Create agent.py

alz_companion/agent.py

@@ -0,0 +1,455 @@
+from __future__ import annotations
+import os
+import json
+import base64
+import time
+import tempfile
+import re # <-- ADD THIS LINE
+
+from typing import List, Dict, Any, Optional
+
+# OpenAI for LLM (optional)
+try:
+    from openai import OpenAI
+except Exception:  # pragma: no cover
+    OpenAI = None  # type: ignore
+
+# LangChain & RAG
+from langchain.schema import Document
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+# TTS
+try:
+    from gtts import gTTS
+except Exception:  # pragma: no cover
+    gTTS = None  # type: ignore
+
+
+from .prompts import (
+    SYSTEM_TEMPLATE, ANSWER_TEMPLATE_CALM, ANSWER_TEMPLATE_ADQ,
+    SAFETY_GUARDRAILS, RISK_FOOTER, render_emotion_guidelines,
+    # --- Import the new decomposed NLU prompts ---
+    NLU_ROUTER_PROMPT, SPECIALIST_CLASSIFIER_PROMPT,
+    EMOTIONAL_SUPPORT_EXAMPLES, PRACTICAL_PLANNING_EXAMPLES,
+    # Other templates
+    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
+
+# In agent.py
+
+def describe_image(image_path: str) -> str:
+    """Uses a vision model to describe an image for context."""
+    client = _openai_client()
+    if not client:
+        return "(Image description failed: OpenAI API key not configured.)"
+    
+    try:
+        # --- FIX START ---
+        # Determine the MIME type based on the file extension
+        extension = os.path.splitext(image_path)[1].lower()
+        if extension == ".png":
+            mime_type = "image/png"
+        elif extension in [".jpg", ".jpeg"]:
+            mime_type = "image/jpeg"
+        elif extension == ".gif":
+            mime_type = "image/gif"
+        elif extension == ".webp":
+            mime_type = "image/webp"
+        else:
+            # Default to JPEG, but this handles the most common cases
+            mime_type = "image/jpeg"
+        # --- FIX END ---
+
+        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 in a concise, factual way for a memory journal. Focus on people, places, and key objects. For example: 'A photo of John and Mary smiling on a bench at the park.'"},
+                        {
+                            "type": "image_url", 
+                            # Use the dynamically determined MIME type
+                            "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
+# -----------------------------
+
+def detect_tags_from_query(query: str, behavior_options: list, emotion_options: list, topic_options: list, context_options: list, settings: dict = None) -> Dict[str, Any]:
+    """Uses a two-step NLU process: Route -> Select Examples -> Classify."""
+    
+    # --- STEP 1: Route the query to determine the primary goal ---
+    router_prompt = NLU_ROUTER_PROMPT.format(query=query)
+    router_messages = [{"role": "user", "content": router_prompt}]
+    primary_goal = call_llm(router_messages, temperature=0.0).strip().lower()
+
+    if "practical" in primary_goal:
+        selected_examples = PRACTICAL_PLANNING_EXAMPLES
+        goal_for_prompt = "Practical Planning"
+    else:
+        selected_examples = EMOTIONAL_SUPPORT_EXAMPLES
+        goal_for_prompt = "Emotional Support"
+
+    if settings and settings.get("debug_mode"):
+        print(f"\n--- NLU Router ---\nGoal: {goal_for_prompt}\n------------------\n")
+
+    # --- STEP 2: Use the Specialist Classifier with selected 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,
+        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. Follow the instructions precisely."}, {"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")
+    
+    result_dict = {
+        "detected_behaviors": [], "detected_emotion": "None",
+        "detected_topic": "None", "detected_contexts": []
+    }
+    
+    try:
+        # --- ROBUST PARSING LOGIC ---
+        start_brace = response_str.find('{')
+        end_brace = response_str.rfind('}')
+        
+        if start_brace != -1 and end_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")
+            result_dict["detected_behaviors"] = [b for b in behaviors if b in behavior_options] if behaviors else []
+
+            emotion = result.get("detected_emotion")
+            result_dict["detected_emotion"] = emotion if emotion in emotion_options else "None"
+
+            topic = result.get("detected_topic")
+            result_dict["detected_topic"] = topic if topic in topic_options else "None"
+            
+            contexts = result.get("detected_contexts")
+            result_dict["detected_contexts"] = [c for c in contexts if c in context_options] if contexts else []
+
+        return result_dict
+    except (json.JSONDecodeError, AttributeError) as e:
+        print(f"ERROR parsing CoT JSON: {e}")
+        return result_dict
+
+
+# -----------------------------
+# Embeddings & VectorStore
+# -----------------------------
+
+def _default_embeddings():
+    """Lightweight, widely available model."""
+    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):
+                line = line.strip()
+                if not line: continue
+                obj = json.loads(line)
+                txt = obj.get("text") or ""
+                if not isinstance(txt, str) or not txt.strip(): continue
+
+                # fix bugs by adding tags for topic and context
+                md = {"source": os.path.basename(path), "chunk": i}
+                for k in ("behaviors", "emotion", "topic_tags", "context_tags"):
+                    if k in obj and obj[k]: # Ensure the key exists and is not empty
+                        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
+# -----------------------------
+# updated the detect_tags_from_query function to call call_llm with a new stop argument, 
+# but I failed to update the call_llm function itself to accept that argument.
+# Now fix call_llm function:
+def call_llm(messages: List[Dict[str, str]], temperature: float = 0.6, stop: Optional[List[str]] = None) -> str:
+    """Call OpenAI Chat Completions if available; else return a fallback."""
+    client = _openai_client()
+    model = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
+    if not client:
+        return "(Offline Mode: OpenAI API key not configured.)"
+    try:
+        # Prepare arguments for the API call to handle the optional 'stop' parameter
+        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 _format_sources(docs: List[Document]) -> List[str]:
+    return list(set(d.metadata.get("source", "unknown") for d in docs))
+
+# In agent.py, replace the existing make_rag_chain function with this new one to handle general & specific conversations .
+# The logic for the "factual_question" path needs to be updated to perform the expansion query
+
+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) -> Dict[str, Any]:
+    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)
+
+        # --- NEW 'general_knowledge_question' PATH ---
+        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"]}
+        # --- END NEW PATH ---
+
+        elif "factual_question" in query_type:
+            # ... (This entire section for query expansion and factual search remains the same)
+            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}")
+            retriever_personal = vs_personal.as_retriever(search_kwargs={"k": 2})
+            retriever_general = vs_general.as_retriever(search_kwargs={"k": 2})
+
+            all_docs = []
+            for q in search_queries:
+                all_docs.extend(retriever_personal.invoke(q))
+                all_docs.extend(retriever_general.invoke(q))
+
+            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)
+            return {"answer": answer, "sources": _format_sources(all_docs)}
+
+        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
+            # ... (This entire section for caregiving scenarios remains the same)
+            search_filter = {}
+            if scenario_tag and scenario_tag != "None":
+                search_filter["behaviors"] = scenario_tag.lower()
+            if emotion_tag and emotion_tag != "None":
+                search_filter["emotion"] = emotion_tag.lower()
+            # fix bug by adding topic tag and context tag
+            if topic_tag and topic_tag != "None": # <-- ADD THESE TWO LINES
+                search_filter["topic_tags"] = topic_tag.lower()
+            if context_tags: # <-- ADD THESE TWO LINES
+                search_filter["context_tags"] = {"in": [tag.lower() for tag in context_tags]}
+
+            # --- Robust Search Strategy ---
+            # 1. Start with a general, unfiltered search to always get text-based matches.
+            retriever_personal = vs_personal.as_retriever(search_kwargs={"k": 3})
+            retriever_general = vs_general.as_retriever(search_kwargs={"k": 3})
+            
+            personal_docs = retriever_personal.invoke(query)
+            general_docs = retriever_general.invoke(query)
+            
+            # 2. If filters exist, perform a second, more specific search and add the results.
+            if search_filter:
+                print(f"Performing additional search with filter: {search_filter}")
+                personal_docs.extend(vs_personal.similarity_search(query, k=3, filter=search_filter))
+                general_docs.extend(vs_general.similarity_search(query, k=3, filter=search_filter))
+
+            # 3. Combine and de-duplicate the results to get the best of both searches.
+            all_personal_docs = list({doc.page_content: doc for doc in personal_docs}.values())
+            all_general_docs = list({doc.page_content: doc for doc in general_docs}.values())
+
+            # 4. Define the context variables based on the new, combined results.
+            personal_context = _format_docs(all_personal_docs, "(No relevant personal memories found.)")
+            general_context = _format_docs(all_general_docs, "(No general guidance found.)")
+
+            first_emotion = None
+            all_docs_care = all_personal_docs + all_general_docs
+
+            # -- end of Robust Search Strategy
+
+            
+            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}"
+
+            return {"answer": answer, "sources": _format_sources(all_docs_care)}
+
+    return _answer_fn
+
+
+# Fix bug by adding topic tag  ... how about context tag??
+def answer_query(chain, question: str, **kwargs) -> Dict[str, Any]:
+    if not callable(chain): return {"answer": "[Error: RAG chain is not callable]", "sources": []}
+    chat_history = kwargs.get("chat_history", [])
+    scenario_tag = kwargs.get("scenario_tag")
+    emotion_tag = kwargs.get("emotion_tag")
+    topic_tag = kwargs.get("topic_tag") # <-- ADD THIS LINE
+    context_tags = kwargs.get("context_tags") # <-- ADD THIS LINE
+    try:
+        return chain(question, chat_history=chat_history, scenario_tag=scenario_tag, emotion_tag=emotion_tag, topic_tag=topic_tag, context_tags=context_tags) # <-- ADD topic_tag and context_tags
+    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:
+        fd, path = tempfile.mkstemp(suffix=".mp3")
+        os.close(fd)
+        tts = gTTS(text=text, lang=(lang or "en"))
+        tts.save(path)
+        return path
+    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