feat: support for pgvectorscale (DiskANN)

docker/docker-compose/timescale/.dockerignore

@@ -0,0 +1,16 @@
+# Git
+.git
+.gitignore
+.gitattributes
+
+# Docker
+docker-compose.yaml
+.dockerignore
+
+# Documentation
+README.md
+*.md
+
+# Environment
+.env
+.env.example

docker/docker-compose/timescale/.env.example

@@ -0,0 +1,25 @@
+# PostgreSQL Configuration
+HINDSIGHT_DB_USER=hindsight_user
+HINDSIGHT_DB_PASSWORD=change-me-to-secure-password
+HINDSIGHT_DB_NAME=hindsight_db
+
+# Hindsight Version
+HINDSIGHT_VERSION=latest
+
+# LLM Configuration
+HINDSIGHT_API_LLM_PROVIDER=openai
+OPENAI_API_KEY=your-openai-api-key-here
+
+# Alternative LLM providers (uncomment and configure as needed):
+# HINDSIGHT_API_LLM_PROVIDER=anthropic
+# ANTHROPIC_API_KEY=your-anthropic-api-key
+
+# HINDSIGHT_API_LLM_PROVIDER=gemini
+# GEMINI_API_KEY=your-gemini-api-key
+
+# HINDSIGHT_API_LLM_PROVIDER=groq
+# GROQ_API_KEY=your-groq-api-key
+
+# Vector and Text Search (already configured in docker-compose.yaml)
+# HINDSIGHT_API_VECTOR_EXTENSION=pgvectorscale
+# HINDSIGHT_API_TEXT_SEARCH_EXTENSION=pg_textsearch

docker/docker-compose/timescale/Dockerfile

@@ -0,0 +1,55 @@
+# PostgreSQL with pgvector, pgvectorscale, and pg_textsearch extensions
+# All three extensions from Timescale/pgvector for high-performance vector and text search
+# Note: Requires PostgreSQL 16+
+FROM postgres:17
+
+# Install build dependencies and Rust toolchain
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    git \
+    postgresql-server-dev-17 \
+    libpq-dev \
+    cmake \
+    curl \
+    pkg-config \
+    libssl-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Rust toolchain (required for pgvectorscale)
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
+ENV PATH="/root/.cargo/bin:${PATH}"
+
+# Install pgvector (required by pgvectorscale)
+RUN cd /tmp && \
+    git clone --branch v0.8.0 https://github.com/pgvector/pgvector.git && \
+    cd pgvector && \
+    make && \
+    make install && \
+    rm -rf /tmp/pgvector
+
+# Install cargo-pgrx (PostgreSQL extension framework for Rust)
+RUN cargo install cargo-pgrx --version 0.12.5 --locked && \
+    cargo pgrx init --pg17 /usr/bin/pg_config
+
+# Install pgvectorscale (DiskANN index support)
+RUN cd /tmp && \
+    git clone --branch 0.5.1 https://github.com/timescale/pgvectorscale.git && \
+    cd pgvectorscale/pgvectorscale && \
+    cargo pgrx install --release && \
+    rm -rf /tmp/pgvectorscale
+
+# Install pg_textsearch (BM25 text search)
+RUN cd /tmp && \
+    git clone https://github.com/timescale/pg_textsearch.git && \
+    cd pg_textsearch && \
+    make && \
+    make install && \
+    rm -rf /tmp/pg_textsearch
+
+# Clean up build dependencies (keep runtime dependencies)
+RUN apt-get purge -y --auto-remove git cmake curl && \
+    rm -rf /root/.cargo/registry /root/.cargo/git
+
+# Ensure extensions are preloaded (pg_textsearch requires preloading)
+RUN echo "shared_preload_libraries = 'pg_textsearch'" >> /usr/share/postgresql/postgresql.conf.sample
+

docker/docker-compose/timescale/README.md

@@ -0,0 +1,101 @@
+# Hindsight with Timescale Extensions
+
+This Docker Compose setup provides a complete Hindsight deployment with **Timescale extensions**:
+- **pgvectorscale** - DiskANN algorithm for disk-based scalable vector search  
+- **pg_textsearch** - High-performance BM25 text search
+
+Both extensions are from [Timescale](https://github.com/timescale) and provide production-grade performance.
+
+## Prerequisites
+
+- Docker and Docker Compose installed
+- OpenAI API key (or another LLM provider)
+
+## Quick Start
+
+```bash
+# Set environment variables
+export HINDSIGHT_DB_PASSWORD="your-secure-password"
+export OPENAI_API_KEY="your-openai-api-key"
+
+# Build and start
+docker compose -f docker/docker-compose/timescale/docker-compose.yaml up -d --build
+
+# Check logs
+
+docker compose -f docker/docker-compose/timescale/docker-compose.yaml logs -f
+```
+
+**Access:**
+- API: http://localhost:8888
+- Control Plane: http://localhost:9999
+
+## Stop and Clean Up
+
+```bash
+# Stop services
+docker compose -f docker/docker-compose/timescale/docker-compose.yaml down
+
+# Remove volumes (deletes all data)
+docker compose -f docker/docker-compose/timescale/docker-compose.yaml down -v
+```
+
+## Configuration
+
+### Environment Variables
+
+| Variable | Description | Default |
+|----------|-------------|---------|
+| `HINDSIGHT_DB_PASSWORD` | PostgreSQL password | `hindsight_password` |
+| `HINDSIGHT_DB_USER` | PostgreSQL username | `hindsight_user` |
+| `HINDSIGHT_DB_NAME` | Database name | `hindsight_db` |
+| `HINDSIGHT_VERSION` | Hindsight Docker image version | `latest` |
+| `OPENAI_API_KEY` | OpenAI API key | (required) |
+| `HINDSIGHT_API_LLM_PROVIDER` | LLM provider | `openai` |
+
+### Why Timescale Extensions?
+
+**pgvectorscale (DiskANN):**
+- 28x lower p95 latency vs dedicated vector databases
+- 16x higher query throughput at 99% recall
+- 60-75% cost reduction (disk is cheaper than RAM)
+- Best for large datasets (10M+ vectors)
+
+**pg_textsearch (BM25):**
+- High-performance keyword retrieval
+- Native BM25 ranking algorithm
+- Optimized for full-text search
+
+## Troubleshooting
+
+### Extensions not installed
+
+Check if extensions are available:
+
+```bash
+docker exec -it hindsight-db-timescale psql -U hindsight_user -d hindsight_db -c "\dx"
+```
+
+You should see:
+- `vector` (pgvector)
+- `vectorscale` (pgvectorscale/DiskANN)
+- `pg_textsearch` (BM25 search)
+
+### Build fails
+
+If the Docker build fails during pgvectorscale compilation:
+
+1. Ensure you have sufficient memory (recommended: 4GB+)
+2. Check Docker build logs for Rust compilation errors
+3. Try building with more resources: `docker compose build --no-cache --memory 4g`
+
+### Port conflicts
+
+If port 5438 is already in use, modify the `ports` section in docker-compose.yaml.
+
+## Learn More
+
+- [pgvectorscale GitHub](https://github.com/timescale/pgvectorscale)
+- [pg_textsearch GitHub](https://github.com/timescale/pg_textsearch)
+- [HNSW vs DiskANN](https://www.tigerdata.com/learn/hnsw-vs-diskann)
+- [Hindsight Documentation](https://hindsight.dev)

docker/docker-compose/timescale/docker-compose.yaml

@@ -0,0 +1,108 @@
+name: hindsight
+# Docker Compose file for Hindsight with Timescale extensions
+# - pgvectorscale: DiskANN vector search (disk-based, scalable)
+# - pg_textsearch: BM25 text search (high-performance keyword retrieval)
+#
+# Quick start:
+# docker compose -f docker/docker-compose/timescale/docker-compose.yaml up -d --build
+#
+# Required environment variables:
+# - HINDSIGHT_DB_PASSWORD: Password for the PostgreSQL user
+# - OPENAI_API_KEY (or configure another LLM provider)
+#
+# Optional environment variables with defaults:
+# - HINDSIGHT_VERSION: Hindsight application version (default: latest)
+# - HINDSIGHT_DB_USER: PostgreSQL user (default: hindsight_user)
+# - HINDSIGHT_DB_NAME: PostgreSQL database name (default: hindsight_db)
+
+services:
+  db:
+    # Custom PostgreSQL image with Timescale extensions (pgvectorscale + pg_textsearch)
+    build:
+      context: .
+      dockerfile: Dockerfile
+    container_name: hindsight-db-timescale
+    restart: always
+    # Expose PostgreSQL port (using 5438 to avoid conflicts with other setups)
+    ports:
+      - "5438:5432"
+    environment:
+      POSTGRES_USER: ${HINDSIGHT_DB_USER:-hindsight_user}
+      POSTGRES_PASSWORD: ${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+      POSTGRES_DB: ${HINDSIGHT_DB_NAME:-hindsight_db}
+    volumes:
+      - pg_data:/var/lib/postgresql/data
+    networks:
+      - hindsight-net
+    # Health check to ensure database is ready
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U hindsight_user"]
+      interval: 5s
+      timeout: 5s
+      retries: 5
+
+  timescale-init:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    depends_on:
+      db:
+        condition: service_healthy
+    environment:
+      - PGPASSWORD=${HINDSIGHT_DB_PASSWORD:-hindsight_password}
+    command: >
+      bash -c "
+        echo 'PostgreSQL is ready - creating hindsight_db database';
+        psql -h hindsight-db-timescale -p 5432 -U hindsight_user -c 'CREATE DATABASE hindsight_db;' 2>/dev/null || echo 'Database already exists';
+        echo 'Installing Timescale extensions...';
+        echo '1/3: Installing pgvector (required by pgvectorscale)...';
+        psql -h hindsight-db-timescale -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vector CASCADE;';
+        echo '2/3: Installing pgvectorscale (DiskANN vector search)...';
+        psql -h hindsight-db-timescale -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS vectorscale CASCADE;';
+        echo '3/3: Installing pg_textsearch (BM25 text search)...';
+        psql -h hindsight-db-timescale -p 5432 -U hindsight_user -d hindsight_db -c 'CREATE EXTENSION IF NOT EXISTS pg_textsearch CASCADE;';
+        echo '';
+        echo '✅ Timescale extensions installed successfully';
+        echo '';
+        echo 'Installed extensions:';
+        psql -h hindsight-db-timescale -p 5432 -U hindsight_user -d hindsight_db -c \"\\dx\" | grep -E '(vector|vectorscale|pg_textsearch)';
+      "
+    restart: "no"
+    networks:
+      - hindsight-net
+
+  hindsight:
+    image: ghcr.io/vectorize-io/hindsight:${HINDSIGHT_VERSION:-latest}
+    container_name: hindsight-app-timescale
+    ports:
+      - "8888:8888"
+      - "9999:9999"
+    environment:
+      # LLM Configuration
+      HINDSIGHT_API_LLM_PROVIDER: ${HINDSIGHT_API_LLM_PROVIDER:-openai}
+      HINDSIGHT_API_LLM_API_KEY: ${OPENAI_API_KEY:-your-api-key}
+
+      # Database Configuration
+      HINDSIGHT_API_DATABASE_URL: postgresql://${HINDSIGHT_DB_USER:-hindsight_user}:${HINDSIGHT_DB_PASSWORD:-hindsight_password}@db:5432/${HINDSIGHT_DB_NAME:-hindsight_db}
+
+      # Timescale Extensions
+      # pgvectorscale: DiskANN algorithm for disk-based scalable vector search
+      HINDSIGHT_API_VECTOR_EXTENSION: pgvectorscale
+      # pg_textsearch: High-performance BM25 text search
+      HINDSIGHT_API_TEXT_SEARCH_EXTENSION: pg_textsearch
+
+    depends_on:
+      db:
+        condition: service_healthy
+      timescale-init:
+        condition: service_completed_successfully
+    networks:
+      - hindsight-net
+
+
+networks:
+  hindsight-net:
+    driver: bridge
+
+volumes:
+  pg_data:

hindsight-api/hindsight_api/alembic/versions/5a366d414dce_initial_schema.py

@@ -24,14 +24,27 @@ depends_on: str | Sequence[str] | None = None
 
 def _detect_vector_extension() -> str:
     """
-    Detect or validate vector extension: 'vchord' or 'pgvector'.
+    Detect or validate vector extension: 'pgvector', 'vchord', or 'pgvectorscale'.
     Respects HINDSIGHT_API_VECTOR_EXTENSION env var if set.
     """
     conn = op.get_bind()
     vector_extension = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
 
     # Validate configured extension is installed
-    if vector_extension == "vchord":
+    if vector_extension == "pgvectorscale":
+        # pgvectorscale requires pgvector
+        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
+        if not pgvector_check:
+            raise RuntimeError(
+                "pgvectorscale requires pgvector. Install with: CREATE EXTENSION vector; CREATE EXTENSION vectorscale CASCADE;"
+            )
+        vectorscale_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vectorscale'")).scalar()
+        if not vectorscale_check:
+            raise RuntimeError(
+                "Configured vector extension 'pgvectorscale' not found. Install it with: CREATE EXTENSION vectorscale CASCADE;"
+            )
+        return "pgvectorscale"
+    elif vector_extension == "vchord":
         vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
         if not vchord_check:
             raise RuntimeError(
@@ -46,7 +59,9 @@ def _detect_vector_extension() -> str:
             )
         return "pgvector"
     else:
-        raise ValueError(f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector' or 'vchord'")
+        raise ValueError(
+            f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector', 'vchord', or 'pgvectorscale'"
+        )
 
 
 def _detect_text_search_extension() -> str:
@@ -289,7 +304,14 @@ def upgrade() -> None:
     # Create vector index - conditional based on available extension
     vector_ext = _detect_vector_extension()
 
-    if vector_ext == "vchord":
+    if vector_ext == "pgvectorscale":
+        # Use DiskANN index for pgvectorscale (disk-based, scalable)
+        op.execute("""
+            CREATE INDEX idx_memory_units_embedding ON memory_units
+            USING diskann (embedding vector_cosine_ops)
+            WITH (num_neighbors = 50)
+        """)
+    elif vector_ext == "vchord":
         # Use vchordrq index for vchord (supports high-dimensional embeddings)
         op.execute("""
             CREATE INDEX idx_memory_units_embedding ON memory_units

hindsight-api/hindsight_api/alembic/versions/n9i0j1k2l3m4_learnings_and_pinned_reflections.py

@@ -31,14 +31,27 @@ def _get_schema_prefix() -> str:
 
 def _detect_vector_extension() -> str:
     """
-    Detect or validate vector extension: 'vchord' or 'pgvector'.
+    Detect or validate vector extension: 'pgvector', 'vchord', or 'pgvectorscale'.
     Respects HINDSIGHT_API_VECTOR_EXTENSION env var if set.
     """
     conn = op.get_bind()
     vector_extension = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
 
     # Validate configured extension is installed
-    if vector_extension == "vchord":
+    if vector_extension == "pgvectorscale":
+        # pgvectorscale requires pgvector
+        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
+        if not pgvector_check:
+            raise RuntimeError(
+                "pgvectorscale requires pgvector. Install with: CREATE EXTENSION vector; CREATE EXTENSION vectorscale CASCADE;"
+            )
+        vectorscale_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vectorscale'")).scalar()
+        if not vectorscale_check:
+            raise RuntimeError(
+                "Configured vector extension 'pgvectorscale' not found. Install it with: CREATE EXTENSION vectorscale CASCADE;"
+            )
+        return "pgvectorscale"
+    elif vector_extension == "vchord":
         vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
         if not vchord_check:
             raise RuntimeError(
@@ -53,7 +66,9 @@ def _detect_vector_extension() -> str:
             )
         return "pgvector"
     else:
-        raise ValueError(f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector' or 'vchord'")
+        raise ValueError(
+            f"Invalid HINDSIGHT_API_VECTOR_EXTENSION: {vector_extension}. Must be 'pgvector', 'vchord', or 'pgvectorscale'"
+        )
 
 
 def _detect_text_search_extension() -> str:
@@ -134,7 +149,13 @@ def upgrade() -> None:
     op.execute(f"CREATE INDEX idx_learnings_bank_id ON {schema}learnings(bank_id)")
 
     # Create vector index based on detected extension
-    if vector_ext == "vchord":
+    if vector_ext == "pgvectorscale":
+        op.execute(f"""
+            CREATE INDEX idx_learnings_embedding ON {schema}learnings
+            USING diskann (embedding vector_cosine_ops)
+            WITH (num_neighbors = 50)
+        """)
+    elif vector_ext == "vchord":
         op.execute(f"""
             CREATE INDEX idx_learnings_embedding ON {schema}learnings
             USING vchordrq (embedding vector_l2_ops)
@@ -201,7 +222,13 @@ def upgrade() -> None:
     op.execute(f"CREATE INDEX idx_pinned_reflections_bank_id ON {schema}pinned_reflections(bank_id)")
 
     # Create vector index based on detected extension
-    if vector_ext == "vchord":
+    if vector_ext == "pgvectorscale":
+        op.execute(f"""
+            CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
+            USING diskann (embedding vector_cosine_ops)
+            WITH (num_neighbors = 50)
+        """)
+    elif vector_ext == "vchord":
         op.execute(f"""
             CREATE INDEX idx_pinned_reflections_embedding ON {schema}pinned_reflections
             USING vchordrq (embedding vector_l2_ops)

hindsight-api/hindsight_api/banner.py

@@ -86,6 +86,8 @@ def print_startup_info(
     reranker_provider: str,
     mcp_enabled: bool = False,
     version: str | None = None,
+    vector_extension: str | None = None,
+    text_search_extension: str | None = None,
 ):
     """Print styled startup information."""
     print(color_start("Starting Hindsight API..."))
@@ -96,6 +98,8 @@ def print_startup_info(
     print(f"  {dim('LLM:')} {color(f'{llm_provider} / {llm_model}', 0.6)}")
     print(f"  {dim('Embeddings:')} {color(embeddings_provider, 0.8)}")
     print(f"  {dim('Reranker:')} {color(reranker_provider, 1.0)}")
+    extensions = f"{vector_extension or 'default'} (vector) / {text_search_extension or 'default'} (text)"
+    print(f"  {dim('Extensions:')} {color(extensions, 0.4)}")
     if mcp_enabled:
         print(f"  {dim('MCP:')} {color_end('enabled at /mcp')}")
     print()

hindsight-api/hindsight_api/config.py

@@ -334,8 +334,8 @@ DEFAULT_RERANKER_FLASHRANK_CACHE_DIR = None  # Use default cache directory
 DEFAULT_EMBEDDINGS_COHERE_MODEL = "embed-english-v3.0"
 DEFAULT_RERANKER_COHERE_MODEL = "rerank-english-v3.0"
 
-# Vector extension (pgvector vs vchord)
-DEFAULT_VECTOR_EXTENSION = "pgvector"  # Options: "pgvector", "vchord"
+# Vector extension (pgvector, vchord, or pgvectorscale)
+DEFAULT_VECTOR_EXTENSION = "pgvector"  # Options: "pgvector", "vchord", "pgvectorscale"
 
 # Text search extension (native PostgreSQL, vchord BM25, or Timescale pg_textsearch)
 DEFAULT_TEXT_SEARCH_EXTENSION = "native"  # Options: "native", "vchord", "pg_textsearch"
@@ -717,7 +717,7 @@ class HindsightConfig:
     def validate(self) -> None:
         """Validate configuration values and raise errors for invalid combinations."""
         # Validate vector_extension
-        valid_extensions = ("pgvector", "vchord")
+        valid_extensions = ("pgvector", "vchord", "pgvectorscale")
         if self.vector_extension not in valid_extensions:
             raise ValueError(
                 f"Invalid vector_extension: {self.vector_extension}. Must be one of: {', '.join(valid_extensions)}"

hindsight-api/hindsight_api/main.py

@@ -374,6 +374,8 @@ def main():
             reranker_provider=config.reranker_provider,
             mcp_enabled=config.mcp_enabled,
             version=__version__,
+            vector_extension=config.vector_extension,
+            text_search_extension=config.text_search_extension,
         )
 
     # Start idle checker in daemon mode

hindsight-api/hindsight_api/migrations.py

@@ -35,20 +35,38 @@ MIGRATION_LOCK_ID = 123456789
 
 def _detect_vector_extension(conn, vector_extension: str = "pgvector") -> str:
     """
-    Validate vector extension: 'vchord' or 'pgvector'.
+    Validate vector extension: 'pgvector', 'vchord', or 'pgvectorscale'.
 
     Args:
         conn: SQLAlchemy connection object
-        vector_extension: Configured extension ("pgvector" or "vchord")
+        vector_extension: Configured extension ("pgvector", "vchord", or "pgvectorscale")
 
     Returns:
-        "vchord" or "pgvector"
+        "pgvector", "vchord", or "pgvectorscale"
 
     Raises:
         RuntimeError: If configured extension is not installed
     """
     # Verify the configured extension is installed
-    if vector_extension == "vchord":
+    if vector_extension == "pgvectorscale":
+        # pgvectorscale requires pgvector to be installed first
+        pgvector_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vector'")).scalar()
+        if not pgvector_check:
+            raise RuntimeError(
+                "pgvectorscale requires pgvector to be installed. "
+                "Install it with: CREATE EXTENSION vector; CREATE EXTENSION vectorscale CASCADE;"
+            )
+
+        # Check for vectorscale extension
+        vectorscale_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vectorscale'")).scalar()
+        if not vectorscale_check:
+            raise RuntimeError(
+                "Configured vector extension 'pgvectorscale' not found. "
+                "Install it with: CREATE EXTENSION vectorscale CASCADE;"
+            )
+        logger.debug("Using configured vector extension: pgvectorscale (DiskANN)")
+        return "pgvectorscale"
+    elif vector_extension == "vchord":
         vchord_check = conn.execute(text("SELECT 1 FROM pg_extension WHERE extname = 'vchord'")).scalar()
         if not vchord_check:
             raise RuntimeError(
@@ -65,7 +83,9 @@ def _detect_vector_extension(conn, vector_extension: str = "pgvector") -> str:
         logger.debug("Using configured vector extension: pgvector")
         return "pgvector"
     else:
-        raise ValueError(f"Invalid vector_extension: {vector_extension}. Must be 'pgvector' or 'vchord'")
+        raise ValueError(
+            f"Invalid vector_extension: {vector_extension}. Must be 'pgvector', 'vchord', or 'pgvectorscale'"
+        )
 
 
 def _get_schema_lock_id(schema: str) -> int:
@@ -277,6 +297,48 @@ def run_migrations(
                                 "Please install it with: CREATE EXTENSION vector;"
                             ) from e
 
+                # If using pgvectorscale, ensure vectorscale extension is also installed
+                vector_extension = os.getenv("HINDSIGHT_API_VECTOR_EXTENSION", "pgvector").lower()
+                if vector_extension == "pgvectorscale":
+                    logger.debug("Checking pgvectorscale (vectorscale) extension availability...")
+
+                    vectorscale_check = conn.execute(
+                        text("SELECT 1 FROM pg_extension WHERE extname = 'vectorscale'")
+                    ).scalar()
+
+                    if vectorscale_check:
+                        logger.info("pgvectorscale extension already installed")
+                    else:
+                        # Extension doesn't exist - try to install
+                        logger.info("pgvectorscale extension not found, attempting to install...")
+                        try:
+                            conn.execute(text("CREATE EXTENSION vectorscale CASCADE"))
+                            conn.commit()
+                            logger.info("pgvectorscale extension installed successfully")
+                        except Exception as e:
+                            # Installation failed - check one more time in case another process installed it
+                            conn.rollback()
+                            vectorscale_recheck = conn.execute(
+                                text("SELECT 1 FROM pg_extension WHERE extname = 'vectorscale'")
+                            ).fetchone()
+
+                            if vectorscale_recheck:
+                                logger.warning(
+                                    "Could not install pgvectorscale extension (permission denied?), "
+                                    "but extension exists. Continuing..."
+                                )
+                            else:
+                                # Extension truly doesn't exist and we can't install it
+                                logger.error(
+                                    f"pgvectorscale extension is not installed and cannot be installed: {e}. "
+                                    f"Please ensure pgvectorscale is installed by a database administrator. "
+                                    f"See: https://github.com/timescale/pgvectorscale#installation"
+                                )
+                                raise RuntimeError(
+                                    "pgvectorscale extension is required but not installed. "
+                                    "Please install it with: CREATE EXTENSION vectorscale CASCADE;"
+                                ) from e
+
                 # Run migrations while holding the lock
                 _run_migrations_internal(database_url, script_location, schema=schema)
             finally:
@@ -475,7 +537,17 @@ def ensure_embedding_dimension(
         conn.commit()
 
         # Recreate index with appropriate type based on detected extension
-        if vector_ext == "vchord":
+        if vector_ext == "pgvectorscale":
+            conn.execute(
+                text(f"""
+                    CREATE INDEX IF NOT EXISTS idx_memory_units_embedding_diskann
+                    ON {schema_name}.memory_units
+                    USING diskann (embedding vector_cosine_ops)
+                    WITH (num_neighbors = 50)
+                """)
+            )
+            logger.info(f"Created DiskANN index for {required_dimension}-dimensional embeddings")
+        elif vector_ext == "vchord":
             conn.execute(
                 text(f"""
                     CREATE INDEX IF NOT EXISTS idx_memory_units_embedding_vchordrq
@@ -537,7 +609,12 @@ def ensure_vector_extension(
         ]
 
         # Determine target index type
-        target_index_type = "vchordrq" if target_ext == "vchord" else "hnsw"
+        if target_ext == "pgvectorscale":
+            target_index_type = "diskann"
+        elif target_ext == "vchord":
+            target_index_type = "vchordrq"
+        else:
+            target_index_type = "hnsw"
 
         mismatched_tables = []
         tables_with_data = []
@@ -576,7 +653,9 @@ def ensure_vector_extension(
                 continue
 
             indexdef = current_index_info[0].lower()
-            if "vchordrq" in indexdef:
+            if "diskann" in indexdef:
+                current_index_type = "diskann"
+            elif "vchordrq" in indexdef:
                 current_index_type = "vchordrq"
             elif "hnsw" in indexdef:
                 current_index_type = "hnsw"
@@ -609,13 +688,18 @@ def ensure_vector_extension(
         # If there's data in any mismatched table, raise error
         if tables_with_data:
             table_list = ", ".join([f"{table}({count} rows)" for table, count in tables_with_data])
+            # Map index type back to extension name for error message
+            current_ext_name = {"diskann": "pgvectorscale", "vchordrq": "vchord", "hnsw": "pgvector"}.get(
+                current_index_type, current_index_type
+            )
+
             raise RuntimeError(
                 f"Cannot change vector extension from {current_index_type} to {target_index_type}: "
                 f"the following tables contain data: {table_list}. "
                 f"To change vector extension, you must either:\n"
                 f"  1. Re-embed all data: DELETE FROM {schema_name}.memory_units; "
                 f"DELETE FROM {schema_name}.learnings; DELETE FROM {schema_name}.pinned_reflections; then restart\n"
-                f"  2. Use the current vector extension (set HINDSIGHT_API_VECTOR_EXTENSION='{current_index_type.replace('vchordrq', 'vchord').replace('hnsw', 'pgvector')}')"
+                f"  2. Use the current vector extension (set HINDSIGHT_API_VECTOR_EXTENSION='{current_ext_name}')"
             )
 
         # Tables are empty, safe to recreate indexes
@@ -628,7 +712,17 @@ def ensure_vector_extension(
                 conn.execute(text(f"DROP INDEX IF EXISTS {schema_name}.{index_name}"))
 
             # Create new index with appropriate type
-            if target_ext == "vchord":
+            if target_ext == "pgvectorscale":
+                logger.info(f"Creating DiskANN index on {table_name}")
+                conn.execute(
+                    text(f"""
+                        CREATE INDEX IF NOT EXISTS {index_name}
+                        ON {schema_name}.{table_name}
+                        USING diskann (embedding vector_cosine_ops)
+                        WITH (num_neighbors = 50)
+                    """)
+                )
+            elif target_ext == "vchord":
                 logger.info(f"Creating vchordrq index on {table_name}")
                 conn.execute(
                     text(f"""

hindsight-docs/docs/developer/configuration.md

@@ -61,31 +61,61 @@ hindsight-admin run-db-migration --schema tenant_acme
 
 | Variable | Description | Default |
 |----------|-------------|---------|
-| `HINDSIGHT_API_VECTOR_EXTENSION` | Vector extension to use: `auto`, `pgvector`, or `vchord` | `auto` |
+| `HINDSIGHT_API_VECTOR_EXTENSION` | Vector index algorithm: `pgvector`, `vchord`, or `pgvectorscale` | `pgvector` |
 
-Hindsight supports two PostgreSQL vector extensions:
-- **pgvector**: Standard extension, works well for most embeddings (up to ~2000 dimensions)
-- **vchord**: Optimized for high-dimensional embeddings (3000+ dimensions), includes BM25 search
+Hindsight supports three PostgreSQL vector extensions:
 
-When set to `auto` (default), Hindsight automatically detects which extension is installed, preferring vchord if both are available.
+#### **pgvector** (HNSW - default)
+- In-memory index using Hierarchical Navigable Small World algorithm
+- Works well for most embeddings and dataset sizes
+- Fast for small-medium datasets (<10M vectors)
+- Higher memory usage for large datasets
+- Most widely deployed and supported
+
+#### **pgvectorscale** (DiskANN - recommended for scale) ⭐
+- Disk-based index using StreamingDiskANN algorithm (by Timescale)
+- **28x lower p95 latency** and **16x higher throughput** vs dedicated vector DBs
+- **60-75% cost reduction** at scale (SSDs cheaper than RAM)
+- Superior filtering performance with streaming retrieval model
+- Optimized for large datasets (10M+ vectors)
+- Requires both `pgvector` and `vectorscale` extensions
+- **Installation:** `CREATE EXTENSION vector; CREATE EXTENSION vectorscale CASCADE;`
+
+#### **vchord** (vchordrq)
+- Alternative high-performance vector index
+- Optimized for high-dimensional embeddings (3000+ dimensions)
+- Includes integrated BM25 search capabilities
+- Requires `vchord` extension
+
+**When to use pgvectorscale (DiskANN):**
+- Large datasets (10M+ vectors) ⭐
+- Complex filtering requirements
+- Cost-sensitive deployments
+- Production workloads requiring high throughput
+- When disk I/O is not a bottleneck
+
+**When to use pgvector (HNSW):**
+- Small-medium datasets (<10M vectors)
+- Maximum query speed when all data fits in memory
+- Simple nearest-neighbor queries without filters
+- Standard PostgreSQL deployment preference
 
 **When to use vchord:**
-- Using high-dimensional embeddings (e.g., `text-embedding-3-large` with 3072 dimensions)
-- Need better performance with large embedding dimensions
-- Want to use vchord's BM25 search capabilities
-
-**When to use pgvector:**
-- Using standard embedding dimensions (384-1536)
-- Prefer the widely-adopted pgvector extension
-- Simpler deployment (pgvector is more commonly available)
+- High-dimensional embeddings (3000+ dimensions)
+- Want integrated BM25 search
+- Already using vchord for text search
 
 **Switching extensions:**
 
 If you need to switch from one extension to another:
-1. Set `HINDSIGHT_API_VECTOR_EXTENSION` to your desired extension (`pgvector` or `vchord`)
+1. Set `HINDSIGHT_API_VECTOR_EXTENSION` to your desired extension (`pgvector`, `vchord`, or `pgvectorscale`)
 2. If your database has existing data, you'll get an error with migration instructions
 3. For empty databases, indexes will be automatically recreated on startup
 
+**Learn more:**
+- [HNSW vs. DiskANN comparison](https://www.tigerdata.com/learn/hnsw-vs-diskann)
+- [pgvectorscale GitHub](https://github.com/timescale/pgvectorscale)
+
 ### Text Search Extension
 
 | Variable | Description | Default |