json(result)
for result in xc['matches']:
st.session_state["chat_history"].append(
{
"role": "assistant",
"content": f"{round(result['score'],2)}: {result['metadata']['text']}",
}, # This can be replaced with your chat response logic
)
if "chat_history" not in st.session_state:
st.session_state["chat_history"] = []
st.chat_input("Enter your message", on_submit=chat_actions, key="chat_input")
for i in st.session_state["chat_history"]:
with st.chat_message(name=i["role"]):
st.write(i["content"])
### Creating a Index(Pinecone Vector Database)
# %%writefile .env
# PINECONE_API_KEY=os.getenv("PINECONE_API_KEY")
# PINECONE_ENV=os.getenv("PINECONE_ENV")
# PINECONE_ENVIRONMENT=os.getenv("PINECONE_ENVIRONMENT")
# import os
# import pinecone
# from pinecone import Index, GRPCIndex
# pinecone.init(api_key=PINECONE_API_KEY, environment=PINECONE_ENV)
# st.text(pinecone)
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