Mostly working

- Add log_queue to PipelineOrchestrator and log LLM inputs to UI
- Use entity_name for lore update logs instead of topic
- Pass log_queue into ConfirmationApp to display logs in UI
- Introduce a log pane and left/right pane layout in the UI
- Poll and render log messages via a new poll_log_updates worker
- Run log polling with Textual workers to avoid GC issues
- Fix ListView insertion by wrapping ListItem in a list
- Relax RAG similarity threshold from 0.7 to 0.5

.gitignore

@@ -1,2 +1,3 @@
 artifacts/
-__pycache__
+**/__pycache__/
+data

.python-version

@@ -0,0 +1 @@
+3.12

main.py

@@ -0,0 +1,48 @@
+import argparse
+
+from src.rag.manager import RAGManager
+
+
+def main():
+    parser = argparse.ArgumentParser(description="D&D Helpers CLI")
+    parser.add_argument(
+        "--ingest-pdf",
+        type=str,
+        help="Path to a PDF file to ingest into the RAG system",
+    )
+    parser.add_argument(
+        "--ingest-file",
+        type=str,
+        help="Path to a markdown file to ingest into the RAG system",
+    )
+    parser.add_argument(
+        "--ingest-dir",
+        type=str,
+        help="Path to a directory of markdown files to ingest into the RAG system",
+    )
+
+    args = parser.parse_args()
+
+    rag_manager = RAGManager()
+
+    if args.ingest_pdf:
+        print(f"Ingesting PDF: {args.ingest_pdf}...")
+        rag_manager.ingest_pdf(args.ingest_pdf)
+        print("PDF ingestion complete.")
+
+    if args.ingest_file:
+        print(f"Ingesting File: {args.ingest_file}...")
+        rag_manager.ingest_file(args.ingest_file)
+        print("File ingestion complete.")
+
+    if args.ingest_dir:
+        print(f"Ingesting Directory: {args.ingest_dir}...")
+        rag_manager.ingest_directory(args.ingest_dir)
+        print("Directory ingestion complete.")
+
+    if not any([args.ingest_pdf, args.ingest_file, args.ingest_dir]):
+        print("Hello from dnd-helpers!")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -0,0 +1,7 @@
+[project]
+name = "dnd-helpers"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = []

requirements.txt

@@ -1,8 +1,11 @@
 # Core dependencies for D&D Helpers
-faster-whisper
+whisperx
 sounddevice
 pydantic
 textual
 typer
 openai
 python-dotenv
+llama-index
+chromadb
+pdfplumber

src/llm/models.py

@@ -44,6 +44,26 @@ class CharacterStateUpdate(BaseModel):
     )
 
 
+class ContextUpdate(BaseModel):
+    query: str = Field(..., description="The search query used to retrieve the context")
+    snippet: str = Field(
+        ..., description="The relevant text snippet retrieved from the source"
+    )
+    source: str = Field(
+        ..., description="The source of the snippet (e.g., 'PHB p. 12')"
+    )
+
+
+class FilterResult(BaseModel):
+    contextual_info: str = Field(
+        ...,
+        description="Information interesting to the user but not useful for structured extraction",
+    )
+    filtered_text: str = Field(
+        ..., description="Cleaned transcript used for structured data extraction"
+    )
+
+
 class ExtractionResult(BaseModel):
     lore_updates: List[LoreUpdate] = Field(
         default_factory=list, description="List of discovered lore facts", alias="lore"
@@ -58,6 +78,11 @@ class ExtractionResult(BaseModel):
         description="List of significant plot points or events",
         alias="events",
     )
+    context_updates: List[ContextUpdate] = Field(
+        default_factory=list,
+        description="List of context updates",
+        alias="context",
+    )
 
     class Config:
         populate_by_name = True

src/llm/processor.py

@@ -1,11 +1,20 @@
+import logging
 import os
+from posix import system
+from this import s
 from typing import Any, Dict, Optional
 
 from openai import OpenAI
 from pydantic import ValidationError
 
-from .models import ExtractionResult
-from .prompts import EXTRACTION_SYSTEM_PROMPT, NOISE_FILTER_SYSTEM_PROMPT
+from .models import ExtractionResult, FilterResult
+from .prompts import (
+    EXTRACTION_SYSTEM_PROMPT,
+    NOISE_FILTER_SYSTEM_PROMPT,
+    QUERY_ANSWER_SYSTEM_PROMPT,
+)
+
+logger = logging.getLogger(__name__)
 
 
 class LLMProcessor:
@@ -47,7 +56,7 @@ class LLMProcessor:
                 # but we can ensure the client is initialized.
                 pass
         except Exception as e:
-            print(f"Error initializing LLM client for backend {backend}: {e}")
+            logger.error(f"Error initializing LLM client for backend {backend}: {e}")
             raise
 
         self.model = model or os.environ.get("LLM_MODEL", "gpt-4o")
@@ -56,73 +65,121 @@ class LLMProcessor:
         self,
         system_prompt: str,
         user_prompt: str,
+        context: Optional[str] = None,
         response_format: Optional[Any] = None,
     ) -> str:
         """
         Generic method to call the LLM.
         """
+        messages = [
+            {"role": "system", "content": system_prompt},
+        ]
+        if context:
+            messages.append(
+                {
+                    "role": "system",
+                    "content": f"Context from previous conversation:\n{context}",
+                }
+            )
+
+        messages.append({"role": "user", "content": user_prompt})
+
         try:
             response = self.client.chat.completions.create(
                 model=self.model,
-                messages=[
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": user_prompt},
-                ],
+                messages=messages,
                 response_format=response_format,
-                extra_body={"include_reasoning": False},
+                extra_body={"enable_thinking": False},
             )
-            return response.choices[0].message.content
+            content = response.choices[0].message.content
+
+            # Strip markdown code blocks if present
+            if content.startswith("```"):
+                import re
+
+                content = re.sub(
+                    r"^```(?:json)?\n?|```$", "", content, flags=re.MULTILINE
+                ).strip()
+
+            return content
         except Exception as e:
-            print(f"LLM Error: {e}")
+            logger.error(f"LLM Error: {e}")
             return ""
 
-    def filter_transcript(self, text: str) -> str:
+    def generate_answer(self, query: str, context: str) -> str:
+        """
+        Generates a natural language answer to a DM query.
+        """
+        return self._call_llm(
+            QUERY_ANSWER_SYSTEM_PROMPT,
+            query,
+            context=context,
+        )
+
+    def filter_transcript(
+        self, text: str, context: Optional[str] = None
+    ) -> FilterResult:
         """
         Stage 1: Raw Transcript -> Filtered Text.
         """
-        result = self._call_llm(NOISE_FILTER_SYSTEM_PROMPT, text)
-        print(f"LLM Processor (Filter): {text} -> {result}")
-        return result
+        result = self._call_llm(
+            NOISE_FILTER_SYSTEM_PROMPT,
+            text,
+            context=context,
+            response_format={"type": "json_object"},
+        )
+        logger.info(f"LLM Processor (Filter): {text} -> {result}")
 
-    def extract_structured_data(self, filtered_text: str) -> ExtractionResult:
+        import json
+
+        try:
+            data = json.loads(result)
+            return FilterResult(**data)
+        except (json.JSONDecodeError, ValidationError) as e:
+            logger.error(f"Filter Parsing Error: {e}")
+            return FilterResult(contextual_info="", filtered_text=result)
+
+    def extract_structured_data(
+        self, filtered_text: str, context: Optional[str] = None
+    ) -> ExtractionResult:
         """
         Stage 2: Filtered Text -> Structured Data.
         """
-        print(f"LLM Processor (Extract): Calling extraction for: {filtered_text}")
+        logger.info(f"LLM Processor (Extract): Calling extraction for: {filtered_text}")
         try:
             # Using standard chat.completions.create with JSON mode for better compatibility with vLLM
-            print("LLM Processor (Extract): Sending request to backend...")
+            logger.info("LLM Processor (Extract): Sending request to backend...")
+
+            system_prompt = EXTRACTION_SYSTEM_PROMPT
+            if context:
+                system_prompt += f"\n{context}"
+
+            messages = [
+                {"role": "system", "content": system_prompt},
+            ]
+            messages.append({"role": "user", "content": filtered_text})
+
+            for message in messages:
+                logger.info(f"LLM Processor (Extract): Message: {message}")
+
             response = self.client.chat.completions.create(
                 model=self.model,
-                messages=[
-                    {"role": "system", "content": EXTRACTION_SYSTEM_PROMPT},
-                    {"role": "user", "content": filtered_text},
-                ],
+                messages=messages,
                 response_format={"type": "json_object"},
-                extra_body={"include_reasoning": False},
+                extra_body={"enable_thinking": False},
             )
-            print("LLM Processor (Extract): Response received from backend.")
+            logger.info("LLM Processor (Extract): Response received from backend.")
 
             import json
 
             content = response.choices[0].message.content
-            print(f"LLM Processor (Extract): Raw JSON response: {content}")
+            logger.info(f"LLM Processor (Extract): Raw JSON response: {content}")
             data = json.loads(content)
 
             # Map the JSON data to the Pydantic model
             return ExtractionResult(**data)
 
         except Exception as e:
-            print(f"Extraction Error: {e}")
+            logger.error(f"Extraction Error: {e}")
             # Return an empty ExtractionResult if parsing fails
             return ExtractionResult()
-
-    def process_pipeline(self, raw_text: str) -> ExtractionResult:
-        """
-        Executes the two-stage pipeline: Raw Transcript -> Filtered Text -> Structured Data.
-        """
-        filtered_text = self.filter_transcript(raw_text)
-        if not filtered_text:
-            return ExtractionResult()
-
-        return self.extract_structured_data(filtered_text)

src/llm/prompts.py

@@ -1,8 +1,20 @@
-# System prompts for the LLM pipeline
+QUERY_ANSWER_SYSTEM_PROMPT = """
+You are a helpful D&D Game Master's assistant. Your goal is to provide accurate, concise, and helpful answers to the DM's questions based on the provided context (conversation history and RAG snippets).
+
+Guidelines:
+- Use the provided context as your primary source of truth.
+- If the answer is not in the context, state that you don't have enough information, but feel free to provide general D&D 5e rules as a fallback.
+- Keep responses natural and professional.
+- Be concise.
+"""
 
 NOISE_FILTER_SYSTEM_PROMPT = """
 You are a D&D Game Master's assistant. Given a transcript, remove all out-of-character (OOC) chatter, logistical discussions (e.g., 'Where is my d20?'), and non-relevant noise.
-Output only the in-character dialogue and game-relevant events.
+
+You must output your response as a JSON object with the following keys:
+- "contextual_info": Information that is interesting or relevant to the story/session but doesn't fit into lore, character state, or significant events (e.g., flavor text, atmospheric descriptions, player commentary that adds context).
+- "filtered_text": The cleaned transcript. IMPORTANT: Keep all player questions, requests for rule clarifications, and mentions of spells, NPCs, or locations in this field, as they are used to trigger knowledge base lookups.
+
 Keep the original speakers' names if they are present in the transcript.
 Do not add any commentary or summaries. Just filter the text.
 """
@@ -26,6 +38,7 @@ Return a JSON object with exactly these keys:
    - "category": (string) 'NPC', 'Location', 'WorldBuilding', or 'Plot'
    - "entity_name": (string) The name of the NPC, Location, or entity
    - "content": (string) The actual lore fact or description
+   - "context": (string, optional) Helpful information for the DM (e.g., descriptions of characters, spell details, game mechanics) discovered via the knowledge base or the transcript.
 2. "character_state": A list of objects. Each object MUST have:
    - "character_name": (string) Name of the character
    - "hp_change": (integer, optional) Change in HP
@@ -33,6 +46,10 @@ Return a JSON object with exactly these keys:
    - "status_effects_removed": (list of strings)
    - "inventory_changes": (list of objects with "item", "quantity", "action")
 3. "events": A list of strings. Each string should be a concise description of a significant plot development.
+4. "context": A list of objects. Each object MUST have:
+   - "query": (string) The original query or topic this context relates to
+   - "snippet": (string) The contextual information or rule explanation
+   - "source": (string) The source of the context (e.g., "players handbook, page 68")
 
 Example Output:
 {
@@ -40,7 +57,7 @@ Example Output:
     {
       "category": "NPC",
       "entity_name": "Thorne",
-      "content": "A gruff dwarf who runs the local tavern."
+      "content": "A gruff dwarf who runs the local tavern.",
     }
   ],
   "character_state": [
@@ -54,6 +71,13 @@ Example Output:
   ],
   "events": [
     "The party discovered the secret entrance to the crypt."
+  ],
+  "context": [
+    {
+      "query": "fireball",
+      "snippet": "fireball does 1d6 damage, with an area of effect of 10 feet circle",
+      "source": "players handbook, page 68"
+    }
   ]
 }
 

src/pipeline/context_pipeline.py

@@ -0,0 +1,67 @@
+import asyncio
+import logging
+from typing import Tuple
+
+from src.llm.models import ContextUpdate
+from src.rag.manager import RAGManager
+
+logger = logging.getLogger(__name__)
+
+
+class ContextPipeline:
+    def __init__(self, rag_manager: RAGManager):
+        self.rag_manager = rag_manager
+
+    async def process_message(
+        self, speaker: str, text: str, context_queue: asyncio.Queue
+    ):
+        """
+        Processes a single message and pushes summarized insights to the context queue.
+        """
+        try:
+            # Use RAGManager.retrieve with summarize=True to get concise insights
+            # Run in a thread to avoid blocking the event loop
+            insights = await asyncio.to_thread(
+                self.rag_manager.retrieve, text, summarize=True
+            )
+
+            if insights:
+                logger.info(
+                    f"ContextPipeline: Found {len(insights)} insights for text: {text}"
+                )
+                for insight in insights:
+                    await context_queue.put(insight)
+            else:
+                logger.debug(f"ContextPipeline: No insights found for text: {text}")
+
+        except Exception as e:
+            logger.error(f"ContextPipeline error processing message: {e}")
+
+    async def run(
+        self,
+        transcript_queue: asyncio.Queue,
+        context_queue: asyncio.Queue,
+        stop_event: asyncio.Event,
+    ):
+        """
+        Main loop that listens to the transcript queue and triggers RAG lookups.
+        """
+        logger.info("Context Pipeline started.")
+        while not stop_event.is_set():
+            try:
+                # Get raw text from transcript queue (speaker, text)
+                speaker, text = await transcript_queue.get()
+
+                # For now, implement the basic flow: every message triggers a lookup.
+                # If performance becomes an issue, a filter can be added here.
+                await self.process_message(speaker, text, context_queue)
+
+                # Mark the task as done
+                transcript_queue.task_done()
+            except Exception as e:
+                logger.error(f"Context Pipeline loop error: {e}")
+
+            # Small sleep to avoid tight loop
+            await asyncio.sleep(0.1)
+
+        logger.info("Context Pipeline stopped.")

src/pipeline/orchestrator.py

@@ -1,13 +1,40 @@
 import asyncio
 import logging
+import os
+from pathlib import Path
+from typing import List, Optional
 
-from src.llm.models import ExtractionResult
+import numpy as np
+
+from src.llm.models import (
+    CharacterStateUpdate,
+    ContextUpdate,
+    ExtractionResult,
+    LoreUpdate,
+)
 from src.llm.processor import LLMProcessor
+from src.llm.prompts import EXTRACTION_SYSTEM_PROMPT, NOISE_FILTER_SYSTEM_PROMPT
+from src.persistence.characters import update_character_state
+from src.persistence.lore import update_lore
+from src.rag.manager import RAGManager
 from src.stt.listener import AudioListener
 from src.stt.transcriber import Transcriber
 from src.ui.tui import ConfirmationApp
 
-logging.basicConfig(level=logging.INFO)
+# Configure logging to write to a file instead of stdout
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    handlers=[
+        logging.FileHandler("pipeline.log"),
+    ],
+)
+# Suppress verbose logging from STT libraries to keep the TUI clean
+logging.getLogger("whisper").setLevel(logging.WARNING)
+logging.getLogger("faster_whisper").setLevel(logging.WARNING)
+logging.getLogger("pyannote").setLevel(logging.WARNING)
+logging.getLogger("whisperx").setLevel(logging.ERROR)
+
 logger = logging.getLogger(__name__)
 
 
@@ -17,15 +44,44 @@ class PipelineOrchestrator:
 
         # Modules
         self.listener = AudioListener(loop=self.loop)
-        self.transcriber = Transcriber()
+        self.transcriber = Transcriber(model_size="small")
         self.processor = LLMProcessor()
+        self.rag_manager = RAGManager()
 
         # Queues
-        self.transcript_queue = asyncio.Queue()
-        self.proposal_queue = asyncio.Queue()
+        self.stt_to_clean_queue = asyncio.Queue()
+        self.ui_to_llm_queue = asyncio.Queue()
+        self.clean_to_llm_queue = asyncio.Queue()
+        self.llm_to_ui_queue = asyncio.Queue()
+        self.log_queue = asyncio.Queue()
 
         self.is_running = False
 
+        # Conversation history for context
+        self.history = []  # List of strings (transcripts)
+        self.history_max_words = 1000
+
+        # STT Sliding Window Buffer
+        self.audio_buffer = []  # List of audio chunks
+        self.buffer_max_seconds = 30
+        self.sample_rate = 16000
+        self.buffer_max_samples = self.buffer_max_seconds * self.sample_rate
+        self.last_processed_end_time = 0.0
+
+    def _get_combined_context(self) -> str:
+        """
+        Returns the trimmed conversation history as a context string.
+        """
+        full_history_text = " ".join(self.history)
+        words = full_history_text.split()
+        if len(words) > self.history_max_words:
+            kept_words = words[-self.history_max_words :]
+            context_text = " ".join(kept_words)
+        else:
+            context_text = full_history_text
+
+        return f"Conversation History:\n{context_text}\n\n"
+
     async def stt_worker(self):
         """
         Worker that handles STT: Audio -> Text.
@@ -36,12 +92,35 @@ class PipelineOrchestrator:
                 # Get audio chunk from listener
                 audio_chunk = await self.listener.get_chunk()
 
-                # Transcribe
-                text = self.transcriber.transcribe(audio_chunk)
+                # Maintain sliding window buffer
+                self.audio_buffer.append(audio_chunk)
+                current_buffer_samples = sum(len(c) for c in self.audio_buffer)
 
-                if text:
-                    logger.info(f"Transcribed: {text}")
-                    await self.transcript_queue.put(text)
+                if current_buffer_samples > self.buffer_max_samples:
+                    # Remove oldest chunks until we are within the buffer limit
+                    while (
+                        sum(len(c) for c in self.audio_buffer) > self.buffer_max_samples
+                    ):
+                        self.audio_buffer.pop(0)
+
+                # Concatenate buffer for transcription
+                full_audio = np.concatenate(self.audio_buffer)
+
+                # Transcribe (WhisperX now returns a list of (speaker, text, start, end))
+                results = self.transcriber.transcribe(full_audio)
+
+                # Filter for only new segments that start after the last processed segment
+                new_segments = [
+                    res for res in results if res[2] >= self.last_processed_end_time
+                ]
+
+                if new_segments:
+                    for speaker, text, start, end in new_segments:
+                        logger.info(f"Transcribed: [{speaker}] {text}")
+                        await self.stt_to_clean_queue.put((speaker, text))
+                        self.last_processed_end_time = max(
+                            self.last_processed_end_time, end
+                        )
 
             except Exception as e:
                 logger.error(f"STT Worker error: {e}")
@@ -49,33 +128,110 @@ class PipelineOrchestrator:
             # Small sleep to prevent tight loop if get_chunk is fast
             await asyncio.sleep(0.1)
 
-    async def llm_worker(self):
+    async def clean_worker(self):
         """
-        Worker that handles LLM: Text -> Proposal.
+        Worker that handles Text Cleaning: Raw STT -> Filtered Text.
         """
-        logger.info("LLM Worker started.")
+        logger.info("Clean Worker started.")
         while self.is_running:
             try:
-                # Get raw text from transcript queue
-                raw_text = await self.transcript_queue.get()
+                # Get raw transcript from STT
+                speaker, raw_text = await self.stt_to_clean_queue.get()
+                logger.info(f"Clean Worker: Filtering text from {speaker}: {raw_text}")
 
-                logger.info(f"LLM Worker: Processing text: {raw_text}")
+                # RAG Retrieval for context
+                context = await asyncio.to_thread(self.rag_manager.retrieve, raw_text)
 
-                # Process via LLM (Filter -> Extract)
-                # Note: this is currently a synchronous call, which blocks the loop.
-                result = self.processor.process_pipeline(raw_text)
-
-                if (
-                    result.lore_updates
-                    or result.character_updates
-                    or result.significant_events
-                ):
-                    logger.info(
-                        f"LLM Worker: Proposal generated. Putting into proposal queue. (Lore: {len(result.lore_updates)}, Char: {len(result.character_updates)})"
+                # Filtering using the processor
+                filter_result = await asyncio.to_thread(
+                    self.processor.filter_transcript,
+                    raw_text,
+                    context=context,
                 )
-                    await self.proposal_queue.put(result)
+
+                # Push filtered text to LLM queue
+                if filter_result.filtered_text:
+                    await self.clean_to_llm_queue.put(
+                        (speaker, filter_result.filtered_text)
+                    )
+                    logger.info(f"Clean Worker: Pushed filtered text to LLM queue.")
                 else:
-                    logger.info("LLM Worker: No relevant game data extracted.")
+                    logger.info("Clean Worker: No filtered text to push.")
+
+            except Exception as e:
+                logger.error(f"Clean Worker error: {e}")
+
+            # Small sleep to prevent tight loop
+            await asyncio.sleep(0.1)
+
+    async def llm_worker(self):
+        """
+        Worker that handles LLM: Filtered Text/UI Input -> Structured Data & UI Updates.
+        """
+        logger.info("LLM Worker started.")
+
+        # Internal queue to serialize processing from multiple sources
+        internal_queue = asyncio.Queue()
+
+        async def feed_clean():
+            while self.is_running:
+                try:
+                    item = await self.clean_to_llm_queue.get()
+                    await internal_queue.put(item)
+                except Exception as e:
+                    logger.error(f"LLM Feeder (Clean) error: {e}")
+
+        async def feed_ui():
+            while self.is_running:
+                try:
+                    text = await self.ui_to_llm_queue.get()
+                    await internal_queue.put(("UI", text))
+                except Exception as e:
+                    logger.error(f"LLM Feeder (UI) error: {e}")
+
+        # Start feeder tasks
+        feeders = [
+            asyncio.create_task(feed_clean()),
+            asyncio.create_task(feed_ui()),
+        ]
+
+        while self.is_running:
+            try:
+                speaker, text = await internal_queue.get()
+                logger.info(f"LLM Worker: Processing text from {speaker}: {text}")
+
+                # RAG Retrieval for context
+                context = await asyncio.to_thread(self.rag_manager.retrieve, text)
+
+                # Log the text sent to the LLM for UI affordance
+                await self.log_queue.put(f"[{speaker}] {text}")
+
+                # Structured extraction using the processor
+                extraction_result = await asyncio.to_thread(
+                    self.processor.extract_structured_data,
+                    text,
+                    context=context,
+                )
+
+                # Persistence: Lore Updates
+                for lore_update in extraction_result.lore_updates:
+                    file_path = await asyncio.to_thread(update_lore, lore_update)
+                    await asyncio.to_thread(self.rag_manager.ingest_file, file_path)
+                    logger.info(
+                        f"LLM Worker: Lore updated and ingested into RAG: {lore_update.entity_name}"
+                    )
+
+                # Persistence: Character State Updates
+                for char_update in extraction_result.character_updates:
+                    await asyncio.to_thread(update_character_state, char_update)
+                    logger.info(
+                        f"LLM Worker: Character {char_update.character_name} state updated."
+                    )
+
+                # UI Notification: Context Updates
+                for context_update in extraction_result.context_updates:
+                    await self.llm_to_ui_queue.put(context_update)
+                    logger.info(f"LLM Worker: Pushed context update to UI.")
 
             except Exception as e:
                 logger.error(f"LLM Worker error: {e}")
@@ -83,18 +239,54 @@ class PipelineOrchestrator:
             # Small sleep
             await asyncio.sleep(0.1)
 
+        # Clean up feeders
+        for f in feeders:
+            f.cancel()
+
+    def _get_wiki_context(self) -> str:
+        """
+        Reads all files in the lore directory and returns them as a 저희 context string.
+        """
+        from src.persistence.lore import DATA_LORE_DIR
+
+        wiki_contents = []
+        # Recursively find all .md files in the lore directory
+        for path in DATA_LORE_DIR.rglob("*.md"):
+            try:
+                with open(path, "r", encoding="utf-8") as f:
+                    content = f.read()
+                    wiki_contents.append(
+                        f"File: {path.relative_to(DATA_LORE_DIR)}\nContent:\n{content}"
+                    )
+            except Exception as e:
+                logger.error(f"Error reading wiki file {path}: {e}")
+
+        return (
+            "\n\n".join(wiki_contents)
+            if wiki_contents
+            else "No wiki knowledge available."
+        )
+
     async def tui_worker(self):
         """
-        Worker that handles TUI: Proposal -> Persistence.
+        Worker that handles TUI: UI interactions.
         """
         logger.info("TUI Worker started.")
         try:
-            # Launch TUI exactly once.
-            # Pass the proposal queue to the app.
-            app = ConfirmationApp(proposal_queue=self.proposal_queue)
+            # Launch TUI.
+            # Use the new queues for the TUI.
+            app = ConfirmationApp(
+                ui_to_llm_queue=self.ui_to_llm_queue,
+                llm_to_ui_queue=self.llm_to_ui_queue,
+                log_queue=self.log_queue,
+            )
             await app.run_async()
+            self.stop()
         except Exception as e:
             logger.error(f"TUI Worker error: {e}")
+            self.stop()
+        except asyncio.CancelledError:
+            pass
 
     async def run(self):
         """
@@ -103,9 +295,16 @@ class PipelineOrchestrator:
         self.is_running = True
         self.listener.start()
 
+        # Initialize Context Pipeline
+        from src.pipeline.context_pipeline import ContextPipeline
+
+        self.context_pipeline = ContextPipeline(self.rag_manager)
+        stop_event = asyncio.Event()
+
         # Start workers as background tasks
         tasks = [
             asyncio.create_task(self.stt_worker()),
+            asyncio.create_task(self.clean_worker()),
             asyncio.create_task(self.llm_worker()),
             asyncio.create_task(self.tui_worker()),
         ]
@@ -118,6 +317,7 @@ class PipelineOrchestrator:
             pass
         finally:
             self.is_running = False
+            stop_event.set()
             self.listener.stop()
             for task in tasks:
                 task.cancel()
@@ -127,6 +327,6 @@ class PipelineOrchestrator:
 
     def stop(self):
         """
-        Stops the pipeline.
+        Stops.
         """
         self.is_running = False

src/rag/manager.py

@@ -0,0 +1,193 @@
+import os
+from typing import Any, List, Optional
+
+import chromadb
+import pdfplumber
+from llama_index.core import Document, Settings, StorageContext, VectorStoreIndex
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.vector_stores.chroma import ChromaVectorStore
+
+from src.llm.models import ContextUpdate
+from src.llm.processor import LLMProcessor
+
+
+class RAGManager:
+    def __init__(self, persist_dir: str = "data/rag_index"):
+        self.persist_dir = persist_dir
+        self.db = chromadb.PersistentClient(path=self.persist_dir)
+        self.collection_name = "phb_collection"
+
+        # Initialize Chroma Vector Store
+        self.vector_store = ChromaVectorStore(
+            chroma_collection=self.db.get_or_create_collection(self.collection_name)
+        )
+
+        # Initialize Storage Context
+        self.storage_context = StorageContext.from_defaults(
+            vector_store=self.vector_store
+        )
+
+        # Use a local HuggingFace embedding model to avoid API key issues during verification
+        Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+
+        # Load index if it exists, otherwise initialize
+        try:
+            self.index = VectorStoreIndex.from_vector_store(
+                self.vector_store, storage_context=self.storage_context
+            )
+        except Exception:
+            self.index = None
+
+    def ingest_pdf(self, pdf_path: str):
+        """
+        Parses a PDF, chunks it, and stores embeddings in ChromaDB.
+        """
+        documents = []
+        with pdfplumber.open(pdf_path) as pdf:
+            for i, page in enumerate(pdf.pages):
+                text = page.extract_text()
+                if text:
+                    # Create a document for each page
+                    # In a real scenario, we might use a recursive character splitter
+                    # but for PHB, page-level chunking is a good start.
+                    doc = Document(
+                        text=text, metadata={"source": f"PHB p. {i + 1}", "page": i + 1}
+                    )
+                    documents.append(doc)
+
+        if not documents:
+            print(f"No text extracted from {pdf_path}")
+            return
+
+        # Create index from documents
+        self.index = VectorStoreIndex.from_documents(
+            documents, storage_context=self.storage_context
+        )
+        print(f"Successfully ingested {pdf_path} into the vector store.")
+
+    def ingest_file(self, file_path: str):
+        """
+        Loads a single markdown file into the index.
+        """
+        with open(file_path, "r", encoding="utf-8") as f:
+            text = f.read()
+
+        # Use the filename as the source
+        source = os.path.basename(file_path)
+        doc = Document(text=text, metadata={"source": source})
+
+        # If index doesn't exist, initialize it
+        if not self.index:
+            self.index = VectorStoreIndex.from_documents(
+                [doc], storage_context=self.storage_context
+            )
+        else:
+            # Insert into existing index
+            self.index.insert(doc)
+
+        print(f"Successfully ingested {file_path} into the vector store.")
+
+    def ingest_directory(self, dir_path: str):
+        """
+        Recursively loads all markdown files in a directory into the index.
+        """
+        files_processed = 0
+        for root, _, files in os.walk(dir_path):
+            for file in files:
+                if file.endswith(".md"):
+                    file_path = os.path.join(root, file)
+                    self.ingest_file(file_path)
+                    files_processed += 1
+
+        print(
+            f"Successfully ingested {files_processed} files from {dir_path} into the vector store."
+        )
+
+    def summarize_results(self, query: str, nodes: List[Any]) -> List[ContextUpdate]:
+        """
+        Uses an LLM to transform raw snippets into concise "insights", filtering out irrelevant content.
+        """
+        if not nodes:
+            return []
+
+        processor = LLMProcessor()
+
+        # Construct the context from retrieved nodes
+        context_text = "\n\n".join(
+            [
+                f"Source: {node.metadata.get('source', 'Unknown')}\nContent: {node.text}"
+                for node in nodes
+            ]
+        )
+
+        system_prompt = (
+            "You are a precise research assistant. Your task is to analyze provided text snippets "
+            "and extract only the information that is directly relevant to the user's query. "
+            "1. If a snippet is irrelevant to the query, discard it completely. "
+            "2. For relevant information, synthesize it into a concise, single-sentence 'insight'. "
+            "3. Do not simply repeat the raw text; summarize it for clarity and brevity. "
+            "4. If no snippets are relevant to the query, return an empty list. "
+            "5. Be factual and do not hallucinate. Use only the provided snippets."
+        )
+
+        user_prompt = (
+            f"Query: {query}\n\n"
+            f"Snippets:\n{context_text}\n\n"
+            "Return a JSON object with a key 'insights' containing a list of objects, each with 'snippet' and 'source'."
+        )
+
+        result = processor._call_llm(
+            system_prompt,
+            user_prompt,
+            response_format={"type": "json_object"},
+        )
+
+        import json
+
+        try:
+            data = json.loads(result)
+            # Expecting a format like {"insights": [{"snippet": "...", "source": "..."}, ...]}
+            insights = data.get("insights", []) if isinstance(data, dict) else data
+
+            if not insights:
+                print(f"Summarization: No relevant insights found for query: {query}")
+
+            return [
+                ContextUpdate(
+                    query=query, snippet=item["snippet"], source=item["source"]
+                )
+                for item in insights
+            ]
+        except (json.JSONDecodeError, KeyError, TypeError) as e:
+            print(f"Summarization parsing error: {e}")
+            return []
+
+    def retrieve(
+        self, query: str, top_k: int = 5, summarize: bool = False
+    ) -> List[ContextUpdate]:
+        """
+        Retrieves the top-K most relevant snippets for a given query,
+        filtering for those with a similarity score > 0.7.
+        """
+        if not self.index:
+            print("Index not initialized. Please ingest documents first.")
+            return []
+
+        # Create a retriever
+        retriever = self.index.as_retriever(similarity_top_k=top_k)
+        nodes = retriever.retrieve(query)
+
+        # Filter nodes by similarity score (threshold > 0.7)
+        nodes = [node for node in nodes if node.score >= 0.5]
+
+        if summarize:
+            return self.summarize_results(query, nodes)
+
+        results = []
+        for node in nodes:
+            # Extract metadata
+            source = node.metadata.get("source", "Unknown Source")
+
+            results.append(ContextUpdate(query=query, snippet=node.text, source=source))
+
+        return results

src/stt/listener.py

@@ -5,7 +5,7 @@ import numpy as np
 import sounddevice as sd
 import torch
 
-logging.basicConfig(level=logging.INFO)
+# Do not call basicConfig here, as it's called in the orchestrator
 logger = logging.getLogger(__name__)
 
 

src/stt/transcriber.py

@@ -1,69 +1,111 @@
 import logging
+import os
 
-from faster_whisper import WhisperModel
+import numpy as np
+import whisperx
+from whisperx.diarize import DiarizationPipeline
 
-logging.basicConfig(level=logging.INFO)
+# Do not call basicConfig here, as it's called in the orchestrator
 logger = logging.getLogger(__name__)
 
 
 class Transcriber:
     """
-    Converts audio chunks (numpy arrays) into text using faster-whisper.
+    Converts audio chunks (numpy arrays) into text and identifies speakers using WhisperX.
     """
 
-    def __init__(self, model_size="base", device="cpu", compute_type="int8"):
+    def __init__(
+        self, model_size="base", device="cpu", compute_type="int8", language="en"
+    ):
         """
-        Initializes the faster-whisper model.
+        Initializes the WhisperX model and diarization pipeline.
 
         Args:
             model_size (str): The size of the model to use (e.g., "tiny", "base", "small").
             device (str): The device to run the model on ("cpu" or "cuda").
             compute_type (str): The compute type to use (e.g., "int8", "float16").
+            language (str): The language code for alignment (e.g., "en").
         """
+        self.device = device
+        self.compute_type = compute_type
+        self.language = language
+
         logger.info(
-            f"Loading faster-whisper model: {model_size} on {device} ({compute_type})..."
+            f"Loading WhisperX model: {model_size} on {device} ({compute_type})..."
         )
         try:
-            self.model = WhisperModel(
+            # Load transcription model
+            self.model = whisperx.load_model(
                 model_size, device=device, compute_type=compute_type
             )
-            logger.info("Model loaded successfully.")
+
+            # Load alignment model (required for accurate speaker assignment)
+            # model_dir=None allows automatic model selection based on the language
+            self.align_model, self.align_metadata = whisperx.load_align_model(
+                device=device, model_dir=None, language_code=self.language
+            )
+
+            self.diarize_model = DiarizationPipeline()
+
+            logger.info("WhisperX and Diarization models loaded successfully.")
         except Exception as e:
-            logger.error(f"Failed to load faster-whisper model: {e}")
+            logger.error(f"Failed to load WhisperX models: {e}")
             raise
 
     def transcribe(self, audio_chunk):
         """
-        Transcribes a single audio chunk.
+        Transcribes an audio chunk and performs speaker diarization.
 
         Args:
             audio_chunk (np.ndarray): The audio data as a numpy array.
 
         Returns:
-            str: The transcribed text.
+            list: A list of tuples (speaker_id, text).
         """
         if audio_chunk is None:
-            return ""
+            return []
 
         try:
-            # faster-whisper expects audio in float32 and 1D array
-            audio_data = audio_chunk.astype("float32").flatten()
+            # WhisperX expects audio in float32 and 1D array
+            audio = audio_chunk.astype("float32").flatten()
 
-            # Transcribe the audio
-            segments, info = self.model.transcribe(audio_data, beam_size=5)
+            # 1. Perform transcription
+            # batch_size is set to 16 for efficiency; can be adjusted based on VRAM
+            result = self.model.transcribe(audio, batch_size=16)
 
-            # Combine segments into a single string
-            text = " ".join([segment.text.strip() for segment in segments])
+            # 2. Perform alignment
+            # Alignment is necessary for the assign_words_to_speakers step
+            result_a = whisperx.align(
+                result["segments"],
+                self.align_model,
+                self.align_metadata,
+                audio,
+                self.device,
+            )
 
-            return text.strip()
+            # 3. Perform diarization
+            diarize_segments = self.diarize_model(audio)
+
+            # 4. Align transcription segments with speakers
+            result_final = whisperx.assign_word_speakers(diarize_segments, result_a)
+
+            # Extract (speaker_id, text, start, end) tuples from the final result
+            output = []
+            for segment in result_final.get("segments", []):
+                speaker = segment.get("speaker", "Unknown")
+                text = segment.get("text", "").strip()
+                start = segment.get("start", 0.0)
+                end = segment.get("end", 0.0)
+                if text:
+                    output.append((speaker, text, start, end))
+
+            return output
         except Exception as e:
-            logger.error(f"Transcription error: {e}")
-            return ""
+            logger.error(f"Transcription/Diarization error: {e}")
+            return []
 
     def close(self):
         """
         Explicitly release model resources if necessary.
         """
-        # faster-whisper's WhisperModel doesn't have a standard close(),
-        # but we'll provide this for consistency.
         pass

src/ui/tui.py

@@ -3,315 +3,321 @@ from typing import List, Optional, Union
 
 from textual.app import App, ComposeResult
 from textual.containers import Container, Horizontal, Vertical
-from textual.widgets import Button, DataTable, Footer, Input, Label, Static
+from textual.message import Message
+from textual.screen import ModalScreen
+from textual.widgets import (
+    Button,
+    DataTable,
+    Footer,
+    Input,
+    Label,
+    ListItem,
+    ListView,
+    Static,
+)
 
 from src.llm.models import CharacterStateUpdate, ExtractionResult, LoreUpdate
 from src.persistence.characters import update_character_state
 from src.persistence.lore import update_lore
 
 
+class EditModal(ModalScreen):
+    def __init__(self, initial_text: str, on_save: callable):
+        super().__init__()
+        self.initial_text = initial_text
+        self.on_save = on_save
+
+    def compose(self) -> ComposeResult:
+        with Vertical(id="modal-container"):
+            yield Label("Edit Fact Content:")
+            yield Input(value=self.initial_text, id="edit-input")
+            with Horizontal(id="modal-actions"):
+                yield Button("Save", id="btn-save")
+                yield Button("Cancel", id="btn-cancel")
+
+    def on_button_pressed(self, event: Button.Pressed) -> None:
+        if event.button.id == "btn-save":
+            edit_input = self.query_one("#edit-input", Input)
+            self.on_save(edit_input.value)
+            self.dismiss()
+        elif event.button.id == "btn-cancel":
+            self.dismiss()
+
+
 class ConfirmationApp(App):
     CSS = """
-    Screen {
+    #main-container {
+        layout: vertical;
+        height: 100%;
+    }
+
+    #content-wrapper {
         layout: horizontal;
+        height: 100%;
     }
 
     #left-pane {
-        width: 40%;
-        border: solid;
-        padding: 1;
+        width: 70%;
+        layout: vertical;
     }
 
     #right-pane {
-        width: 60%;
-        border: solid;
-        padding: 1;
+        width: 30%;
         layout: vertical;
+        border: solid white;
     }
 
-    #details-container {
-        height: auto;
-        margin-bottom: 1;
+    #pending-facts-table {
+        height: 40%;
+        border: solid white;
     }
 
-    #actions-container {
+    #llm-input-container {
+        height: 10%;
+        border: solid white;
+        padding: 1;
+    }
+
+    #context-pane {
+        height: 50%;
+        border: solid white;
+    }
+
+    #log-pane {
+        height: 30%;
+        border: solid white;
+        background: #111;
+    }
+
+    #log-footer {
+        height: 70%;
+        border: solid white;
+    }
+
+    #modal-container {
+        width: 60%;
         height: auto;
-        layout: horizontal;
+        border: double white;
+        background: #222;
+        padding: 2;
         align: center middle;
     }
 
-    #edit-container {
-        display: none;
+    #modal-actions {
         height: auto;
-        layout: vertical;
-        border: solid;
-        padding: 1;
+        margin-top: 1;
+        align: right middle;
     }
 
-    Button {
-        margin: 0 1;
+    #edit-input {
+        margin: 1 0;
+    }
+
+    #llm-input {
+        width: 100%;
+    }
+
+    ListItem Static {
+        border: solid grey;
+        margin: 1 0;
+        padding: 1;
     }
     """
 
     BINDINGS = [
         ("q", "quit", "Quit"),
+        ("a", "accept", "Accept"),
+        ("r", "reject", "Reject"),
+        ("e", "edit", "Edit"),
+        ("enter", "send", "Send"),
     ]
 
     def __init__(
         self,
         result: Optional[ExtractionResult] = None,
-        proposal_queue: Optional[asyncio.Queue] = None,
+        ui_to_llm_queue: Optional[asyncio.Queue] = None,
+        llm_to_ui_queue: Optional[asyncio.Queue] = None,
+        log_queue: Optional[asyncio.Queue] = None,
     ):
         super().__init__()
         self.result = result
-        self.proposal_queue = proposal_queue
+        self.ui_to_llm_queue = ui_to_llm_queue
+        self.llm_to_ui_queue = llm_to_ui_queue
+        self.log_queue = log_queue
         self.pending_updates: List[Union[LoreUpdate, CharacterStateUpdate]] = []
 
         if result:
-            # Populate pending updates from result
             self.pending_updates.extend(result.lore_updates)
             self.pending_updates.extend(result.character_updates)
 
-        self.selected_index = -1
-
     def compose(self) -> ComposeResult:
-        yield Container(
+        yield Vertical(
             Horizontal(
                 Vertical(
-                    DataTable(id="update-table"),
+                    DataTable(id="pending-facts-table"),
+                    Vertical(
+                        Input(placeholder="Message LLM...", id="llm-input"),
+                        id="llm-input-container",
+                    ),
+                    ListView(id="context-pane"),
                     id="left-pane",
                 ),
                 Vertical(
-                    Vertical(
-                        Label("Details:", id="details-label"),
-                        Static("No update selected", id="details-text"),
-                        id="details-container",
-                    ),
-                    Vertical(
-                        Label("Edit Value:"),
-                        Input(id="edit-input"),
-                        Button("Save Edit", id="save-edit"),
-                        id="edit-container",
-                    ),
-                    Horizontal(
-                        Button("Accept", id="btn-accept"),
-                        Button("Reject", id="btn-reject"),
-                        Button("Edit", id="btn-edit"),
-                        id="actions-container",
-                    ),
+                    ListView(id="log-pane"),
+                    Static("LATEST LLM INPUTS", id="log-footer"),
                     id="right-pane",
                 ),
+                id="content-wrapper",
             ),
-            Footer(),
+            id="main-container",
         )
+        yield Footer()
 
     def on_mount(self) -> None:
-        table = self.query_one("#update-table", DataTable)
+        table = self.query_one("#pending-facts-table", DataTable)
         table.cursor_type = "row"
-        table.add_columns("Type", "Target", "Update")
+        table.add_columns("Type", "Target", "Content")
 
         for i, update in enumerate(self.pending_updates):
-            if isinstance(update, LoreUpdate):
-                table.add_row(
-                    "Lore", update.entity_name or "General", update.content, key=str(i)
-                )
-            elif isinstance(update, CharacterStateUpdate):
-                change_text = f"HP: {update.hp_change or 0}"
-                if update.status_effects_added:
-                    change_text += f", Added: {', '.join(update.status_effects_added)}"
-                if update.status_effects_removed:
-                    change_text += (
-                        f", Removed: {', '.join(update.status_effects_removed)}"
-                    )
-                table.add_row("Char", update.character_name, change_text, key=str(i))
+            self.add_update_to_table(update, i)
 
-        if self.pending_updates:
-            self.handle_row_highlight(0)
-            self.query_one("#btn-accept", Button).focus()
-
-        if self.proposal_queue:
-            self.run_worker(self.poll_proposal_queue, thread=False)
-
-    async def poll_proposal_queue(self) -> None:
-        """
-        Background worker that polls the proposal queue for new extraction results.
-        """
-        while True:
-            try:
-                result = await self.proposal_queue.get()
-                self.add_result(result)
-            except Exception as e:
-                # Log error but keep the worker running
-                self.log(f"Error polling proposal queue: {e}")
-            finally:
-                # Signal that the item has been processed
-                if hasattr(self.proposal_queue, "task_done"):
-                    self.proposal_queue.task_done()
-
-    def add_result(self, result: ExtractionResult) -> None:
-        """
-        Adds results from the LLM processor to the TUI table.
-        """
-        table = self.query_one("#update-table", DataTable)
-        start_index = len(self.pending_updates)
-
-        for update in result.lore_updates + result.character_updates:
-            self.pending_updates.append(update)
-            actual_index = len(self.pending_updates) - 1
-
-            if isinstance(update, LoreUpdate):
-                table.add_row(
-                    "Lore",
-                    update.entity_name or "General",
-                    update.content,
-                    key=str(actual_index),
-                )
-            elif isinstance(update, CharacterStateUpdate):
-                change_text = f"HP: {update.hp_change or 0}"
-                if update.status_effects_added:
-                    change_text += f", Added: {', '.join(update.status_effects_added)}"
-                if update.status_effects_removed:
-                    change_text += (
-                        f", Removed: {', '.join(update.status_effects_removed)}"
-                    )
-                table.add_row(
-                    "Char", update.character_name, change_text, key=str(actual_index)
-                )
-
-        # If the table was previously empty and we added updates, focus the first one.
-        if start_index == 0 and self.pending_updates:
-            self.handle_row_highlight(0)
-            self.query_one("#btn-accept", Button).focus()
-
-    def on_data_table_row_highlighted(self, event: DataTable.RowHighlighted) -> None:
-        self.handle_row_highlight(event.cursor_row)
-
-    def handle_row_highlight(self, row: int) -> None:
-        self.selected_index = row
-        if self.selected_index < 0 or self.selected_index >= len(self.pending_updates):
-            return
-
-        update = self.pending_updates[self.selected_index]
-
-        details_text = self.query_one("#details-text", Static)
-        if isinstance(update, LoreUpdate):
-            details_text.update(
-                f"Category: {update.category}\nTarget: {update.entity_name}\nContent: {update.content}"
-            )
-        elif isinstance(update, CharacterStateUpdate):
-            details_text.update(
-                f"Character: {update.character_name}\nHP Change: {update.hp_change}\nAdded Effects: {update.status_effects_added}\nRemoved Effects: {update.status_effects_removed}"
-            )
-
-        # Reset to detail view
-        self.query_one("#edit-container", Vertical).styles.display = "none"
-        self.query_one("#details-container", Vertical).styles.display = "block"
-
-    def on_button_pressed(self, event: Button.Pressed) -> None:
-        if self.selected_index == -1:
-            return
-
-        update = self.pending_updates[self.selected_index]
-
-        if event.button.id == "btn-accept":
-            if isinstance(update, LoreUpdate):
-                update_lore(update)
-            elif isinstance(update, CharacterStateUpdate):
-                update_character_state(update)
-
-            self.remove_update(self.selected_index)
-
-        elif event.button.id == "btn-reject":
-            self.remove_update(self.selected_index)
-
-        elif event.button.id == "btn-edit":
-            self.show_edit_mode(update)
-
-        elif event.button.id == "save-edit":
-            self.save_edit(update)
-
-    def show_edit_mode(self, update: Union[LoreUpdate, CharacterStateUpdate]) -> None:
-        edit_input = self.query_one("#edit-input", Input)
-
-        if isinstance(update, LoreUpdate):
-            edit_input.value = update.content
-        elif isinstance(update, CharacterStateUpdate):
-            # For simplicity, only allow editing HP change in this TUI
-            edit_input.value = str(update.hp_change or 0)
-
-        self.query_one("#edit-container", Vertical).styles.display = "block"
-        self.query_one("#details-container", Vertical).styles.display = "none"
-
-    def save_edit(self, update: Union[LoreUpdate, CharacterStateUpdate]) -> None:
-        new_val = self.query_one("#edit-input", Input).value
-
-        if isinstance(update, LoreUpdate):
-            update.content = new_val
-        elif isinstance(update, CharacterStateUpdate):
-            try:
-                update.hp_change = int(new_val)
-            except ValueError:
-                # Ignore invalid integer input
+        if self.ui_to_llm_queue:
+            # We don't need a poller for this, just the action_send
             pass
+        if self.llm_to_ui_queue:
+            # Use Textual workers so the task isn't garbage-collected and
+            # exceptions are surfaced via the worker manager.
+            self.run_worker(self.poll_llm_updates(), exclusive=False)
+        if self.log_queue:
+            self.run_worker(self.poll_log_updates(), exclusive=False)
 
-        # Refresh the table
-        table = self.query_one("#update-table", DataTable)
-        # Textual DataTable doesn't have a simple 'update_row', so we clear and refill
-        # or we can use update_cell.
+        self.query_one("#llm-input", Input).focus()
 
-        # Update the table row
+    def add_update_to_table(
+        self, update: Union[LoreUpdate, CharacterStateUpdate], index: int
+    ) -> None:
+        table = self.query_one("#pending-facts-table", DataTable)
         if isinstance(update, LoreUpdate):
-            table.update_cell(self.selected_index, 2, update.content)
+            table.add_row(
+                "Lore", update.entity_name or "General", update.content, key=str(index)
+            )
         elif isinstance(update, CharacterStateUpdate):
             change_text = f"HP: {update.hp_change or 0}"
             if update.status_effects_added:
                 change_text += f", Added: {', '.join(update.status_effects_added)}"
             if update.status_effects_removed:
                 change_text += f", Removed: {', '.join(update.status_effects_removed)}"
-            table.update_cell(self.selected_index, 2, change_text)
+            table.add_row("Char", update.character_name, change_text, key=str(index))
 
-        self.show_edit_mode(update)  # just to refresh the value maybe? No,’
-        # Actually let's go back to detail view
-        self.query_one("#edit-container", Vertical).styles.display = "none"
-        self.query_one("#details-container", Vertical).styles.display = "block"
+    async def poll_llm_updates(self) -> None:
+        while True:
+            try:
+                update = await self.llm_to_ui_queue.get()
+                display_text = f"Query: {update.query}\nSource: {update.source}\n\n{update.snippet}"
+                context_list = self.query_one("#context-pane", ListView)
+                # ListView.insert takes an *iterable* of ListItems; passing a
+                # bare ListItem raises TypeError because ListItem is not iterable.
+                # Insert at the top to show most recent first.
+                await context_list.insert(0, [ListItem(Static(display_text))])
+                if hasattr(self.llm_to_ui_queue, "task_done"):
+                    self.llm_to_ui_queue.task_done()
+            except Exception as e:
+                self.log(f"Error polling LLM updates: {e}")
 
-        # Update details text
-        details_text = self.query_one("#details-text", Static)
+    async def poll_log_updates(self) -> None:
+        while True:
+            try:
+                log_text = await self.log_queue.get()
+                log_list = self.query_one("#log-pane", ListView)
+                # See poll_llm_updates: wrap the ListItem in a list.
+                # Insert at the top to show most recent first.
+                await log_list.insert(0, [ListItem(Static(log_text))])
+                if hasattr(self.log_queue, "task_done"):
+                    self.log_queue.task_done()
+            except Exception as e:
+                self.log(f"Error polling log updates: {e}")
+
+    def handle_proposal_result(self, result: ExtractionResult) -> None:
+        table = self.query_one("#pending-facts-table", DataTable)
+        for update in result.lore_updates + result.character_updates:
+            index = len(self.pending_updates)
+            self.pending_updates.append(update)
+            self.add_update_to_table(update, index)
+
+    async def poll_context_queue(self) -> None:
+        # Obsolete
+        pass
+
+    async def poll_response_queue(self) -> None:
+        # Obsolete
+        pass
+
+    def on_input_submitted(self, event: Input.Submitted) -> None:
+        if event.input.id == "llm-input":
+            self.action_send()
+
+    def action_send(self) -> None:
+        input_widget = self.query_one("#llm-input", Input)
+        text = input_widget.value
+        if text and self.ui_to_llm_queue:
+            self.ui_to_llm_queue.put_nowait(text)
+            input_widget.value = ""
+
+    def action_accept(self) -> None:
+        table = self.query_one("#pending-facts-table", DataTable)
+        row_index = table.cursor_row
+        if row_index < 0 or row_index >= len(self.pending_updates):
+            return
+
+        update = self.pending_updates[row_index]
         if isinstance(update, LoreUpdate):
-            details_text.update(
-                f"Category: {update.category}\nTarget: {update.entity_name}\nContent: {update.content}"
-            )
+            update_lore(update)
         elif isinstance(update, CharacterStateUpdate):
-            details_text.update(
-                f"Character: {update.character_name}\nHP Change: {update.hp_change}\nAdded Effects: {update.status_effects_added}\nRemoved Effects: {update.status_effects_removed}"
-            )
+            update_character_state(update)
+
+        self.remove_update(row_index)
+
+    def action_reject(self) -> None:
+        table = self.query_one("#pending-facts-table", DataTable)
+        row_index = table.cursor_row
+        if row_index < 0 or row_index >= len(self.pending_updates):
+            return
+
+        self.remove_update(row_index)
+
+    def action_edit(self) -> None:
+        table = self.query_one("#pending-facts-table", DataTable)
+        row_index = table.cursor_row
+        if row_index < 0 or row_index >= len(self.pending_updates):
+            return
+
+        update = self.pending_updates[row_index]
+        initial_text = ""
+        if isinstance(update, LoreUpdate):
+            initial_text = update.content
+        elif isinstance(update, CharacterStateUpdate):
+            initial_text = str(update.hp_change or 0)
+
+        def save_callback(new_text: str):
+            if isinstance(update, LoreUpdate):
+                update.content = new_text
+            elif isinstance(update, CharacterStateUpdate):
+                try:
+                    update.hp_change = int(new_text)
+                except ValueError:
+                    pass
+
+            # Update the table
+            self.refresh_table()
+
+        self.push_screen(EditModal(initial_text, save_callback))
 
     def remove_update(self, index: int) -> None:
-        # Remove from the pending list
         del self.pending_updates[index]
+        self.refresh_table()
 
-        # Clear and refill the table
-        table = self.query_one("#update-table", DataTable)
+    def refresh_table(self) -> None:
+        table = self.query_one("#pending-facts-table", DataTable)
         table.clear()
-
         for i, update in enumerate(self.pending_updates):
-            if isinstance(update, LoreUpdate):
-                table.add_row(
-                    "Lore", update.entity_name or "General", update.content, key=str(i)
-                )
-            elif isinstance(update, CharacterStateUpdate):
-                change_text = f"HP: {update.hp_change or 0}"
-                if update.status_effects_added:
-                    change_text += f", Added: {', '.join(update.status_effects_added)}"
-                if update.status_effects_removed:
-                    change_text += (
-                        f", Removed: {', '.join(update.status_effects_removed)}"
-                    )
-                table.add_row("Char", update.character_name, change_text, key=str(i))
-
-        if self.pending_updates:
-            self.handle_row_highlight(0)
-            self.query_one("#btn-accept", Button).focus()
-        else:
-            self.selected_index = -1
-            self.query_one("#details-text", Static).update("All updates processed.")
+            self.add_update_to_table(update, i)

tests/test_rag.py

@@ -0,0 +1,98 @@
+import os
+
+from reportlab.pdfgen import canvas
+
+from src.rag.manager import RAGManager
+
+
+def create_dummy_phb(pdf_path: str):
+    """
+    Creates a dummy PDF file to simulate the Player's Handbook for verification.
+    """
+    print(f"Creating dummy PHB at {pdf_path}...")
+    c = canvas.Canvas(pdf_path)
+
+    # Page 1: Fireball
+    c.drawString(100, 750, "Fireball")
+    c.drawString(
+        100,
+        730,
+        "A bright streak flashes from your pointing finger to a point you choose within range.",
+    )
+    c.drawString(
+        100,
+        710,
+        "Each creature in a 20-foot-radius sphere centered on that point must make a Dexterity saving throw.",
+    )
+    c.showPage()
+
+    # Page 2: Grappling
+    c.drawString(100, 750, "Grappling")
+    c.drawString(
+        100,
+        730,
+        "Grappling is a special option available to any attack that hits with a melee attack.",
+    )
+    c.drawString(
+        100,
+        710,
+        "A creature is grappled if it is the target of the grapple attack and the attack hits.",
+    )
+    c.showPage()
+
+    # Page 3: General Rules
+    c.drawString(100, 750, "General Rules")
+    c.drawString(
+        100,
+        730,
+        "Combat is resolved in rounds, and each round represents 6 seconds of in-game time.",
+    )
+    c.showPage()
+
+    c.save()
+    print(f"Dummy PHB created successfully at {pdf_path}")
+
+
+def test_rag():
+    pdf_path = "data/phb_dummy.pdf"
+    create_dummy_phb(pdf_path)
+
+    # Initialize RAG Manager
+    rag = RAGManager(persist_dir="data/rag_index_test")
+
+    # Task 2.2: Ingest PDF
+    print("\nTesting Ingestion...")
+    rag.ingest_pdf(pdf_path)
+
+    # Task 2.3: Retrieve Logic
+    print("\nTesting Retrieval...")
+
+    # Test 1: Fireball
+    query1 = "What is Fireball?"
+    results1 = rag.retrieve(query1)
+    print(f"Query: {query1}")
+    for res in results1:
+        print(f"Source: {res.source} | Snippet: {res.snippet[:100]}...")
+
+    assert len(results1) > 0, "Should have retrieved at least one result for 'Fireball'"
+    assert "Fireball" in results1[0].snippet, "The top result should contain 'Fireball'"
+
+    # Test 2: Grappling
+    query2 = "How does grappling work?"
+    results2 = rag.retrieve(query2)
+    print(f"Query: {query2}")
+    for res in results2:
+        print(f"Source: {res.source} | Snippet: {res.snippet[:100]}...")
+
+    assert len(results2) > 0, (
+        "Should have retrieved at least one result for 'Grappling'"
+    )
+    assert "Grappling" in results2[0].snippet, (
+        "The top result should contain 'Grappling'"
+    )
+
+    print("\n✅ RAG Verification Successful!")
+
+
+if __name__ == "__main__":
+    test_rag()