| """ |
| Research Paper Answer Bot — Streamlit UI |
| Retrieves from an existing Pinecone index (built by Research_Paper_Answer_Bot.ipynb) |
| and answers questions with Groq's Llama model, grounded with citations. |
| """ |
|
|
| import os |
| import streamlit as st |
| from sentence_transformers import SentenceTransformer, CrossEncoder |
| from pinecone import Pinecone |
| from groq import Groq |
|
|
| |
| |
| |
| st.set_page_config(page_title="Research Paper Answer Bot", page_icon="📚", layout="centered") |
|
|
| |
| |
| |
| st.markdown(""" |
| <style> |
| .stApp { background-color: #F5EBDD; } |
| |
| section[data-testid="stSidebar"] { |
| background-color: #EFDFC5; |
| border-right: 1px solid #D8BE93; |
| } |
| |
| h1, h2, h3 { color: #4A2E12 !important; } |
| p, span, li, label { color: #3E2A18; } |
| |
| /* chat bubbles */ |
| div[data-testid="stChatMessage"] { |
| background-color: #FFF9F0; |
| border: 1px solid #E1C79A; |
| border-radius: 14px; |
| padding: 6px 10px; |
| margin-bottom: 6px; |
| } |
| |
| /* force readable text inside chat bubbles — this is what was invisible */ |
| div[data-testid="stChatMessage"] p, |
| div[data-testid="stChatMessage"] li, |
| div[data-testid="stChatMessage"] span, |
| div[data-testid="stChatMessage"] div[data-testid="stMarkdownContainer"] { |
| color: #3E2A18 !important; |
| } |
| |
| .stCaption, [data-testid="stCaptionContainer"], |
| div[data-testid="stChatMessage"] [data-testid="stCaptionContainer"] p { |
| color: #7A5C3E !important; |
| } |
| |
| .stButton>button { |
| background-color: #8B5E3C; |
| color: #FFF9F0; |
| border-radius: 8px; |
| border: none; |
| } |
| .stButton>button:hover { background-color: #6E4A2E; color: #FFF9F0; } |
| |
| div[data-testid="stChatInput"] { |
| background-color: #FFF9F0; |
| border: 1px solid #D8BE93; |
| border-radius: 12px; |
| } |
| </style> |
| """, unsafe_allow_html=True) |
|
|
| |
| |
| |
| with st.sidebar: |
| st.markdown("### ⚙️ Settings") |
|
|
| |
| index_name = st.text_input("Pinecone index name", value="research-bot") |
| top_k = st.slider("Chunks to retrieve (k)", 2, 10, 5) |
| use_reranker = st.checkbox("Use cross-encoder re-ranker", value=True) |
|
|
| st.divider() |
| if st.button("🗑️ Clear chat"): |
| st.session_state.messages = [] |
| st.session_state.history = [] |
| st.rerun() |
|
|
| st.caption("This app only queries the Pinecone index — run the notebook first to embed and upsert the PDFs.") |
|
|
|
|
| groq_api_key= "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" |
| pinecone_api_key= "pcsk_3jtVHH_5GGvLrt3pEgoexxL4TF17Su3TQ5a1Qjm1CEnG4rTcHreADx1R4F72Ui1KJJEgwo" |
|
|
|
|
| if not groq_api_key or not pinecone_api_key: |
| st.title("📚 Research Paper Answer Bot") |
| st.info("Enter your Groq and Pinecone API keys in the sidebar to start chatting.") |
| st.stop() |
|
|
| |
| |
| |
| @st.cache_resource(show_spinner="Loading embedding model…") |
| def load_embedder(): |
| return SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") |
|
|
|
|
| @st.cache_resource(show_spinner="Loading re-ranker…") |
| def load_reranker(): |
| return CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2") |
|
|
|
|
| @st.cache_resource(show_spinner=False) |
| def get_clients(groq_key, pine_key): |
| return Groq(api_key=groq_key), Pinecone(api_key=pine_key) |
|
|
|
|
| embedder = load_embedder() |
| groq_client, pc = get_clients(groq_api_key, pinecone_api_key) |
|
|
| existing_indexes = [ix["name"] for ix in pc.list_indexes()] |
| if index_name not in existing_indexes: |
| st.title("📚 Research Paper Answer Bot") |
| st.error( |
| f"Pinecone index '{index_name}' doesn't exist yet. " |
| "Run the notebook's indexing steps first (Sections 3–6) to create and populate it." |
| ) |
| st.stop() |
|
|
| index = pc.Index(index_name) |
| reranker = load_reranker() if use_reranker else None |
|
|
| GROQ_MODEL = "llama-3.1-8b-instant" |
| SYSTEM_PROMPT = ( |
| "You are a precise research assistant. Answer the user's question using ONLY the " |
| "provided context. If the answer is not in the context, say you don't know. " |
| "Cite sources inline as [source p.PAGE]." |
| ) |
|
|
| |
| |
| |
| def embed(texts): |
| return embedder.encode(texts, show_progress_bar=False, normalize_embeddings=True).tolist() |
|
|
|
|
| def retrieve_cosine(query, k=5): |
| qv = embed([query])[0] |
| res = index.query(vector=qv, top_k=k, include_metadata=True) |
| return [ |
| { |
| "text": m["metadata"]["text"], |
| "source": m["metadata"]["source"], |
| "page": m["metadata"]["page"], |
| "score": m["score"], |
| } |
| for m in res["matches"] |
| ] |
|
|
|
|
| def retrieve_rerank(query, k=5, pool=20): |
| candidates = retrieve_cosine(query, k=pool) |
| pairs = [(query, c["text"]) for c in candidates] |
| scores = reranker.predict(pairs) |
| for c, s in zip(candidates, scores): |
| c["rerank_score"] = float(s) |
| return sorted(candidates, key=lambda c: c["rerank_score"], reverse=True)[:k] |
|
|
|
|
| def build_context(hits): |
| blocks = [f"[{h['source']} p.{h['page']}]\n{h['text']}" for h in hits] |
| return "\n\n---\n\n".join(blocks) |
|
|
|
|
| def rag_answer(query, k=5, use_rerank=True): |
| hits = retrieve_rerank(query, k=k) if use_rerank else retrieve_cosine(query, k=k) |
| context = build_context(hits) |
| resp = groq_client.chat.completions.create( |
| model=GROQ_MODEL, |
| temperature=0.1, |
| messages=[ |
| {"role": "system", "content": SYSTEM_PROMPT}, |
| {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}, |
| ], |
| ) |
| answer = resp.choices[0].message.content |
| sources = sorted({(h["source"], h["page"]) for h in hits}) |
| return answer, sources |
|
|
|
|
| def condense(question, history): |
| if not history: |
| return question |
| convo = "\n".join(f"{r}: {c}" for r, c in history[-6:]) |
| resp = groq_client.chat.completions.create( |
| model=GROQ_MODEL, |
| temperature=0.0, |
| messages=[ |
| { |
| "role": "system", |
| "content": "Rewrite the follow-up question as a standalone question " |
| "using the chat history. Return only the rewritten question.", |
| }, |
| {"role": "user", "content": f"Chat history:\n{convo}\n\nFollow-up: {question}"}, |
| ], |
| ) |
| return resp.choices[0].message.content.strip() |
|
|
|
|
| |
| |
| |
| if "messages" not in st.session_state: |
| st.session_state.messages = [] |
| if "history" not in st.session_state: |
| st.session_state.history = [] |
|
|
| |
| |
| |
| st.title("📚 Research Paper Answer Bot") |
| st.caption("Ask about the indexed papers — grounded, cited answers powered by Pinecone + Groq.") |
|
|
| for msg in st.session_state.messages: |
| with st.chat_message(msg["role"]): |
| st.markdown(msg["content"]) |
| if msg.get("sources"): |
| st.caption("Sources: " + ", ".join(f"{s}:p{p}" for s, p in msg["sources"])) |
|
|
| if prompt := st.chat_input("Ask something about the papers…"): |
| st.session_state.messages.append({"role": "user", "content": prompt}) |
| with st.chat_message("user"): |
| st.markdown(prompt) |
|
|
| with st.chat_message("assistant"): |
| with st.spinner("Thinking…"): |
| standalone = condense(prompt, st.session_state.history) |
| answer, sources = rag_answer(standalone, k=top_k, use_rerank=use_reranker) |
| st.markdown(answer) |
| if sources: |
| st.caption("Sources: " + ", ".join(f"{s}:p{p}" for s, p in sources)) |
|
|
| st.session_state.history.append(("user", prompt)) |
| st.session_state.history.append(("assistant", answer)) |
| st.session_state.messages.append({"role": "assistant", "content": answer, "sources": sources}) |
|
|
