feat: ✨ Working again with proper use of enriched content

TODO

@@ -0,0 +1,17 @@
+Test new embeddings 
+
+Benchmark / rate embeddings & vectors 
+
+Is RAG still the "thing"? - What is the cutting edge 
+ - "Context Engineering" is the current evolution, although GraphRaG has been a thing?
+ - Context Engineering seems to be finding the balance of how to provide just the right amount of context to get best results. 
+    Too little context and the llm doesnt have enough info to give an accurate answer 
+    Too much conflicting context (poison)
+    too much context (confusion)
+
+
+
+Turso - better? vector store, but sqlite so data <3 
+
+How can we RaG in AW? DSAR 
+

src/config.yaml

@@ -1,6 +1,6 @@
 # --- Connection Settings ---
 api:
-  base_url: "http://192.168.0.49:1234"
+  base_url: "http://framework.tawny-bellatrix.ts.net:1234"
   api_version: "/v1/"
 
 # --- Model Settings ---

src/experts/dnd_agent.py

@@ -58,7 +58,17 @@ class DnDRAG(dspy.Module):
         context_parts = []
         for i, doc in enumerate(results):
             source = doc.metadata.get("source", "Unknown")
-            context_parts.append(f"--- Chunk {i + 1} from {source} ---\n{doc.page_content}")
+            synopsis = doc.metadata.get("synopsis", "None")
+            tags = doc.metadata.get("tags", "None")
+            entities = doc.metadata.get("entities", "None")
+            context_parts.append(f"""
+--- Chunk {i+1} from {source} ---
+synpsis: {synopsis}, 
+tags: {tags}, 
+entities: {entities}
+{doc.page_content}
+""")
+        #print(context_parts)
 
         # 3. Add the Full Content of the top match (optional, but requested!)
         # We'll just take the top 1 file to avoid context window explosion

src/experts/ingestion_agent.py

@@ -1,33 +1,21 @@
 import dspy
-from pydantic import BaseModel, Field
+from typing import List
-
-
-# 1. Define the structure of your metadata
-class DocMetadata(BaseModel):
-    synopsis: str = Field(description="A one-sentence summary of the document.")
-    tags: list[str] = Field(description="Relevant tags (NPCs, Locations, Items, Plot Points).")
-    entities: list[str] = Field(description="Key names of people, places, or factions.")
-
 
 class IngestionSignature(dspy.Signature):
     """You are an expert Dungeon Master's assistant.
     Analyze the provided notes and extract a concise synopsis and relevant metadata.
+    synopsis = A one-sentence summary of the document.
+    tags = Relevant tags (NPCs, Locations, Items, Plot Points).
+    entities = Key names of people, places, or factions.
+    "note -> synopsis:str, tags: list[str], entities: list[str]"
+    /no_think
     """
 
     note: str = dspy.InputField(desc="The DM notes or session recap content.")
-    # By using the Pydantic model as the type, DSPy handles the JSON formatting for you
+    answer: dict[str,str|List] = dspy.OutputField(desc="the metadata dictionary with the keys; synopsis, tags, entities")
-    answer: DocMetadata = dspy.OutputField()
+
 
 
 class IngestionAgent(dspy.Module):
     def __init__(self):
-        super().__init__()
+        self.ingest = dspy.Predict(IngestionSignature)
-        # We use TypedPredictor to enforce the Pydantic schema
-        # We use ChainOfThought because it helps 8B models "reason" through the tags
-        # before committing to the final JSON structure.
-        self.process = dspy.TypedPredictor(IngestionSignature)
-
-    def forward(self, note: str):
-        # The .answer will now be a DocMetadata object, not a string!
-        prediction = self.process(note=note)
-        return prediction

src/ingest.py

@@ -18,11 +18,10 @@ MODEL_BASE = CFG["models"]["enrich"]
 EMBEDDING_MODEL = CFG["models"]["embedding"]
 API_BASE = CFG["api"]["base_url"]
 API_VERSION = CFG["api"]["api_version"]
-MAX_WORKERS = CFG["ingestion"]["max_workers"]
+MAX_WORKERS=CFG["ingestion"]["max_workers"]
-CHUNK_SIZE = (CFG["ingestion"]["chunk_size"],)
+CHUNK_SIZE=CFG["ingestion"]["chunk_size"]
-CHUNK_OVERLAP = CFG["ingestion"]["chunk_overlap"]
+CHUNK_OVERLAP=CFG["ingestion"]["chunk_overlap"]
-EMBEDDING_BATCH_SIZE = CFG["ingestion"]["embedding_batch_size"]
+EMBEDDING_BATCH_SIZE=CFG["ingestion"]["embedding_batch_size"]
-
 
 def load_documents():
     docs = []
@@ -64,20 +63,19 @@ def enrich_chunks(chunks: list) -> list:
         lm_index = idx % 8
 
         try:
-            with dspy.context(
+            with dspy.context(lm=dspy.LM(model=MODEL_BASE, api_base=API_BASE+API_VERSION)):
-                lm=dspy.LM(f"{MODEL_BASE}:{lm_index}", api_base=API_BASE + API_VERSION)
+                response = IngestionAgent().ingest(note=chunk.page_content)
-            ):
+                
-                response = IngestionAgent().forward(note=chunk.page_content)
-
                 # This is now an object, not a string!
-                metadata = response.answer.dict()
+                metadata = response.answer 
 
         except Exception as e:
             print(f"⚠️ Failed for chunk {idx}: {e}")
             metadata = {"synopsis": "Summary failed", "tags": ["error"], "entities": []}
 
-            chunk.metadata.update(metadata)
+        chunk.metadata.update(metadata)
-            return chunk
+        return (idx, chunk)
+            
 
     enriched_results = []
     with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor: