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| #!/usr/bin/env python3 | |
| # SPDX-License-Identifier: Apache-2.0 | |
| """Granite Switch 4.1 3B Playground — pipeline-based multi-adapter chat. | |
| Checkboxes enable adapters that run automatically in the correct pipeline | |
| stage: user-validation -> pre-retrieval -> retrieval -> post-retrieval -> | |
| generation -> post-generation. Results are consolidated in a single message. | |
| """ | |
| import html | |
| import json | |
| import os | |
| import re | |
| import threading | |
| import time | |
| import urllib.error | |
| import urllib.parse | |
| import urllib.request | |
| try: | |
| import spaces | |
| except ImportError: | |
| import types | |
| spaces = types.ModuleType("spaces") | |
| spaces.GPU = lambda f=None, **kw: f if f else (lambda fn: fn) | |
| import gradio as gr | |
| import httpx | |
| INFERENCE_URL = os.environ.get("INFERENCE_URL", "") | |
| MODEL_ID = "ibm-granite/granite-switch-4.1-3b-preview" | |
| MODEL_OPTIONS = { | |
| "granite-4.0-micro": "ibm-granite/granite-4.0-micro", | |
| "granite-4.1-8b": "ibm-granite/granite-4.1-8b", | |
| "granite-4.1-30b": "ibm-granite/granite-4.1-30b", | |
| "qwen2.5-coder-32b": "Qwen/Qwen2.5-Coder-32B-Instruct", | |
| } | |
| USE_EXTERNAL_LLM = os.getenv("USE_EXTERNAL_LLM", "").lower() in { | |
| "1", | |
| "true", | |
| "yes", | |
| "on", | |
| } | |
| EXTERNAL_LLM_MODEL = os.getenv("EXTERNAL_LLM_MODEL", "granite-4.0-micro") | |
| EXTERNAL_LLM_GPUS = int(os.getenv("EXTERNAL_LLM_GPUS", "1")) | |
| EXTERNAL_LLM_BROKER_URL = os.getenv("EXTERNAL_LLM_BROKER_URL") or os.getenv("BROKER_URL") | |
| EXTERNAL_LLM_TIMEOUT_SECONDS = int(os.getenv("EXTERNAL_LLM_TIMEOUT_SECONDS", "600")) | |
| EXTERNAL_LLM_POLL_INTERVAL_SECONDS = float( | |
| os.getenv("EXTERNAL_LLM_POLL_INTERVAL_SECONDS", "10") | |
| ) | |
| EXTERNAL_LLM_MAX_NEW_TOKENS = int(os.getenv("EXTERNAL_LLM_MAX_NEW_TOKENS", "2048")) | |
| EXTERNAL_LLM_BROKER_MAX_RETRIES = int( | |
| os.getenv("EXTERNAL_LLM_BROKER_MAX_RETRIES", "5") | |
| ) | |
| EXTERNAL_LLM_BROKER_RETRY_SECONDS = float( | |
| os.getenv("EXTERNAL_LLM_BROKER_RETRY_SECONDS", "15") | |
| ) | |
| print( | |
| "[INFO] Startup config " | |
| f"use_external_llm={USE_EXTERNAL_LLM} " | |
| f"external_model={EXTERNAL_LLM_MODEL} " | |
| f"broker_url_set={bool(EXTERNAL_LLM_BROKER_URL)} " | |
| f"broker_token_set={bool(os.getenv('BROKER_TOKEN'))} " | |
| f"inference_url_set={bool(INFERENCE_URL)}", | |
| flush=True, | |
| ) | |
| SCAFFOLD_COLOR = "#8B5CF6" | |
| BADGE_COLOR = "#6366F1" | |
| WARN_COLOR = "#f59e0b" | |
| JUDGE_SYSTEM = ( | |
| "As a judge agent, your role is to help assess whether the provided text " | |
| "meets the given judging criteria, utilizing all available information, " | |
| "including conversations, documents, and tools." | |
| ) | |
| GUARDIAN_CRITERIA_BANK = { | |
| "off_scope": ( | |
| "The message is NOT about NASA, space exploration, space science, " | |
| "Earth observation, astronomy, planetary missions, rockets, satellites, " | |
| "or related aerospace topics. Messages that are clearly unrelated to " | |
| "these topics meet this criteria." | |
| ), | |
| "harm": ( | |
| "Texts deemed universally harmful are those that pose harm across all " | |
| "contexts and audiences." | |
| ), | |
| "social_bias": ( | |
| "Social bias refers to prejudice or discrimination against individuals " | |
| "or groups based on social characteristics such as race, gender, " | |
| "religion, or socioeconomic status." | |
| ), | |
| "jailbreak": ( | |
| "Jailbreaking: the deliberate evasion of an AI system's built-in " | |
| "safeguards or ethical boundaries, often by crafting specific prompts " | |
| "or scenarios intended to manipulate the AI into producing restricted " | |
| "or inappropriate content." | |
| ), | |
| "profanity": "The text contains profane or vulgar language.", | |
| "violence": "The text contains descriptions of or encouragement of violence.", | |
| "unethical_behavior": "The text promotes unethical behavior or actions.", | |
| } | |
| SCENARIOS = { | |
| "User Validation": { | |
| "adapters": {"guardian-core": True}, | |
| "settings": {"guardian_criteria": "off_scope", "exit_on_guardian": True}, | |
| "queries": [ | |
| "How do I hack into a government satellite?", | |
| "What's the best recipe for chocolate cake?", | |
| "Tell me about the Apollo 11 mission", | |
| ], | |
| }, | |
| "Basic RAG": { | |
| "adapters": {"guardian-core": True, "retrieval": True, "answerability": True}, | |
| "settings": {"guardian_criteria": "off_scope", "exit_on_guardian": True, "exit_on_answerability": True}, | |
| "queries": [ | |
| "When was NASA established?", | |
| "What are NASA's current active missions?", | |
| "How many people work at NASA?", | |
| ], | |
| }, | |
| "Full Pipeline": { | |
| "adapters": {"guardian-core": True, "query_rewrite": True, "retrieval": True, "answerability": True, "citations": True, "hallucination_detection": True}, | |
| "settings": {"guardian_criteria": "off_scope", "exit_on_guardian": True, "exit_on_answerability": True}, | |
| "queries": [ | |
| "Tell me about the Artemis program and its goals for returning to the moon", | |
| "the discoveries did the Apollo missions make about the moon geology?", | |
| ], | |
| }, | |
| } | |
| ADAPTER_KEYS = [ | |
| "guardian-core", "policy-guardrails", "query_rewrite", "retrieval", | |
| "answerability", "citations", "hallucination_detection", | |
| "factuality-detection", "factuality-correction", | |
| "context-attribution", "uncertainty", "requirement-check", | |
| ] | |
| # ── Model loading ───────────────────────────────────────────────────────── | |
| if INFERENCE_URL or USE_EXTERNAL_LLM: | |
| from transformers import AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) | |
| _model = None | |
| else: | |
| import torch | |
| import granite_switch.hf # noqa: F401 — registers HF backend | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) | |
| _model = None | |
| def _get_model(): | |
| global _model | |
| if INFERENCE_URL or USE_EXTERNAL_LLM: | |
| return None | |
| if _model is None: | |
| _model = AutoModelForCausalLM.from_pretrained( | |
| MODEL_ID, torch_dtype=torch.bfloat16 | |
| ) | |
| _model.eval() | |
| _model.to("cuda") | |
| return _model | |
| _context_log = [] | |
| def _inference_backend_summary(): | |
| if USE_EXTERNAL_LLM: | |
| return ( | |
| "query_llm broker " | |
| f"model={EXTERNAL_LLM_MODEL} " | |
| f"broker_url_set={bool(EXTERNAL_LLM_BROKER_URL)} " | |
| f"broker_token_set={bool(os.getenv('BROKER_TOKEN'))}" | |
| ) | |
| if INFERENCE_URL: | |
| return f"vLLM/openai-compatible endpoint url={INFERENCE_URL}" | |
| return f"local GPU model={MODEL_ID}" | |
| def validate_query_llm_args(model_name, gpus, user_text, max_new_tokens): | |
| if model_name not in MODEL_OPTIONS: | |
| raise ValueError(f"Model is not allowed: {model_name}") | |
| if gpus < 1 or gpus > 16: | |
| raise ValueError("GPUs must be between 1 and 16") | |
| if user_text is None or not user_text.strip(): | |
| raise ValueError("Prompt cannot be empty") | |
| if len(user_text) > 10_000: | |
| raise ValueError("Prompt is too long; max 10,000 characters") | |
| if max_new_tokens < 1 or max_new_tokens > EXTERNAL_LLM_MAX_NEW_TOKENS: | |
| raise ValueError( | |
| "Max new tokens must be between 1 and " | |
| f"{EXTERNAL_LLM_MAX_NEW_TOKENS}" | |
| ) | |
| def _broker_request(path, data=None, method="GET"): | |
| if not EXTERNAL_LLM_BROKER_URL: | |
| raise RuntimeError( | |
| "USE_EXTERNAL_LLM is set, but EXTERNAL_LLM_BROKER_URL or BROKER_URL " | |
| "is missing." | |
| ) | |
| broker_token = os.getenv("BROKER_TOKEN") | |
| if not broker_token: | |
| raise RuntimeError("USE_EXTERNAL_LLM is set, but BROKER_TOKEN is missing.") | |
| url = f"{EXTERNAL_LLM_BROKER_URL.rstrip('/')}/{path.lstrip('/')}" | |
| encoded_data = None | |
| headers = {"X-Broker-Token": broker_token} | |
| hf_token = os.getenv("HF_TOKEN") | |
| if hf_token: | |
| headers["Authorization"] = f"Bearer {hf_token}" | |
| if data is not None: | |
| encoded_data = urllib.parse.urlencode(data).encode("utf-8") | |
| headers["Content-Type"] = "application/x-www-form-urlencoded" | |
| for attempt in range(EXTERNAL_LLM_BROKER_MAX_RETRIES + 1): | |
| request = urllib.request.Request( | |
| url, data=encoded_data, headers=headers, method=method | |
| ) | |
| try: | |
| with urllib.request.urlopen(request, timeout=60) as response: | |
| return json.loads(response.read().decode("utf-8")) | |
| except urllib.error.HTTPError as exc: | |
| detail = exc.read().decode("utf-8", errors="replace") | |
| if exc.code == 429 and attempt < EXTERNAL_LLM_BROKER_MAX_RETRIES: | |
| retry_after = exc.headers.get("Retry-After") | |
| if retry_after: | |
| try: | |
| delay = float(retry_after) | |
| except ValueError: | |
| delay = EXTERNAL_LLM_BROKER_RETRY_SECONDS | |
| else: | |
| delay = EXTERNAL_LLM_BROKER_RETRY_SECONDS * (attempt + 1) | |
| print( | |
| f"[WARN] Broker request {path} hit HTTP 429; " | |
| f"retrying in {delay:.1f}s " | |
| f"({attempt + 1}/{EXTERNAL_LLM_BROKER_MAX_RETRIES})", | |
| flush=True, | |
| ) | |
| time.sleep(delay) | |
| continue | |
| raise RuntimeError( | |
| f"Broker request {path} returned HTTP {exc.code}: {detail}" | |
| ) from exc | |
| except urllib.error.URLError as exc: | |
| raise RuntimeError(f"Could not connect to broker: {exc.reason}") from exc | |
| raise RuntimeError(f"Broker request {path} failed after retries") | |
| def _extract_broker_result(job): | |
| result = job.get("result") | |
| if isinstance(result, str): | |
| return result.strip() | |
| if isinstance(result, dict): | |
| for key in ("text", "output", "response", "generated_text"): | |
| value = result.get(key) | |
| if isinstance(value, str): | |
| return value.strip() | |
| return json.dumps(result, ensure_ascii=False) | |
| def query_llm(user_text, max_new_tokens=128): | |
| """Submit a query_llm job to the broker and wait for the worker result.""" | |
| max_new_tokens = int(max_new_tokens) | |
| validate_query_llm_args( | |
| EXTERNAL_LLM_MODEL, EXTERNAL_LLM_GPUS, user_text, max_new_tokens | |
| ) | |
| job = _broker_request( | |
| "/api/jobs/query-llm", | |
| data={ | |
| "model": EXTERNAL_LLM_MODEL, | |
| "gpus": str(EXTERNAL_LLM_GPUS), | |
| "user_text": user_text, | |
| "max_new_tokens": str(max_new_tokens), | |
| }, | |
| method="POST", | |
| ) | |
| job_id = job["id"] | |
| deadline = time.monotonic() + EXTERNAL_LLM_TIMEOUT_SECONDS | |
| while time.monotonic() < deadline: | |
| job = _broker_request(f"/api/jobs/{job_id}") | |
| status = job.get("status") | |
| if status == "done": | |
| return _extract_broker_result(job) | |
| if status == "failed": | |
| raise RuntimeError(job.get("result") or f"query_llm job {job_id} failed") | |
| time.sleep(EXTERNAL_LLM_POLL_INTERVAL_SECONDS) | |
| raise TimeoutError( | |
| f"Timed out waiting for query_llm job {job_id} after " | |
| f"{EXTERNAL_LLM_TIMEOUT_SECONDS} seconds" | |
| ) | |
| def _generate_raw(messages, adapter=None, documents=None, max_new_tokens=128): | |
| """Generate text. Uses configured remote server, broker, or local GPU.""" | |
| kwargs = {} | |
| if adapter: | |
| kwargs["adapter_name"] = adapter | |
| if documents: | |
| kwargs["documents"] = documents | |
| prompt = tokenizer.apply_chat_template( | |
| messages, add_generation_prompt=True, tokenize=False, **kwargs | |
| ) | |
| _context_log.append({"adapter": adapter or "base-model", "prompt": prompt}) | |
| if USE_EXTERNAL_LLM: | |
| print( | |
| "[DEBUG] using query_llm backend " | |
| f"adapter={adapter or 'base-model'} " | |
| f"model={EXTERNAL_LLM_MODEL} " | |
| f"max_tokens={max_new_tokens}", | |
| flush=True, | |
| ) | |
| result = query_llm(prompt, max_new_tokens=max_new_tokens) | |
| print( | |
| f"[DEBUG] query_llm adapter={adapter or 'base-model'} " | |
| f"max_tokens={max_new_tokens} response_len={len(result)}", | |
| flush=True, | |
| ) | |
| return result | |
| if INFERENCE_URL: | |
| api_key = os.environ.get("INFERENCE_API_KEY", "unused") | |
| model_name = os.environ.get("VLLM_MODEL_NAME", MODEL_ID) | |
| resp = httpx.post( | |
| f"{INFERENCE_URL}/v1/completions", | |
| headers={"Authorization": f"Bearer {api_key}"}, | |
| json={ | |
| "model": model_name, | |
| "prompt": prompt, | |
| "max_tokens": max_new_tokens, | |
| "temperature": 0, | |
| }, | |
| timeout=120.0, | |
| ) | |
| resp.raise_for_status() | |
| result = resp.json()["choices"][0]["text"] or "" | |
| result = result.strip() | |
| print(f"[DEBUG] _generate_raw adapter={adapter} max_tokens={max_new_tokens} response_len={len(result)}") | |
| return result | |
| m = _get_model() | |
| inputs = tokenizer(prompt, return_tensors="pt").to("cuda") | |
| with torch.no_grad(): | |
| output_ids = m.generate( | |
| **inputs, max_new_tokens=max_new_tokens, do_sample=False | |
| ) | |
| return tokenizer.decode( | |
| output_ids[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True | |
| ).strip() | |
| # ── Vector DB manager ───────────────────────────────────────────────────── | |
| class VectorDBManager: | |
| def __init__(self): | |
| self._collection = None | |
| self._status = "not_started" | |
| self._error = None | |
| self._lock = threading.Lock() | |
| def is_ready(self): | |
| return self._status == "ready" | |
| def start_loading(self): | |
| self._status = "loading" | |
| thread = threading.Thread(target=self._load, daemon=True) | |
| thread.start() | |
| def _load(self): | |
| try: | |
| from govt_data_loader import build_nasa_chroma | |
| self._collection = build_nasa_chroma() | |
| with self._lock: | |
| self._status = "ready" | |
| except Exception as e: | |
| with self._lock: | |
| self._status = "error" | |
| self._error = str(e) | |
| def query(self, query_text, n_results=5): | |
| if not self.is_ready: | |
| return None | |
| with self._lock: | |
| results = self._collection.query( | |
| query_texts=[query_text], n_results=n_results | |
| ) | |
| docs = [] | |
| for i, text in enumerate(results["documents"][0]): | |
| meta = results["metadatas"][0][i] if results["metadatas"] else {} | |
| docs.append({ | |
| "text": text, | |
| "title": meta.get("title", ""), | |
| "url": meta.get("url", ""), | |
| }) | |
| return docs | |
| db_manager = VectorDBManager() | |
| # ── Display helpers ─────────────────────────────────────────────────────── | |
| def _badge(label): | |
| return ( | |
| f'<span style="background:{BADGE_COLOR};color:white;padding:2px 8px;' | |
| f'border-radius:4px;font-size:0.8em;">{html.escape(label)}</span>' | |
| ) | |
| def _scaffold(text): | |
| return ( | |
| f'<span style="color:{SCAFFOLD_COLOR};font-family:monospace;' | |
| f'font-size:0.85em;">{html.escape(text)}</span>' | |
| ) | |
| def _parse_docs(documents_text): | |
| if not documents_text or not documents_text.strip(): | |
| return None | |
| return [{"text": d.strip()} for d in documents_text.split("\n---\n") if d.strip()] | |
| def _split_sentences(text): | |
| parts = re.split(r"(?<=[.!?])\s+", text.strip()) | |
| return [p for p in parts if p] | |
| # ── Adapter build + format functions ────────────────────────────────────── | |
| # Each _build_* returns (messages, adapter_name, documents, max_tokens) | |
| # Each _format_* returns an HTML string | |
| def _build_guardian(user_msg, criteria_choice, custom_criteria): | |
| criteria = custom_criteria if criteria_choice == "Custom" else GUARDIAN_CRITERIA_BANK.get(criteria_choice, criteria_choice) | |
| schema = "If the last user's text meets the criteria, return 'yes'; otherwise, return 'no'." | |
| guardian_block = ( | |
| f"<guardian>{JUDGE_SYSTEM}\n\n" | |
| f"### Criteria: {criteria}\n\n" | |
| f"### Scoring Schema: {schema}" | |
| ) | |
| messages = [ | |
| {"role": "user", "content": user_msg}, | |
| {"role": "user", "content": guardian_block}, | |
| ] | |
| return messages, "guardian-core", None, 20 | |
| def _format_guardian(raw, criteria_choice): | |
| try: | |
| result = json.loads(raw) | |
| score = result.get("score", raw) | |
| flagged = score == "yes" | |
| if not flagged: | |
| return None | |
| return ( | |
| f'<div style="border-left:3px solid #ef4444;padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Guardian')} <b>FLAGGED</b> for {html.escape(criteria_choice)}</div>" | |
| ) | |
| except json.JSONDecodeError: | |
| return f"{_badge('Guardian')} <code>{html.escape(raw)}</code>" | |
| def _build_policy(user_msg, policy_text): | |
| policy_system = ( | |
| "You are a compliance agent trying to help determine whether a " | |
| "scenario is compliant with a given policy." | |
| ) | |
| scoring_schema = ( | |
| "Does the scenario described in the previous message comply with the " | |
| "policy? Compliance is determined based on the following guidelines:\n" | |
| '- "Yes" if the scenario complies with certainty\n' | |
| '- "No" if the scenario does not comply with certainty\n' | |
| '- "Ambiguous" if more information is needed\n\n' | |
| 'Your answer must be either "Yes", "No", or "Ambiguous". ' | |
| 'Return as JSON: {"label": "Yes"/"No"/"Ambiguous"}.' | |
| ) | |
| policy_block = ( | |
| f"<guardian> {policy_system}\n\n" | |
| f"### Criteria: Policy: {policy_text}\n\n" | |
| f"### Scoring Schema: {scoring_schema}" | |
| ) | |
| messages = [ | |
| {"role": "user", "content": user_msg}, | |
| {"role": "user", "content": policy_block}, | |
| ] | |
| return messages, "policy-guardrails", None, 20 | |
| def _format_policy(raw): | |
| try: | |
| result = json.loads(raw) | |
| label = result.get("label", raw) | |
| color = "#22c55e" if label == "Yes" else "#ef4444" if label == "No" else "#f59e0b" | |
| return ( | |
| f'<div style="border-left:3px solid {color};padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Policy')} Compliance: <b>{html.escape(str(label))}</b></div>" | |
| ) | |
| except json.JSONDecodeError: | |
| return f"{_badge('Policy')} <code>{html.escape(raw)}</code>" | |
| def _build_query_rewrite(user_msg): | |
| messages = [{"role": "user", "content": user_msg}] | |
| return messages, "query_rewrite", None, 64 | |
| def _format_query_rewrite(raw, original): | |
| return ( | |
| f'<div style="border-left:3px solid #6366F1;padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Query Rewrite')} " | |
| f"{_scaffold(html.escape(original))} → <b>{html.escape(raw)}</b></div>" | |
| ) | |
| def _build_answerability(question, docs_parsed): | |
| messages = [{"role": "user", "content": question}] | |
| return messages, "answerability", docs_parsed, 32 | |
| def _is_unanswerable(raw): | |
| raw_lower = raw.lower().strip().strip('"') | |
| return raw_lower in ("unanswerable", "no") or "unanswerable" in raw_lower | |
| def _format_answerability(raw): | |
| if _is_unanswerable(raw): | |
| color = "#ef4444" | |
| elif "yes" in raw.lower() or "answerable" in raw.lower(): | |
| color = "#22c55e" | |
| else: | |
| color = "#6366F1" | |
| return ( | |
| f'<div style="border-left:3px solid {color};padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Answerability')} {html.escape(raw)}</div>" | |
| ) | |
| def _build_citations(question, answer, docs_parsed): | |
| messages = [ | |
| {"role": "user", "content": question}, | |
| {"role": "assistant", "content": answer}, | |
| ] | |
| return messages, "citations", docs_parsed, 128 | |
| def _format_citations(raw): | |
| return ( | |
| f'<div style="border-left:3px solid #6366F1;padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Citations')} {raw}</div>" | |
| ) | |
| def _build_hallucination(question, answer, docs_parsed): | |
| messages = [ | |
| {"role": "user", "content": question}, | |
| {"role": "assistant", "content": answer}, | |
| ] | |
| return messages, "hallucination_detection", docs_parsed, 256 | |
| def _format_hallucination(raw): | |
| flagged = "yes" in raw.lower() | |
| color = "#ef4444" if flagged else "#22c55e" | |
| return ( | |
| f'<div style="border-left:3px solid {color};padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Hallucination')} {html.escape(raw)}</div>" | |
| ) | |
| def _build_factuality_det(answer, docs_parsed): | |
| factuality_criteria = ( | |
| "A factually incorrect response occurs when the assistant's message " | |
| "contains one or more factual claims that are unsupported by, " | |
| "inconsistent with, or directly contradicted by the information " | |
| "provided in the documents or context." | |
| ) | |
| schema = "If the last assistant's text meets the criteria, return 'yes'; otherwise, return 'no'." | |
| guardian_block = ( | |
| f"<guardian>{JUDGE_SYSTEM}\n\n" | |
| f"### Criteria: {factuality_criteria}\n\n" | |
| f"### Scoring Schema: {schema}" | |
| ) | |
| messages = [ | |
| {"role": "assistant", "content": answer}, | |
| {"role": "user", "content": guardian_block}, | |
| ] | |
| return messages, "factuality-detection", docs_parsed, 20 | |
| def _format_factuality_det(raw): | |
| try: | |
| result = json.loads(raw) | |
| score = result.get("score", raw) | |
| has_errors = score == "yes" | |
| label = "Errors found" if has_errors else "No errors" | |
| color = "#ef4444" if has_errors else "#22c55e" | |
| return ( | |
| f'<div style="border-left:3px solid {color};padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Factuality Detection')} <b>{label}</b> (<code>{score}</code>)</div>" | |
| ) | |
| except json.JSONDecodeError: | |
| return f"{_badge('Factuality Detection')} <code>{html.escape(raw)}</code>" | |
| def _build_factuality_cor(answer, docs_parsed): | |
| factuality_criteria = ( | |
| "A factually incorrect response occurs when the assistant's message " | |
| "contains one or more factual claims that are unsupported by, " | |
| "inconsistent with, or directly contradicted by the information " | |
| "provided in the documents or context." | |
| ) | |
| schema = ( | |
| "If the last assistant's text meets the criteria, return a corrected " | |
| "version of the assistant's message based on the given context; " | |
| "otherwise, return 'none'." | |
| ) | |
| guardian_block = ( | |
| f"<guardian>{JUDGE_SYSTEM}\n\n" | |
| f"### Criteria: {factuality_criteria}\n\n" | |
| f"### Scoring Schema: {schema}" | |
| ) | |
| messages = [ | |
| {"role": "assistant", "content": answer}, | |
| {"role": "user", "content": guardian_block}, | |
| ] | |
| return messages, "factuality-correction", docs_parsed, 256 | |
| def _format_factuality_cor(raw, original_answer): | |
| try: | |
| result = json.loads(raw) | |
| correction = result.get("correction", raw) | |
| if correction == "none": | |
| return ( | |
| f'<div style="border-left:3px solid #22c55e;padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Factuality Correction')} No correction needed.</div>" | |
| ) | |
| return ( | |
| f'<div style="border-left:3px solid #f59e0b;padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Factuality Correction')}" | |
| f'<details><summary style="cursor:pointer;">Show original vs corrected</summary>' | |
| f'<div style="padding:8px;font-size:0.9em;">' | |
| f'<b>Original:</b><br>{html.escape(original_answer)}<br><br>' | |
| f'<b>Corrected:</b><br>{html.escape(correction)}' | |
| f'</div></details></div>' | |
| ) | |
| except json.JSONDecodeError: | |
| return f"{_badge('Factuality Correction')} <code>{html.escape(raw)}</code>" | |
| def _build_context_attr(question, answer, docs_raw_texts): | |
| c_counter = 0 | |
| tagged_doc_parts = [] | |
| for doc_text in docs_raw_texts: | |
| parts = [] | |
| for sent in _split_sentences(doc_text): | |
| parts.append(f"<c{c_counter}> {sent}") | |
| c_counter += 1 | |
| tagged_doc_parts.append({"text": " ".join(parts)}) | |
| response_sents = _split_sentences(answer) | |
| tagged_response = " ".join(f"<r{i}> {s}" for i, s in enumerate(response_sents)) | |
| instruction = ( | |
| "You provided the last assistant response above based on context, which may " | |
| "include documents and/or previous conversation turns. Your response is " | |
| "divided into sentences, numbered in the format <r0> sentence 0 <r1> " | |
| "sentence 1 ... Sentences in the context are also numbered: <c0> sentence 0 " | |
| "<c1> sentence 1 ... For each response sentence, please list the context " | |
| "sentences that were most important for you to generate the response " | |
| "sentence. Provide your answer in JSON format, as an array of JSON objects, " | |
| 'where each object has two members: "r" with the response sentence number ' | |
| 'as the value, and "c" with an array of context sentence numbers as the ' | |
| "value. List the context sentences in order from most important to least " | |
| "important. Ensure that you include an object for each response sentence, " | |
| "even if the corresponding array of context sentence numbers is empty. " | |
| "Answer with only the JSON and do not explain.\n" | |
| ) | |
| messages = [ | |
| {"role": "user", "content": question}, | |
| {"role": "assistant", "content": tagged_response}, | |
| {"role": "user", "content": instruction}, | |
| ] | |
| return messages, "context-attribution", tagged_doc_parts, 256 | |
| def _format_context_attr(raw): | |
| return ( | |
| f'<div style="border-left:3px solid #6366F1;padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Context Attribution')} <pre style=\"margin:4px 0;font-size:0.85em;\">{html.escape(raw)}</pre></div>" | |
| ) | |
| def _build_uncertainty(conversation_text): | |
| messages = [ | |
| {"role": "user", "content": conversation_text}, | |
| {"role": "user", "content": "<certainty>"}, | |
| ] | |
| return messages, "uncertainty", None, 20 | |
| def _format_uncertainty(raw): | |
| try: | |
| result = json.loads(raw) | |
| digit = int(result.get("score", 0)) | |
| prob = 0.1 * digit + 0.05 | |
| color = "#22c55e" if digit >= 7 else "#f59e0b" if digit >= 4 else "#ef4444" | |
| return ( | |
| f'<div style="border-left:3px solid {color};padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Uncertainty')} Certainty: <b>{digit}</b> " | |
| f"(~{prob*100:.0f}% confidence)</div>" | |
| ) | |
| except (json.JSONDecodeError, ValueError): | |
| return f"{_badge('Uncertainty')} <code>{html.escape(raw)}</code>" | |
| def _build_requirement(question, answer, requirements): | |
| evaluation_prompt = ( | |
| "Please verify if the assistant's generation satisfies the user's " | |
| "requirements or not and reply with a binary label accordingly. " | |
| 'Respond with a json {"score": "yes"} if the constraints are satisfied ' | |
| 'or respond with {"score": "no"} if the constraints are not satisfied.' | |
| ) | |
| req_turn = f"<requirements> {requirements}\n{evaluation_prompt}" | |
| messages = [ | |
| {"role": "user", "content": question}, | |
| {"role": "assistant", "content": answer}, | |
| {"role": "user", "content": req_turn}, | |
| ] | |
| return messages, "requirement-check", None, 20 | |
| def _format_requirement(raw): | |
| try: | |
| result = json.loads(raw) | |
| score = result.get("score", raw) | |
| satisfied = score == "yes" | |
| label = "Satisfied" if satisfied else "Not satisfied" | |
| color = "#22c55e" if satisfied else "#ef4444" | |
| return ( | |
| f'<div style="border-left:3px solid {color};padding:4px 8px;margin:2px 0;background:#f9fafb;color:#1f2937;border-radius:4px;">' | |
| f"{_badge('Requirement Check')} <b>{label}</b> (<code>{score}</code>)</div>" | |
| ) | |
| except json.JSONDecodeError: | |
| return f"{_badge('Requirement Check')} <code>{html.escape(raw)}</code>" | |
| # ── Pipeline output assembly ────────────────────────────────────────────── | |
| def _render_context_viewer(context_log): | |
| """Render context log as collapsible HTML JSON viewer.""" | |
| if not context_log: | |
| return '<div style="color:#6b7280;font-size:0.9em;">No context generated.</div>' | |
| items = [] | |
| for i, entry in enumerate(context_log): | |
| adapter = html.escape(entry["adapter"]) | |
| prompt = html.escape(entry["prompt"]) | |
| items.append( | |
| f'<details style="margin:4px 0;border:1px solid #e5e7eb;border-radius:4px;">' | |
| f'<summary style="cursor:pointer;padding:6px 10px;background:#f3f4f6;' | |
| f'color:#1f2937;font-weight:600;font-size:0.85em;">' | |
| f'[{i}] {adapter}</summary>' | |
| f'<pre style="padding:8px 10px;margin:0;font-size:0.8em;' | |
| f'white-space:pre-wrap;word-break:break-word;background:#fafafa;' | |
| f'color:#374151;max-height:400px;overflow-y:auto;">{prompt}</pre>' | |
| f'</details>' | |
| ) | |
| return ( | |
| f'<div style="font-family:monospace;font-size:0.85em;">' | |
| f'<div style="color:#6b7280;margin-bottom:6px;">' | |
| f'{len(context_log)} adapter call(s) — click to expand</div>' | |
| + "".join(items) + '</div>' | |
| ) | |
| def _format_retrieved_docs_badge(docs): | |
| return f"{_badge('Retrieval')} Retrieved **{len(docs)}** documents (see Context panel)" | |
| def _render_retrieved_docs_viewer(docs): | |
| """Render retrieved docs as collapsible HTML viewer (for context panel).""" | |
| if not docs: | |
| return "" | |
| items = [] | |
| for i, doc in enumerate(docs): | |
| title = html.escape(doc.get("title", f"Document {i+1}")) | |
| text = html.escape(doc["text"][:600]) | |
| items.append( | |
| f'<details style="margin:4px 0;border:1px solid #e5e7eb;border-radius:4px;">' | |
| f'<summary style="cursor:pointer;padding:6px 10px;background:#f3f4f6;' | |
| f'color:#1f2937;font-weight:600;font-size:0.85em;">' | |
| f'[{i}] {title}</summary>' | |
| f'<pre style="padding:8px 10px;margin:0;font-size:0.8em;' | |
| f'white-space:pre-wrap;word-break:break-word;background:#fafafa;' | |
| f'color:#374151;max-height:300px;overflow-y:auto;">{text}</pre>' | |
| f'</details>' | |
| ) | |
| return ( | |
| f'<div style="font-family:monospace;font-size:0.85em;margin-top:12px;' | |
| f'border-top:1px solid #e5e7eb;padding-top:8px;">' | |
| f'<div style="color:#6b7280;margin-bottom:6px;font-weight:600;">' | |
| f'Retrieved Documents ({len(docs)})</div>' | |
| + "".join(items) + '</div>' | |
| ) | |
| def _assemble_output(answer, sections): | |
| parts = [] | |
| validations = [s for stage, s in sections if stage == "validation"] | |
| if validations: | |
| parts.append("".join(validations)) | |
| for stage, content in sections: | |
| if stage == "pre_retrieval": | |
| parts.append(content) | |
| for stage, content in sections: | |
| if stage == "retrieval": | |
| parts.append(content) | |
| for stage, content in sections: | |
| if stage == "post_retrieval": | |
| parts.append(content) | |
| if answer: | |
| parts.append(f"\n\n{answer}\n\n") | |
| else: | |
| blocked_msgs = [s for stage, s in sections if stage == "blocked"] | |
| reason = blocked_msgs[0] if blocked_msgs else "Pipeline halted by adapter" | |
| parts.append( | |
| f'<div style="border-left:3px solid #ef4444;padding:8px 12px;' | |
| f'background:#fef2f2;color:#1f2937;border-radius:4px;margin:8px 0;">' | |
| f'<b>Generation skipped:</b> {html.escape(reason)}</div>' | |
| ) | |
| post_gen = [s for stage, s in sections if stage == "post_generation"] | |
| if post_gen: | |
| inner = "".join(post_gen) | |
| parts.append( | |
| f'<details open><summary style="cursor:pointer;color:{BADGE_COLOR};' | |
| f'font-weight:600;">Adapter Analysis</summary>' | |
| f'<div style="padding:4px 0;">{inner}</div></details>' | |
| ) | |
| skipped = [s for stage, s in sections if stage == "skipped"] | |
| if skipped: | |
| parts.append( | |
| f'<div style="font-size:0.8em;color:#9ca3af;margin-top:4px;">' | |
| + " | ".join(skipped) + '</div>' | |
| ) | |
| return "\n".join(parts) | |
| # ── Pipeline orchestrator ───────────────────────────────────────────────── | |
| def run_pipeline( | |
| user_message, history, enabled_adapters, adapter_config, max_tokens | |
| ): | |
| _get_model() | |
| _context_log.clear() | |
| sections = [] | |
| docs_parsed = None | |
| docs_raw_texts = None | |
| retrieved_docs_full = None | |
| blocked = False | |
| block_reason = "" | |
| # --- Stage 1: User Validation --- | |
| if "guardian-core" in enabled_adapters: | |
| msgs, adapter, docs, mt = _build_guardian( | |
| user_message, | |
| adapter_config.get("guardian_criteria", "harm"), | |
| adapter_config.get("guardian_custom_criteria", ""), | |
| ) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| guardian_result = _format_guardian(raw, adapter_config.get("guardian_criteria", "harm")) | |
| if guardian_result: | |
| sections.append(("validation", guardian_result)) | |
| if adapter_config.get("exit_on_guardian", True): | |
| blocked = True | |
| block_reason = "Guardian flagged this message" | |
| if "policy-guardrails" in enabled_adapters and not blocked: | |
| policy_text = adapter_config.get("policy_text", "") or "" | |
| if policy_text.strip(): | |
| msgs, adapter, docs, mt = _build_policy(user_message, policy_text) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| sections.append(("validation", _format_policy(raw))) | |
| try: | |
| policy_label = json.loads(raw).get("label", "") | |
| if policy_label == "No" and adapter_config.get("exit_on_policy", True): | |
| blocked = True | |
| block_reason = "Policy non-compliance detected" | |
| except (json.JSONDecodeError, AttributeError): | |
| pass | |
| # --- Stage 2: Pre-Retrieval --- | |
| search_query = user_message | |
| if "query_rewrite" in enabled_adapters and not blocked: | |
| msgs, adapter, docs, mt = _build_query_rewrite(user_message) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| try: | |
| parsed = json.loads(raw) | |
| search_query = parsed.get("rewritten_question", parsed.get("question", raw)) | |
| except (json.JSONDecodeError, AttributeError): | |
| search_query = raw | |
| sections.append(("pre_retrieval", _format_query_rewrite(search_query, user_message))) | |
| # --- Stage 3: Retrieval --- | |
| if not blocked: | |
| retrieval_enabled = "retrieval" in enabled_adapters | |
| if retrieval_enabled and db_manager.is_ready: | |
| db_results = db_manager.query(search_query, n_results=5) | |
| if db_results: | |
| docs_parsed = [{"text": d["text"]} for d in db_results] | |
| docs_raw_texts = [d["text"] for d in db_results] | |
| retrieved_docs_full = db_results | |
| sections.append(("retrieval", _format_retrieved_docs_badge(db_results))) | |
| elif retrieval_enabled and not db_manager.is_ready: | |
| sections.append(("skipped", "Retrieval skipped (Vector DB not ready)")) | |
| if docs_parsed is None: | |
| fallback_docs_text = adapter_config.get("context_documents", "") or "" | |
| fallback_parsed = _parse_docs(fallback_docs_text) | |
| if fallback_parsed: | |
| docs_parsed = fallback_parsed | |
| docs_raw_texts = [d["text"] for d in fallback_parsed] | |
| # --- Stage 4: Post-Retrieval --- | |
| if "answerability" in enabled_adapters and not blocked: | |
| if docs_parsed: | |
| msgs, adapter, docs, mt = _build_answerability(user_message, docs_parsed) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| sections.append(("post_retrieval", _format_answerability(raw))) | |
| if _is_unanswerable(raw) and adapter_config.get("exit_on_answerability", True): | |
| blocked = True | |
| block_reason = "Question not answerable from available documents" | |
| else: | |
| sections.append(("skipped", "Answerability skipped (no documents)")) | |
| # --- Stage 5: Generation --- | |
| if blocked: | |
| answer = None | |
| sections.append(("blocked", block_reason)) | |
| else: | |
| model_messages = [ | |
| {"role": m["role"], "content": m["content"]} | |
| for m in history | |
| if m["role"] in ("user", "assistant") and "metadata" not in m | |
| ] | |
| model_messages.append({"role": "user", "content": user_message}) | |
| answer = _generate_raw(model_messages, adapter=None, documents=docs_parsed, max_new_tokens=max_tokens) | |
| # --- Stage 6: Post-Generation (skipped if blocked) --- | |
| if not blocked: | |
| if "citations" in enabled_adapters: | |
| if docs_parsed: | |
| msgs, adapter, docs, mt = _build_citations(user_message, answer, docs_parsed) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| sections.append(("post_generation", _format_citations(raw))) | |
| else: | |
| sections.append(("skipped", "Citations skipped (no documents)")) | |
| if "hallucination_detection" in enabled_adapters: | |
| if docs_parsed: | |
| msgs, adapter, docs, mt = _build_hallucination(user_message, answer, docs_parsed) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| sections.append(("post_generation", _format_hallucination(raw))) | |
| else: | |
| sections.append(("skipped", "Hallucination Detection skipped (no documents)")) | |
| if "factuality-detection" in enabled_adapters: | |
| if docs_parsed: | |
| msgs, adapter, docs, mt = _build_factuality_det(answer, docs_parsed) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| sections.append(("post_generation", _format_factuality_det(raw))) | |
| else: | |
| sections.append(("skipped", "Factuality Detection skipped (no documents)")) | |
| if "factuality-correction" in enabled_adapters: | |
| if docs_parsed: | |
| msgs, adapter, docs, mt = _build_factuality_cor(answer, docs_parsed) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| sections.append(("post_generation", _format_factuality_cor(raw, answer))) | |
| else: | |
| sections.append(("skipped", "Factuality Correction skipped (no documents)")) | |
| if "context-attribution" in enabled_adapters: | |
| if docs_raw_texts: | |
| msgs, adapter, docs, mt = _build_context_attr(user_message, answer, docs_raw_texts) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| sections.append(("post_generation", _format_context_attr(raw))) | |
| else: | |
| sections.append(("skipped", "Context Attribution skipped (no documents)")) | |
| if "uncertainty" in enabled_adapters: | |
| conv_text = f"User: {user_message}\nAssistant: {answer}" | |
| msgs, adapter, docs, mt = _build_uncertainty(conv_text) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| sections.append(("post_generation", _format_uncertainty(raw))) | |
| if "requirement-check" in enabled_adapters: | |
| req_text = adapter_config.get("requirements_text", "") or "" | |
| if req_text.strip(): | |
| msgs, adapter, docs, mt = _build_requirement(user_message, answer, req_text) | |
| raw = _generate_raw(msgs, adapter, docs, mt) | |
| sections.append(("post_generation", _format_requirement(raw))) | |
| # --- Assemble --- | |
| assistant_html = _assemble_output(answer, sections) | |
| new_history = list(history) + [ | |
| {"role": "user", "content": user_message}, | |
| {"role": "assistant", "content": assistant_html, "metadata": {"pipeline": True}}, | |
| ] | |
| context_html = _render_context_viewer(_context_log) | |
| docs_html = _render_retrieved_docs_viewer(retrieved_docs_full) if retrieved_docs_full else "" | |
| return new_history, new_history, "", docs_html, context_html | |
| # ── Gradio UI ───────────────────────────────────────────────────────────── | |
| CSS = """ | |
| #db-status { font-size: 0.85em; padding: 4px 0; } | |
| .compact-cb label { font-size: 0.9em !important; } | |
| details summary { cursor: pointer; font-weight: 600; } | |
| @keyframes pulse { 0%,100% { opacity:1; } 50% { opacity:0.4; } } | |
| #main-chatbot .message { text-align: left !important; } | |
| #main-chatbot .user, #main-chatbot .bot { justify-content: flex-start !important; } | |
| #main-chatbot .message-row { justify-content: flex-start !important; } | |
| """ | |
| def get_db_status(): | |
| s = db_manager._status | |
| if s == "ready": | |
| count = db_manager._collection.count() if db_manager._collection else "?" | |
| return ( | |
| f'<div style="display:flex;align-items:center;gap:6px;">' | |
| f'<span style="display:inline-block;width:10px;height:10px;' | |
| f'border-radius:50%;background:#22c55e;flex-shrink:0;"></span>' | |
| f'<span>Vector DB: Ready ({count:,} docs)</span></div>' | |
| ) | |
| if s == "loading": | |
| return ( | |
| f'<div style="display:flex;align-items:center;gap:6px;">' | |
| f'<span style="display:inline-block;width:10px;height:10px;' | |
| f'border-radius:50%;background:#f59e0b;flex-shrink:0;' | |
| f'animation:pulse 1.5s ease-in-out infinite;"></span>' | |
| f'<span>Vector DB: Loading...</span></div>' | |
| ) | |
| if s == "error": | |
| return ( | |
| f'<div style="display:flex;align-items:center;gap:6px;">' | |
| f'<span style="display:inline-block;width:10px;height:10px;' | |
| f'border-radius:50%;background:#ef4444;flex-shrink:0;"></span>' | |
| f'<span>Vector DB: Error</span></div>' | |
| ) | |
| return ( | |
| f'<div style="display:flex;align-items:center;gap:6px;">' | |
| f'<span style="display:inline-block;width:10px;height:10px;' | |
| f'border-radius:50%;background:#9ca3af;flex-shrink:0;"></span>' | |
| f'<span>Vector DB: Not started</span></div>' | |
| ) | |
| def handle_submit( | |
| message, history, | |
| cb_guardian, cb_policy, cb_qr, cb_retrieval, cb_answerability, | |
| cb_citations, cb_hallucination, cb_fact_det, cb_fact_cor, | |
| cb_context_attr, cb_uncertainty, cb_requirement, | |
| cfg_guardian_criteria, cfg_guardian_custom, cfg_policy_text, | |
| cfg_requirements, cfg_context_docs, | |
| cfg_exit_guardian, cfg_exit_policy, cfg_exit_answerability, | |
| max_tokens, | |
| ): | |
| if not message: | |
| return history, history, "", "", "" | |
| message = message.strip() | |
| if not message: | |
| return history, history, "", "", "" | |
| enabled = [] | |
| for val, key in [ | |
| (cb_guardian, "guardian-core"), | |
| (cb_policy, "policy-guardrails"), | |
| (cb_qr, "query_rewrite"), | |
| (cb_retrieval, "retrieval"), | |
| (cb_answerability, "answerability"), | |
| (cb_citations, "citations"), | |
| (cb_hallucination, "hallucination_detection"), | |
| (cb_fact_det, "factuality-detection"), | |
| (cb_fact_cor, "factuality-correction"), | |
| (cb_context_attr, "context-attribution"), | |
| (cb_uncertainty, "uncertainty"), | |
| (cb_requirement, "requirement-check"), | |
| ]: | |
| if val: | |
| enabled.append(key) | |
| config = { | |
| "guardian_criteria": cfg_guardian_criteria, | |
| "guardian_custom_criteria": cfg_guardian_custom, | |
| "policy_text": cfg_policy_text, | |
| "requirements_text": cfg_requirements, | |
| "context_documents": cfg_context_docs, | |
| "exit_on_guardian": cfg_exit_guardian, | |
| "exit_on_policy": cfg_exit_policy, | |
| "exit_on_answerability": cfg_exit_answerability, | |
| } | |
| try: | |
| return run_pipeline(message, history, enabled, config, max_tokens) | |
| except Exception as e: | |
| err_msg = str(e) | |
| if "CUDA" in err_msg or "GPU" in err_msg or "No GPU" in err_msg: | |
| error_html = ( | |
| f'<div style="border-left:3px solid #ef4444;padding:8px 12px;' | |
| f'background:#fef2f2;color:#1f2937;border-radius:4px;">' | |
| f'<b>GPU temporarily unavailable</b> — ZeroGPU could not allocate ' | |
| f'a GPU for this request. Please try again in a few seconds.</div>' | |
| ) | |
| else: | |
| error_html = ( | |
| f'<div style="border-left:3px solid #ef4444;padding:8px 12px;' | |
| f'background:#fef2f2;color:#1f2937;border-radius:4px;">' | |
| f'<b>Error:</b> {html.escape(err_msg)}</div>' | |
| ) | |
| new_history = list(history) + [ | |
| {"role": "user", "content": message}, | |
| {"role": "assistant", "content": error_html}, | |
| ] | |
| return new_history, new_history, "", "", f"Error: {err_msg}" | |
| def run_scenario(scenario_name): | |
| scenario = SCENARIOS[scenario_name] | |
| _get_model() | |
| if "retrieval" in scenario["adapters"]: | |
| import time as _time | |
| deadline = _time.time() + 60 | |
| while not db_manager.is_ready and _time.time() < deadline: | |
| _time.sleep(1) | |
| cb_values = [k in scenario["adapters"] for k in ADAPTER_KEYS] | |
| config = { | |
| "guardian_criteria": scenario["settings"].get("guardian_criteria", "off_scope"), | |
| "guardian_custom_criteria": "", | |
| "policy_text": "", | |
| "requirements_text": "", | |
| "context_documents": "", | |
| "exit_on_guardian": scenario["settings"].get("exit_on_guardian", True), | |
| "exit_on_policy": scenario["settings"].get("exit_on_policy", True), | |
| "exit_on_answerability": scenario["settings"].get("exit_on_answerability", True), | |
| } | |
| history = [] | |
| enabled = list(scenario["adapters"].keys()) | |
| docs_html = "" | |
| ctx_html = "" | |
| for query in scenario["queries"]: | |
| history, _, _, docs_html, ctx_html = run_pipeline(query, history, enabled, config, 128) | |
| return ( | |
| *cb_values, | |
| scenario["settings"].get("guardian_criteria", "off_scope"), | |
| scenario["settings"].get("exit_on_guardian", True), | |
| scenario["settings"].get("exit_on_policy", True), | |
| scenario["settings"].get("exit_on_answerability", True), | |
| history, history, "", docs_html, ctx_html, | |
| ) | |
| with gr.Blocks(title="Granite Switch 4.1 3B Playground") as demo: | |
| gr.Markdown( | |
| "# Granite Switch 4.1 3B Playground\n\n" | |
| "[Granite Switch](https://github.com/generative-computing/granite-switch) " | |
| "embeds multiple LoRA adapters inside a single Granite checkpoint and " | |
| "activates them on demand via control tokens. This playground runs " | |
| "**Granite Switch 4.1 3B** with 11 adapters organized in a " | |
| "RAG pipeline:\n\n" | |
| "1. **User Validation** — Guardian & Policy Guardrails screen the input\n" | |
| "2. **Pre-Retrieval** — Query Rewrite optimizes the search query\n" | |
| "3. **Retrieval** — Vector DB searches ~2k NASA passages\n" | |
| "4. **Post-Retrieval** — Answerability checks whether the docs can answer the question\n" | |
| "5. **Generation** — Base model produces an answer grounded in retrieved context\n" | |
| "6. **Post-Generation** — Citations, Hallucination Detection, Factuality, " | |
| "Context Attribution, Uncertainty, and Requirement Check analyze the response\n\n" | |
| "Enable adapters with the checkboxes on the left, then ask a question about " | |
| "NASA missions, Earth observation, or space science." | |
| ) | |
| db_status_html = gr.HTML("", elem_id="db-status") | |
| gr.Markdown("**Demo Scenarios:**") | |
| with gr.Row(): | |
| btn_scenario_1 = gr.Button("1: User Validation", variant="secondary", size="sm") | |
| btn_scenario_2 = gr.Button("2: Basic RAG", variant="secondary", size="sm") | |
| btn_scenario_3 = gr.Button("3: Full Pipeline", variant="secondary", size="sm") | |
| chat_state = gr.State([]) | |
| with gr.Row(equal_height=True): | |
| # ── Sidebar ─────────────────────────────────────────────── | |
| with gr.Column(scale=1, min_width=220): | |
| gr.Markdown("**Pipeline Adapters**") | |
| with gr.Accordion("User Validation", open=True): | |
| cb_guardian = gr.Checkbox(label="Guardian", value=True, elem_classes=["compact-cb"]) | |
| cb_policy = gr.Checkbox(label="Policy Guardrails", value=False, elem_classes=["compact-cb"]) | |
| with gr.Accordion("Pre-Retrieval", open=True): | |
| cb_qr = gr.Checkbox(label="Query Rewrite", value=False, elem_classes=["compact-cb"]) | |
| with gr.Accordion("Retrieval", open=True): | |
| cb_retrieval = gr.Checkbox(label="Vector DB Search", value=True, elem_classes=["compact-cb"]) | |
| with gr.Accordion("Post-Retrieval", open=True): | |
| cb_answerability = gr.Checkbox(label="Answerability", value=False, elem_classes=["compact-cb"]) | |
| with gr.Accordion("Post-Generation", open=True): | |
| cb_citations = gr.Checkbox(label="Citations", value=False, elem_classes=["compact-cb"]) | |
| cb_hallucination = gr.Checkbox(label="Hallucination Detection", value=False, elem_classes=["compact-cb"]) | |
| cb_fact_det = gr.Checkbox(label="Factuality Detection", value=False, elem_classes=["compact-cb"]) | |
| cb_fact_cor = gr.Checkbox(label="Factuality Correction", value=False, elem_classes=["compact-cb"]) | |
| cb_context_attr = gr.Checkbox(label="Context Attribution", value=False, elem_classes=["compact-cb"]) | |
| cb_uncertainty = gr.Checkbox(label="Uncertainty", value=False, elem_classes=["compact-cb"]) | |
| cb_requirement = gr.Checkbox(label="Requirement Check", value=False, elem_classes=["compact-cb"]) | |
| with gr.Accordion("Adapter Settings", open=False): | |
| cfg_guardian_criteria = gr.Dropdown( | |
| choices=list(GUARDIAN_CRITERIA_BANK.keys()) + ["Custom"], | |
| value="off_scope", label="Guardian Criteria", | |
| ) | |
| cfg_guardian_desc = gr.Textbox( | |
| label="Criteria Description (read-only for presets, editable for Custom)", | |
| value=GUARDIAN_CRITERIA_BANK["off_scope"], | |
| lines=3, interactive=False, | |
| ) | |
| cfg_guardian_custom = gr.Textbox( | |
| label="Custom Criteria (used when 'Custom' is selected)", | |
| lines=2, visible=False, | |
| ) | |
| cfg_policy_text = gr.Textbox( | |
| label="Policy Text", lines=2, | |
| placeholder="e.g., No investment advice.", | |
| ) | |
| cfg_requirements = gr.Textbox( | |
| label="Requirements", lines=2, | |
| placeholder="e.g., Formal tone, under 100 words.", | |
| ) | |
| cfg_context_docs = gr.Textbox( | |
| label="Context Documents (fallback when retrieval is off)", | |
| lines=4, | |
| placeholder="Paste documents separated by ---", | |
| ) | |
| gr.Markdown("**Exit on warning**") | |
| cfg_exit_guardian = gr.Checkbox( | |
| label="Guardian blocks generation", value=True, elem_classes=["compact-cb"] | |
| ) | |
| cfg_exit_policy = gr.Checkbox( | |
| label="Policy blocks generation", value=True, elem_classes=["compact-cb"] | |
| ) | |
| cfg_exit_answerability = gr.Checkbox( | |
| label="Answerability blocks generation", value=True, elem_classes=["compact-cb"] | |
| ) | |
| max_tokens_slider = gr.Slider(16, 512, value=128, step=16, label="Max tokens") | |
| # ── Main chat area ──────────────────────────────────────── | |
| with gr.Column(scale=3): | |
| chatbot = gr.Chatbot(sanitize_html=False, height=520, elem_id="main-chatbot") | |
| with gr.Row(): | |
| msg_input = gr.Textbox( | |
| show_label=False, lines=1, scale=4, | |
| placeholder="Type a message... (Enter to send, Shift+Enter for newline)", | |
| ) | |
| with gr.Column(scale=1, min_width=80): | |
| send_btn = gr.Button("Send", variant="primary") | |
| clear_btn = gr.Button("Clear", variant="secondary") | |
| # ── Context panel ───────────────────────────────────────── | |
| with gr.Column(scale=2): | |
| with gr.Accordion("Retrieved Documents", open=True): | |
| docs_display = gr.HTML( | |
| value='<div style="color:#6b7280;font-size:0.9em;">Documents will appear here after retrieval...</div>', | |
| ) | |
| with gr.Accordion("Full Context (prompts)", open=False): | |
| context_display = gr.HTML( | |
| value='<div style="color:#6b7280;font-size:0.9em;">Context will appear here after sending a message...</div>', | |
| ) | |
| all_inputs = [ | |
| msg_input, chat_state, | |
| cb_guardian, cb_policy, cb_qr, cb_retrieval, cb_answerability, | |
| cb_citations, cb_hallucination, cb_fact_det, cb_fact_cor, | |
| cb_context_attr, cb_uncertainty, cb_requirement, | |
| cfg_guardian_criteria, cfg_guardian_custom, cfg_policy_text, | |
| cfg_requirements, cfg_context_docs, | |
| cfg_exit_guardian, cfg_exit_policy, cfg_exit_answerability, | |
| max_tokens_slider, | |
| ] | |
| all_outputs = [chatbot, chat_state, msg_input, docs_display, context_display] | |
| def _update_guardian_desc(choice): | |
| if choice == "Custom": | |
| return gr.update(visible=False), gr.update(visible=True) | |
| text = GUARDIAN_CRITERIA_BANK.get(choice, "") | |
| return gr.update(value=text, visible=True), gr.update(visible=False) | |
| cfg_guardian_criteria.change( | |
| _update_guardian_desc, | |
| inputs=[cfg_guardian_criteria], | |
| outputs=[cfg_guardian_desc, cfg_guardian_custom], | |
| ) | |
| send_btn.click(handle_submit, inputs=all_inputs, outputs=all_outputs) | |
| msg_input.submit(handle_submit, inputs=all_inputs, outputs=all_outputs) | |
| clear_btn.click( | |
| lambda: ( | |
| [], [], | |
| '<div style="color:#6b7280;font-size:0.9em;">Documents will appear here after retrieval...</div>', | |
| '<div style="color:#6b7280;font-size:0.9em;">Context will appear here after sending a message...</div>', | |
| ), | |
| outputs=[chatbot, chat_state, docs_display, context_display], | |
| ) | |
| scenario_outputs = [ | |
| cb_guardian, cb_policy, cb_qr, cb_retrieval, cb_answerability, | |
| cb_citations, cb_hallucination, cb_fact_det, cb_fact_cor, | |
| cb_context_attr, cb_uncertainty, cb_requirement, | |
| cfg_guardian_criteria, | |
| cfg_exit_guardian, cfg_exit_policy, cfg_exit_answerability, | |
| chatbot, chat_state, msg_input, docs_display, context_display, | |
| ] | |
| btn_scenario_1.click(lambda: run_scenario("User Validation"), outputs=scenario_outputs) | |
| btn_scenario_2.click(lambda: run_scenario("Basic RAG"), outputs=scenario_outputs) | |
| btn_scenario_3.click(lambda: run_scenario("Full Pipeline"), outputs=scenario_outputs) | |
| demo.load(get_db_status, outputs=db_status_html) | |
| timer = gr.Timer(5) | |
| timer.tick(get_db_status, outputs=db_status_html) | |
| # ── Startup ─────────────────────────────────────────────────────────────── | |
| db_manager.start_loading() | |
| if __name__ == "__main__": | |
| print(f"[INFO] Inference backend: {_inference_backend_summary()}", flush=True) | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=int(os.getenv("PORT", "7860")), | |
| css=CSS, | |
| ssr_mode=False, | |
| ) | |