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import os
from dotenv import load_dotenv
from langchain_google_genai import GoogleGenerativeAI
from langchain.chains import RetrievalQA
# from langchain.vectorstores import FAISS
from langchain_community.vectorstores import FAISS
from langchain.prompts import PromptTemplate
load_dotenv() # take environment variables from .env (especially openai api key)
# Create Google Palm LLM model
model_name = "models/text-bison-001"
llm = GoogleGenerativeAI(google_api_key=os.environ["GOOGLE_PALM_API"], model=model_name)
vectordb_file_path = "faiss_index_V2"
def get_qa_chain(embeddings):
# Load the vector database from the local folder
vectordb = FAISS.load_local(vectordb_file_path, embeddings,allow_dangerous_deserialization=True)
# Create a retriever for querying the vector database
retriever = vectordb.as_retriever(score_threshold=0.7)
prompt_template = """Given the following context and a question, generate an answer based on this context only.
In the answer try to provide as much text as possible from the source document context without making much changes.
If the answer is not found in the context, kindly state "I don't know." Don't try to make up an answer.
CONTEXT: {context}
QUESTION: {question}"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
chain = RetrievalQA.from_chain_type(llm=llm,
chain_type="stuff",
retriever=retriever,
input_key="query",
return_source_documents=True,
chain_type_kwargs={"prompt": PROMPT})
return chain |