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Build error
File size: 2,221 Bytes
2ee953c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | import streamlit as st
# from src.helper import download_hugging_face_embeddings
from langchain_pinecone import PineconeVectorStore
from langchain_openai import OpenAI, ChatOpenAI
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_huggingface import HuggingFaceEmbeddings
from src.prompt import *
from dotenv import load_dotenv
import os
#Download the Embeddings from Hugging Face
def download_hugging_face_embeddings():
embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
return embeddings
# Load env Variables
load_dotenv()
PINECONE_API_KEY=os.environ.get('PINECONE_API_KEY')
OPENAI_API_KEY=os.environ.get('OPENAI_API_KEY')
GOOGLE_API_KEY= os.environ.get("GOOGLE_API_KEY")
os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
# Embedding model
embeddings = download_hugging_face_embeddings()
# load exisiting pinecone index
index_name = "yolotest"
Vector_store = PineconeVectorStore.from_existing_index(
index_name=index_name,
embedding=embeddings
)
# Retriever
retriever = Vector_store.as_retriever(search_type="similarity", search_kwargs={"k":5})
# llm
# llm = OpenAI(api_key=OPENAI_API_KEY, temperature=0, max_tokens=500)
# llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro",temperature=0,max_tokens=None,timeout=None)
llm = ChatOpenAI(api_key=OPENAI_API_KEY, temperature=0, model='gpt-3.5-turbo-0125')
# streamlit
st.title("RAG Application built on Gemini Model")
query = st.chat_input("Say something: ")
prompt = query
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
if query:
question_answer_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
response = rag_chain.invoke({"input": query})
st.write(response["answer"]) |