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| import os | |
| import re | |
| import time | |
| import json | |
| import hashlib | |
| import requests | |
| import matplotlib | |
| matplotlib.use("Agg") | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| from collections import defaultdict | |
| from bs4 import BeautifulSoup | |
| from langchain_core.documents import Document | |
| from langchain_text_splitters import RecursiveCharacterTextSplitter | |
| from langchain_huggingface import HuggingFaceEmbeddings | |
| from langchain_community.vectorstores import Chroma | |
| from rank_bm25 import BM25Okapi | |
| from sentence_transformers import CrossEncoder | |
| # ═══════════════════════════════════════════════════════ | |
| # CONFIG | |
| # ═══════════════════════════════════════════════════════ | |
| PERSIST_DIR = "k8s_chroma_db" | |
| URLS = { | |
| "pods": "https://kubernetes.io/docs/concepts/workloads/pods/", | |
| "deployments": "https://kubernetes.io/docs/concepts/workloads/controllers/deployment/", | |
| "services": "https://kubernetes.io/docs/concepts/services-networking/service/", | |
| "namespaces": "https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/", | |
| "nodes": "https://kubernetes.io/docs/concepts/architecture/nodes/", | |
| "statefulsets": "https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/", | |
| "rbac": "https://kubernetes.io/docs/reference/access-authn-authz/rbac/", | |
| "persistent-volumes": "https://kubernetes.io/docs/concepts/storage/persistent-volumes/", | |
| "ingress": "https://kubernetes.io/docs/concepts/services-networking/ingress/", | |
| "autoscaling": "https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/", | |
| "docker-overview": "https://docs.docker.com/get-started/overview/", | |
| "docker-images": "https://docs.docker.com/get-started/docker-concepts/the-basics/what-is-an-image/", | |
| "docker-containers": "https://docs.docker.com/get-started/docker-concepts/the-basics/what-is-a-container/", | |
| "docker-volumes": "https://docs.docker.com/storage/volumes/", | |
| "docker-networking": "https://docs.docker.com/network/", | |
| "docker-compose": "https://docs.docker.com/compose/", | |
| } | |
| # Cost estimates (llama-3.1-8b-instruct on OpenRouter, per 1K tokens) | |
| COST_PER_1K_INPUT_TOKENS = 0.00006 | |
| COST_PER_1K_OUTPUT_TOKENS = 0.00006 | |
| AVG_CHARS_PER_TOKEN = 4 | |
| MAX_RETRIES = 3 | |
| RETRY_BACKOFF_BASE = 1.5 | |
| MAX_HISTORY_TURNS = 4 | |
| CONFIDENCE_THRESHOLD = 0.35 | |
| GROUNDEDNESS_THRESHOLD = 40 | |
| # ═══════════════════════════════════════════════════════ | |
| # LLM GATEWAY — Caching, Retry, Fallback, Cost Tracking | |
| # ═══════════════════════════════════════════════════════ | |
| _response_cache: dict = {} | |
| CACHE_STATS = {"hits": 0, "misses": 0} | |
| COST_LOG: list = [] | |
| def _cache_key(prompt: str) -> str: | |
| return hashlib.sha256(prompt.encode()).hexdigest() | |
| def cache_get(prompt: str): | |
| return _response_cache.get(_cache_key(prompt)) | |
| def cache_set(prompt: str, answer: str) -> None: | |
| _response_cache[_cache_key(prompt)] = answer | |
| def estimate_tokens(text: str) -> int: | |
| return max(1, len(text) // AVG_CHARS_PER_TOKEN) | |
| def log_cost(query: str, prompt: str, answer: str, cached: bool = False) -> float: | |
| input_tok = estimate_tokens(prompt) | |
| output_tok = estimate_tokens(answer) | |
| cost_usd = (input_tok / 1000 * COST_PER_1K_INPUT_TOKENS + | |
| output_tok / 1000 * COST_PER_1K_OUTPUT_TOKENS) | |
| COST_LOG.append({ | |
| "query": query, "input_tokens": input_tok, | |
| "output_tokens": output_tok, | |
| "cost_usd": round(cost_usd, 6), "cached": cached, | |
| }) | |
| return cost_usd | |
| def _build_fallback(context_chunks) -> str: | |
| if not context_chunks: | |
| return "⚠️ The language model is unavailable and no docs were found. Please try again." | |
| snippets = "\n\n".join(f"• {c[:300]}..." for c in context_chunks[:3]) | |
| return ("⚠️ The language model is temporarily unavailable. " | |
| "Here are the most relevant documentation excerpts:\n\n" | |
| + snippets + "\n\n_Please try again for a full AI-generated answer._") | |
| def call_llm(prompt: str, context_chunks=None, max_tokens: int = 400) -> tuple: | |
| """ | |
| LLM Gateway: Cache → Retry (exponential backoff) → Fallback. | |
| Returns (answer, was_cached). | |
| """ | |
| api_key = os.getenv("OPENROUTER_API_KEY") | |
| if not api_key: | |
| return _build_fallback(context_chunks), False | |
| cached = cache_get(prompt) | |
| if cached: | |
| CACHE_STATS["hits"] += 1 | |
| log_cost("(cached)", prompt, cached, cached=True) | |
| return cached, True | |
| CACHE_STATS["misses"] += 1 | |
| last_error = None | |
| for attempt in range(1, MAX_RETRIES + 1): | |
| try: | |
| res = requests.post( | |
| "https://openrouter.ai/api/v1/chat/completions", | |
| headers={ | |
| "Authorization": f"Bearer {api_key}", | |
| "HTTP-Referer": "https://huggingface.co/", | |
| "X-Title": "Kubernetes RAG Assistant", | |
| }, | |
| json={ | |
| "model": "meta-llama/llama-3.1-8b-instruct", | |
| "messages": [{"role": "user", "content": prompt}], | |
| "max_tokens": max_tokens, | |
| "temperature": 0.0, | |
| }, | |
| timeout=60, | |
| ) | |
| res.raise_for_status() | |
| answer = res.json()["choices"][0]["message"]["content"] | |
| cache_set(prompt, answer) | |
| return answer, False | |
| except Exception as e: | |
| last_error = e | |
| if attempt < MAX_RETRIES: | |
| wait = RETRY_BACKOFF_BASE ** attempt | |
| print(f"[WARN] LLM attempt {attempt} failed: {e} — retrying in {wait:.1f}s") | |
| time.sleep(wait) | |
| print(f"[ERROR] All {MAX_RETRIES} LLM attempts failed: {last_error}. Using fallback.") | |
| return _build_fallback(context_chunks), False | |
| # ═══════════════════════════════════════════════════════ | |
| # GUARDRAILS | |
| # ═══════════════════════════════════════════════════════ | |
| PII_PATTERNS = { | |
| "email": r"[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+", | |
| "phone": r"\+?\d[\d\s\-]{8,15}\d", | |
| "ssn": r"\b\d{3}-\d{2}-\d{4}\b", | |
| "credit": r"\b(?:\d[ -]?){13,16}\b", | |
| "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b", | |
| } | |
| INJECTION_PATTERNS = [ | |
| r"ignore.*instructions", r"reveal.*prompt", r"bypass.*security", | |
| r"system.*prompt", r"forget.*previous", r"pretend.*you.*are", | |
| r"act.*as.*if", r"jailbreak", r"dan.*mode", | |
| ] | |
| _rate_store: dict = defaultdict(list) | |
| RATE_LIMIT_WINDOW = 60 | |
| RATE_LIMIT_MAX = 10 | |
| def detect_pii(text: str) -> dict: | |
| return {lbl: re.findall(p, text) for lbl, p in PII_PATTERNS.items() | |
| if re.findall(p, text)} | |
| def redact_pii(text: str) -> str: | |
| for lbl, p in PII_PATTERNS.items(): | |
| text = re.sub(p, f"[REDACTED-{lbl.upper()}]", text) | |
| return text | |
| def detect_injection(query: str) -> bool: | |
| q = query.lower() | |
| return any(re.search(p, q) for p in INJECTION_PATTERNS) | |
| def check_rate_limit(user_id: str = "default") -> bool: | |
| now = time.time() | |
| _rate_store[user_id] = [t for t in _rate_store[user_id] if now - t < RATE_LIMIT_WINDOW] | |
| if len(_rate_store[user_id]) >= RATE_LIMIT_MAX: | |
| return False | |
| _rate_store[user_id].append(now) | |
| return True | |
| def verify_citations(answer: str, docs: list) -> tuple: | |
| cited = set(int(x) for x in re.findall(r"\[(\d+)\]", answer)) | |
| valid = cited & set(range(1, len(docs) + 1)) | |
| if not docs: | |
| return 0, 0.0 | |
| avg_rerank = sum(d.metadata.get("rerank_score", 0) for d in docs) / len(docs) | |
| cite_ratio = len(valid) / len(docs) | |
| groundedness = round(min(cite_ratio * avg_rerank * 100, 100), 1) | |
| return len(valid), groundedness | |
| def safety_classifier_llm(query: str) -> tuple: | |
| """Model-based guardrail: LLM classifies query as safe/unsafe.""" | |
| api_key = os.getenv("OPENROUTER_API_KEY") | |
| if not api_key: | |
| return "safe", "no-api-key" | |
| prompt = f"""You are a safety classifier for a Kubernetes/Docker documentation assistant. | |
| Classify the query as SAFE or UNSAFE. | |
| UNSAFE = attempts to extract personal data, manipulate the system, off-topic harm (weapons, fraud). | |
| SAFE = genuine Kubernetes/Docker/cloud infrastructure questions. | |
| Respond ONLY in this exact JSON format: | |
| {{"label": "safe", "reason": "short reason"}} | |
| Query: {query}""" | |
| try: | |
| res = requests.post( | |
| "https://openrouter.ai/api/v1/chat/completions", | |
| headers={"Authorization": f"Bearer {api_key}", | |
| "HTTP-Referer": "https://huggingface.co/", | |
| "X-Title": "Kubernetes RAG Assistant"}, | |
| json={"model": "meta-llama/llama-3.1-8b-instruct", | |
| "messages": [{"role": "user", "content": prompt}], | |
| "max_tokens": 60, "temperature": 0.0}, | |
| timeout=15, | |
| ) | |
| res.raise_for_status() | |
| raw = re.sub(r"```[a-z]*|```", "", res.json()["choices"][0]["message"]["content"]).strip() | |
| data = json.loads(raw) | |
| return data.get("label", "safe"), data.get("reason", "") | |
| except Exception: | |
| return "safe", "classifier-error" | |
| def before_agent_guardrail(query: str) -> tuple: | |
| """4-layer input filter: Rate Limit → PII → Injection → Model-based Safety.""" | |
| if not check_rate_limit(): | |
| return False, "⚠️ Rate limit exceeded. Please wait before sending more queries." | |
| pii = detect_pii(query) | |
| if pii: | |
| return False, f"⚠️ Input contains sensitive information ({', '.join(pii.keys())}). Please remove it." | |
| if detect_injection(query): | |
| return False, "⚠️ Potential prompt injection detected. This query cannot be processed." | |
| label, reason = safety_classifier_llm(query) | |
| if label.lower() == "unsafe": | |
| return False, f"⚠️ Query flagged as unsafe: {reason}" | |
| return True, "" | |
| def after_agent_guardrail(answer: str, docs: list) -> tuple: | |
| """4-layer output filter: PII Redact → Citation Check → Groundedness → Human Review flag.""" | |
| answer = redact_pii(answer) | |
| cite_count, ground = verify_citations(answer, docs) | |
| avg_score = sum(d.metadata.get("rerank_score", 0) for d in docs) / len(docs) if docs else 0 | |
| needs_review = (avg_score < CONFIDENCE_THRESHOLD) or (ground < GROUNDEDNESS_THRESHOLD) | |
| answer = re.sub(r"Groundedness:\s*\d+%", "", answer).strip() | |
| answer += f"\n\n📊 **Groundedness: {ground}%** | Citations verified: {cite_count}/{len(docs)}" | |
| if needs_review: | |
| answer += "\n\n🔍 **Human Review Recommended** — confidence below threshold." | |
| return answer, needs_review, ground | |
| # ═══════════════════════════════════════════════════════ | |
| # KNOWLEDGE BASE | |
| # ═══════════════════════════════════════════════════════ | |
| def scrape_page(name, url): | |
| try: | |
| r = requests.get(url, timeout=20) | |
| r.raise_for_status() | |
| soup = BeautifulSoup(r.text, "html.parser") | |
| content = (soup.find("div", class_="td-content") or | |
| soup.find("div", class_="docs-content")) | |
| if not content: | |
| return None | |
| return Document(page_content=content.get_text(separator="\n").strip(), | |
| metadata={"doc_id": name, "url": url}) | |
| except Exception as e: | |
| print(f"[ERROR] scraping {url}: {e}") | |
| return None | |
| def build_or_load_kb(): | |
| emb = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") | |
| if os.path.isdir(PERSIST_DIR): | |
| print("[INFO] Loading existing DB...") | |
| vectordb = Chroma(embedding_function=emb, persist_directory=PERSIST_DIR) | |
| raw = vectordb._collection.get(include=["documents", "metadatas"]) | |
| chunks = [Document(page_content=d, metadata=m) | |
| for d, m in zip(raw["documents"], raw["metadatas"])] | |
| return vectordb, chunks | |
| print("[INFO] No DB found — scraping docs...") | |
| docs = [d for name, url in URLS.items() if (d := scrape_page(name, url))] | |
| splitter = RecursiveCharacterTextSplitter(chunk_size=900, chunk_overlap=200) | |
| chunks = splitter.split_documents(docs) | |
| vectordb = Chroma.from_documents(chunks, emb, persist_directory=PERSIST_DIR) | |
| vectordb.persist() | |
| print(f"[INFO] DB created with {len(chunks)} chunks.") | |
| return vectordb, chunks | |
| vectordb, chunks = build_or_load_kb() | |
| bm25 = BM25Okapi([c.page_content.split() for c in chunks]) | |
| reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-12-v2") | |
| retriever = vectordb.as_retriever( | |
| search_type="similarity_score_threshold", | |
| search_kwargs={"k": 8, "score_threshold": 0.35}, | |
| ) | |
| # ═══════════════════════════════════════════════════════ | |
| # RETRIEVAL — Hybrid BM25 + Vector + CrossEncoder Rerank | |
| # ═══════════════════════════════════════════════════════ | |
| def hybrid_search(query: str, top_k: int = 5) -> list: | |
| vector_results = retriever.invoke(query) | |
| bm_scores = bm25.get_scores(query.lower().split()) | |
| bm_results = [d for _, d in sorted(zip(bm_scores, chunks), key=lambda x: x[0], reverse=True)[:top_k]] | |
| combined = vector_results + bm_results | |
| seen, unique = set(), [] | |
| for d in combined: | |
| key = (d.metadata.get("doc_id"), d.page_content[:80]) | |
| if key not in seen: | |
| seen.add(key); unique.append(d) | |
| if not unique: | |
| return [] | |
| scores = reranker.predict([(query, d.page_content) for d in unique]) | |
| ranked = sorted(zip(scores, unique), key=lambda x: x[0], reverse=True)[:top_k] | |
| for s, doc in ranked: | |
| doc.metadata["rerank_score"] = float(s) | |
| return [doc for _, doc in ranked] | |
| # ═══════════════════════════════════════════════════════ | |
| # METRICS | |
| # ═══════════════════════════════════════════════════════ | |
| METRICS = { | |
| "q": [], "lat": [], "tok": [], "g": [], "r": [], | |
| "c": [], "t": [], "review": [], "guardrail": [], | |
| "cached": [], "cost": [], | |
| } | |
| def classify_query(q: str) -> str: | |
| q = q.lower() | |
| if "how" in q: return "how-to" | |
| if "error" in q or "fail" in q: return "debug" | |
| return "general" | |
| # ═══════════════════════════════════════════════════════ | |
| # CONVERSATION MEMORY | |
| # ═══════════════════════════════════════════════════════ | |
| def build_prompt_with_history(query: str, history: list, context: str) -> str: | |
| recent = history[-(MAX_HISTORY_TURNS * 2):] | |
| history_text = "" | |
| for msg in recent: | |
| role, content = msg.get("role", ""), msg.get("content", "") | |
| if role == "user": | |
| history_text += f"User: {content}\n" | |
| elif role == "assistant": | |
| history_text += f"Assistant: {content[:400]}...\n" | |
| history_block = (f"Previous conversation (for context only):\n{history_text}\n---\n" | |
| if history_text else "") | |
| return f"""{history_block}Answer using ONLY the context below. | |
| Every sentence MUST include at least one citation like [1] or [2]. | |
| Do NOT add any groundedness score — that is computed separately. | |
| Current question: {query} | |
| Context: | |
| {context} | |
| """ | |
| # ═══════════════════════════════════════════════════════ | |
| # FEATURE 1 — SUGGESTED FOLLOW-UP QUESTIONS | |
| # ═══════════════════════════════════════════════════════ | |
| def generate_followup_questions(query: str, answer: str) -> list: | |
| """ | |
| Asks the LLM to suggest 3 natural follow-up questions based on | |
| what was just asked and answered. Returns list of 3 strings. | |
| Falls back to empty list on any error so the main flow is never broken. | |
| """ | |
| api_key = os.getenv("OPENROUTER_API_KEY") | |
| if not api_key: | |
| return [] | |
| prompt = f"""A user asked a Kubernetes/Docker assistant: | |
| "{query}" | |
| The assistant answered it. Suggest exactly 3 short follow-up questions the user might ask next. | |
| Rules: | |
| - Must be about Kubernetes or Docker only | |
| - Keep each question under 12 words | |
| - Return ONLY a valid JSON array of 3 strings, no other text | |
| - Example: ["Question one?", "Question two?", "Question three?"] | |
| JSON array:""" | |
| try: | |
| res = requests.post( | |
| "https://openrouter.ai/api/v1/chat/completions", | |
| headers={"Authorization": f"Bearer {api_key}", | |
| "HTTP-Referer": "https://huggingface.co/", | |
| "X-Title": "Kubernetes RAG Assistant"}, | |
| json={"model": "meta-llama/llama-3.1-8b-instruct", | |
| "messages": [{"role": "user", "content": prompt}], | |
| "max_tokens": 120, "temperature": 0.7}, | |
| timeout=15, | |
| ) | |
| res.raise_for_status() | |
| raw = re.sub(r"```[a-z]*|```", "", | |
| res.json()["choices"][0]["message"]["content"]).strip() | |
| questions = json.loads(raw) | |
| if isinstance(questions, list): | |
| return [str(q) for q in questions[:3]] | |
| except Exception as e: | |
| print(f"[WARN] follow-up generation failed: {e}") | |
| return [] | |
| # ═══════════════════════════════════════════════════════ | |
| # FEATURE 2 — RETRIEVAL DETAILS PANEL | |
| # Shows exactly which chunks were retrieved, their reranker | |
| # scores, and whether each was cited. Makes the RAG pipeline | |
| # visible — this is what separates the app from ChatGPT. | |
| # ═══════════════════════════════════════════════════════ | |
| def build_retrieval_details(docs: list, answer: str) -> str: | |
| cited_indices = set(int(x) for x in re.findall(r"\[(\d+)\]", answer)) | |
| lines = ["**🔍 Retrieval Details** — how this answer was found\n", | |
| "| # | Source | Rerank Score | Cited? | Snippet |", | |
| "|---|--------|-------------|--------|---------|"] | |
| for i, doc in enumerate(docs, 1): | |
| score = doc.metadata.get("rerank_score", 0) | |
| url = doc.metadata.get("url", "unknown") | |
| cited = "✅ Yes" if i in cited_indices else "❌ No" | |
| snippet = doc.page_content[:120].replace("\n", " ").strip() + "..." | |
| short_url = (url.replace("https://kubernetes.io/docs/", "k8s/") | |
| .replace("https://docs.docker.com/", "docker/")) | |
| lines.append(f"| [{i}] | `{short_url}` | `{score:.3f}` | {cited} | {snippet} |") | |
| lines.append(f"\n**Chunks retrieved:** {len(docs)} | " | |
| f"**Chunks cited:** {len(cited_indices)}") | |
| return "\n".join(lines) | |
| # ═══════════════════════════════════════════════════════ | |
| # MAIN CHAT HANDLER | |
| # ═══════════════════════════════════════════════════════ | |
| def answer_question(query: str, history: list) -> tuple: | |
| """ | |
| Returns: | |
| history — updated chat history | |
| clear_input — empty string to clear the textbox | |
| fq1, fq2, fq3 — three follow-up question strings (may be empty) | |
| retrieval_md — markdown string for the retrieval details panel | |
| """ | |
| start = time.time() | |
| NO_EXTRAS = ("", "", "", "") # fq1, fq2, fq3, retrieval_md | |
| # ── Before-Agent Guardrail ──────────────────────── | |
| is_safe, block_reason = before_agent_guardrail(query) | |
| if not is_safe: | |
| _log(query, time.time()-start, 0, 0, 0, 0, blocked=True) | |
| history.append({"role": "user", "content": query}) | |
| history.append({"role": "assistant", "content": block_reason}) | |
| return history, "", *NO_EXTRAS | |
| # ── Retrieval ───────────────────────────────────── | |
| docs = hybrid_search(query) | |
| if not docs: | |
| history.append({"role": "user", "content": query}) | |
| history.append({"role": "assistant", | |
| "content": "❌ Not found in the knowledge base. " | |
| "Please ask about Kubernetes or Docker."}) | |
| return history, "", *NO_EXTRAS | |
| # ── Build context & prompt (with conversation memory) ─ | |
| ctx, sources = "", [] | |
| for i, d in enumerate(docs, 1): | |
| ctx += f"[{i}] {d.page_content[:900]}\nSource: {d.metadata['url']}\n\n" | |
| sources.append(f"[{i}] → {d.metadata['url']}") | |
| prompt = build_prompt_with_history(query, history, ctx) | |
| # ── LLM call (cache → retry → fallback) ────────── | |
| raw_answer, was_cached = call_llm(prompt, context_chunks=[d.page_content for d in docs]) | |
| cost = log_cost(query, prompt, raw_answer, cached=was_cached) | |
| latency = time.time() - start | |
| # ── After-Agent Guardrail ───────────────────────── | |
| final_answer, needs_review, groundedness = after_agent_guardrail(raw_answer, docs) | |
| cache_tag = " ⚡ _Served from cache_" if was_cached else "" | |
| final_answer += "\n\n---\n**Sources:**\n" + "\n".join(sources) + cache_tag | |
| if needs_review: | |
| final_answer += "\n\n> ℹ️ A human expert should verify this answer." | |
| # ── Feature 1: Follow-up questions ─────────────── | |
| followups = generate_followup_questions(query, raw_answer) | |
| while len(followups) < 3: | |
| followups.append("") | |
| fq1, fq2, fq3 = followups[0], followups[1], followups[2] | |
| # ── Feature 2: Retrieval details panel ─────────── | |
| retrieval_md = build_retrieval_details(docs, raw_answer) | |
| # ── Log metrics ─────────────────────────────────── | |
| avg_score = sum(d.metadata.get("rerank_score", 0) for d in docs) / len(docs) | |
| cite_count = len(set(re.findall(r"\[(\d+)\]", raw_answer))) | |
| _log(query, latency, groundedness, avg_score, cite_count, | |
| cost, tokens=len(raw_answer.split()), | |
| review=needs_review, cached=was_cached) | |
| history.append({"role": "user", "content": query}) | |
| history.append({"role": "assistant", "content": final_answer}) | |
| return history, "", fq1, fq2, fq3, retrieval_md | |
| def _log(query, latency, ground, avg_score, cite_count, cost, | |
| tokens=0, review=False, cached=False, blocked=False): | |
| METRICS["q"].append(query) | |
| METRICS["lat"].append(round(latency, 2)) | |
| METRICS["tok"].append(tokens) | |
| METRICS["g"].append(ground) | |
| METRICS["r"].append(round(avg_score, 3)) | |
| METRICS["c"].append(cite_count) | |
| METRICS["t"].append(classify_query(query)) | |
| METRICS["review"].append(review) | |
| METRICS["guardrail"].append(blocked) | |
| METRICS["cached"].append(cached) | |
| METRICS["cost"].append(round(cost, 6)) | |
| def use_followup(question: str, history: list) -> tuple: | |
| """Called when user clicks a follow-up suggestion button.""" | |
| if not question.strip(): | |
| return history, "", "", "", "", "" | |
| return answer_question(question, history) | |
| # ═══════════════════════════════════════════════════════ | |
| # ANALYTICS CHARTS | |
| # ═══════════════════════════════════════════════════════ | |
| def make_groundedness_chart(): | |
| fig, ax = plt.subplots(figsize=(7, 3)) | |
| if not METRICS["g"]: | |
| ax.text(0.5, 0.5, "No data yet", ha="center", va="center", transform=ax.transAxes) | |
| return fig | |
| idx = list(range(1, len(METRICS["g"]) + 1)) | |
| colors = ["#2ecc71" if g >= 60 else "#f39c12" if g >= 40 else "#e74c3c" | |
| for g in METRICS["g"]] | |
| ax.bar(idx, METRICS["g"], color=colors, width=0.6, edgecolor="none") | |
| ax.axhline(40, color="#e74c3c", linestyle="--", lw=1, label="Review threshold (40%)") | |
| ax.axhline(60, color="#2ecc71", linestyle="--", lw=1, label="Good (60%)") | |
| ax.set_xlabel("Query #"); ax.set_ylabel("Groundedness (%)") | |
| ax.set_title("Groundedness per Query", fontweight="bold") | |
| ax.set_ylim(0, 105); ax.set_xticks(idx); ax.legend(fontsize=8) | |
| ax.spines["top"].set_visible(False); ax.spines["right"].set_visible(False) | |
| fig.tight_layout(); return fig | |
| def make_query_type_chart(): | |
| fig, ax = plt.subplots(figsize=(4, 3)) | |
| if not METRICS["t"]: | |
| ax.text(0.5, 0.5, "No data yet", ha="center", va="center", transform=ax.transAxes) | |
| return fig | |
| counts = {} | |
| for t in METRICS["t"]: | |
| counts[t] = counts.get(t, 0) + 1 | |
| palette = ["#3498db", "#9b59b6", "#1abc9c", "#e67e22"][:len(counts)] | |
| wedges, texts, autotexts = ax.pie( | |
| counts.values(), labels=counts.keys(), autopct="%1.0f%%", | |
| colors=palette, startangle=90, | |
| wedgeprops={"edgecolor": "white", "linewidth": 1.5}) | |
| for at in autotexts: at.set_fontsize(9) | |
| ax.set_title("Query Types", fontweight="bold") | |
| fig.tight_layout(); return fig | |
| def make_latency_chart(): | |
| fig, ax = plt.subplots(figsize=(7, 3)) | |
| if not METRICS["lat"]: | |
| ax.text(0.5, 0.5, "No data yet", ha="center", va="center", transform=ax.transAxes) | |
| return fig | |
| idx = list(range(1, len(METRICS["lat"]) + 1)) | |
| colors = ["#f39c12" if c else "#3498db" for c in METRICS["cached"]] | |
| ax.bar(idx, METRICS["lat"], color=colors, width=0.6, edgecolor="none") | |
| ax.set_xlabel("Query #"); ax.set_ylabel("Latency (s)") | |
| ax.set_title("Latency per Query (orange = cache hit)", fontweight="bold") | |
| ax.set_xticks(idx) | |
| ax.spines["top"].set_visible(False); ax.spines["right"].set_visible(False) | |
| fig.tight_layout(); return fig | |
| def make_cost_chart(): | |
| fig, ax = plt.subplots(figsize=(7, 3)) | |
| if not METRICS["cost"]: | |
| ax.text(0.5, 0.5, "No data yet", ha="center", va="center", transform=ax.transAxes) | |
| return fig | |
| running, total = [], 0 | |
| for c in METRICS["cost"]: | |
| total += c; running.append(round(total, 6)) | |
| idx = list(range(1, len(running) + 1)) | |
| ax.plot(idx, running, marker="o", color="#9b59b6", lw=2, markersize=5) | |
| ax.fill_between(idx, running, alpha=0.15, color="#9b59b6") | |
| ax.set_xlabel("Query #"); ax.set_ylabel("Cumulative cost (USD)") | |
| ax.set_title("Running Cost Tracker", fontweight="bold") | |
| ax.set_xticks(idx) | |
| ax.spines["top"].set_visible(False); ax.spines["right"].set_visible(False) | |
| fig.tight_layout(); return fig | |
| def update_dashboard(): | |
| if not METRICS["q"]: | |
| empty = plt.figure() | |
| return [], 0, 0, 0, 0, 0, 0, empty, empty, empty, empty | |
| rows = list(zip( | |
| range(1, len(METRICS["q"]) + 1), METRICS["q"], | |
| METRICS["lat"], METRICS["tok"], METRICS["g"], METRICS["r"], | |
| METRICS["c"], METRICS["t"], | |
| ["✅" if v else "—" for v in METRICS["review"]], | |
| ["🛡️" if v else "—" for v in METRICS["guardrail"]], | |
| ["⚡" if v else "—" for v in METRICS["cached"]], | |
| METRICS["cost"], | |
| )) | |
| n = len(METRICS["g"]) | |
| return ( | |
| rows, | |
| round(sum(METRICS["g"]) / n, 2), | |
| round(sum(METRICS["lat"]) / n, 2), | |
| round(sum(METRICS["tok"]) / n, 2), | |
| round(sum(METRICS["review"]) / n * 100, 1), | |
| round(sum(METRICS["guardrail"]) / n * 100, 1), | |
| round(sum(METRICS["cost"]), 6), | |
| make_groundedness_chart(), make_query_type_chart(), | |
| make_latency_chart(), make_cost_chart(), | |
| ) | |
| # ═══════════════════════════════════════════════════════ | |
| # UI | |
| # ═══════════════════════════════════════════════════════ | |
| with gr.Blocks(title="Kubernetes & Docker RAG Assistant") as app: | |
| gr.Markdown( | |
| "# ☸️ Kubernetes & Docker RAG Assistant\n" | |
| "> Hybrid RAG · Layered Guardrails · Conversation Memory · " | |
| "Follow-up Suggestions · Retrieval Transparency" | |
| ) | |
| # ── Tab 1: Chat ─────────────────────────────────── | |
| with gr.Tab("💬 Chat"): | |
| chat = gr.Chatbot(height=460) | |
| user_in = gr.Textbox( | |
| label="Ask about Kubernetes or Docker", | |
| placeholder="e.g. How do I set resource limits for a Pod?", | |
| ) | |
| with gr.Row(): | |
| submit_btn = gr.Button("Send", variant="primary") | |
| clear_btn = gr.Button("Clear conversation") | |
| # ── Feature 1: Follow-up question buttons ───── | |
| gr.Markdown("**💡 Suggested follow-ups** — click to ask:") | |
| with gr.Row(): | |
| fq1_btn = gr.Button("", visible=True, size="sm") | |
| fq2_btn = gr.Button("", visible=True, size="sm") | |
| fq3_btn = gr.Button("", visible=True, size="sm") | |
| # ── Feature 2: Retrieval details accordion ──── | |
| with gr.Accordion("🔍 Why this answer? (Retrieval Details)", open=False): | |
| retrieval_panel = gr.Markdown( | |
| "_Ask a question to see which documentation chunks were retrieved, " | |
| "their reranker scores, and which ones were cited._" | |
| ) | |
| # Wire up main send | |
| all_outputs = [chat, user_in, fq1_btn, fq2_btn, fq3_btn, retrieval_panel] | |
| user_in.submit(answer_question, [user_in, chat], all_outputs) | |
| submit_btn.click(answer_question, [user_in, chat], all_outputs) | |
| clear_btn.click( | |
| lambda: ([], "", "", "", "", "_Ask a question to see retrieval details._"), | |
| None, all_outputs | |
| ) | |
| # Wire up follow-up buttons — clicking one runs it as a new query | |
| fq1_btn.click(use_followup, [fq1_btn, chat], all_outputs) | |
| fq2_btn.click(use_followup, [fq2_btn, chat], all_outputs) | |
| fq3_btn.click(use_followup, [fq3_btn, chat], all_outputs) | |
| # ── Tab 2: Guardrails ───────────────────────────── | |
| with gr.Tab("🛡️ Guardrails"): | |
| gr.Markdown(""" | |
| ### Guardrails Architecture | |
| **Before-Agent (input filtering)** | |
| | Layer | Type | What it checks | | |
| |---|---|---| | |
| | Rate Limiting | Deterministic | Max 10 req / min per user | | |
| | PII Detection | Deterministic | Email, phone, SSN, credit card, IP | | |
| | Prompt Injection | Deterministic | Regex patterns on known attack phrases | | |
| | Safety Classifier | **Model-based** | LLM classifies Safe / Unsafe with reason | | |
| **After-Agent (output safety)** | |
| | Layer | Type | What it checks | | |
| |---|---|---| | |
| | PII Redaction | Deterministic | Scrubs leaked PII from LLM output | | |
| | Citation Verification | Deterministic | Checks [N] refs exist and are valid | | |
| | Groundedness Score | Deterministic | Citation ratio × reranker score — not LLM self-report | | |
| | Confidence Threshold | Deterministic | Flags for human review if score < 0.35 or ground < 40% | | |
| **Human-in-the-Loop** | |
| When confidence is low the assistant shows a _Human Review Recommended_ banner. | |
| The reviewer sees groundedness %, reranker scores, and source URLs in the Retrieval Details panel. | |
| """) | |
| # ── Tab 3: Analytics ────────────────────────────── | |
| with gr.Tab("📊 Analytics"): | |
| gr.Markdown("### Query Analytics Dashboard") | |
| refresh_btn = gr.Button("🔄 Refresh Dashboard") | |
| with gr.Row(): | |
| avgG_box = gr.Number(label="Avg Groundedness (%)") | |
| avgL_box = gr.Number(label="Avg Latency (s)") | |
| avgT_box = gr.Number(label="Avg Tokens") | |
| pctReview_box = gr.Number(label="% Human Review") | |
| pctGuardrail_box = gr.Number(label="% Blocked") | |
| totalCost_box = gr.Number(label="Total Cost (USD)") | |
| with gr.Row(): | |
| ground_chart = gr.Plot(label="Groundedness over time") | |
| latency_chart = gr.Plot(label="Latency (orange = cache hit)") | |
| with gr.Row(): | |
| type_chart = gr.Plot(label="Query types") | |
| cost_chart = gr.Plot(label="Running cost tracker") | |
| gr.Markdown("#### Raw query log") | |
| table = gr.DataFrame( | |
| headers=["ID", "Query", "Latency (s)", "Tokens", "Groundedness (%)", | |
| "Rerank Score", "Citations", "Type", | |
| "Human Review", "Guardrail", "Cached", "Cost (USD)"], | |
| interactive=False, | |
| ) | |
| refresh_btn.click( | |
| update_dashboard, None, | |
| [table, avgG_box, avgL_box, avgT_box, | |
| pctReview_box, pctGuardrail_box, totalCost_box, | |
| ground_chart, type_chart, latency_chart, cost_chart], | |
| ) | |
| app.launch() |