Create app.py

app.py

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+import os
+import streamlit as st
+import pickle
+import time
+import langchain
+#from langchain import OpenAI
+#from langchain.chains import RetrievalQAWithSourcesChain
+#from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.document_loaders import UnstructuredURLLoader
+#from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+import requests
+import pandas as pd
+from langchain_community.llms import HuggingFaceEndpoint
+from sentence_transformers import SentenceTransformer
+from langchain.document_loaders import TextLoader
+from sentence_transformers import SentenceTransformer, util
+from langchain.schema import SystemMessage, HumanMessage, AIMessage
+import faiss
+from dotenv import load_dotenv
+load_dotenv()  # take environment variables from .env (especially openai api key)
+
+def querypreprocess(query: str ):
+  vec = model.encode(query)  #again embeddings of query by sentencetransformer and able to search the index vector.
+  #svec = np.array(vec).reshape(1,-1)  # as 2D needed
+  distances, I = index.search(vec, k=2)
+  row_indices = I.tolist()[0]
+  list1 = [docs[i].page_content for i in row_indices]
+  str1 = " "
+  str1 = str1.join(list1)
+  #str1 = '\n'.join([str(message) for message in list1])
+  #results = ' '.join(map(str, list1)) #list to string convert
+  return str1
+    
+def augmented_prompt(query: str):
+  messages = querypreprocess(query)
+  source_knowledge =''.join([str(message) for message in messages])
+  #source_knowledge =results
+  augmented_prompt = f"""
+  using the contexts below, answer the query.
+
+  Contexts:
+  {source_knowledge}
+  Question: {query}
+  Answer:"""
+  return augmented_prompt
+
+    
+st.title("RockyBot: News Research Tool 📈")
+st.sidebar.title("News Article URLs")
+
+urls = []
+for i in range(3):
+    url = st.sidebar.text_input(f"URL {i+1}")
+    urls.append(url)
+
+process_url_clicked = st.sidebar.button("Process URLs")
+file_path = "vector_index.pkl"
+
+main_placeholder = st.empty()
+#llm = OpenAI(temperature=0.9, max_tokens=500)
+llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.2")
+
+if process_url_clicked:
+    # load data
+    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,
+        chunk_overlap=0
+    )
+    main_placeholder.text("Text Splitter...Started...✅✅✅")
+    docs = text_splitter.split_documents(data)
+    # Create an array of text to embed
+    sentences = []
+    for i, row in enumerate(docs):
+        sentences.append(row.page_content)
+    # create embeddings and save it to FAISS index
+    #embeddings = OpenAIEmbeddings()
+    #vectorstore_openai = FAISS.from_documents(docs, embeddings)
+    # initialize sentence transformer model
+    model = SentenceTransformer('bert-base-nli-mean-tokens')
+    # create sentence embeddings
+    sentence_embeddings = model.encode(sentences)
+    main_placeholder.text("Embedding Vector Started Building...✅✅✅")
+    time.sleep(2)
+
+    # Save the FAISS index to a pickle file
+    with open(file_path, "wb") as f:
+        pickle.dump(vector_index, f)
+
+query = main_placeholder.text_input("Question: ")
+if query:
+    if os.path.exists(file_path):
+        with open(file_path, "rb") as f:
+            query = pickle.load(f)
+            import faiss
+            d = sentence_embeddings.shape[1]
+            index = faiss.IndexFlatL2(d)   # build the index, d=size of vectors
+            # here we assume xb contains a n-by-d numpy matrix of type float32
+            index.add(sentence_embeddings)                  # add vectors to the index
+            #chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vectorstore.as_retriever())
+            #result = chain({"question": query}, return_only_outputs=True)
+            # result will be a dictionary of this format --> {"answer": "", "sources": [] }
+            #xq = model.encode([query])
+            #k=2
+            #D, I = index.search(xq, k=k)
+            #result1 = [f'{i}: {sentences[i]}' for i in I[0]]
+            messages = [
+                SystemMessage(content="You are a helpful assistant."),
+                HumanMessage(content=query),
+                AIMessage(content="I am Great, Thank You, How Can I Help You.")
+                ]
+            prompt = augmented_prompt(query)
+            messages.append(prompt)
+            result = llm.invoke(messages)
+            st.header("Answer")
+            # 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)