added toon-python submodule

feat: ✨ AI Powered enhanced queries to get better results

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "toon-python"]
+	path = toon-python
+	url = git@github.com:toon-format/toon-python.git

ROADMAP.md

@@ -16,4 +16,6 @@
 ## Planned Later
 
 * entity chunking & re-ranking
-* Logging in Ingestion
+* Logging in Ingestion
+* database retrieve for tag or entity
+*

config.yaml

@@ -8,11 +8,13 @@ models:
   enrich: "lm_studio/qwen-" # will have an identifier, based on amount of active LLMs see ./load_ingestion_llms.sh
   embedding: "text-embedding-qwen3-embedding-8b"
   retrieval: "lm_studio/qwen/qwen3-30b-a3b-2507"
+  expansion: "lm_studio/qwen/qwen3-30b-a3b-2507"
 
 # --- Ingestion Settings ---
 ingestion:
-  data_dir: "/home/cosmic/DnD"
-  db_path: "./data/dmv.db"
+  data_dir: "/home/jake/DnD"
+  db_path: "./data/"
+  db_name: "dmv.db"
   active_llms: 2
   parallel_requests_per_llm: 2
   chunk_size: 800
@@ -21,7 +23,7 @@ ingestion:
   time_file_location: "./data/time_file.txt"
 
 # ---- Agent Settings ----
-ingestion_agent: 
+ingestion_agent:
   ingestion_signature: |
     You are an expert Dungeon Master's assistant.
     Analyze the provided notes and extract a concise synopsis and relevant metadata.
@@ -36,3 +38,10 @@ retrieval_agent:
     Given the context and the question, answer the question.
     Do not make things up, base all of your answers on the context.
     Always site the file location of your source of information.
+
+expansion_agent:
+  expansion_signature: |
+    You are a query expansion expert, specialised in Dungeons and Dragons.
+    Given a user's question, generate 3-5 similar but enhanced search queries that would help find more relevant information.
+    Each expanded query should be distinct and add different perspective to the original question.
+    Return only the queries as a JSON list with key "queries"."""

src/embedding.py

@@ -1,5 +1,6 @@
 import requests
 from langchain_core.embeddings import Embeddings
+
 from config_loader import load_config
 
 CFG = load_config()
@@ -37,7 +38,7 @@ class LocalLMEmbeddings(Embeddings):
 
         for i in range(0, len(texts), self.batch_size):
             batch = texts[i : i + self.batch_size]
-            print(f"🚀 Processing batch {(i // self.batch_size) + 1} (Size: {len(batch)})...")
+            # print(f"🚀 Processing batch {(i // self.batch_size) + 1} (Size: {len(batch)})...")
 
             batch_vectors = self._post_request(batch)
             all_embeddings.extend(batch_vectors)

src/experts/retrieval_agent.py

@@ -1,7 +1,7 @@
 import os
-import turso
-import dspy
 
+import dspy
+import turso
 
 from config_loader import load_config
 from embedding import LocalLMEmbeddings
@@ -9,27 +9,28 @@ from embedding import LocalLMEmbeddings
 CFG = load_config()
 
 DATABASE_PATH = CFG["ingestion"]["db_path"]
+DATABASE_NAME = CFG["ingestion"]["db_name"]
 EMBEDDING_MODEL = CFG["models"]["embedding"]
 API_BASE = CFG["api"]["base_url"]
 RETRIEVAL_CONFIG = CFG["retrieval_agent"]
+EXPANSION_CONFIG = CFG["expansion_agent"]
 
 
 def retrieve_from_turso(embedded_question, k=5):
     query = f"""
     SELECT file_path, synopsis, tags, entities, chunk_data,
-    vector_distance_cos(embedding, vector32('{embedded_question[0]}')) AS distance
+    vector_distance_cos(embedding, vector32('{embedded_question}')) AS distance
     FROM notes
     ORDER BY distance ASC
     LIMIT {k};
     """
-    con = turso.connect(DATABASE_PATH)
+    con = turso.connect(DATABASE_PATH + DATABASE_NAME)
     cur = con.cursor()
     cur.execute(query)
     rows = cur.fetchall()
     return rows
 
 
-# --- DSPy Signature ---
 class DnDContextQA(dspy.Signature):
     f"{RETRIEVAL_CONFIG['retrieval_signature']}"
 
@@ -38,47 +39,71 @@ class DnDContextQA(dspy.Signature):
     answer = dspy.OutputField(desc="A detailed answer based on the notes, citing the source file.")
 
 
+class ExpansionSignature(dspy.Signature):
+    f"{EXPANSION_CONFIG['expansion_signature']}"
+    question = dspy.InputField()
+    answer = dspy.OutputField(
+        desc="A list of questions that will be used to vector search the database."
+    )
+
+
 class DnDRAG(dspy.Module):
     def __init__(self):
         super().__init__()
         self.embeddings_model = LocalLMEmbeddings(
             model=EMBEDDING_MODEL,
             base_url=API_BASE,
-            batch_size=1,  # we only send 1 question at a time.
+            # batch_size=1,
         )
-        # Tools exposed to the ReAct loop
+        self.retrieval_lm = dspy.LM(
+            model=CFG["models"]["retrieval"], api_base=API_BASE + CFG["api"]["api_version"]
+        )
+        with dspy.context(lm=self.retrieval_lm, signature=ExpansionSignature):
+            self.query_expander = dspy.Predict("question -> queries:list[str]")
+
         self.tools = [self.load_file]
         self.generate_answer = dspy.ReAct(signature=DnDContextQA, tools=self.tools)
 
     def forward(self, question):
-        # TODO: Add step here to LLM Expand
-        # given the current question, generate 3-5 distinct search queries.
-        # embed all the questions
-        embedded_question = self.embeddings_model._post_request(question)
-        # store the 5 from all 3-5 questions (15 - 25 results)
-        results = retrieve_from_turso(embedded_question, k=5)  # k is limit to return
+        print("Enhancing Question")
+        with dspy.context(lm=self.retrieval_lm):
+            expanded_queries = self.query_expander(question=question).queries
+        print("Enhanced Queries:")
+        for q in expanded_queries:
+            print("    ", q)
+        all_embeddings = self.embeddings_model.embed_documents([question] + expanded_queries)
+        # print(all_embeddings)
+        all_results = []
+        for embedded_question in all_embeddings:
+            results = retrieve_from_turso(embedded_question, k=5)
+            all_results.extend(results)
+
+        seen = set()
+        unique_results = []
+        for row in all_results:
+            key = (row[0], row[4])
+            if key not in seen:
+                seen.add(key)
+                unique_results.append(row)
 
-        # Format context as before
         context_parts = []
-        for i, row in enumerate(results):
-            source = row[0]  # file_path
-            synopsis = row[1]  # synopsis
-            tags = row[2]  # tags
-            entities = row[3]  # entities
-            content = row[4]  # chunk_data
+        for i, row in enumerate(unique_results):
+            source = row[0]
+            synopsis = row[1]
+            tags = row[2]
+            entities = row[3]
+            content = row[4]
+            closeness = row[5]
 
             context_parts.append(f"""
 --- Chunk {i + 1} from {source} ---
 synopsis: {synopsis},
 tags: {tags},
-entities: {entities}
+entities: {entities},
+closeness: {closeness},
 {content}
 """)
 
-        # print('Closest embedding hits')
-        # for part in context_parts:
-        #     print(part)
-
         context = "\n\n".join(context_parts)
 
         prediction = self.generate_answer(context=context, question=question)

src/ingest.py

@@ -16,6 +16,7 @@ from experts.ingestion_agent import IngestionAgent
 CFG = load_config()
 DATA_DIR = CFG["ingestion"]["data_dir"]
 DATABASE_PATH = CFG["ingestion"]["db_path"]
+DATABASE_NAME = CFG["ingestion"]["db_name"]
 MODEL_BASE = CFG["models"]["enrich"]
 EMBEDDING_MODEL = CFG["models"]["embedding"]
 API_BASE = CFG["api"]["base_url"]
@@ -139,13 +140,10 @@ def embed_chunks(chunks: List[Any], batch_size: int = EMBEDDING_BATCH_SIZE) -> L
     # Process chunks in batches
     for i in tqdm(range(0, total_chunks, batch_size), desc="Embedding batches"):
         batch = chunks[i : i + batch_size]
+        print(f"🚀 Processing batch {(i // batch_size) + 1} (Size: {len(batch)})...")
         batch_content = [chunk.page_content for chunk in batch]
-
         try:
-            # Use model's batched embedding method
-            # batch_embeddings = embeddings_model.embed_query(batch_content)
             batch_embeddings = embeddings_model.embed_documents(batch_content)
-
             # Process each chunk in the batch
             for j, (chunk, embedding) in enumerate(zip(batch, batch_embeddings)):
                 # Extract metadata
@@ -228,7 +226,7 @@ def save_to_db(chunk_dicts):
     Each dict maps to a row in the 'notes' table.
     """
     print("connecting to db")
-    con = turso.connect(DATABASE_PATH)
+    con = turso.connect(DATABASE_PATH + DATABASE_NAME)
     print("opening cursor")
     cur = con.cursor()
 
@@ -267,7 +265,8 @@ def save_to_db(chunk_dicts):
 
 
 def create_db():
-    con = turso.connect(DATABASE_PATH)
+    Path(DATABASE_PATH).mkdir(exist_ok=True)
+    con = turso.connect(DATABASE_PATH + DATABASE_NAME)
     cur = con.cursor()
 
     cur.execute("""
@@ -334,7 +333,7 @@ def delete_from_db(embedded_chunks):
 
     print(f"Deleting existing rows for {len(file_paths)} file(s)")
 
-    con = turso.connect(DATABASE_PATH)
+    con = turso.connect(DATABASE_PATH + DATABASE_NAME)
     cur = con.cursor()
 
     # Use a single DELETE statement with IN clause for efficiency