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README.md

@@ -1,14 +1,14 @@
----
-title: RAG Chatbot
-emoji: 🔥
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 5.46.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-short_description: RAG Assignment
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: RAG Chatbot
+emoji: 🔥
+colorFrom: yellow
+colorTo: gray
+sdk: gradio
+sdk_version: 5.46.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: RAG Assignment
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,238 @@
+import os
+import PyPDF2
+from qdrant_client import QdrantClient
+from dotenv import load_dotenv
+from langchain_openai import AzureChatOpenAI, AzureOpenAIEmbeddings
+from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
+import streamlit as st
+import hashlib
+
+# Load environment variables from .env
+load_dotenv(".env")
+
+# Initialize Azure OpenAI (as in notebook)
+llm = AzureChatOpenAI(
+    temperature=0,
+    api_key=os.getenv("AZURE_OPENAI_KEY"),
+    api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
+    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+    model=os.getenv("AZURE_OPENAI_MODEL_NAME")  # Must match deployment name
+)
+
+# Qdrant configuration from environment
+QDRANT_API_KEY = os.getenv('QDRANT_API_KEY')
+QDRANT_URL = os.getenv('QDRANT_CLOUD_URL')
+
+# Helper functions from notebook
+
+def load_pdf_text(pdf_path):
+    text = ""
+    with open(pdf_path, 'rb') as f:
+        reader = PyPDF2.PdfReader(f)
+        for page in reader.pages:
+            page_text = page.extract_text() or ""
+            text += page_text + "\n"
+    return text
+
+def split_text(text, chunk_size=800, chunk_overlap=150):
+    sentences = text.split('. ')
+    chunks, chunk = [], ''
+    for sentence in sentences:
+        next_piece = (sentence + '. ').strip()
+        if len(chunk) + len(next_piece) <= chunk_size:
+            chunk += (next_piece + ' ')
+        else:
+            if chunk:
+                chunks.append(chunk.strip())
+            # start new chunk with overlap
+            overlap = chunk[-chunk_overlap:] if chunk_overlap and len(chunk) > chunk_overlap else ''
+            chunk = (overlap + next_piece + ' ')
+    if chunk:
+        chunks.append(chunk.strip())
+    return chunks
+
+# Azure embeddings helper
+
+def _azure_base(url: str | None) -> str | None:
+    if not url:
+        return None
+    idx = url.find("/openai")
+    return url[:idx] if idx > 0 else url
+
+
+def _init_azure_embedder():
+    return AzureOpenAIEmbeddings(
+        api_key=os.getenv("AZURE_OPENAI_EMBEDDING_API_KEY") or os.getenv("AZURE_OPENAI_KEY"),
+        azure_endpoint=_azure_base(os.getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT") or os.getenv("AZURE_OPENAI_ENDPOINT")),
+        api_version=os.getenv("AZURE_OPENAI_EMBEDDING_API_VERSION") or os.getenv("AZURE_OPENAI_API_VERSION"),
+        model=os.getenv("AZURE_OPENAI_EMBEDDING_MODEL_NAME")
+    )
+
+# Streamlit UI
+st.title("Chatbot using PDF Documents")
+
+# Sidebar: upload PDFs
+with st.sidebar:
+    st.header("Upload PDFs")
+    uploaded_files = st.file_uploader(
+        "Upload one or more PDF files",
+        type=["pdf"],
+        accept_multiple_files=True
+    )
+
+# Automatically process when files are uploaded or changed
+files_sig = (lambda files: (None if not files else hashlib.sha1("|".join(sorted([
+    f"{uf.name}:{len((uf.getvalue() if hasattr(uf, 'getvalue') else uf.read()))}:{hashlib.sha1((uf.getvalue() if hasattr(uf, 'getvalue') else (uf.seek(0) or uf.read() or b''))).hexdigest()}"  # type: ignore
+    for uf in files
+])).encode()).hexdigest()))(uploaded_files)
+if uploaded_files:
+    if not QDRANT_URL or not QDRANT_API_KEY:
+        st.error("QDRANT_URL or QDRANT_API_KEY is missing in the .env file.")
+    elif files_sig != st.session_state.get('files_sig'):
+        with st.spinner("Processing PDFs and building index..."):
+            # Load and process uploaded PDF(s) with metadata and better chunking
+            pdf_chunks, pdf_meta = [], []
+            for uf in uploaded_files:
+                try:
+                    uf.seek(0)
+                    reader = PyPDF2.PdfReader(uf)
+                    for page_idx, page in enumerate(reader.pages, start=1):
+                        page_text = page.extract_text() or ""
+                        if not page_text.strip():
+                            continue
+                        for ch in split_text(page_text, chunk_size=800, chunk_overlap=150):
+                            pdf_chunks.append(ch)
+                            pdf_meta.append({"source": uf.name, "page": page_idx})
+                except Exception as e:
+                    st.error(f"Failed to read {uf.name}: {e}")
+
+            # Generate embeddings using Azure OpenAI Embeddings
+            embedder = _init_azure_embedder()
+            embeddings = embedder.embed_documents(pdf_chunks) if pdf_chunks else []
+
+            # Initialize Qdrant (always recreate to match embedding dimension)
+            client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)
+            collection_name = 'pdf-chatbot-collection'
+            dim = (len(embeddings[0]) if embeddings else 1536)
+            client.recreate_collection(
+                collection_name=collection_name,
+                vectors_config={"size": dim, "distance": "Cosine"}
+            )
+
+            # Index embeddings with metadata
+            points = [
+                {
+                    "id": i,
+                    "vector": emb,
+                    "payload": {"text": chunk, **meta}
+                }
+                for i, (emb, chunk, meta) in enumerate(zip(embeddings, pdf_chunks, pdf_meta))
+            ]
+            if points:
+                client.upsert(collection_name=collection_name, points=points)
+
+            # Store in session for querying
+            st.session_state['qdrant_client'] = client
+            st.session_state['collection_name'] = collection_name
+            st.session_state['embedder'] = embedder
+            st.session_state['index_ready'] = True
+            st.session_state['files_sig'] = files_sig
+        st.success("Index built successfully. You can now ask questions.")
+
+# Text cleaning utility for retrieved chunks
+
+def clean_text(t: str) -> str:
+    if not t:
+        return ""
+    # Normalize whitespace
+    t = t.replace('\u00A0', ' ').replace('\t', ' ')
+    # Fix hyphenation across line breaks: "exam-\nple" -> "example"
+    t = t.replace('-\n', '')
+    # Collapse newlines and multiple spaces
+    t = '\n'.join(line.strip() for line in t.splitlines())
+    while '  ' in t:
+        t = t.replace('  ', ' ')
+    # Trim
+    return t.strip()
+
+# Retrieval logic — synthesize a single structured answer with history-aware prompting
+
+def retrieve_answer(query, top_k=4):
+    embedder = st.session_state.get('embedder')
+    client = st.session_state.get('qdrant_client')
+    collection_name = st.session_state.get('collection_name')
+
+    if not embedder or not client or not collection_name:
+        return "Index not initialized. Upload PDFs to build the index first."
+
+    query_emb = embedder.embed_query(query)
+    hits = client.search(collection_name=collection_name, query_vector=query_emb, limit=top_k)
+    contexts, citations = [], []
+    for h in hits:
+        payload = getattr(h, 'payload', {}) or {}
+        text = clean_text(payload.get('text', ''))
+        src = payload.get('source', 'document')
+        page = payload.get('page', None)
+        if text:
+            contexts.append(text)
+            citations.append(f"{src} (page {page})" if page else src)
+
+    context_block = "\n\n---\n\n".join(contexts[:top_k]) if contexts else ""
+
+    # Build system prompt to enforce structured, user-friendly answers (generic for any PDF)
+    system_prompt = (
+        "You are a reliable retrieval-augmented assistant that answers questions about any kind of PDF content "
+        "(technical, legal, scientific, financial, educational, etc.). Use ONLY the provided context snippets. "
+        "Do not speculate or invent facts. If the information is not present, reply exactly: 'Not found in documents.' "
+        "Return a clear, structured, user-friendly response with: a brief summary, bullet-point key facts, and a short conclusion. "
+        "Include short citations with source filename and page numbers when available. Be concise and neutral."
+    )
+
+    # Include brief chat history for continuity (last 3 exchanges)
+    history = st.session_state.get('messages', [])[-6:]
+    history_msgs = []
+    for m in history:
+        role = m.get('role')
+        content = m.get('content', '')
+        if role == 'user':
+            history_msgs.append(HumanMessage(content=content))
+        elif role == 'assistant':
+            history_msgs.append(AIMessage(content=content))
+
+    user_content = (
+        f"CONTEXT:\n{context_block}\n\n"
+        f"QUESTION: {query}\n\n"
+        "Format:\n# Answer\n\n- Bullet points of key facts\n\nConclusion\n\nCitations: list source and page numbers if available."
+    )
+
+    messages = [SystemMessage(content=system_prompt), *history_msgs, HumanMessage(content=user_content)]
+    result = llm.invoke(messages)
+    answer_text = getattr(result, 'content', str(result))
+
+    if citations:
+        answer_text += "\n\nSources: " + "; ".join(dict.fromkeys(citations))
+    return answer_text
+
+# Simple chat-style UI (only shown after index is ready)
+ready = st.session_state.get('index_ready')
+if 'messages' not in st.session_state:
+    st.session_state['messages'] = []
+
+if ready:
+    for msg in st.session_state['messages']:
+        with st.chat_message(msg['role']):
+            st.markdown(msg['content'])
+
+    user_input = st.chat_input("Ask a question about the uploaded PDFs")
+    if user_input:
+        st.session_state['messages'].append({"role": "user", "content": user_input})
+        with st.chat_message("user"):
+            st.markdown(user_input)
+
+        with st.chat_message("assistant"):
+            with st.spinner("Retrieving answer..."):
+                answer_text = retrieve_answer(user_input, top_k=4)
+            st.markdown(answer_text)
+        st.session_state['messages'].append({"role": "assistant", "content": answer_text})
+else:
+    st.caption("Upload PDFs in the sidebar to start chatting.")

requirements.txt

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+langchain
+openai
+python-dotenv
+streamlit
+langchain_openai
+langchain_community
+langchain_qdrant