Delete alz_companion/agent.py

alz_companion/agent.py

@@ -1,396 +0,0 @@
-
-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
-