initial commit

app.py

@@ -0,0 +1,167 @@
+# Install necessary packages
+#!pip install streamlit
+#!pip install wikipedia
+#!pip install langchain_community
+#!pip install sentence-transformers
+#!pip install chromadb
+#!pip install huggingface_hub
+#!pip install transformers
+
+import streamlit as st
+from langchain_community.document_loaders import WikipediaLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter, SentenceTransformersTokenTextSplitter
+import chromadb
+from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction
+from huggingface_hub import login, InferenceClient
+from sentence_transformers import CrossEncoder
+import numpy as np
+import random
+import string
+
+# User variables
+topic = st.sidebar.text_input("Enter the Wikipedia topic:", "Wikipedia_Keyword")
+query = st.sidebar.text_input("Enter your first query:", "First query")
+model_name = st.sidebar.selectbox("Select model:", ["mistralai/Mistral-7B-Instruct-v0.3", "meta-llama/Meta-Llama-3.1-8B-Instruct"])
+HF_TOKEN = st.sidebar.text_input("Enter your Hugging Face token:", "YOUR_HF_TOKEN", type="password")
+
+# Hugging Face login
+login(token=HF_TOKEN)
+
+# Memory for chat history
+if "history" not in st.session_state:
+    st.session_state.history = []
+
+# Function to generate a random string for collection name
+def generate_random_string(max_length=60):
+    if max_length > 60:
+        raise ValueError("The maximum length cannot exceed 60 characters.")
+    length = random.randint(1, max_length)
+    characters = string.ascii_letters + string.digits
+    return ''.join(random.choice(characters) for _ in range(length))
+
+collection_name = generate_random_string()
+
+# Function for query expansion
+def augment_multiple_query(query):
+    client = InferenceClient(model_name, token=HF_TOKEN)
+    content = client.chat_completion(
+        messages=[
+            {
+                "role": "system",
+                "content": f"""You are a helpful expert in {topic}. Your users are asking questions about {topic}. 
+                Suggest up to five additional related questions to help them find the information they need for the provided question. 
+                Suggest only short questions without compound sentences. Suggest a variety of questions that cover different aspects of the topic. 
+                Make sure they are complete questions, and that they are related to the original question."""
+            },
+            {
+                "role": "user",
+                "content": query
+            }
+        ],
+        max_tokens=500,
+    )
+    return content.choices[0].message.content.split("\n")
+
+# Custom function to handle fuzzy keyword searches
+def load_wikipedia_page(topic):
+    try:
+        # Attempt to load the page directly
+        page = wikipedia.page(topic)
+        return page.content
+    
+    except wikipedia.exceptions.DisambiguationError as e:
+        # Handle disambiguation page case
+        st.write(f"The keyword '{topic}' returned multiple possible pages.")
+        st.write("Please refine your search or select one of the following options:")
+        
+        # List disambiguation options
+        options = e.options
+        selected_option = st.selectbox("Select the closest match:", options)
+        
+        # Load the selected option
+        page = wikipedia.page(selected_option)
+        return page.content
+    
+    except wikipedia.exceptions.PageError:
+        # Handle the case where no page is found
+        st.write(f"No page found for '{topic}'. Please try a different keyword.")
+        return None
+
+# Function to handle RAG-based question answering
+def rag_advanced(user_query):
+    # Document Loading
+    docs_content = load_wikipedia_page(topic)
+    if docs_content:
+        docs = [docs_content]  # Wrap the content in a list for consistency
+
+    # Text Splitting
+    character_splitter = RecursiveCharacterTextSplitter(separators=["\n\n", "\n", ". ", " ", ""], chunk_size=1000, chunk_overlap=0)
+    concat_texts = "".join([doc.page_content for doc in docs])
+    character_split_texts = character_splitter.split_text(concat_texts)
+
+    token_splitter = SentenceTransformersTokenTextSplitter(chunk_overlap=0, tokens_per_chunk=256)
+    token_split_texts = [text for text in character_split_texts for text in token_splitter.split_text(text)]
+
+    # Embedding and Document Storage
+    embedding_function = SentenceTransformerEmbeddingFunction()
+    chroma_client = chromadb.Client()
+    chroma_collection = chroma_client.create_collection(collection_name, embedding_function=embedding_function)
+
+    ids = [str(i) for i in range(len(token_split_texts))]
+    chroma_collection.add(ids=ids, documents=token_split_texts)
+
+    # Document Retrieval
+    augmented_queries = augment_multiple_query(user_query)
+    joint_query = [user_query] + augmented_queries
+
+    results = chroma_collection.query(query_texts=joint_query, n_results=5, include=['documents', 'embeddings'])
+    retrieved_documents = results['documents']
+
+    unique_documents = list(set(doc for docs in retrieved_documents for doc in docs))
+
+    # Re-Ranking
+    cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+    pairs = [[user_query, doc] for doc in unique_documents]
+    scores = cross_encoder.predict(pairs)
+
+    top_indices = np.argsort(scores)[::-1][:5]
+    top_documents = [unique_documents[idx] for idx in top_indices]
+
+    # LLM Reference
+    client = InferenceClient(model_name, token=HF_TOKEN)
+    response = ""
+    for message in client.chat_completion(
+        messages=[
+            {
+                "role": "system",
+                "content": f"""You are a helpful expert in {topic}. Your users are asking questions about {topic}. 
+                You will be shown the user's questions, and the relevant information from the documents related to {topic}. 
+                Answer the user's question using only this information."""
+            },
+            {
+                "role": "user",
+                "content": f"Questions: {user_query}. \n Information: {top_documents}"
+            }
+        ],
+        max_tokens=500,
+        stream=True,
+    ):
+        response += message.choices[0].delta.content
+
+    return response
+
+# Streamlit UI
+st.title("Wikipedia RAG Chatbot")
+
+# Input box for the user to type their message
+user_input = st.text_input("You: ", "")
+
+# Generate response and update conversation history
+if user_input:
+    response = rag_advanced(user_input)
+    st.session_state.history.append({"user": user_input, "bot": response})
+
+# Display the conversation history
+for chat in st.session_state.history:
+    st.write(f"You: {chat['user']}")
+    st.write(f"Bot: {chat['bot']}")