Update UI and prompts

src/llm/models.py

@@ -54,6 +54,16 @@ class ContextUpdate(BaseModel):
     )
 
 
+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"
@@ -68,6 +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 +1,13 @@
 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 .models import ExtractionResult, FilterResult
 from .prompts import EXTRACTION_SYSTEM_PROMPT, NOISE_FILTER_SYSTEM_PROMPT
 
 logger = logging.getLogger(__name__)
@@ -90,13 +92,28 @@ class LLMProcessor:
             logger.error(f"LLM Error: {e}")
             return ""
 
-    def filter_transcript(self, text: str, context: Optional[str] = None) -> str:
+    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, context=context)
+        result = self._call_llm(
+            NOISE_FILTER_SYSTEM_PROMPT,
+            text,
+            context=context,
+            response_format={"type": "json_object"},
+        )
         logger.info(f"LLM Processor (Filter): {text} -> {result}")
-        return result
+
+        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
@@ -109,18 +126,18 @@ class LLMProcessor:
             # Using standard chat.completions.create with JSON mode for better compatibility with vLLM
             logger.info("LLM Processor (Extract): Sending request to backend...")
 
-            messages = [
+            system_prompt = EXTRACTION_SYSTEM_PROMPT
-                {"role": "system", "content": EXTRACTION_SYSTEM_PROMPT},
-            ]
             if context:
-                messages.append(
+                system_prompt += f"\n{context}"
-                    {
+
-                        "role": "system",
+            messages = [
-                        "content": f"Context from previous conversation:\n{context}",
+                {"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=messages,
@@ -142,15 +159,3 @@ class LLMProcessor:
             logger.error(f"Extraction Error: {e}")
             # Return an empty ExtractionResult if parsing fails
             return 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, context=context)
-        if not filtered_text:
-            return ExtractionResult()
-
-        return self.extract_structured_data(filtered_text, context=context)

src/llm/prompts.py

@@ -2,7 +2,11 @@
 
 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 questions, or player commentary that adds context).
+- "filtered_text": The cleaned transcript used for structured data extraction.
+
 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 +30,7 @@ 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 +38,9 @@ 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:
+   - "contextual_info": (string) The contextual information for the event
+   - "source": (string) The source of the context (e.g., "players handbook, page 68")
 
 Example Output:
 {
@@ -40,7 +48,7 @@ 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 +62,12 @@ Example Output:
   ],
   "events": [
     "The party discovered the secret entrance to the crypt."
+  ],
+  "context": [
+    {
+      "contextual_info": "fireball does 1d6 damage, with an area of effect of 10 feet circle",
+      "source": "players handbook, page 68"
+    }
   ]
 }
 

src/pipeline/orchestrator.py

@@ -131,32 +131,76 @@ class PipelineOrchestrator:
                     context_text = full_history_text
 
                 # 2. Prepare Context (Wiki / Database of Knowledge)
-                wiki_context = self._get_wiki_context()
+                # wiki_context = self._get_wiki_context()
 
                 # Combine both
-                combined_context = f"Conversation History:\n{context_text}\n\nWiki Knowledge:\n{wiki_context}"
+                combined_context = f"Conversation History:\n{context_text}\n\n"
 
-                # Process via LLM (Filter -> Extract)
+                # --- New RAG Flow ---
-                # Run in a separate thread to avoid blocking the event loop
+                # a. Filter transcript first to get cleaned text
-                result = await asyncio.to_thread(
+                filter_result = await asyncio.to_thread(
-                    self.processor.process_pipeline, raw_text, context=combined_context
+                    self.processor.filter_transcript, raw_text, context=combined_context
+                )
+
+                # b. Use filtered text to retrieve relevant snippets from RAG
+                rag_snippets = []
+                if filter_result.filtered_text:
+                    try:
+                        snippets = await asyncio.to_thread(
+                            self.rag_manager.retrieve, filter_result.filtered_text
+                        )
+                        rag_snippets = snippets
+                    except Exception as e:
+                        logger.error(f"RAG Retrieval Error in llm_worker: {e}")
+
+                # c. Combine RAG snippets with existing combined_context
+                logger.info(f"LLM Processor (Extract): rag_snippets: {rag_snippets}")
+                rag_context_text = "\n".join([s.snippet for s in rag_snippets])
+                augmented_context = combined_context
+                if rag_context_text:
+                    augmented_context += (
+                        f"\n\nRelevant RAG Context:\n{rag_context_text}"
+                    )
+
+                # d. Extract structured data using the augmented context
+                extraction_result = await asyncio.to_thread(
+                    self.processor.extract_structured_data,
+                    filter_result.filtered_text if filter_result.filtered_text else "",
+                    context=augmented_context,
                 )
 
                 if (
-                    result.lore_updates
+                    extraction_result.lore_updates
-                    or result.character_updates
+                    or extraction_result.character_updates
-                    or result.significant_events
+                    or extraction_result.significant_events
                 ):
                     logger.info(
-                        f"LLM Worker: Proposal generated. Putting into proposal queue. (Lore: {len(result.lore_updates)}, Char: {len(result.character_updates)})"
+                        f"LLM Worker: Proposal generated. Putting into proposal queue. (Lore: {len(extraction_result.lore_updates)}, Char: {len(extraction_result.character_updates)})"
                     )
-                    await self.proposal_queue.put(result)
+                    await self.proposal_queue.put(extraction_result)
 
-                    # Trigger RAG query based on extracted entities
+                    # Trigger RAG query based on extracted entities (for TUI updates)
-                    await self._trigger_rag_queries(result)
+                    await self._trigger_rag_queries(extraction_result)
                 else:
                     logger.info("LLM Worker: No relevant game data extracted.")
 
+                # e. If the filter found contextual info, push it to the context queue
+                if filter_result.contextual_info:
+                    logger.info(
+                        f"LLM Worker: Contextual info found: {filter_result.contextual_info}"
+                    )
+                    await self.context_queue.put(
+                        ContextUpdate(
+                            query="Filter",
+                            snippet=filter_result.contextual_info,
+                            source="Transcript",
+                        )
+                    )
+
+                # f. Also push the RAG snippets used for extraction to the context queue
+                for snippet in rag_snippets:
+                    await self.context_queue.put(snippet)
+
             except Exception as e:
                 logger.error(f"LLM Worker error: {e}")
 

src/ui/tui.py

@@ -3,7 +3,7 @@ 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.widgets import Button, DataTable, Footer, Input, Label, ListView, Static
 
 from src.llm.models import CharacterStateUpdate, ExtractionResult, LoreUpdate
 from src.persistence.characters import update_character_state
@@ -12,18 +12,16 @@ from src.persistence.lore import update_lore
 
 class ConfirmationApp(App):
     CSS = """
-    Screen {
+    #main-container {
-        layout: horizontal;
+        height: 100%;
+    }
+
+    #top-pane {
+        height: 70%;
     }
 
     #left-pane {
-        width: 30%;
+        width: 60%;
-        border: solid;
-        padding: 1;
-    }
-
-    #middle-pane {
-        width: 30%;
         border: solid;
         padding: 1;
     }
@@ -35,6 +33,12 @@ class ConfirmationApp(App):
         layout: vertical;
     }
 
+    #middle-pane {
+        height: 30%;
+        border: solid;
+        padding: 1;
+    }
+
     #details-container {
         height: auto;
         margin-bottom: 1;
@@ -84,35 +88,38 @@ class ConfirmationApp(App):
 
     def compose(self) -> ComposeResult:
         yield Container(
-            Horizontal(
+            Vertical(
-                Vertical(
+                Horizontal(
-                    DataTable(id="update-table"),
+                    Vertical(
-                    id="left-pane",
+                        DataTable(id="update-table"),
+                        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",
+                        ),
+                        id="right-pane",
+                    ),
                 ),
-                Vertical(
+                Horizontal(
-                    Static("No context available", id="context-pane"),
+                    ListView(id="context-pane"),
                     id="middle-pane",
                 ),
-                Vertical(
+                id="main-container",
-                    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",
-                    ),
-                    id="right-pane",
-                ),
             ),
             Footer(),
         )
@@ -169,11 +176,13 @@ class ConfirmationApp(App):
         while True:
             try:
                 update = await self.context_queue.get()
-                context_pane = self.query_one("#context-pane", Static)
+                context_pane = self.query_one("#context-pane", ListView)
 
                 # Format the update for display
                 display_text = f"Query: {update.query}\nSource: {update.source}\n\n{update.snippet}"
-                context_pane.update(display_text)
+
+                # Add a new Static widget to the ListView
+                context_pane.mount(Static(display_text))
 
                 if hasattr(self.context_queue, "task_done"):
                     self.context_queue.task_done()