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faiss_index/index.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73becf24b1238d656c0f8261be6a0666f422200bc5eabc5ff0d9b46b553f2045
+size 47382

main.py

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+import streamlit as st
+# from langchain_helper import get_qa_chain, create_vector_db
+
+
+
+from langchain.vectorstores import FAISS
+from langchain.llms import GooglePalm
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
+from dotenv import load_dotenv
+load_dotenv()
+import os
+# get this free api key from https://makersuite.google.com/
+llm = GooglePalm(google_api_key=os.environ["GOOGLE_API_KEY"], temperature=0.1)
+
+instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-large")
+vectordb_file_path = "faiss_index"
+
+
+def get_qa_chain():
+    # Load the vector database from the local folder
+    vectordb = FAISS.load_local(vectordb_file_path, instructor_embeddings)
+
+    # 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 "response" section in 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
+
+
+
+st.title("Q&A for My Courses ")
+# btn = st.button("Create Knowledgebase")
+# if btn:
+#     create_vector_db()
+
+question = st.text_input("Question: ")
+
+if question:
+    chain = get_qa_chain()
+    response = chain(question)
+
+    st.header("Answer")
+    st.write(response["result"])

requirements.txt

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+langchain==0.0.284
+python-dotenv==1.0.0
+streamlit==1.22.0
+tiktoken==0.4.0
+faiss-cpu==1.7.4
+protobuf~=3.19.0
+huggingface
+transformers
+InstructorEmbedding
+torch
+sentence_transformers
+google-generativeai