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app.py

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+import os
+import streamlit as st
+import pickle
+import pinecone
+import time
+from langchain import OpenAI
+from langchain.chains import RetrievalQAWithSourcesChain
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import UnstructuredURLLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.chains.question_answering import load_qa_chain
+from langchain.vectorstores import FAISS
+from langchain.vectorstores import Pinecone
+
+from dotenv import load_dotenv
+load_dotenv()  # take environment variables from .env (especially openai api key)
+
+st.title("Research Tool 📈")
+st.sidebar.title("Article URLs")
+
+
+urls = []
+for i in range(3):
+    url = st.sidebar.text_input(f"URL {i+1}")
+    urls.append(url)
+
+main_placeholder = st.empty()
+
+
+query = main_placeholder.text_input("Question: ")
+if query:
+    loader = UnstructuredURLLoader(urls=urls)
+    main_placeholder.text("Data Loading...Started...✅✅✅")
+    data = loader.load()
+    # split data
+    text_splitter = RecursiveCharacterTextSplitter(
+        separators=['\n\n', '\n', '.', ','],
+        chunk_size=1000
+    )
+    main_placeholder.text("Text Splitter...Started...✅✅✅")
+    docs = text_splitter.split_documents(data)
+    # create embeddings and save it to FAISS index
+    embeddings = OpenAIEmbeddings(api_key=os.getenv('OPENAI_API_KEY'))
+
+    pinecone.init(
+    api_key=os.getenv('PINECONE_API_KEY'),
+    environment="gcp-starter"
+    )
+    index_name = "langchainvector"
+    index = Pinecone.from_documents(docs, embeddings, index_name=index_name)
+    main_placeholder.text("Embedding Vector Started Building...✅✅✅")
+    def retrieve_query(mquery, k=3):
+        matching_results = index.similarity_search(mquery, k=k)
+        return matching_results
+    llm = OpenAI(temperature=0.5)
+    chain = load_qa_chain(llm, chain_type="stuff")
+    def retrieve_ans(mquery):
+        doc_search = retrieve_query(mquery)
+        print(doc_search)
+        response = chain.run(input_documents = doc_search, question=query)
+        return response   
+    
+
+    result = retrieve_ans(query)
+    st.header("Answer")
+    st.write(result)
+    # Display sources, if available
+    # sources = result.get("sources", "")
+    # if sources:
+    #     st.subheader("Sources:")
+    #     sources_list = sources.split("\n")  # Split the sources by newline
+    #     for source in sources_list:
+    #     st.write(source)
+
+
+

requirements.txt

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+langchain
+python-dotenv
+streamlit
+unstructured
+tiktoken
+libmagic
+python-magic
+python-magic-bin
+OpenAI 
+pandas
+numpy
+scipy
+pinecone-client
+scikit-learn
+matplotlib