feat: Enhance SPLADE indexing command to support multiple index databases and add chunk ID management

codex-lens/src/codexlens/cli/commands.py

@@ -2456,6 +2456,9 @@ def splade_index_command(
     Encodes all semantic chunks with SPLADE model and builds inverted index
     for efficient sparse retrieval.
 
+    This command discovers all _index.db files recursively in the project's
+    index directory and builds SPLADE encodings for chunks across all of them.
+
     Examples:
         codexlens splade-index ~/projects/my-app
         codexlens splade-index . --rebuild
@@ -2473,17 +2476,17 @@ def splade_index_command(
         console.print("[dim]Install with: pip install transformers torch[/dim]")
         raise typer.Exit(1)
 
-    # Find index database
+    # Find index root directory
     target_path = path.expanduser().resolve()
 
-    # Try to find _index.db
+    # Determine index root directory (containing _index.db files)
     if target_path.is_file() and target_path.name == "_index.db":
-        index_db = target_path
+        index_root = target_path.parent
     elif target_path.is_dir():
         # Check for local .codexlens/_index.db
         local_index = target_path / ".codexlens" / "_index.db"
         if local_index.exists():
-            index_db = local_index
+            index_root = local_index.parent
         else:
             # Try to find via registry
             registry = RegistryStore()
@@ -2495,29 +2498,66 @@ def splade_index_command(
                     console.print(f"[red]Error:[/red] No index found for {target_path}")
                     console.print("Run 'codexlens init' first to create an index")
                     raise typer.Exit(1)
+                index_root = index_db.parent
             finally:
                 registry.close()
     else:
         console.print(f"[red]Error:[/red] Path must be _index.db file or indexed directory")
         raise typer.Exit(1)
 
-    splade_db = index_db.parent / "_splade.db"
+    # Discover all _index.db files recursively
+    all_index_dbs = sorted(index_root.rglob("_index.db"))
+    if not all_index_dbs:
+        console.print(f"[red]Error:[/red] No _index.db files found in {index_root}")
+        raise typer.Exit(1)
+
+    console.print(f"[blue]Discovered {len(all_index_dbs)} index databases[/blue]")
+
+    # SPLADE index is stored alongside the root _index.db
+    splade_db = index_root / "_splade.db"
 
     if splade_db.exists() and not rebuild:
         console.print("[yellow]SPLADE index exists. Use --rebuild to regenerate.[/yellow]")
         return
 
-    # Load chunks from vector store
+    # If rebuild, delete existing splade database
-    console.print(f"[blue]Loading chunks from {index_db.name}...[/blue]")
+    if splade_db.exists() and rebuild:
+        splade_db.unlink()
+
+    # Collect all chunks from all distributed index databases
+    # Assign globally unique IDs to avoid collisions (each DB starts with ID 1)
+    console.print(f"[blue]Loading chunks from {len(all_index_dbs)} distributed indexes...[/blue]")
+    all_chunks = []  # (global_id, chunk) pairs
+    total_files_checked = 0
+    indexes_with_chunks = 0
+    global_id = 0  # Sequential global ID across all databases
+
+    for index_db in all_index_dbs:
+        total_files_checked += 1
+        try:
             vector_store = VectorStore(index_db)
             chunks = vector_store.get_all_chunks()
+            if chunks:
+                indexes_with_chunks += 1
+                # Assign sequential global IDs to avoid collisions
+                for chunk in chunks:
+                    global_id += 1
+                    all_chunks.append((global_id, chunk, index_db))
+                if verbose:
+                    console.print(f"  [dim]{index_db.parent.name}: {len(chunks)} chunks[/dim]")
+            vector_store.close()
+        except Exception as e:
+            if verbose:
+                console.print(f"  [yellow]Warning: Failed to read {index_db}: {e}[/yellow]")
 
-    if not chunks:
+    if not all_chunks:
-        console.print("[yellow]No chunks found in vector store[/yellow]")
+        console.print("[yellow]No chunks found in any index database[/yellow]")
-        console.print("[dim]Generate embeddings first with 'codexlens embeddings-generate'[/dim]")
+        console.print(f"[dim]Checked {total_files_checked} index files, found 0 chunks[/dim]")
+        console.print("[dim]Generate embeddings first with 'codexlens embeddings-generate --recursive'[/dim]")
         raise typer.Exit(1)
 
-    console.print(f"[blue]Encoding {len(chunks)} chunks with SPLADE...[/blue]")
+    console.print(f"[blue]Found {len(all_chunks)} chunks across {indexes_with_chunks} indexes[/blue]")
+    console.print(f"[blue]Encoding with SPLADE...[/blue]")
 
     # Initialize SPLADE
     encoder = get_splade_encoder()
@@ -2525,6 +2565,7 @@ def splade_index_command(
     splade_index.create_tables()
 
     # Encode in batches with progress bar
+    chunk_metadata_batch = []
     with Progress(
         SpinnerColumn(),
         TextColumn("[progress.description]{task.description}"),
@@ -2533,12 +2574,31 @@ def splade_index_command(
         TimeElapsedColumn(),
         console=console,
     ) as progress:
-        task = progress.add_task("Encoding...", total=len(chunks))
+        task = progress.add_task("Encoding...", total=len(all_chunks))
-        for chunk in chunks:
+        for global_id, chunk, source_db_path in all_chunks:
             sparse_vec = encoder.encode_text(chunk.content)
-            splade_index.add_posting(chunk.id, sparse_vec)
+            splade_index.add_posting(global_id, sparse_vec)
+            # Store chunk metadata for self-contained search
+            # Serialize metadata dict to JSON string
+            metadata_str = None
+            if hasattr(chunk, 'metadata') and chunk.metadata:
+                try:
+                    metadata_str = json.dumps(chunk.metadata) if isinstance(chunk.metadata, dict) else chunk.metadata
+                except Exception:
+                    pass
+            chunk_metadata_batch.append((
+                global_id,
+                chunk.file_path or "",
+                chunk.content,
+                metadata_str,
+                str(source_db_path)
+            ))
             progress.advance(task)
 
+    # Batch insert chunk metadata
+    if chunk_metadata_batch:
+        splade_index.add_chunks_metadata_batch(chunk_metadata_batch)
+
     # Set metadata
     splade_index.set_metadata(
         model_name=encoder.model_name,
@@ -2546,7 +2606,8 @@ def splade_index_command(
     )
 
     stats = splade_index.get_stats()
-    console.print(f"[green]✓[/green] SPLADE index built: {stats['unique_chunks']} chunks, {stats['total_postings']} postings")
+    console.print(f"[green]OK[/green] SPLADE index built: {stats['unique_chunks']} chunks, {stats['total_postings']} postings")
+    console.print(f"  Source indexes: {indexes_with_chunks}")
     console.print(f"  Database: [dim]{splade_db}[/dim]")
 
 

codex-lens/src/codexlens/entities.py

@@ -36,6 +36,8 @@ class SemanticChunk(BaseModel):
     content: str = Field(..., min_length=1)
     embedding: Optional[List[float]] = Field(default=None, description="Vector embedding for semantic search")
     metadata: Dict[str, Any] = Field(default_factory=dict)
+    id: Optional[int] = Field(default=None, description="Database row ID")
+    file_path: Optional[str] = Field(default=None, description="Source file path")
 
     @field_validator("embedding")
     @classmethod

codex-lens/src/codexlens/search/hybrid_search.py

@@ -656,8 +656,25 @@ class HybridSearchEngine:
                 self.logger.debug("SPLADE not available: %s", err)
                 return []
 
-            # Use main index database (SPLADE tables are in _index.db, not separate _splade.db)
+            # SPLADE index is stored in _splade.db at the project index root
-            splade_index = SpladeIndex(index_path)
+            # Traverse up from the current index to find the root _splade.db
+            current_dir = index_path.parent
+            splade_db_path = None
+            for _ in range(10):  # Limit search depth
+                candidate = current_dir / "_splade.db"
+                if candidate.exists():
+                    splade_db_path = candidate
+                    break
+                parent = current_dir.parent
+                if parent == current_dir:  # Reached root
+                    break
+                current_dir = parent
+
+            if not splade_db_path:
+                self.logger.debug("SPLADE index not found in ancestor directories of %s", index_path)
+                return []
+
+            splade_index = SpladeIndex(splade_db_path)
             if not splade_index.has_index():
                 self.logger.debug("SPLADE index not initialized")
                 return []
@@ -672,22 +689,12 @@ class HybridSearchEngine:
             if not raw_results:
                 return []
 
-            # Fetch chunk details from main index database
+            # Fetch chunk details from splade_chunks table (self-contained)
             chunk_ids = [chunk_id for chunk_id, _ in raw_results]
             score_map = {chunk_id: score for chunk_id, score in raw_results}
 
-            # Query semantic_chunks table for full details
+            # Get chunk metadata from SPLADE database
-            placeholders = ",".join("?" * len(chunk_ids))
+            rows = splade_index.get_chunks_by_ids(chunk_ids)
-            with sqlite3.connect(index_path) as conn:
-                conn.row_factory = sqlite3.Row
-                rows = conn.execute(
-                    f"""
-                    SELECT id, file_path, content, metadata
-                    FROM semantic_chunks
-                    WHERE id IN ({placeholders})
-                    """,
-                    chunk_ids
-                ).fetchall()
 
             # Build SearchResult objects
             results = []

codex-lens/src/codexlens/storage/splade_index.py

@@ -102,21 +102,22 @@ class SpladeIndex:
     def create_tables(self) -> None:
         """Create SPLADE schema if not exists.
         
-        Note: The splade_posting_list table has a FOREIGN KEY constraint
+        Note: When used with distributed indexes (multiple _index.db files),
-        referencing semantic_chunks(id). Ensure VectorStore.create_tables()
+        the SPLADE database stores chunk IDs from multiple sources. In this case,
-        is called first to create the semantic_chunks table.
+        foreign key constraints are not enforced to allow cross-database references.
         """
         with self._lock:
             conn = self._get_connection()
             try:
                 # Inverted index for sparse vectors
+                # Note: No FOREIGN KEY constraint to support distributed index architecture
+                # where chunks may come from multiple _index.db files
                 conn.execute("""
                     CREATE TABLE IF NOT EXISTS splade_posting_list (
                         token_id INTEGER NOT NULL,
                         chunk_id INTEGER NOT NULL,
                         weight REAL NOT NULL,
-                        PRIMARY KEY (token_id, chunk_id),
+                        PRIMARY KEY (token_id, chunk_id)
-                        FOREIGN KEY (chunk_id) REFERENCES semantic_chunks(id) ON DELETE CASCADE
                     )
                 """)
                 
@@ -141,6 +142,18 @@ class SpladeIndex:
                     )
                 """)
 
+                # Chunk metadata for self-contained search results
+                # Stores all chunk info needed to build SearchResult without querying _index.db
+                conn.execute("""
+                    CREATE TABLE IF NOT EXISTS splade_chunks (
+                        id INTEGER PRIMARY KEY,
+                        file_path TEXT NOT NULL,
+                        content TEXT NOT NULL,
+                        metadata TEXT,
+                        source_db TEXT
+                    )
+                """)
+                
                 conn.commit()
                 logger.debug("SPLADE schema created successfully")
             except sqlite3.Error as e:
@@ -234,6 +247,113 @@ class SpladeIndex:
                     operation="add_postings_batch"
                 ) from e
 
+    def add_chunk_metadata(
+        self,
+        chunk_id: int,
+        file_path: str,
+        content: str,
+        metadata: Optional[str] = None,
+        source_db: Optional[str] = None
+    ) -> None:
+        """Store chunk metadata for self-contained search results.
+
+        Args:
+            chunk_id: Global chunk ID.
+            file_path: Path to source file.
+            content: Chunk text content.
+            metadata: JSON metadata string.
+            source_db: Path to source _index.db.
+        """
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                conn.execute(
+                    """
+                    INSERT OR REPLACE INTO splade_chunks
+                    (id, file_path, content, metadata, source_db)
+                    VALUES (?, ?, ?, ?, ?)
+                    """,
+                    (chunk_id, file_path, content, metadata, source_db)
+                )
+                conn.commit()
+            except sqlite3.Error as e:
+                raise StorageError(
+                    f"Failed to add chunk metadata for chunk_id={chunk_id}: {e}",
+                    db_path=str(self.db_path),
+                    operation="add_chunk_metadata"
+                ) from e
+
+    def add_chunks_metadata_batch(
+        self,
+        chunks: List[Tuple[int, str, str, Optional[str], Optional[str]]]
+    ) -> None:
+        """Batch insert chunk metadata.
+
+        Args:
+            chunks: List of (chunk_id, file_path, content, metadata, source_db) tuples.
+        """
+        if not chunks:
+            return
+
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                conn.executemany(
+                    """
+                    INSERT OR REPLACE INTO splade_chunks
+                    (id, file_path, content, metadata, source_db)
+                    VALUES (?, ?, ?, ?, ?)
+                    """,
+                    chunks
+                )
+                conn.commit()
+                logger.debug("Batch inserted %d chunk metadata records", len(chunks))
+            except sqlite3.Error as e:
+                raise StorageError(
+                    f"Failed to batch insert chunk metadata: {e}",
+                    db_path=str(self.db_path),
+                    operation="add_chunks_metadata_batch"
+                ) from e
+
+    def get_chunks_by_ids(self, chunk_ids: List[int]) -> List[Dict]:
+        """Get chunk metadata by IDs.
+
+        Args:
+            chunk_ids: List of chunk IDs to retrieve.
+
+        Returns:
+            List of dicts with id, file_path, content, metadata, source_db.
+        """
+        if not chunk_ids:
+            return []
+
+        with self._lock:
+            conn = self._get_connection()
+            try:
+                placeholders = ",".join("?" * len(chunk_ids))
+                rows = conn.execute(
+                    f"""
+                    SELECT id, file_path, content, metadata, source_db
+                    FROM splade_chunks
+                    WHERE id IN ({placeholders})
+                    """,
+                    chunk_ids
+                ).fetchall()
+
+                return [
+                    {
+                        "id": row["id"],
+                        "file_path": row["file_path"],
+                        "content": row["content"],
+                        "metadata": row["metadata"],
+                        "source_db": row["source_db"]
+                    }
+                    for row in rows
+                ]
+            except sqlite3.Error as e:
+                logger.error("Failed to get chunks by IDs: %s", e)
+                return []
+    
     def remove_chunk(self, chunk_id: int) -> int:
         """Remove all postings for a chunk.