Improvements

.gitignore

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

src/llm/processor.py

@@ -1,3 +1,4 @@
+import logging
 import os
 from typing import Any, Dict, Optional
 
@@ -7,6 +8,8 @@ from pydantic import ValidationError
 from .models import ExtractionResult
 from .prompts import EXTRACTION_SYSTEM_PROMPT, NOISE_FILTER_SYSTEM_PROMPT
 
+logger = logging.getLogger(__name__)
+
 
 class LLMProcessor:
     def __init__(
@@ -47,7 +50,7 @@ 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 +59,98 @@ 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},
             )
             return response.choices[0].message.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 filter_transcript(self, text: str, context: Optional[str] = None) -> str:
         """
         Stage 1: Raw Transcript -> Filtered Text.
         """
-        result = self._call_llm(NOISE_FILTER_SYSTEM_PROMPT, text)
-        print(f"LLM Processor (Filter): {text} -> {result}")
+        result = self._call_llm(NOISE_FILTER_SYSTEM_PROMPT, text, context=context)
+        logger.info(f"LLM Processor (Filter): {text} -> {result}")
         return result
 
-    def extract_structured_data(self, filtered_text: str) -> ExtractionResult:
+    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...")
+
+            messages = [
+                {"role": "system", "content": EXTRACTION_SYSTEM_PROMPT},
+            ]
+            if context:
+                messages.append(
+                    {
+                        "role": "system",
+                        "content": f"Context from previous conversation:\n{context}",
+                    }
+                )
+            messages.append({"role": "user", "content": filtered_text})
+
             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},
             )
-            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:
+    def process_pipeline(
+        self, raw_text: str, context: Optional[str] = None
+    ) -> ExtractionResult:
         """
         Executes the two-stage pipeline: Raw Transcript -> Filtered Text -> Structured Data.
         """
-        filtered_text = self.filter_transcript(raw_text)
+        filtered_text = self.filter_transcript(raw_text, context=context)
         if not filtered_text:
             return ExtractionResult()
 
-        return self.extract_structured_data(filtered_text)
+        return self.extract_structured_data(filtered_text, context=context)

src/pipeline/orchestrator.py

@@ -1,5 +1,8 @@
 import asyncio
 import logging
+import os
+from pathlib import Path
+from typing import List, Optional
 
 from src.llm.models import ExtractionResult
 from src.llm.processor import LLMProcessor
@@ -7,7 +10,14 @@ 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"),
+    ],
+)
 logger = logging.getLogger(__name__)
 
 
@@ -17,7 +27,7 @@ class PipelineOrchestrator:
 
         # Modules
         self.listener = AudioListener(loop=self.loop)
-        self.transcriber = Transcriber()
+        self.transcriber = Transcriber(model_size="small")
         self.processor = LLMProcessor()
 
         # Queues
@@ -26,6 +36,10 @@ class PipelineOrchestrator:
 
         self.is_running = False
 
+        # Conversation history for context
+        self.history = []  # List of strings (transcripts)
+        self.history_max_words = 1000
+
     async def stt_worker(self):
         """
         Worker that handles STT: Audio -> Text.
@@ -61,9 +75,29 @@ class PipelineOrchestrator:
 
                 logger.info(f"LLM Worker: Processing text: {raw_text}")
 
+                # 1. Prepare Context (Conversation History)
+                # Maintain history and truncate to max words
+                self.history.append(raw_text)
+                full_history_text = " ".join(self.history)
+                words = full_history_text.split()
+                if len(words) > self.history_max_words:
+                    # Keep the last N words
+                    kept_words = words[-self.history_max_words :]
+                    context_text = " ".join(kept_words)
+                else:
+                    context_text = full_history_text
+
+                # 2. Prepare Context (Wiki / Database of Knowledge)
+                wiki_context = self._get_wiki_context()
+
+                # Combine both
+                combined_context = f"Conversation History:\n{context_text}\n\nWiki Knowledge:\n{wiki_context}"
+
                 # Process via LLM (Filter -> Extract)
-                # Note: this is currently a synchronous call, which blocks the loop.
-                result = self.processor.process_pipeline(raw_text)
+                # Run in a separate thread to avoid blocking the event loop
+                result = await asyncio.to_thread(
+                    self.processor.process_pipeline, raw_text, context=combined_context
+                )
 
                 if (
                     result.lore_updates
@@ -83,6 +117,30 @@ class PipelineOrchestrator:
             # Small sleep
             await asyncio.sleep(0.1)
 
+    def _get_wiki_context(self) -> str:
+        """
+        Reads all files in the lore directory and returns them as a single 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.
@@ -93,8 +151,11 @@ class PipelineOrchestrator:
             # Pass the proposal queue to the app.
             app = ConfirmationApp(proposal_queue=self.proposal_queue)
             await app.run_async()
+            # Once the TUI exits, stop the entire pipeline
+            self.stop()
         except Exception as e:
             logger.error(f"TUI Worker error: {e}")
+            self.stop()
 
     async def run(self):
         """

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

@@ -2,7 +2,7 @@ import logging
 
 from faster_whisper import WhisperModel
 
-logging.basicConfig(level=logging.INFO)
+# Do not call basicConfig here, as it's called in the orchestrator
 logger = logging.getLogger(__name__)
 
 

src/ui/tui.py

@@ -140,13 +140,12 @@ class ConfirmationApp(App):
             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()
+            except Exception as e:
+                # Log error but keep the worker running
+                self.log(f"Error polling proposal queue: {e}")
 
     def add_result(self, result: ExtractionResult) -> None:
         """