Spaces:
Running
Running
File size: 19,015 Bytes
10a67b7 302a4d3 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 fe8f736 10a67b7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 |
from __future__ import annotations
import os
import json
import base64
import time
import tempfile
import re
from typing import List, Dict, Any, Optional
try:
from openai import OpenAI
except Exception:
OpenAI = None
from langchain.schema import Document
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
try:
from gtts import gTTS
except Exception:
gTTS = None
from .prompts import (
SYSTEM_TEMPLATE, ANSWER_TEMPLATE_CALM, ANSWER_TEMPLATE_ADQ,
SAFETY_GUARDRAILS, RISK_FOOTER, render_emotion_guidelines,
NLU_ROUTER_PROMPT, SPECIALIST_CLASSIFIER_PROMPT,
ROUTER_PROMPT,
ANSWER_TEMPLATE_FACTUAL,
ANSWER_TEMPLATE_GENERAL_KNOWLEDGE,
ANSWER_TEMPLATE_GENERAL,
QUERY_EXPANSION_PROMPT
)
# -----------------------------
# Multimodal Processing Functions
# -----------------------------
def _openai_client() -> Optional[OpenAI]:
api_key = os.getenv("OPENAI_API_KEY", "").strip()
return OpenAI(api_key=api_key) if api_key and OpenAI else None
def describe_image(image_path: str) -> str:
client = _openai_client()
if not client:
return "(Image description failed: OpenAI API key not configured.)"
try:
extension = os.path.splitext(image_path)[1].lower()
mime_type = f"image/{'jpeg' if extension in ['.jpg', '.jpeg'] else extension.strip('.')}"
with open(image_path, "rb") as image_file:
base64_image = base64.b64encode(image_file.read()).decode('utf-8')
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "Describe this image concisely for a memory journal. Focus on people, places, and key objects. Example: 'A photo of John and Mary smiling on a bench at the park.'"},
{"type": "image_url", "image_url": {"url": f"data:{mime_type};base64,{base64_image}"}}
],
}
], max_tokens=100)
return response.choices[0].message.content or "No description available."
except Exception as e:
return f"[Image description error: {e}]"
# -----------------------------
# NLU Classification Function (Dynamic Version)
# -----------------------------
def detect_tags_from_query(
query: str,
nlu_vectorstore: FAISS,
behavior_options: list,
emotion_options: list,
topic_options: list,
context_options: list,
settings: dict = None
) -> Dict[str, Any]:
"""Uses a dynamic two-step NLU process: Route -> Retrieve Examples -> Classify."""
# --- STEP 1: Route the query to determine the primary goal ---
router_prompt = NLU_ROUTER_PROMPT.format(query=query)
primary_goal_raw = call_llm([{"role": "user", "content": router_prompt}], temperature=0.0).strip().lower()
# --- FIX START: Use separate variables for the filter (lowercase) and the prompt (Title Case) ---
goal_for_filter = "practical_planning" if "practical" in primary_goal_raw else "emotional_support"
goal_for_prompt = "Practical Planning" if "practical" in primary_goal_raw else "Emotional Support"
# --- FIX END ---
if settings and settings.get("debug_mode"):
print(f"\n--- NLU Router ---\nGoal: {goal_for_prompt} (Filter: '{goal_for_filter}')\n------------------\n")
# --- STEP 2: Retrieve relevant examples from the NLU vector store ---
retriever = nlu_vectorstore.as_retriever(
search_kwargs={"k": 2, "filter": {"primary_goal": goal_for_filter}} # <-- Use the correct lowercase filter
)
retrieved_docs = retriever.invoke(query)
# Format the retrieved examples for the prompt
selected_examples = "\n".join(
f"User Query: \"{doc.page_content}\"\n{json.dumps(doc.metadata['classification'], indent=4)}"
for doc in retrieved_docs
)
if not selected_examples:
selected_examples = "(No relevant examples found)"
if settings and settings.get("debug_mode"):
print("WARNING: NLU retriever found no examples for this query.")
# --- STEP 3: Use the Specialist Classifier with retrieved examples ---
behavior_str = ", ".join(f'"{opt}"' for opt in behavior_options if opt != "None")
emotion_str = ", ".join(f'"{opt}"' for opt in emotion_options if opt != "None")
topic_str = ", ".join(f'"{opt}"' for opt in topic_options if opt != "None")
context_str = ", ".join(f'"{opt}"' for opt in context_options if opt != "None")
prompt = SPECIALIST_CLASSIFIER_PROMPT.format(
primary_goal=goal_for_prompt, # Use Title Case for the prompt text
examples=selected_examples,
behavior_options=behavior_str,
emotion_options=emotion_str,
topic_options=topic_str,
context_options=context_str,
query=query
)
messages = [{"role": "system", "content": "You are a helpful NLU classification assistant."}, {"role": "user", "content": prompt}]
response_str = call_llm(messages, temperature=0.1)
if settings and settings.get("debug_mode"):
print(f"\n--- NLU Specialist Full Response ---\n{response_str}\n----------------------------------\n")
# --- STEP 4: Parse the final result ---
result_dict = {"detected_behaviors": [], "detected_emotion": "None", "detected_topic": "None", "detected_contexts": []}
try:
start_brace = response_str.find('{')
end_brace = response_str.rfind('}')
if start_brace != -1 and end_brace > start_brace:
json_str = response_str[start_brace : end_brace + 1]
result = json.loads(json_str)
behaviors = result.get("detected_behaviors")
if behaviors: # This checks for both None and empty list
result_dict["detected_behaviors"] = [b for b in behaviors if b in behavior_options]
# Fix bug to properly handle null values from the LLM and will no longer raise the TypeError.
# Use `or` to safely handle None, empty strings, etc.
result_dict["detected_emotion"] = result.get("detected_emotion") or "None"
result_dict["detected_topic"] = result.get("detected_topic") or "None"
contexts = result.get("detected_contexts")
if contexts: # This checks for both None and empty list
result_dict["detected_contexts"] = [c for c in contexts if c in context_options]
# Buggy code that can't handle a NULL case from LLM.
# result_dict["detected_behaviors"] = [b for b in result.get("detected_behaviors", []) if b in behavior_options]
# result_dict["detected_emotion"] = result.get("detected_emotion", "None")
# result_dict["detected_topic"] = result.get("detected_topic", "None")
# result_dict["detected_contexts"] = [c for c in result.get("detected_contexts", []) if c in context_options]
return result_dict
except (json.JSONDecodeError, AttributeError) as e:
print(f"ERROR parsing NLU Specialist JSON: {e}")
return result_dict
# -----------------------------
# Embeddings & VectorStore
# -----------------------------
def _default_embeddings():
model_name = os.getenv("EMBEDDINGS_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
return HuggingFaceEmbeddings(model_name=model_name)
def build_or_load_vectorstore(docs: List[Document], index_path: str, is_personal: bool = False) -> FAISS:
os.makedirs(os.path.dirname(index_path), exist_ok=True)
if os.path.isdir(index_path) and os.path.exists(os.path.join(index_path, "index.faiss")):
try:
return FAISS.load_local(index_path, _default_embeddings(), allow_dangerous_deserialization=True)
except Exception: pass
if is_personal and not docs:
docs = [Document(page_content="(This is the start of the personal memory journal.)", metadata={"source": "placeholder"})]
vs = FAISS.from_documents(docs, _default_embeddings())
vs.save_local(index_path)
return vs
def texts_from_jsonl(path: str) -> List[Document]:
out: List[Document] = []
try:
with open(path, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
obj = json.loads(line.strip())
txt = obj.get("text") or ""
if not txt.strip(): continue
md = {"source": os.path.basename(path), "chunk": i}
for k in ("behaviors", "emotion", "topic_tags", "context_tags"):
if k in obj and obj[k]: md[k] = obj[k]
out.append(Document(page_content=txt, metadata=md))
except Exception: return []
return out
def bootstrap_vectorstore(sample_paths: List[str] | None = None, index_path: str = "data/faiss_index") -> FAISS:
docs: List[Document] = []
for p in (sample_paths or []):
try:
if p.lower().endswith(".jsonl"):
docs.extend(texts_from_jsonl(p))
else:
with open(p, "r", encoding="utf-8", errors="ignore") as fh:
docs.append(Document(page_content=fh.read(), metadata={"source": os.path.basename(p)}))
except Exception: continue
if not docs:
docs = [Document(page_content="(empty index)", metadata={"source": "placeholder"})]
return build_or_load_vectorstore(docs, index_path=index_path)
# -----------------------------
# LLM Call
# -----------------------------
def call_llm(messages: List[Dict[str, str]], temperature: float = 0.6, stop: Optional[List[str]] = None) -> str:
client = _openai_client()
model = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
if not client:
return "(Offline Mode: OpenAI API key not configured.)"
try:
api_args = {"model": model, "messages": messages, "temperature": float(temperature if temperature is not None else 0.6)}
if stop: api_args["stop"] = stop
resp = client.chat.completions.create(**api_args)
return (resp.choices[0].message.content or "").strip()
except Exception as e:
return f"[LLM API Error: {e}]"
# -----------------------------
# Prompting & RAG Chain
# -----------------------------
def make_rag_chain(
vs_general: FAISS,
vs_personal: FAISS,
*,
role: str = "patient",
temperature: float = 0.6,
language: str = "English",
patient_name: str = "the patient",
caregiver_name: str = "the caregiver",
tone: str = "warm",
):
"""Returns a callable that performs the complete, intelligent RAG process."""
def _format_docs(docs: List[Document], default_msg: str) -> str:
if not docs: return default_msg
unique_docs = {doc.page_content: doc for doc in docs}.values()
return "\n".join([f"- {d.page_content.strip()}" for d in unique_docs])
def _answer_fn(query: str, chat_history: List[Dict[str, str]], scenario_tag: Optional[str] = None, emotion_tag: Optional[str] = None, topic_tag: Optional[str] = None, context_tags: Optional[List[str]] = None) -> Dict[str, Any]:
router_messages = [{"role": "user", "content": ROUTER_PROMPT.format(query=query)}]
query_type = call_llm(router_messages, temperature=0.0).strip().lower()
print(f"Query classified as: {query_type}")
system_message = SYSTEM_TEMPLATE.format(tone=tone, language=language, patient_name=patient_name or "the patient", caregiver_name=caregiver_name or "the caregiver", guardrails=SAFETY_GUARDRAILS)
messages = [{"role": "system", "content": system_message}]
messages.extend(chat_history)
if "general_knowledge_question" in query_type:
user_prompt = ANSWER_TEMPLATE_GENERAL_KNOWLEDGE.format(question=query, language=language)
messages.append({"role": "user", "content": user_prompt})
answer = call_llm(messages, temperature=temperature)
return {"answer": answer, "sources": ["General Knowledge"]}
elif "factual_question" in query_type:
print(f"Performing query expansion for: '{query}'")
expansion_prompt = QUERY_EXPANSION_PROMPT.format(question=query)
expansion_response = call_llm([{"role": "user", "content": expansion_prompt}], temperature=0.1)
try:
clean_response = expansion_response.strip().replace("```json", "").replace("```", "")
expanded_queries = json.loads(clean_response)
search_queries = [query] + expanded_queries
except json.JSONDecodeError:
search_queries = [query]
print(f"Searching with queries: {search_queries}")
all_docs = []
for q in search_queries:
all_docs.extend(vs_personal.similarity_search(q, k=2))
all_docs.extend(vs_general.similarity_search(q, k=2))
context = _format_docs(all_docs, "(No relevant information found in the memory journal.)")
user_prompt = ANSWER_TEMPLATE_FACTUAL.format(context=context, question=query, language=language)
messages.append({"role": "user", "content": user_prompt})
answer = call_llm(messages, temperature=temperature)
sources = list(set(d.metadata.get("source", "unknown") for d in all_docs))
return {"answer": answer, "sources": sources}
elif "general_conversation" in query_type:
user_prompt = ANSWER_TEMPLATE_GENERAL.format(question=query, language=language)
messages.append({"role": "user", "content": user_prompt})
answer = call_llm(messages, temperature=temperature)
return {"answer": answer, "sources": []}
else: # Default to the original caregiving logic
# --- Reworked search strategy to handle filters correctly ---
# 1. Start with a general, unfiltered search to always get text-based matches.
personal_docs = vs_personal.similarity_search(query, k=3)
general_docs = vs_general.similarity_search(query, k=3)
# 2. Build a filter for simple equality checks (FAISS supported).
simple_search_filter = {}
if scenario_tag and scenario_tag != "None":
simple_search_filter["behaviors"] = scenario_tag.lower()
if emotion_tag and emotion_tag != "None":
simple_search_filter["emotion"] = emotion_tag.lower()
if topic_tag and topic_tag != "None":
simple_search_filter["topic_tags"] = topic_tag.lower()
# 3. If simple filters exist, perform a second, more specific search.
if simple_search_filter:
print(f"Performing additional search with filter: {simple_search_filter}")
personal_docs.extend(vs_personal.similarity_search(query, k=2, filter=simple_search_filter))
general_docs.extend(vs_general.similarity_search(query, k=2, filter=simple_search_filter))
# 4. If context_tags exist (unsupported by 'in'), loop through them and perform separate searches.
if context_tags:
print(f"Performing looped context tag search for: {context_tags}")
for tag in context_tags:
context_filter = {"context_tags": tag.lower()}
personal_docs.extend(vs_personal.similarity_search(query, k=1, filter=context_filter))
general_docs.extend(vs_general.similarity_search(query, k=1, filter=context_filter))
# 5. Combine and de-duplicate all results.
all_docs_care = list({doc.page_content: doc for doc in personal_docs + general_docs}.values())
# --- End of reworked search strategy ---
personal_context = _format_docs([d for d in all_docs_care if d in personal_docs], "(No relevant personal memories found.)")
general_context = _format_docs([d for d in all_docs_care if d in general_docs], "(No general guidance found.)")
first_emotion = None
for doc in all_docs_care:
if "emotion" in doc.metadata and doc.metadata["emotion"]:
emotion_data = doc.metadata["emotion"]
if isinstance(emotion_data, list): first_emotion = emotion_data[0]
else: first_emotion = emotion_data
if first_emotion: break
emotions_context = render_emotion_guidelines(first_emotion or emotion_tag)
is_tagged_scenario = (scenario_tag and scenario_tag != "None") or (emotion_tag and emotion_tag != "None") or (first_emotion is not None)
template = ANSWER_TEMPLATE_ADQ if is_tagged_scenario else ANSWER_TEMPLATE_CALM
if template == ANSWER_TEMPLATE_ADQ:
user_prompt = template.format(general_context=general_context, personal_context=personal_context, question=query, scenario_tag=scenario_tag, emotions_context=emotions_context, role=role, language=language)
else:
combined_context = f"General Guidance:\n{general_context}\n\nPersonal Memories:\n{personal_context}"
user_prompt = template.format(context=combined_context, question=query, language=language)
messages.append({"role": "user", "content": user_prompt})
answer = call_llm(messages, temperature=temperature)
high_risk_scenarios = ["exit_seeking", "wandering", "elopement"]
if scenario_tag and scenario_tag.lower() in high_risk_scenarios:
answer += f"\n\n---\n{RISK_FOOTER}"
sources = list(set(d.metadata.get("source", "unknown") for d in all_docs_care))
return {"answer": answer, "sources": sources}
return _answer_fn
def answer_query(chain, question: str, **kwargs) -> Dict[str, Any]:
if not callable(chain): return {"answer": "[Error: RAG chain is not callable]", "sources": []}
try:
return chain(question, **kwargs)
except Exception as e:
print(f"ERROR in answer_query: {e}")
return {"answer": f"[Error executing chain: {e}]", "sources": []}
# -----------------------------
# TTS & Transcription
# -----------------------------
def synthesize_tts(text: str, lang: str = "en"):
if not text or gTTS is None: return None
try:
with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp:
tts = gTTS(text=text, lang=(lang or "en"))
tts.save(fp.name)
return fp.name
except Exception:
return None
def transcribe_audio(filepath: str, lang: str = "en"):
client = _openai_client()
if not client: return "[Transcription failed: API key not configured]"
api_args = {"model": "whisper-1"}
if lang and lang != "auto": api_args["language"] = lang
with open(filepath, "rb") as audio_file:
transcription = client.audio.transcriptions.create(file=audio_file, **api_args)
return transcription.text
|