feat: ✨ Refactor it Turso!

.gitignore

@@ -1,3 +1,5 @@
+data/*
+
 # Python-generated files
 __pycache__/
 *.py[oc]

.python-version

@@ -1 +1 @@
-3.14
+3.13

TODO

@@ -9,9 +9,6 @@ Is RAG still the "thing"? - What is the cutting edge
     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 
+How can we RaG in AW?
-

pyproject.toml

@@ -3,7 +3,7 @@ name = "dungeon-masters-vault"
 version = "0.1.0"
 description = "RAG application to help you with your DnD Notes."
 readme = "README.md"
-requires-python = ">=3.14"
+requires-python = ">=3.13"
 dependencies = [
     "dspy>=3.1.2",
     "faiss-cpu>=1.13.2",

src/config.yaml

@@ -12,11 +12,12 @@ models:
 # --- Ingestion Settings ---
 ingestion:
   data_dir: "/home/cosmic/DnD"
-  db_path: "./local_faiss_db"
+  db_path: "./data/dmv.db"
   max_workers: 8
   chunk_size: 800
   chunk_overlap: 100
   embedding_batch_size: 32
+  time_file_location: "./data/time_file.txt"
 
 # --- Retrieval Settings ---
 retrieval:

src/ingest.py

@@ -1,6 +1,7 @@
+import turso
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
-
+from datetime import datetime
 import dspy
 from langchain_community.document_loaders import TextLoader
 from langchain_community.vectorstores import FAISS
@@ -22,8 +23,9 @@ MAX_WORKERS=CFG["ingestion"]["max_workers"]
 CHUNK_SIZE=CFG["ingestion"]["chunk_size"]
 CHUNK_OVERLAP=CFG["ingestion"]["chunk_overlap"]
 EMBEDDING_BATCH_SIZE=CFG["ingestion"]["embedding_batch_size"]
+TIMEFILE = CFG["ingestion"]["time_file_location"]
 
-def load_documents():
+def load_documents(last_update_time):
     docs = []
     data_path = Path(DATA_DIR)
 
@@ -32,6 +34,10 @@ def load_documents():
         return docs
 
     for file_path in data_path.rglob("*.md"):
+        file_modified_date = datetime.fromtimestamp(file_path.stat().st_mtime)
+
+        if file_modified_date < last_update_time:
+            continue
         try:
             loader = TextLoader(str(file_path))
             loaded_docs = loader.load()
@@ -90,31 +96,212 @@ def enrich_chunks(chunks: list) -> list:
     return [item[1] for item in enriched_results]
 
 
-def store_chunks_locally(chunks, db_path=DATABASE_PATH):
+def embed_chunks(chunks):
+    """
+    Embed chunks and return a list of dictionaries with full metadata.
+    Each dict contains:
+        - file_path
+        - file_name
+        - chunk_data
+        - synopsis
+        - tags
+        - entities
+        - embedding
+    """
     embeddings_model = LocalLMEmbeddings(
         model=EMBEDDING_MODEL,
         base_url=API_BASE,
         batch_size=EMBEDDING_BATCH_SIZE,
     )
 
-    print(f"Index creation started for {len(chunks)} chunks...")
+    # Prepare list of dictionaries for output
-    # FAISS.from_documents extracts metadata directly from the Document objects
+    embedded_chunks = []
-    vectorstore = FAISS.from_documents(documents=chunks, embedding=embeddings_model)
+
-    vectorstore.save_local(db_path)
+    for i, chunk in enumerate(tqdm(chunks, desc="Embedding chunks")):
-    print(f"✅ Successfully stored in FAISS at '{db_path}'")
+        try:
-    return vectorstore
+            # Extract metadata
+            file_path = chunk.metadata.get("full_path", "unknown")
+            file_name = chunk.metadata.get("source", "unknown")
+            content = chunk.page_content
+
+            # Extract enriched metadata (from IngestionAgent)
+            synopsis = chunk.metadata.get("synopsis", "No summary")
+            tags = chunk.metadata.get("tags", [])
+            entities = chunk.metadata.get("entities", [])
+
+            # Generate embedding
+            embedding = embeddings_model.embed_query(content)
+
+            # Create structured dictionary
+            chunk_data = {
+                "file_path": file_path,
+                "file_name": file_name,
+                "chunk_data": content,
+                "synopsis": synopsis,
+                "tags": tags,
+                "entities": entities,
+                "embedding": embedding,
+                "timestamp": datetime.now().isoformat()
+            }
+
+            embedded_chunks.append(chunk_data)
+
+        except Exception as e:
+            print(f"⚠️ Failed to embed chunk {i}: {e}")
+            # Fallback entry
+            embedded_chunks.append({
+                "file_path": chunk.metadata.get("full_path", "unknown"),
+                "file_name": chunk.metadata.get("source", "unknown"),
+                "chunk_data": content,
+                "synopsis": "Embedding failed",
+                "tags": ["error"],
+                "entities": [],
+                "embedding": [],
+                "timestamp": datetime.now().isoformat()
+            })
+
+    return embedded_chunks
+
+
+def save_to_db(chunk_dicts):
+    """
+    Save a list of dictionaries to the Turso database.
+    Each dict maps to a row in the 'notes' table.
+    """
+    print('connecting to db')
+    con = turso.connect(DATABASE_PATH)
+    print('opening cursor')
+    cur = con.cursor()
+
+    # SQL with named placeholders for clarity and safety
+    insert_sql = """
+    INSERT INTO notes (
+        file_path, file_name, chunk_data, synopsis, tags, entities, embedding, timestamp
+    ) VALUES (?, ?, ?, ?, ?, ?, vector32(?), ?)
+    """
+
+    # Prepare batch data: convert each dict to a tuple in correct order
+    batch_data = []
+    for entry in chunk_dicts:
+        # Convert list of floats to comma-separated string for Turso vector32
+        embedding_str = str(entry["embedding"])
+
+        batch_data.append((
+            entry["file_path"],
+            entry["file_name"],
+            entry["chunk_data"],
+            entry["synopsis"],
+            ",".join(entry["tags"]),  # Store as comma-separated string
+            ",".join(entry["entities"]),  # Store as comma-separated string
+            embedding_str,
+            entry["timestamp"]
+        ))
+    print('data to insert:',len(batch_data))
+    # Execute batch insert
+    cur.executemany(insert_sql, batch_data)
+    con.commit()
+    con.close()
+
+    print(f"✅ Saved {len(batch_data)} chunks to database.")
+
+def create_db():
+    con = turso.connect(DATABASE_PATH)
+    cur = con.cursor()
+
+    cur.execute("""
+    CREATE TABLE IF NOT EXISTS notes (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        file_path TEXT NOT NULL,
+        file_name TEXT NOT NULL,
+        chunk_data TEXT NOT NULL,
+        synopsis TEXT,
+        tags TEXT,  -- comma-separated
+        entities TEXT,  -- comma-separated
+        embedding F32_BLOB(4096),
+        timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
+        UNIQUE(file_path, chunk_data)  -- avoid duplicates
+    )
+    """)
+
+    # Indexes for faster queries
+    cur.execute("CREATE INDEX IF NOT EXISTS idx_embedding ON notes(embedding);")
+    cur.execute("CREATE INDEX IF NOT EXISTS idx_file_path ON notes(file_path);")
+    cur.execute("CREATE INDEX IF NOT EXISTS idx_tags ON notes(tags);")
+    cur.execute("CREATE INDEX IF NOT EXISTS idx_synopsis ON notes(synopsis);")
+
+    con.commit()
+    con.close()
+    print("✅ Database and indexes created.")
+
+def get_last_update_time():
+    try: 
+        with open(TIMEFILE, "r") as file:
+            last_update_str = file.read()
+        last_update = datetime.strptime(last_update_str,"%Y/%m/%d - %H:%M:%S")
+    except FileNotFoundError:
+        print("File Not found, setting time to ingest all files")
+        last_update = datetime(year=2000,month=1,day=1)
+    return last_update
+
+def update_timefile():
+    current_time = datetime.now()
+    current_time_str = current_time.strftime("%Y/%m/%d - %H:%M:%S")
+    with open(TIMEFILE, "w") as file:
+        file.write(current_time_str)
+    return current_time_str
 
 
 def main():
-    docs = load_documents()
+    create_db()
+    last_update_time = get_last_update_time()
+    print(f"Last update time: {last_update_time}")
+
+    docs = load_documents(last_update_time)
     if not docs:
+        print("No Recently Updated Files to Ingest")
         return
 
     chunks = chunk_documents(docs)
+    print(f"Split into {len(chunks)} chunks.")
+
     enriched_chunks = enrich_chunks(chunks)
-    store_chunks_locally(enriched_chunks)
+    print(f"Enriched {len(enriched_chunks)} chunks.")
+
+    embedded_chunks = embed_chunks(enriched_chunks)
+    print(f"Embedded {len(embedded_chunks)} chunks.")
+
+    save_to_db(embedded_chunks)
     print("🎉 Ingestion complete!")
 
+    updated = update_timefile()
+    print(f"Updated timefile to: {updated}")
 
 if __name__ == "__main__":
     main()
+
+#TODO: create function to delete rows that match new files coming in
+# and handle danabase insert fails 
+"""
+Traceback (most recent call last):
+  File "/home/cosmic/source/dungeon_masters_vault/.venv/lib/python3.13/site-packages/turso/lib.py", line 643, in executemany
+    result = _run_execute_with_io(stmt, self._connection.extra_io)
+  File "/home/cosmic/source/dungeon_masters_vault/.venv/lib/python3.13/site-packages/turso/lib.py", line 163, in _run_execute_with_io
+    result = stmt.execute()
+turso.Constraint: UNIQUE constraint failed: notes.(file_path, chunk_data) (19)
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "/home/cosmic/source/dungeon_masters_vault/src/ingest.py", line 280, in <module>
+    main()
+    ~~~~^^
+  File "/home/cosmic/source/dungeon_masters_vault/src/ingest.py", line 273, in main
+    save_to_db(embedded_chunks)
+    ~~~~~~~~~~^^^^^^^^^^^^^^^^^
+  File "/home/cosmic/source/dungeon_masters_vault/src/ingest.py", line 201, in save_to_db
+    cur.executemany(insert_sql, batch_data)
+    ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/cosmic/source/dungeon_masters_vault/.venv/lib/python3.13/site-packages/turso/lib.py", line 656, in executemany
+    raise _map_turso_exception(exc)
+turso.lib.IntegrityError: UNIQUE constraint failed: notes.(file_path, chunk_data) (19)
+"""

src/testing_turso.py

@@ -0,0 +1,83 @@
+import turso
+
+from config_loader import load_config
+from embedding import LocalLMEmbeddings
+
+CFG = load_config()
+EMBEDDING_MODEL = CFG["models"]["embedding"]
+API_BASE = CFG["api"]["base_url"]
+EMBEDDING_BATCH_SIZE=CFG["ingestion"]["embedding_batch_size"]
+
+con = turso.connect("dmv.db")
+cur = con.cursor()
+cur.execute("""
+CREATE TABLE IF NOT EXISTS notes (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    file_path TEXT NOT NULL,
+    file_name TEXT NOT NULL,
+    chunk_data TEXT,
+    embedding F32_BLOB(4096),
+    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
+  )""")
+
+cur.execute("CREATE INDEX IF NOT EXISTS idx_embedding ON notes(embedding);")
+# OR, if using libsql vector extension:
+# cur.execute("CREATE INDEX IF NOT EXISTS idx_embedding_vector ON notes(libsql_vector_idx(embedding));")
+
+embeddings_model = LocalLMEmbeddings(
+    model=EMBEDDING_MODEL,
+    base_url=API_BASE,
+    batch_size=EMBEDDING_BATCH_SIZE,
+)
+
+texts_to_embed = [
+"The quick brown fox jumped over the lazy dog",
+"Tiffany is my wife, she writes books and watches films",
+"Mazie and Bella are my labradour dogs that are two and three years old, they are white and have a pink nose",
+"The movie Titanic is about a love story on a big boat. but the boat sinks in the end"
+]
+
+reply = embeddings_model._post_request(texts_to_embed)
+zipped = zip(texts_to_embed,reply)
+
+
+# Instead of looping and executing one INSERT at a time
+# Batch insert using multiple VALUES
+batch_insert_sql = """
+INSERT INTO notes (file_path, file_name, chunk_data, embedding)
+VALUES (?, ?, ?, vector32(?))
+"""
+
+# Prepare batch data
+batch_data = []
+for number, (text, embed) in enumerate(zipped):
+    batch_data.append((
+        f"path/to/file_{number}",
+        f"file_{number}",
+        text,
+        str(embed)  # format as comma-separated string
+    ))
+
+cur.executemany(batch_insert_sql, batch_data)
+con.commit()
+
+query_string = ["tell me about a film on a ship"]
+query_reply = embeddings_model._post_request(query_string)
+
+
+cur.execute(f"""
+SELECT id,
+       file_path,
+       file_name,
+       chunk_data, 
+       vector_distance_cos(embedding, vector32('{query_reply[0]}')) AS distance
+FROM notes
+ORDER BY distance ASC;
+""")
+       # vector_extract(embedding)
+
+print(query_string[0])
+
+rows = cur.fetchall()
+for row in rows:
+    print(row)

uv.lock

@@ -1,6 +1,6 @@
 version = 1
 revision = 3
-requires-python = ">=3.14"
+requires-python = ">=3.13"
 
 [[package]]
 name = "aiohappyeyeballs"
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