diff --git a/week8/community_contributions/agentic_legal_qna_with_rag_on_bare_acts/README.md b/week8/community_contributions/agentic_legal_qna_with_rag_on_bare_acts/README.md
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+# Agentic Legal Q&A on Bare Acts (Week 8)
+
+An **agentic RAG** demo that answers legal questions from Indian Bare Acts (IPC/BNS/Constitution).  
+Pipeline: **Query expansion (Modal+Qwen3) → Multi-retrieval (Chroma) → Neighbor-aware context merge → LLM answer → Self-critique → Optional second pass**.  
+UI: lightweight **Gradio** chat with live agent logs.
+
+## Features
+- **Modal-first expander:** `modal_expander.py` (Qwen3-4B via vLLM, GPU) with local LLM fallback.
+- **Vector store:** Chroma + `all-MiniLM-L6-v2`, token-span aware chunking, ±neighbor merge.
+- **Agentic loop:** critic validates citations and triggers follow-up retrievals if needed.
+- **Config knobs:** top-k per rewrite, neighbor radius, max merged blocks, model dropdown.
+
+## Setup
+```bash
+python -m pip install -U openai chromadb transformers gradio python-dotenv modal
+````
+
+Create `.env` with your keys:
+
+```bash
+OPENAI_API_KEY=...
+```
+
+Place Bare Acts as UTF-8 `.txt` files in:
+
+```
+knowledge_base/bare_acts/   # e.g., ipc.txt, bns.txt, coi.txt
+```
+
+## Deploy the Modal expander
+
+Set a Modal secret named `huggingface-secret` containing `HUGGINGFACE_HUB_TOKEN`, then:
+
+```bash
+modal deploy -m modal_expander
+```
+
+## Run the notebook app
+
+```bash
+jupyter notebook agentic_legal_qna_with_rag_on_bare_acts.ipynb
+```
+
+Run all cells; a Gradio chat appears. Tune **Top-K**, **Neighbor radius**, and **Max blocks** under *Advanced*.
+
+## Notes
+
+* Default OpenAI model: `gpt-4o-mini` (change via UI).
+* Vector DB is persisted in `vector_db_w8`; re-run the indexing cell to rebuild after data changes.
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diff --git a/week8/community_contributions/agentic_legal_qna_with_rag_on_bare_acts/agentic_legal_qna_with_rag_on_bare_acts.md b/week8/community_contributions/agentic_legal_qna_with_rag_on_bare_acts/agentic_legal_qna_with_rag_on_bare_acts.md
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+Download and extract the notebook and knowledge base from below gdrive link
+
+https://drive.google.com/file/d/1ogRxIhD_JEXnPtjbxWbIGGPCCsGOUqAb/view?usp=sharing
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diff --git a/week8/community_contributions/agentic_legal_qna_with_rag_on_bare_acts/modal_expander.py b/week8/community_contributions/agentic_legal_qna_with_rag_on_bare_acts/modal_expander.py
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+# week8/community_contributions/agentic_legal_qna_with_rag_on_bare_acts/modal_expander.py
+import os, json, re
+from typing import List
+import modal
+
+# minimal image: vLLM + torch + HF hub
+image = (
+    modal.Image.from_registry("nvidia/cuda:12.8.0-devel-ubuntu22.04", add_python="3.12")
+    .entrypoint([])
+    .uv_pip_install(
+        "vllm==0.10.2",
+        "torch==2.8.0",
+        "huggingface_hub[hf_transfer]==0.35.0",
+    )
+    .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
+)
+
+app = modal.App("legal-query-expander-qwen3-v2", image=image)
+
+MODEL_NAME = "Qwen/Qwen3-4B-Instruct"   # use instruct, defaults everywhere
+_llm = None  # warm-container cache
+
+
+def _extract_json_array(text: str) -> List[str]:
+    try:
+        parsed = json.loads(text)
+        return [x for x in parsed if isinstance(x, str)]
+    except Exception:
+        pass
+    m = re.search(r"\[(?:.|\n|\r)*\]", text)
+    if m:
+        try:
+            parsed = json.loads(m.group(0))
+            return [x for x in parsed if isinstance(x, str)]
+        except Exception:
+            return []
+    return []
+
+
+def _sanitize_and_dedupe(items: List[str], n: int) -> List[str]:
+    out, seen = [], set()
+    for q in items:
+        q = re.sub(r"[^\w\s\-./]", "", (q or "")).strip()
+        k = q.lower()
+        if q and k not in seen:
+            seen.add(k)
+            out.append(q)
+        if len(out) >= n:
+            break
+    return out
+
+
+@app.function(
+    image=image,
+    gpu=modal.gpu.L4(),  # pick any available GPU (A100/H100 also fine)
+    timeout=600,
+    secrets=[modal.Secret.from_name("huggingface-secret")],  # set HF token here
+)
+def expand(question: str, n: int = 5) -> List[str]:
+    """
+    Return up to n short, diverse retrieval keyphrases for Bare Acts.
+    Uses Qwen3-4B-Instruct with its default chat template.
+    """
+    global _llm
+    from vllm import LLM, SamplingParams
+
+    # ensure HF token is available to vLLM
+    tok = os.environ.get("HUGGINGFACE_HUB_TOKEN") or os.environ.get("HF_TOKEN")
+    if tok and not os.environ.get("HUGGINGFACE_HUB_TOKEN"):
+        os.environ["HUGGINGFACE_HUB_TOKEN"] = tok
+
+    if _llm is None:
+        _llm = LLM(
+            model=MODEL_NAME,
+            trust_remote_code=True,
+            dtype="auto",
+            tensor_parallel_size=1,
+        )
+
+    user = (
+        "You are Search Query Expander."
+        "For given search query you give 4-5 different variants of it to search the database better. It is a legal search query and our database is of legal data like bare acts."
+        "Respond ONLY as a JSON array of strings; no prose, no section numbers."
+        f"Question:\n{question}\n\n"
+        f"Return {n} distinct keyphrases (4–20 words each), which captures the what to search inside rag database. Return as a JSON array. No commentary."
+    )
+
+    messages = [
+        {"role": "user", "content": user},
+    ]
+
+    tokenizer = _llm.get_tokenizer()
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+    result = _llm.generate(
+        [prompt],
+        SamplingParams(
+            max_tokens=256,
+            temperature=0.2,
+        ),
+    )
+    text = result[0].outputs[0].text
+
+    arr = _sanitize_and_dedupe(_extract_json_array(text), n)
+
+    if not arr:
+        # deterministic fallback (keeps things non-empty)
+        base = re.sub(r"[?]+$", "", (question or "")).strip()
+        pool = [
+            f"{base} section",
+            f"{base} provision bare act",
+            f"{base} indian penal code",
+            f"{base} bharatiya nyaya sanhita",
+            f"{base} punishment section keywords",
+        ]
+        arr = _sanitize_and_dedupe(pool, n)
+
+    return arr