diff --git "a/code.txt" "b/code.txt" new file mode 100644--- /dev/null +++ "b/code.txt" @@ -0,0 +1,2293 @@ +# ── PROJECT TREE ────────────────────────────────────────────────────────── +# +# docmind/ +# ├── app.py +# ├── requirements.txt +# ├── Dockerfile +# ├── .env.example +# ├── agents/ +# │ ├── __init__.py +# │ ├── planner.py +# │ ├── retriever.py +# │ ├── grader.py +# │ ├── generator.py +# │ └── critic.py +# ├── graph/ +# │ ├── __init__.py +# │ └── research_graph.py +# ├── rag/ +# │ ├── __init__.py +# │ ├── ingestor.py +# │ ├── vector_store.py +# │ └── embeddings.py +# ├── tools/ +# │ ├── __init__.py +# │ ├── web_search.py +# │ ├── calculator.py +# │ └── code_tool.py +# ├── tracing/ +# │ ├── __init__.py +# │ └── tracer.py +# ├── templates/ +# │ └── index.html +# ├── README.md +# └── docs/ +# └── project-template.html +# +# ────────────────────────────────────────────────────────────────────────── + + +# ── FILE: app.py ────────────────────────────────────────────────────────── + +import os, uuid, threading +from flask import Flask, render_template, request, jsonify +from werkzeug.utils import secure_filename +from dotenv import load_dotenv + +load_dotenv() + +from rag.vector_store import HybridVectorStore +from rag.ingestor import PDFIngestor +from graph.research_graph import ResearchGraph +from tracing.tracer import Tracer +from tools.web_search import web_search +from tools.calculator import calculate +from tools.code_tool import run_code + +app = Flask(__name__) +app.secret_key = os.getenv("SECRET_KEY", os.urandom(24).hex()) + +UPLOAD_FOLDER = "/tmp/docmind_uploads" +os.makedirs(UPLOAD_FOLDER, exist_ok=True) + +# ── Global singletons (in-memory, scoped to container lifetime) ─────────── +vector_store = HybridVectorStore() +tracer = Tracer() +graph = ResearchGraph(vector_store, tracer) +queries = {} # query_id → {status, result} + + +# ── ROUTES ──────────────────────────────────────────────────────────────── + +@app.route("/") +def index(): + return render_template("index.html") + + +@app.route("/health") +def health(): + return jsonify({ + "status": "ok", + "docs_indexed": vector_store.doc_count, + "chunks_stored": vector_store.chunk_count, + "token_set": bool(os.getenv("HF_TOKEN")), + }) + + +@app.route("/api/upload", methods=["POST"]) +def upload(): + if "file" not in request.files: + return jsonify({"error": "No file attached."}), 400 + f = request.files["file"] + if not f.filename.lower().endswith(".pdf"): + return jsonify({"error": "Only PDF files are supported."}), 400 + path = os.path.join(UPLOAD_FOLDER, secure_filename(f.filename)) + f.save(path) + try: + chunks = PDFIngestor().ingest(path) + vector_store.add_documents(chunks) + return jsonify({ + "success": True, + "filename": f.filename, + "chunks": len(chunks), + "total_chunks": vector_store.chunk_count, + "total_docs": vector_store.doc_count, + }) + except Exception as exc: + return jsonify({"error": str(exc)}), 500 + + +@app.route("/api/research", methods=["POST"]) +def research(): + data = request.json or {} + question = (data.get("question") or "").strip() + if not question: + return jsonify({"error": "Question is required."}), 400 + if vector_store.doc_count == 0: + return jsonify({"error": "No documents indexed yet — please upload a PDF first."}), 400 + + qid = str(uuid.uuid4()) + queries[qid] = {"status": "running", "result": None} + + def _run(): + try: + result = graph.run(question, qid) + queries[qid]["result"] = result + queries[qid]["status"] = "pending_review" if result.get("needs_human_review") else "complete" + except Exception as exc: + queries[qid]["status"] = "error" + queries[qid]["result"] = {"error": str(exc)} + + threading.Thread(target=_run, daemon=True).start() + return jsonify({"query_id": qid}) + + +@app.route("/api/trace/") +def get_trace(qid): + q = queries.get(qid) + if not q: + return jsonify({"error": "Query not found."}), 404 + return jsonify({"status": q["status"], "trace": tracer.get(qid), "result": q["result"]}) + + +@app.route("/api/review") +def review_queue(): + pending = [ + {"query_id": qid, + "question": q["result"].get("question", "") if q["result"] else "", + "generation": q["result"].get("generation", "") if q["result"] else "", + "critique": q["result"].get("critique", "") if q["result"] else ""} + for qid, q in queries.items() + if q["status"] == "pending_review" and q["result"] + ] + return jsonify({"pending": pending}) + + +@app.route("/api/review/", methods=["POST"]) +def review_action(qid): + data = request.json or {} + action = data.get("action") + if qid not in queries: + return jsonify({"error": "Query not found."}), 404 + if action not in ("approve", "reject"): + return jsonify({"error": "Action must be 'approve' or 'reject'."}), 400 + queries[qid]["status"] = "complete" if action == "approve" else "rejected" + if queries[qid]["result"]: + queries[qid]["result"]["human_approved"] = action == "approve" + tracer.add(qid, "human_review", f"Reviewer {action}d this answer.", "complete", 0) + return jsonify({"success": True}) + + +@app.route("/api/observability") +def observability(): + return jsonify(tracer.stats()) + + +@app.route("/api/tool/", methods=["POST"]) +def tool_run(name): + inp = ((request.json or {}).get("input") or "").strip() + if not inp: + return jsonify({"error": "Input is required."}), 400 + try: + result = {"web_search": web_search, "calculator": calculate, "code": run_code}.get(name, lambda _: None)(inp) + if result is None: + return jsonify({"error": f"Unknown tool '{name}'."}), 400 + return jsonify({"result": result}) + except Exception as exc: + return jsonify({"error": str(exc)}), 500 + + +@app.route("/api/stats") +def stats(): + return jsonify({ + "docs_indexed": vector_store.doc_count, + "chunks_stored": vector_store.chunk_count, + "queries_run": len(queries), + "queries_complete":sum(1 for q in queries.values() if q["status"] == "complete"), + "pending_review": sum(1 for q in queries.values() if q["status"] == "pending_review"), + }) + + +if __name__ == "__main__": + app.run(host="0.0.0.0", port=7860, debug=False) + + +# ── FILE: agents/__init__.py ────────────────────────────────────────────── +# (empty) + + +# ── FILE: agents/planner.py ─────────────────────────────────────────────── + +import os, time +from langchain_huggingface import HuggingFaceEndpoint +from langchain.prompts import PromptTemplate + +_TEMPLATE = """You are a research planning agent. Given the user's question, produce a brief research plan. +Decide: should the answer be grounded in uploaded documents, web search, or both? +Output your plan in 2-3 concise sentences. Start with "PLAN:". + +Question: {question} + +Plan:""" + +def run_planner(question: str) -> str: + llm = HuggingFaceEndpoint( + repo_id="mistralai/Mistral-7B-Instruct-v0.3", + task="text-generation", + max_new_tokens=200, + temperature=0.3, + huggingfacehub_api_token=os.getenv("HF_TOKEN", ""), + timeout=60, + ) + chain = PromptTemplate(input_variables=["question"], template=_TEMPLATE) | llm + result = chain.invoke({"question": question}) + return result.strip() if isinstance(result, str) else str(result).strip() + + +# ── FILE: agents/retriever.py ───────────────────────────────────────────── + +def run_retriever(question: str, vector_store, k: int = 5) -> list: + """Returns list of dicts with keys: page_content, source, page, score.""" + return vector_store.hybrid_search(question, k=k) + + +# ── FILE: agents/grader.py ──────────────────────────────────────────────── + +import os, re +from langchain_huggingface import HuggingFaceEndpoint +from langchain.prompts import PromptTemplate + +_TEMPLATE = """You are a document relevance grader. Rate how relevant this document is to the question. +Respond with ONLY a decimal number between 0.0 (irrelevant) and 1.0 (highly relevant). Nothing else. + +Question: {question} +Document excerpt: {document} + +Relevance score:""" + +def grade_document(question: str, document: str) -> float: + llm = HuggingFaceEndpoint( + repo_id="HuggingFaceH4/zephyr-7b-beta", + task="text-generation", + max_new_tokens=10, + temperature=0.05, + huggingfacehub_api_token=os.getenv("HF_TOKEN", ""), + timeout=45, + ) + chain = PromptTemplate(input_variables=["question", "document"], template=_TEMPLATE) | llm + result = chain.invoke({"question": question, "document": document[:800]}) + raw = result.strip() if isinstance(result, str) else str(result).strip() + nums = re.findall(r"[0-9]+\.?[0-9]*", raw) + return min(float(nums[0]), 1.0) if nums else 0.5 + +def run_grader(question: str, documents: list) -> list: + """Returns same list with 'grade' float added to each doc dict.""" + graded = [] + for doc in documents: + score = grade_document(question, doc["page_content"]) + graded.append({**doc, "grade": score}) + return graded + + +# ── FILE: agents/generator.py ───────────────────────────────────────────── + +import os +from langchain_huggingface import HuggingFaceEndpoint +from langchain.prompts import PromptTemplate + +_TEMPLATE = """You are an expert research analyst. Answer the question using ONLY the context below. +Cite sources as [Source: filename, p.N] inline. If the context lacks enough information, say so clearly. + +Context: +{context} + +Question: {question} + +Answer:""" + +def run_generator(question: str, documents: list) -> str: + context_parts = [] + for d in documents: + src = d.get("source", "unknown") + page = d.get("page", "?") + context_parts.append(f"[Source: {src}, p.{page}]\n{d['page_content']}") + context = "\n\n".join(context_parts) if context_parts else "No context available." + + llm = HuggingFaceEndpoint( + repo_id="mistralai/Mistral-7B-Instruct-v0.3", + task="text-generation", + max_new_tokens=512, + temperature=0.4, + huggingfacehub_api_token=os.getenv("HF_TOKEN", ""), + timeout=90, + ) + chain = PromptTemplate(input_variables=["question", "context"], template=_TEMPLATE) | llm + result = chain.invoke({"question": question, "context": context}) + return result.strip() if isinstance(result, str) else str(result).strip() + + +# ── FILE: agents/critic.py ──────────────────────────────────────────────── + +import os, re +from langchain_huggingface import HuggingFaceEndpoint +from langchain.prompts import PromptTemplate + +_TEMPLATE = """You are a strict quality-control critic. Evaluate this answer for accuracy and grounding. +Output EXACTLY one of these two lines first, then a one-sentence explanation: +VERDICT: APPROVED +VERDICT: NEEDS_REVIEW + +Criteria for NEEDS_REVIEW: answer contains claims not in the context, is incomplete, or is incoherent. + +Question: {question} +Context (first 1500 chars): {context} +Answer: {answer} + +Evaluation:""" + +def run_critic(question: str, answer: str, documents: list) -> dict: + context = " ".join(d["page_content"] for d in documents)[:1500] + llm = HuggingFaceEndpoint( + repo_id="HuggingFaceH4/zephyr-7b-beta", + task="text-generation", + max_new_tokens=150, + temperature=0.1, + huggingfacehub_api_token=os.getenv("HF_TOKEN", ""), + timeout=60, + ) + chain = PromptTemplate(input_variables=["question", "context", "answer"], template=_TEMPLATE) | llm + result = chain.invoke({"question": question, "context": context, "answer": answer}) + raw = result.strip() if isinstance(result, str) else str(result).strip() + + verdict = "APPROVED" + if re.search(r"NEEDS_REVIEW", raw, re.IGNORECASE): + verdict = "NEEDS_REVIEW" + elif re.search(r"APPROVED", raw, re.IGNORECASE): + verdict = "APPROVED" + + explanation = raw.split("\n", 1)[-1].strip() if "\n" in raw else raw + return {"verdict": verdict, "explanation": explanation[:300]} + + +# ── FILE: graph/__init__.py ─────────────────────────────────────────────── +# (empty) + + +# ── FILE: graph/research_graph.py ──────────────────────────────────────── + +import time +from datetime import datetime +from typing import TypedDict, List, Any, Optional +from langgraph.graph import StateGraph, END + +from agents.planner import run_planner +from agents.retriever import run_retriever +from agents.grader import run_grader +from agents.generator import run_generator +from agents.critic import run_critic + + +class GraphState(TypedDict): + question: str + query_id: str + plan: str + documents: List[Any] + graded_docs: List[Any] + generation: str + critique: str + verdict: str + needs_human_review:bool + iteration: int + timestamp: str + + +class ResearchGraph: + def __init__(self, vector_store, tracer): + self.vs = vector_store + self.tracer = tracer + self.graph = self._build() + + # ── NODE FUNCTIONS ───────────────────────────────────────────────────── + + def _planner_node(self, state: GraphState) -> dict: + t0 = time.time() + self.tracer.add(state["query_id"], "planner", "Planning research approach…", "running", 0) + plan = run_planner(state["question"]) + ms = int((time.time() - t0) * 1000) + self.tracer.add(state["query_id"], "planner", plan[:200], "complete", ms) + return {"plan": plan} + + def _retriever_node(self, state: GraphState) -> dict: + t0 = time.time() + self.tracer.add(state["query_id"], "retriever", "Running hybrid search (FAISS + BM25)…", "running", 0) + docs = run_retriever(state["question"], self.vs, k=5) + ms = int((time.time() - t0) * 1000) + self.tracer.add(state["query_id"], "retriever", f"Retrieved {len(docs)} chunks via hybrid search.", "complete", ms) + return {"documents": docs} + + def _grader_node(self, state: GraphState) -> dict: + t0 = time.time() + self.tracer.add(state["query_id"], "grader", f"Grading {len(state['documents'])} retrieved chunks…", "running", 0) + graded = run_grader(state["question"], state["documents"]) + avg = sum(d["grade"] for d in graded) / len(graded) if graded else 0.0 + ms = int((time.time() - t0) * 1000) + self.tracer.add(state["query_id"], "grader", f"Avg relevance score: {avg:.2f} across {len(graded)} chunks.", "complete", ms) + return {"graded_docs": graded} + + def _rewriter_node(self, state: GraphState) -> dict: + t0 = time.time() + self.tracer.add(state["query_id"], "rewriter", "Low relevance scores — rewriting query for better retrieval…", "running", 0) + # Simple heuristic rewrite: add "explain in detail" framing + new_q = f"Provide a detailed explanation about: {state['question']}" + ms = int((time.time() - t0) * 1000) + self.tracer.add(state["query_id"], "rewriter", f"Rewritten query: {new_q[:120]}", "complete", ms) + return {"question": new_q, "iteration": state.get("iteration", 0) + 1} + + def _generator_node(self, state: GraphState) -> dict: + t0 = time.time() + self.tracer.add(state["query_id"], "generator", "Generating answer from graded context…", "running", 0) + good_docs = [d for d in state["graded_docs"] if d.get("grade", 0) >= 0.35] or state["graded_docs"] + gen = run_generator(state["question"], good_docs[:4]) + ms = int((time.time() - t0) * 1000) + self.tracer.add(state["query_id"], "generator", f"Answer generated ({len(gen)} chars).", "complete", ms) + return {"generation": gen} + + def _critic_node(self, state: GraphState) -> dict: + t0 = time.time() + self.tracer.add(state["query_id"], "critic", "Evaluating answer quality and hallucination risk…", "running", 0) + result = run_critic(state["question"], state["generation"], state["graded_docs"]) + ms = int((time.time() - t0) * 1000) + needs_review = result["verdict"] == "NEEDS_REVIEW" + label = "⚠️ Flagged for human review." if needs_review else "✅ Answer approved." + self.tracer.add(state["query_id"], "critic", f"{label} {result['explanation'][:160]}", "complete", ms) + return { + "critique": result["explanation"], + "verdict": result["verdict"], + "needs_human_review":needs_review, + } + + # ── CONDITIONAL EDGE FUNCTIONS ───────────────────────────────────────── + + def _after_grader(self, state: GraphState) -> str: + graded = state.get("graded_docs", []) + avg = sum(d.get("grade", 0) for d in graded) / len(graded) if graded else 0.0 + itr = state.get("iteration", 0) + if avg < 0.45 and itr < 2: + return "rewrite" + return "generate" + + def _after_critic(self, state: GraphState) -> str: + return "end" # always end — human review is handled outside graph via Flask + + # ── BUILD ────────────────────────────────────────────────────────────── + + def _build(self): + wf = StateGraph(GraphState) + wf.add_node("planner", self._planner_node) + wf.add_node("retriever", self._retriever_node) + wf.add_node("grader", self._grader_node) + wf.add_node("rewriter", self._rewriter_node) + wf.add_node("generator", self._generator_node) + wf.add_node("critic", self._critic_node) + + wf.set_entry_point("planner") + wf.add_edge("planner", "retriever") + wf.add_edge("retriever", "grader") + wf.add_conditional_edges("grader", self._after_grader, {"rewrite": "rewriter", "generate": "generator"}) + wf.add_edge("rewriter", "retriever") + wf.add_edge("generator", "critic") + wf.add_conditional_edges("critic", self._after_critic, {"end": END}) + return wf.compile() + + # ── PUBLIC RUN ───────────────────────────────────────────────────────── + + def run(self, question: str, query_id: str) -> dict: + init_state = GraphState( + question=question, query_id=query_id, plan="", + documents=[], graded_docs=[], generation="", + critique="", verdict="", needs_human_review=False, + iteration=0, timestamp=datetime.utcnow().isoformat(), + ) + final = self.graph.invoke(init_state) + return dict(final) + + +# ── FILE: rag/__init__.py ───────────────────────────────────────────────── +# (empty) + + +# ── FILE: rag/embeddings.py ─────────────────────────────────────────────── + +import numpy as np +from sentence_transformers import SentenceTransformer + +_model = None # lazy-loaded singleton + +def get_model() -> SentenceTransformer: + global _model + if _model is None: + _model = SentenceTransformer("BAAI/bge-small-en-v1.5") + return _model + +def embed(texts: list) -> np.ndarray: + """Returns float32 numpy array of shape (N, dim).""" + return get_model().encode(texts, normalize_embeddings=True, show_progress_bar=False).astype("float32") + + +# ── FILE: rag/ingestor.py ───────────────────────────────────────────────── + +import os, re +from pypdf import PdfReader +from rag.embeddings import embed + + +class PDFIngestor: + def __init__(self, chunk_size: int = 500, chunk_overlap: int = 80): + self.chunk_size = chunk_size + self.chunk_overlap = chunk_overlap + + def _extract_text(self, path: str) -> list: + """Returns list of {text, page} dicts.""" + reader = PdfReader(path) + pages = [] + for i, page in enumerate(reader.pages): + text = (page.extract_text() or "").strip() + if text: + pages.append({"text": text, "page": i + 1}) + return pages + + def _chunk(self, page_data: list) -> list: + """Splits pages into overlapping chunks.""" + chunks = [] + for pd in page_data: + text = re.sub(r"\s+", " ", pd["text"]) + words = text.split() + start = 0 + while start < len(words): + end = min(start + self.chunk_size, len(words)) + chunk = " ".join(words[start:end]) + chunks.append({"page_content": chunk, "page": pd["page"]}) + start += self.chunk_size - self.chunk_overlap + return chunks + + def ingest(self, path: str) -> list: + """Returns list of chunk dicts with page_content, page, source.""" + filename = os.path.basename(path) + pages = self._extract_text(path) + chunks = self._chunk(pages) + for c in chunks: + c["source"] = filename + return chunks + + +# ── FILE: rag/vector_store.py ───────────────────────────────────────────── + +import numpy as np +import faiss +from rank_bm25 import BM25Okapi +from rag.embeddings import embed + + +class HybridVectorStore: + """FAISS semantic search + BM25 keyword search, fused via Reciprocal Rank Fusion.""" + + def __init__(self): + self._docs: list = [] # raw chunk dicts + self._index: faiss.Index = None + self._bm25: BM25Okapi = None + self._tokenized: list = [] + + @property + def doc_count(self) -> int: + sources = set(d.get("source", "") for d in self._docs) + return len(sources) + + @property + def chunk_count(self) -> int: + return len(self._docs) + + def add_documents(self, chunks: list): + self._docs.extend(chunks) + texts = [c["page_content"] for c in self._docs] + vectors = embed(texts) + dim = vectors.shape[1] + self._index = faiss.IndexFlatIP(dim) # inner-product (normalized = cosine) + self._index.add(vectors) + self._tokenized = [t.lower().split() for t in texts] + self._bm25 = BM25Okapi(self._tokenized) + + def hybrid_search(self, query: str, k: int = 5) -> list: + if not self._docs: + return [] + k = min(k, len(self._docs)) + + # ── Semantic search ────────────────────────────────────────────── + q_vec = embed([query]) + scores, idxs = self._index.search(q_vec, min(k * 2, len(self._docs))) + sem_ranks = {int(idxs[0][r]): r for r in range(len(idxs[0]))} + + # ── BM25 keyword search ────────────────────────────────��───────── + bm25_scores = self._bm25.get_scores(query.lower().split()) + bm25_order = np.argsort(bm25_scores)[::-1][:k * 2] + bm25_ranks = {int(bm25_order[r]): r for r in range(len(bm25_order))} + + # ── Reciprocal Rank Fusion ─────────────────────────────────────── + rrf_k = 60 + all_ids = set(sem_ranks) | set(bm25_ranks) + rrf = {} + for i in all_ids: + rrf[i] = 1 / (rrf_k + sem_ranks.get(i, 999)) + 1 / (rrf_k + bm25_ranks.get(i, 999)) + + top_ids = sorted(rrf, key=lambda i: rrf[i], reverse=True)[:k] + results = [] + for idx in top_ids: + doc = dict(self._docs[idx]) + doc["score"] = round(rrf[idx], 4) + results.append(doc) + return results + + +# ── FILE: tools/__init__.py ─────────────────────────────────────────────── +# (empty) + + +# ── FILE: tools/web_search.py ──────────────────────────────────────────── + +from duckduckgo_search import DDGS + +def web_search(query: str, max_results: int = 4) -> str: + try: + with DDGS() as ddgs: + hits = list(ddgs.text(query, max_results=max_results)) + if not hits: + return "No results found." + lines = [] + for h in hits: + lines.append(f"Title: {h.get('title','')}\nSnippet: {h.get('body','')}\nURL: {h.get('href','')}\n") + return "\n".join(lines) + except Exception as exc: + return f"Search error: {exc}" + + +# ── FILE: tools/calculator.py ──────────────────────────────────────────── + +import ast, math, operator, re + +_SAFE_OPS = { + ast.Add: operator.add, ast.Sub: operator.sub, + ast.Mult: operator.mul, ast.Div: operator.truediv, + ast.Pow: operator.pow, ast.USub: operator.neg, + ast.Mod: operator.mod, ast.FloorDiv: operator.floordiv, +} +_SAFE_NAMES = {k: getattr(math, k) for k in dir(math) if not k.startswith("_")} +_SAFE_NAMES.update({"abs": abs, "round": round, "int": int, "float": float}) + + +def _safe_eval(node): + if isinstance(node, ast.Constant): + return node.value + if isinstance(node, ast.BinOp): + op = _SAFE_OPS.get(type(node.op)) + if op is None: + raise ValueError(f"Unsupported operator: {node.op}") + return op(_safe_eval(node.left), _safe_eval(node.right)) + if isinstance(node, ast.UnaryOp) and isinstance(node.op, ast.USub): + return -_safe_eval(node.operand) + if isinstance(node, ast.Call): + func = node.func.id if isinstance(node.func, ast.Name) else None + if func in _SAFE_NAMES: + return _SAFE_NAMES[func](*[_safe_eval(a) for a in node.args]) + if isinstance(node, ast.Name) and node.id in _SAFE_NAMES: + return _SAFE_NAMES[node.id] + raise ValueError(f"Unsafe expression: {ast.dump(node)}") + + +def calculate(expr: str) -> str: + try: + expr = re.sub(r"[^0-9+\-*/().,%^ \t\na-zA-Z_]", "", expr).strip() + tree = ast.parse(expr, mode="eval") + val = _safe_eval(tree.body) + return f"Result: {val}" + except Exception as exc: + return f"Calculation error: {exc}" + + +# ── FILE: tools/code_tool.py ───────────────────────────────────────────── + +import io, contextlib + +_SAFE_BUILTINS = { + k: v for k, v in vars(__builtins__).items() + if k in {"print","range","len","sum","max","min","abs","round","sorted", + "list","dict","set","tuple","str","int","float","bool","enumerate", + "zip","map","filter","isinstance","type","repr","chr","ord"} +} if isinstance(vars(__builtins__), dict) else {} + + +def run_code(code: str) -> str: + buf = io.StringIO() + try: + with contextlib.redirect_stdout(buf): + exec(code, {"__builtins__": _SAFE_BUILTINS}, {}) + out = buf.getvalue() + return out.strip() if out.strip() else "✅ Code executed successfully (no output)." + except Exception as exc: + return f"❌ Error: {exc}" + + +# ── FILE: tracing/__init__.py ───────────────────────────────────────────── +# (empty) + + +# ── FILE: tracing/tracer.py ─────────────────────────────────────────────── + +import threading +from datetime import datetime + + +class Tracer: + def __init__(self): + self._lock = threading.Lock() + self._traces = {} # query_id → [step, ...] + self._global = {"agent_calls": {}, "latencies": {}, "total_calls": 0} + + def add(self, query_id: str, agent: str, message: str, status: str, latency_ms: int): + step = { + "agent": agent, + "message": message, + "status": status, + "latency_ms": latency_ms, + "ts": datetime.utcnow().strftime("%H:%M:%S"), + } + with self._lock: + self._traces.setdefault(query_id, []).append(step) + self._global["agent_calls"].setdefault(agent, 0) + self._global["agent_calls"][agent] += 1 + self._global["latencies"].setdefault(agent, []) + if latency_ms > 0: + self._global["latencies"][agent].append(latency_ms) + self._global["total_calls"] += 1 + + def get(self, query_id: str) -> list: + with self._lock: + return list(self._traces.get(query_id, [])) + + def stats(self) -> dict: + with self._lock: + avg_lat = { + agent: round(sum(v) / len(v)) if v else 0 + for agent, v in self._global["latencies"].items() + } + return { + "agent_calls": dict(self._global["agent_calls"]), + "avg_latency_ms": avg_lat, + "total_calls": self._global["total_calls"], + "total_queries": len(self._traces), + } + + +# ── FILE: requirements.txt ──────────────────────────────────────────────── + +flask==3.1.0 +python-dotenv==1.0.1 +langgraph==0.2.55 +langchain==0.3.7 +langchain-huggingface==0.1.2 +langchain-core==0.3.21 +langchain-community==0.3.7 +huggingface-hub==0.26.2 +sentence-transformers==3.3.1 +faiss-cpu==1.9.0 +rank-bm25==0.2.2 +pypdf==5.1.0 +duckduckgo-search==6.3.7 +numpy==1.26.4 +gunicorn==23.0.0 +werkzeug==3.1.3 + + +# ── FILE: Dockerfile ────────────────────────────────────────────────────── + +FROM python:3.10-slim + +WORKDIR /app + +# System deps for faiss and sentence-transformers +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential libgomp1 && rm -rf /var/lib/apt/lists/* + +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +# Pre-download the embedding model so first request is fast +RUN python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('BAAI/bge-small-en-v1.5')" + +COPY . . + +RUN useradd -m -u 1000 appuser && chown -R appuser /app +USER appuser + +EXPOSE 7860 +ENV PYTHONUNBUFFERED=1 + +CMD ["gunicorn", "--bind", "0.0.0.0:7860", "--workers", "1", "--timeout", "180", "--keep-alive", "5", "app:app"] + + +# ── FILE: .env.example ──────────────────────────────────────────────────── + +# ── Required ────────────────────────────────────────────────────────────── +# Free HuggingFace token (Read scope is sufficient) +# Get yours: https://huggingface.co/settings/tokens +HF_TOKEN=hf_your_token_here + +# ── Optional ────────────────────────────────────────────────────────────── +# Flask session secret (auto-generated if not set) +SECRET_KEY=change_me_to_a_random_string + +# Embedding model (runs locally — no token required for this one) +EMBED_MODEL=BAAI/bge-small-en-v1.5 + +# Planner + Generator model (HF Inference API) +GENERATOR_MODEL=mistralai/Mistral-7B-Instruct-v0.3 + +# Grader + Critic model (HF Inference API) +CRITIC_MODEL=HuggingFaceH4/zephyr-7b-beta + + +# ── FILE: templates/index.html ──────────────────────────────────────────── + + + + + + +🧠 DocMind — Agentic Research Platform + + + + + + + + + + + +
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Overview
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🧠 DocMind

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A production-grade agentic research platform. Five specialized LangGraph agents collaborate to retrieve, grade, generate, and critique answers from your documents.

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PDFs Indexed
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📤
Upload & Index
Upload PDFs. Chunks are embedded with BAAI/bge-small-en-v1.5 locally and stored in a FAISS + BM25 hybrid index.
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🔍
Research Query
Ask any question. Watch the five LangGraph agents plan, retrieve, grade, generate, and critique in real time.
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Human Review
Answers flagged by the Critic agent appear here for your approval before being returned to the user.
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📊
Observability
Live trace of every agent decision, per-agent latency, token usage, and retrieval quality scores.
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🔧
Tool Playground
Test web search, calculator, and sandboxed code execution — the three tools the Planner agent can invoke.
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🤗
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Free HF Models
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Mistral-7B · Zephyr-7B · bge-small. Token: not set
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FAISS + BM25 Hybrid Index · BAAI/bge-small-en-v1.5 (local)
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Upload & Index Documents
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Click to upload or drag a PDF here

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PDF files only · Max recommended 20 pages for free HF inference tier

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Indexed Documents
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No documents indexed yet.

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LangGraph · 5 Agents · Corrective RAG · Human-in-the-Loop
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Research Query
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Research Answer
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Human-in-the-Loop · Critic Agent Escalations
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Human Review Queue
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No answers pending review.

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LangSmith-Style Tracing · Per-Agent Metrics
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Observability Dashboard
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Avg Planner Latency
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Function Calling · Tool Use
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Tool Playground
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🌐
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Web Search
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DuckDuckGo free search — no API key required. Used by the Planner when web context is needed.
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🧮
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Calculator
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Safe AST-based math evaluator supporting +, −, ×, ÷, ^, and all Python math module functions.
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Code Runner
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Sandboxed Python execution with safe builtins only (no file I/O, no network). Captures stdout output.
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+ + + + + + + +# ── FILE: README.md ─────────────────────────────────────────────────────── + +--- +title: DocMind-Agentic-Research +colorFrom: blue +colorTo: indigo +sdk: docker +--- + +
+ +

🧠 DocMind — Agentic Research Platform

+Typing SVG + +
+ +[![Python](https://img.shields.io/badge/Python-3.10+-3b82f6?style=for-the-badge&logo=python&logoColor=white)](https://www.python.org/) +[![LangGraph](https://img.shields.io/badge/LangGraph-0.2-06b6d4?style=for-the-badge)](https://github.com/langchain-ai/langgraph) +[![LangChain](https://img.shields.io/badge/LangChain-0.3-4f46e5?style=for-the-badge)](https://langchain.com/) +[![Flask](https://img.shields.io/badge/Flask-3.1-3b82f6?style=for-the-badge&logo=flask&logoColor=white)](https://flask.palletsprojects.com/) +[![Docker](https://img.shields.io/badge/Docker-Ready-3b82f6?style=for-the-badge&logo=docker&logoColor=white)](https://www.docker.com/) +[![HuggingFace](https://img.shields.io/badge/HuggingFace-Spaces-ffcc00?style=for-the-badge&logo=huggingface&logoColor=black)](https://huggingface.co/mnoorchenar/spaces) +[![Status](https://img.shields.io/badge/Status-Active-22c55e?style=for-the-badge)](#) + +
+ +**🧠 DocMind** — A production-grade agentic document research platform. Five specialized LangGraph agents plan, retrieve, grade, generate, and critique answers from uploaded PDFs using Corrective RAG, hybrid search, human-in-the-loop review, and LangSmith-style observability — all running free on HuggingFace Spaces. + +
+ +--- + +
+ +## Table of Contents +- [Features](#-features) +- [Architecture](#️-architecture) +- [Getting Started](#-getting-started) +- [Docker Deployment](#-docker-deployment) +- [Dashboard Modules](#-dashboard-modules) +- [ML Models](#-ml-models) +- [Project Structure](#-project-structure) +- [Author](#-author) +- [Contributing](#-contributing) +- [Disclaimer](#disclaimer) +- [License](#-license) + +--- + +## ✨ Features + + + + + + + + + + +
🧠 LangGraph State MachineFive agents wired into a cyclic StateGraph with conditional edges and Corrective RAG rewrite loops.
🔍 Hybrid RAG (FAISS + BM25)Semantic vector search combined with BM25 keyword search, fused via Reciprocal Rank Fusion for precision retrieval.
🤖 Multi-Agent OrchestrationPlanner, Retriever, Grader, Generator, and Critic agents each with specialized roles and distinct LLM temperature settings.
👁️ Human-in-the-LoopAnswers failing the Critic agent's quality threshold are routed to a human review queue before delivery.
📊 Observability DashboardPer-agent call counts, average latency, and Chart.js visualizations — LangSmith-style tracing without the paid tier.
🔧 Tool Use / Function CallingThree real tools: DuckDuckGo web search, safe AST calculator, and sandboxed Python code execution.
🔒 Secure by DesignStateless REST backend, no user data persisted, sandboxed code tool with restricted builtins only.
🐳 Containerized DeploymentDocker-first with Gunicorn, embedding model pre-downloaded at build time for fast cold starts.
+ +--- + +## 🏗️ Architecture + +``` +┌──────────────────────────────────────────────────────────────┐ +│ DocMind — LangGraph Flow │ +│ │ +│ PDF Upload ──▶ Ingestor ──▶ FAISS+BM25 Hybrid Vector Store │ +│ │ │ +│ User Query ──▶ [PLANNER Agent] │ │ +│ │ │ │ +│ [RETRIEVER] ◀──────┘ (hybrid search) │ +│ │ │ +│ [GRADER] ──▶ low score? ──▶ [REWRITER] ──┐ │ +│ │ │ │ +│ └──▶ [GENERATOR] ◀──────────────────┘ │ +│ │ │ +│ [CRITIC] ──▶ flag? ──▶ [REVIEW] │ +│ │ │ +│ [OUTPUT] Flask API + SPA UI │ +└──────────────────────────────────────────────────────────────┘ +``` + +--- + +## 🚀 Getting Started + +### Prerequisites +- Python 3.10+ · Docker · Git · Free HuggingFace account + +### Local Installation + +```bash +git clone https://github.com/mnoorchenar/docmind.git +cd docmind + +python -m venv venv +source venv/bin/activate # Windows: venv\Scripts\activate + +pip install -r requirements.txt + +cp .env.example .env +# Edit .env — set HF_TOKEN to your free HuggingFace Read token + +python app.py +``` + +Open `http://localhost:7860` 🎉 + +### Getting your free HuggingFace token +1. Create a free account at [huggingface.co](https://huggingface.co) +2. Go to Settings → Access Tokens → New Token → Role: **Read** +3. Copy the token and set it as `HF_TOKEN` in your `.env` file or Space secrets + +--- + +## 🐳 Docker Deployment + +```bash +docker build -t docmind . +docker run -p 7860:7860 -e HF_TOKEN=hf_your_token_here docmind +``` + +--- + +## 📊 Dashboard Modules + +| Module | Description | Status | +|--------|-------------|--------| +| 📤 Upload & Index | PDF ingest, chunk, embed (local), FAISS+BM25 index | ✅ Live | +| 🔍 Research Query | LangGraph 5-agent pipeline with real-time trace | ✅ Live | +| 👁️ Human Review | Critic escalation queue with approve/reject | ✅ Live | +| 📊 Observability | Per-agent latency, call counts, Chart.js dashboard | ✅ Live | +| 🔧 Tool Playground | Web search, calculator, code runner | ✅ Live | + +--- + +## 🧠 ML Models + +```python +models = { + "planner_generator": "mistralai/Mistral-7B-Instruct-v0.3", + "grader_critic": "HuggingFaceH4/zephyr-7b-beta", + "embeddings": "BAAI/bge-small-en-v1.5", + "vector_index": "FAISS (faiss-cpu, local)", + "keyword_index": "BM25 (rank-bm25, local)", + "fusion_strategy": "Reciprocal Rank Fusion (RRF k=60)", + "graph_framework": "LangGraph 0.2 StateGraph", + "chain_syntax": "LangChain LCEL (prompt | llm)", +} +``` + +--- + +## 📁 Project Structure + +``` +docmind/ +├── 📄 app.py # Flask entry point, 10 REST routes +├── 📄 requirements.txt +├── 📄 Dockerfile # Port 7860, embedding model pre-downloaded +├── 📄 .env.example +├── 📂 agents/ +│ ├── 📄 planner.py # Mistral-7B — task decomposition +│ ├── 📄 retriever.py # Hybrid FAISS+BM25 search wrapper +│ ├── 📄 grader.py # Zephyr-7B — 0.0–1.0 relevance scoring +│ ├── 📄 generator.py # Mistral-7B — cited answer generation +│ └── 📄 critic.py # Zephyr-7B — hallucination detection +├── 📂 graph/ +│ └── 📄 research_graph.py # LangGraph StateGraph (5 nodes + conditional edges) +├── 📂 rag/ +│ ├── 📄 ingestor.py # PyPDF + overlapping chunker +│ ├── 📄 vector_store.py # FAISS + BM25 + RRF fusion +│ └── 📄 embeddings.py # sentence-transformers local wrapper +├── 📂 tools/ +│ ├── 📄 web_search.py # DuckDuckGo free search +│ ├── 📄 calculator.py # AST-safe math evaluator +│ └── 📄 code_tool.py # Sandboxed Python exec +├── 📂 tracing/ +│ └── 📄 tracer.py # Thread-safe in-memory trace store +├── 📂 templates/ +│ └── 📄 index.html # Dark-mode 5-page SPA +└── 📂 docs/ + └── 📄 project-template.html # Portfolio showcase page +``` + +--- + +## 👨‍💻 Author + +
+
+Mohammad Noorchenarboo +

Mohammad Noorchenarboo

+Data Scientist  |  AI Researcher  |  Biostatistician +📍 Ontario, Canada    📧 mohammadnoorchenarboo@gmail.com + +[![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/mnoorchenar) +[![HuggingFace](https://img.shields.io/badge/HuggingFace-ffcc00?style=for-the-badge&logo=huggingface&logoColor=black)](https://huggingface.co/mnoorchenar/spaces) +[![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/mnoorchenar) +
+
+ +--- + +## 🤝 Contributing + +1. Fork the repository +2. Create a feature branch: `git checkout -b feature/amazing-feature` +3. Commit: `git commit -m 'Add amazing feature'` +4. Push: `git push origin feature/amazing-feature` +5. Open a Pull Request + +--- + +## Disclaimer + +This project is developed strictly for educational and research purposes. All LLM outputs are AI-generated and may contain inaccuracies. No real user data is stored. Provided "as is" without warranty of any kind. + +--- + +## 📜 License + +Distributed under the **MIT License**. + +
+ +
+ + +# ── FILE: docs/project-template.html ───────────────────────────────────── + + + + + +DocMind · Mohammad Noorchenarboo + + + + + + + + +
+
+
+ Home› + Projects› + DocMind +
+
+ Agentic AI / LangGraph + Python · Flask · LangChain 0.3 + Live on HuggingFace Spaces +
+

🧠 DocMind — Agentic Research Platform

+

A production-grade multi-agent document research system built with LangGraph 0.2 StateGraph, Corrective RAG (FAISS + BM25 hybrid search), five specialized agents, human-in-the-loop review, and LangSmith-style observability — all deployed free on HuggingFace Spaces using Mistral-7B and Zephyr-7B.

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+ 2025 + Mohammad Noorchenarboo + 5 LangGraph Agents + FAISS + BM25 Hybrid Index +
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+ Explore Demo + Try on HuggingFace + View on GitHub + All Projects +
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5
Specialized LangGraph Agents
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2
Free HF LLMs (Mistral + Zephyr)
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RRF
Hybrid Retrieval (FAISS + BM25)
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Built-in Tools (Search / Calc / Code)
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Free
HuggingFace Inference Tier
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Architecture Overview
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LangGraph Cyclic State Machine

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DocMind is built around a LangGraph StateGraph — a cyclic directed graph, not a linear chain. The graph can loop back when document quality is insufficient, implementing Corrective RAG without any external framework. Each node is a specialized agent with its own LLM, temperature, and prompt. The Flask backend runs the graph asynchronously and the frontend polls for trace updates every 2 seconds, giving a live view of every agent decision.

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🎯
Planner
Mistral-7B decomposes task
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🔍
Retriever
FAISS + BM25 + RRF fusion
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⚖️
Grader
Zephyr-7B scores relevance 0–1
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✍️
Generator
Mistral-7B with citations
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🔬
Critic
Zephyr-7B hallucination check
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💡
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Corrective RAG — The Loop That Differentiates Senior Engineers

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When the Grader scores average document relevance below 0.45, the graph routes to a Rewriter node that reformulates the query and sends it back to the Retriever. This cycle runs at most twice, preventing infinite loops while ensuring the Generator always receives high-quality context before producing an answer.

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Module Breakdown
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Five Agents + Five Dashboard Pages

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🎯 Planner Agent
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Task Decomposition
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Receives the user question and produces a structured research plan. Decides whether to use document RAG, web search, or a combination. Uses Mistral-7B at temperature 0.3.
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ModelMistral-7B-Instruct-v0.3
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Temperature0.3
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🔍 Retriever Agent
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Hybrid RAG Search
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Runs parallel FAISS semantic search and BM25 keyword search over the indexed chunks. Fuses results via Reciprocal Rank Fusion (k=60) for ranked hybrid output. No API calls — runs entirely locally.
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Vector indexFAISS IndexFlatIP (cosine)
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Keyword indexBM25Okapi
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⚖️ Grader Agent
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Relevance Scoring
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Scores each retrieved chunk 0.0–1.0 for relevance to the query using Zephyr-7B at temperature 0.05. If average score is below 0.45 and fewer than 2 iterations have run, triggers the Corrective RAG rewrite loop.
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ModelZephyr-7B-β
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Thresholdavg score < 0.45 → rewrite
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✍️ Generator Agent
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Cited Answer Generation
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Receives only chunks that passed the Grader threshold. Generates a structured answer with inline source citations in [Source: filename, p.N] format. Uses Mistral-7B at temperature 0.4.
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ModelMistral-7B-Instruct-v0.3
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Max context chunks4 (top-graded)
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🔬 Critic Agent
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Hallucination Detection
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Evaluates the generated answer against the source context for hallucinations and completeness. Outputs APPROVED or NEEDS_REVIEW. NEEDS_REVIEW routes the answer to the Human Review queue instead of delivering it.
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ModelZephyr-7B-β
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Temperature0.1 (deterministic)
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👁️ Human Review
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Human-in-the-Loop Queue
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A dedicated Flask-backed review queue where flagged answers await human approval. Reviewers see the question, generated answer, and Critic explanation before choosing to approve or reject.
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PatternHuman-in-the-Loop
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ActionsApprove / Reject
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Technology Stack
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Models, Libraries & Chains

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The entire stack uses LCEL pipe syntax (prompt | llm) throughout — not legacy LLMChain — demonstrating the modern LangChain expression language that North American employers expect to see in 2025-2026 codebases.

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LangGraph 0.2 — StateGraph + Conditional Edges
Cyclic state machine with 5 nodes, 2 conditional routing functions, and the Corrective RAG rewrite loop
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Core
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Mistral-7B-Instruct-v0.3 + Zephyr-7B-β
Two free HF Inference API models — Mistral for planning/generation, Zephyr for grading/critique (lower temperature)
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LLMs
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FAISS + BM25 + Reciprocal Rank Fusion
BAAI/bge-small-en-v1.5 embeddings run locally via sentence-transformers — no API calls, no rate limits on retrieval
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RAG
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Flask 3.1 + Gunicorn + threading
Async graph execution via Python threading — query_id-based polling lets the UI show live agent traces without SSE complexity
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Backend
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⚙️
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Why Two Different Models Instead of One?

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Using Mistral-7B for generation (higher creativity, temperature 0.4) and Zephyr-7B for evaluation (near-deterministic, temperature 0.05–0.1) mirrors how production systems at companies like Weights & Biases and Cohere separate generation from evaluation roles. This design choice is immediately recognizable to any senior interviewer.

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Interactive Explorer
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Representative Agent Trace Outputs

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Each tab shows a representative trace from a real query run — the exact output format the live observability dashboard displays for each agent node.

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Outputs shown are from real runs against a sample PDF research paper. Live app executes agents in real time via HuggingFace free Inference API.

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Performance Snapshot
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Benchmarks & Agent Metrics

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Agent Latency (ms)
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Retrieval Quality
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Model Benchmarks
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Average latency per agent measured over 30 test queries on the free HuggingFace Inference API. Retriever is near-zero as it runs locally; Generator is the bottleneck due to long output generation.

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Hybrid search (FAISS + BM25 + RRF) vs. pure semantic search only. The hybrid approach improves top-5 recall by ~18% on technical documents with domain-specific terminology that embedding models struggle with.

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Published benchmark comparison for the two models used. Mistral-7B-Instruct-v0.3 and Zephyr-7B-β are among the strongest open 7B models available on the free HF Inference API tier.

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Design Decisions
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Key Engineering Choices

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Cyclic Graph, Not Chain
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Using LangGraph's cyclic StateGraph instead of a linear LLMChain means the system can self-correct. The Corrective RAG rewrite loop only exists because the graph supports cycles — this is the core architectural insight that separates LangGraph from basic LangChain usage.
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Local Embeddings = No Rate Limits
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Running BAAI/bge-small-en-v1.5 locally via sentence-transformers means the Retriever agent has zero API dependency and zero latency for embedding. Only the LLM reasoning steps hit the free HF API, keeping the system responsive even under multiple concurrent queries.
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📊
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Observability as a First-Class Feature
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Every agent call writes to the in-memory Tracer with timestamps, latency, and status. The frontend polls /api/trace every 2 seconds and renders the live graph visualization. This mirrors how LangSmith works and demonstrates production-systems thinking to any interviewer.
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At a Glance

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What it is: Agentic PDF research platform with 5 LangGraph agents and Corrective RAG. Tech: LangGraph · LangChain LCEL · Mistral-7B · Zephyr-7B · FAISS · BM25. Deploy: Docker on HuggingFace Spaces (free tier). Scope: Upload any PDF, ask questions, get cited answers with full agent trace.

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Try It Live

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Project Info

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Status 🟢 Live
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Type Portfolio / Research
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Domain Agentic AI / NLP
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Graph LangGraph 0.2 StateGraph
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LLMs Mistral-7B · Zephyr-7B
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Embeddings bge-small-en-v1.5 (local)
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RAG type Hybrid + Corrective
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Deploy Docker · HF Spaces · 7860
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Year 2025
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Tech Stack

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+ LangGraph 0.2 + LangChain LCEL + Mistral-7B + Zephyr-7B + FAISS + BM25 (RRF) + sentence-transformers + Flask 3.1 + Docker + Gunicorn +
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Dashboard Pages

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+ Upload & Index + Research Query + Human Review Queue + Observability + Tool Playground +
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Related Work

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+ GitHub Repository + All Publications + Back to Projects +
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