Update app.py

app.py

@@ -1,165 +1,19 @@
-#####################################
-##   
-#####################################
-
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
 from langchain_community.llms import HuggingFaceHub
-
-
-###### other models:
-# "Trelis/Llama-2-7b-chat-hf-sharded-bf16"
-# "bn22/Mistral-7B-Instruct-v0.1-sharded"
-# "HuggingFaceH4/zephyr-7b-beta"
-
-# function for loading 4-bit quantized model
-def load_model( ):
-
-    model =  HuggingFaceHub(
-        repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
-        model_kwargs={"max_length": 1048, "temperature":0.2, "max_new_tokens":256, "top_p":0.95, "repetition_penalty":1.0},
-    )
-    
-    return model
-
-##################################################
-## vs chat
-##################################################
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
-
-from langchain_core.messages import AIMessage, HumanMessage
-from langchain_community.document_loaders import WebBaseLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-from langchain.vectorstores.faiss import FAISS
-
-
-from dotenv import load_dotenv
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain.chains import create_history_aware_retriever, create_retrieval_chain
 from langchain.chains.combine_documents import create_stuff_documents_chain
-
-
-load_dotenv()
-
-def get_vectorstore():
-    ''' 
-        FAISS
-        A FAISS vector store containing the embeddings of the text chunks.
-   '''
-    model = "BAAI/bge-base-en-v1.5"
-    encode_kwargs = {
-        "normalize_embeddings": True
-    }  # set True to compute cosine similarity
-    embeddings = HuggingFaceBgeEmbeddings(
-        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
-    )
-    # load from disk
-    vector_store = Chroma(persist_directory="/home/user/.cache/chroma_db", embedding_function=embeddings)
-    return vector_store
-
-def get_vectorstore_from_url(url):
-    # get the text in document form
-    loader = WebBaseLoader(url)
-    document = loader.load()
-    
-    # split the document into chunks
-    text_splitter = RecursiveCharacterTextSplitter()
-    document_chunks = text_splitter.split_documents(document)
-    ####### 
-    ''' 
-        FAISS
-        A FAISS vector store containing the embeddings of the text chunks.
-   '''
-    model = "BAAI/bge-base-en-v1.5"
-    encode_kwargs = {
-        "normalize_embeddings": True
-    }  # set True to compute cosine similarity
-    embeddings = HuggingFaceBgeEmbeddings(
-        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
-    )
-    # load from disk
-    #vector_store = Chroma(persist_directory="/home/user/.cache/chroma_db", embedding_function=embeddings)
- 
-    #vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    vector_store = Chroma.from_documents(document_chunks, embeddings, persist_directory="/home/user/.cache/chroma_db")
-    
-    all_documents = vector_store.get()['documents']
-    total_records = len(all_documents)
-    print("Total records in the collection: ", total_records)
-
-    return vector_store
-
-def get_context_retriever_chain(vector_store): 
-
-    llm = load_model( )
-    
-    retriever = vector_store.as_retriever()
-    
-    prompt = ChatPromptTemplate.from_messages([
-      MessagesPlaceholder(variable_name="chat_history"),
-      ("user", "{input}"),
-      ("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation")
-    ])
-    
-    retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
-    
-    return retriever_chain
-    
-def get_conversational_rag_chain(retriever_chain): 
-    
-    llm = load_model( )
-    
-    prompt = ChatPromptTemplate.from_messages([
-      ("system", "Du bist eine freundlicher Mitarbeiterin Namens Susie und arbeitest in einenm Call Center. Du beantwortest basierend auf dem Context. Benutze nur den Inhalt des Context. Füge wenn möglich die Quelle hinzu. Antworte mit: Ich bin mir nicht sicher. Wenn die Antwort nicht aus dem Context hervorgeht. Antworte auf Deutsch, bitte? CONTEXT:\n\n{context}"),
-      MessagesPlaceholder(variable_name="chat_history"),
-      ("user", "{input}"),
-    ])
-    
-    stuff_documents_chain = create_stuff_documents_chain(llm,prompt)
-    
-    return create_retrieval_chain(retriever_chain, stuff_documents_chain)
-
-
-###################
-
-###################
-import gradio as gr
-
-
-chat_history = []     # Set your chat history here
-
-# Define your function here
-def get_response(user_input):
-
-    vs = get_vectorstore()
-    chat_history =[]
-    retriever_chain = get_context_retriever_chain(vs)
-    conversation_rag_chain = get_conversational_rag_chain(retriever_chain)
-    
-    response = conversation_rag_chain.invoke({
-        "chat_history": chat_history,
-        "input": user_input
-    })
-    #print("get_response " +response)
-    res = response['answer']
-    parts = res.split(" Assistant: ")
-    last_part = parts[-1]
-    return last_part
-
-
-###############
-#####
-#####
-#####
-####
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
+from langchain_community.document_loaders import WebBaseLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from urllib.parse import urlparse, urljoin
+import requests
+from bs4 import BeautifulSoup
 
 app = FastAPI()
 
-# middlewares to allow cross orgin communications
+# Middleware to allow cross-origin communications
 app.add_middleware(
     CORSMiddleware,
     allow_origins=['*'], 
@@ -168,100 +22,20 @@ app.add_middleware(
     allow_headers=['*'],
 )
 
+# Function to crawl all URLs from a domain
+def get_all_links_from_domain(domain_url):
+    visited_urls = set()
+    domain_links = set()
+    parsed_initial_url = urlparse(domain_url)
+    base_domain = parsed_initial_url.netloc
+    get_links_from_page(domain_url, visited_urls, domain_links, base_domain)
+    return domain_links
 
-@app.post("/generate/")
-def generate(user_input, history=[]):
-    print("----yuhu -----")   
-    return get_response(user_input, history)
-##################
-
-def history_to_dialog_format(chat_history: list[str]):
-    dialog = []
-    if len(chat_history) > 0:
-        for idx, message in enumerate(chat_history[0]):
-            role = "user" if idx % 2 == 0 else "assistant"
-            dialog.append({
-                "role": role,
-                "content": message,
-            })
-    return dialog
-
-def get_response(message, history):
-    dialog = history_to_dialog_format(history)
-    dialog.append({"role": "user", "content": message})
-
-      # Define the prompt as a ChatPromptValue object
-    #user_input = ChatPromptValue(user_input)
-    
-    # Convert the prompt to a tensor
-    #input_ids = user_input.tensor
-    
-
-    #vs = get_vectorstore_from_url(user_url, all_domain)
-    vs = get_vectorstore()
-  
-    history =[]
-    retriever_chain = get_context_retriever_chain(vs)
-    conversation_rag_chain = get_conversational_rag_chain(retriever_chain)
-    
-    response = conversation_rag_chain.invoke({
-        "chat_history": history,
-        "input": message  + " Assistant: ",
-        "chat_message": message + " Assistant: "
-    })
-    #print("get_response " +response)
-    res = response['answer']
-    parts = res.split(" Assistant: ")
-    last_part = parts[-1]
-    return last_part#[-1]['generation']['content']    
-
-
-
-
- 
-
-######
-
-########
-import requests
-from bs4 import BeautifulSoup
-from urllib.parse import urlparse, urljoin
-
-
-def get_links_from_page(url, visited_urls, all_links):
-    if url in visited_urls:
-        return
-
-    if len(visited_urls) > 2:
-        return
-
-    visited_urls.add(url)
-     
-    print("Getting next" + url)
-    response = requests.get(url)
-
-    if response.status_code == 200:
-        soup = BeautifulSoup(response.content, 'html.parser')
-        base_url = urlparse(url).scheme + '://' + urlparse(url).netloc
-        links = soup.find_all('a', href=True)
-
-        for link in links:
-            href = link.get('href')
-            absolute_url = urljoin(base_url, href)
-            all_links.add(absolute_url)
-            get_links_from_page(absolute_url, visited_urls, all_links)
-
-    else:
-        print(f"Failed to retrieve content from {url}. Status code: {response.status_code}")
-
-
+# Function to crawl links from a page within the same domain
 def get_links_from_page(url, visited_urls, all_links, base_domain):
     if url in visited_urls:
         return
 
-    if len(visited_urls) > 2:
-        return
-
     visited_urls.add(url)
     print("Getting next" + url)
     response = requests.get(url)
@@ -283,48 +57,60 @@ def get_links_from_page(url, visited_urls, all_links, base_domain):
     else:
         print(f"Failed to retrieve content from {url}. Status code: {response.status_code}")
 
+# Function to load the RAG model
+def load_rag_model():
+    model =  HuggingFaceHub(
+        repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        model_kwargs={"max_length": 1048, "temperature":0.2, "max_new_tokens":256, "top_p":0.95, "repetition_penalty":1.0},
+    )
+    return model
 
-def get_all_links_from_domain(domain_url):
-    visited_urls = set()
-    domain_links = set()
-    parsed_initial_url = urlparse(domain_url)
-    base_domain = parsed_initial_url.netloc
-    get_links_from_page(domain_url, visited_urls, domain_links, base_domain)
-    return domain_links
-
-
+# Function to index URLs in RAG
+def index_urls_in_rag(urls):
+    # Create a vector store for storing embeddings of documents
+    vector_store = Chroma(persist_directory="/home/user/.cache/chroma_db")
+    
+    # Load the RAG model
+    rag_model = load_rag_model()
 
+    for url in urls:
+        # Get text from the URL
+        loader = WebBaseLoader(url)
+        document = loader.load()
 
+        # Split the document into chunks
+        text_splitter = RecursiveCharacterTextSplitter()
+        document_chunks = text_splitter.split_documents(document)
 
-def simple(text:str):
-  return text +" hhhmmm "
+        # Index document chunks into the vector store
+        vector_store.add_documents(document_chunks)
 
-fe_app = gr.ChatInterface(
-    fn=get_response,
-    #fn=simple,
-   # inputs=["text"],
-   # outputs="text",
-    title="Chat with Websites",
-    description="Schreibe hier deine Frage rein...",
-    #allow_flagging=False
-    retry_btn=None,
-    undo_btn=None,
-    clear_btn=None 
-)
+    # Convert vector store to retriever
+    retriever = vector_store.as_retriever()
 
-#fe_app.launch(debug=True, share=True) 
+    # Define prompt for RAG model
+    prompt = ChatPromptTemplate.from_messages([
+      MessagesPlaceholder(variable_name="chat_history"),
+      ("user", "{input}"),
+    ])
 
+    # Create history-aware retriever chain
+    retriever_chain = create_history_aware_retriever(rag_model, retriever, prompt)
 
+    return retriever_chain
 
-# load the model asynchronously on startup and save it into memory 
+# Index URLs on app startup
 @app.on_event("startup")
 async def startup():
     domain_url = 'https://www.bofrost.de/faq/'
-    links = get_all_links_from_domain(domain_url)
-    print("Links from the domain:", links)
-    
-    #########
-    # Assuming visited_urls is a list of URLs
-    for url in links:
-        vs = get_vectorstore_from_url(url)
-    #load_model()
+    urls = get_all_links_from_domain(domain_url)
+    retriever_chain = index_urls_in_rag(urls)
+
+# Define API endpoint to receive queries and provide responses
+@app.post("/generate/")
+def generate(user_input):
+    response = retriever_chain.invoke({
+        "chat_history": [],
+        "input": user_input
+    })
+    return response['answer']