fix docker cp image build on ci (#10)

* fix docker cp image build on ci

* fix docker

* fix docker again

.github/workflows/test.yml

@@ -53,6 +53,25 @@ jobs:
       working-directory: ./hindsight-clients/typescript
       run: npm run build
 
+  build-docs:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '20'
+
+    - name: Install dependencies
+      working-directory: ./hindsight-docs
+      run: npm ci
+
+    - name: Build docs
+      working-directory: ./hindsight-docs
+      run: npm run build
+
   build-rust-cli:
     runs-on: ubuntu-latest
 
@@ -129,24 +148,7 @@ jobs:
   test-api:
     runs-on: ubuntu-latest
     needs: [build-python-packages]
-
-    services:
-      postgres:
-        image: pgvector/pgvector:pg16
-        env:
-          POSTGRES_USER: postgres
-          POSTGRES_PASSWORD: postgres
-          POSTGRES_DB: hindsight_test
-        options: >-
-          --health-cmd pg_isready
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-        ports:
-          - 5432:5432
-
     env:
-      HINDSIGHT_API_DATABASE_URL: postgresql://postgres:postgres@localhost:5432/hindsight_test
       HINDSIGHT_API_LLM_PROVIDER: groq
       HINDSIGHT_API_LLM_API_KEY: ${{ secrets.GROQ_API_KEY }}
       HINDSIGHT_API_LLM_MODEL: openai/gpt-oss-20b
@@ -170,4 +172,4 @@ jobs:
 
     - name: Run tests
       working-directory: ./hindsight-api
-      run: uv run pytest tests -v --ignore=tests/test_fact_extraction_quality.py
+      run: uv run pytest tests -v

docker/standalone/Dockerfile

@@ -74,11 +74,15 @@ WORKDIR /app
 COPY --from=sdk-builder /app/sdk /app/sdk
 
 # Install Control Plane dependencies
-COPY hindsight-control-plane/package*.json ./
+# Only copy package.json (not package-lock.json) to ensure npm installs
-RUN npm ci
+# correct platform-specific native bindings for lightningcss/tailwindcss
+COPY hindsight-control-plane/package.json ./
+RUN npm install
 
-# Copy Control Plane source
+# Copy Control Plane source (excluding node_modules via .dockerignore)
 COPY hindsight-control-plane/ ./
+# Remove package-lock.json to avoid conflicts with installed native bindings
+RUN rm -f package-lock.json
 
 # Link SDK (temporary for build)
 RUN cd /app/sdk && npm link && cd /app && npm link @vectorize-io/hindsight-client

hindsight-api/hindsight_api/engine/retain/fact_extraction.py

@@ -16,6 +16,24 @@ from pydantic import BaseModel, Field, field_validator, ConfigDict
 from ..llm_wrapper import OutputTooLongError, LLMConfig
 
 
+def _sanitize_text(text: str) -> str:
+    """
+    Sanitize text by removing invalid Unicode surrogate characters.
+
+    Surrogate characters (U+D800 to U+DFFF) are used in UTF-16 encoding
+    but cannot be encoded in UTF-8. They can appear in Python strings
+    from improperly decoded data (e.g., from JavaScript or broken files).
+
+    This function removes unpaired surrogates to prevent UnicodeEncodeError
+    when the text is sent to the LLM API.
+    """
+    if not text:
+        return text
+    # Remove surrogate characters (U+D800 to U+DFFF) using regex
+    # These are invalid in UTF-8 and cause encoding errors
+    return re.sub(r'[\ud800-\udfff]', '', text)
+
+
 class Entity(BaseModel):
     """An entity extracted from text."""
     text: str = Field(
@@ -470,6 +488,10 @@ WHAT TO EXTRACT vs SKIP
     max_retries = 2
     last_error = None
 
+    # Sanitize input text to prevent Unicode encoding errors (e.g., unpaired surrogates)
+    sanitized_chunk = _sanitize_text(chunk)
+    sanitized_context = _sanitize_text(context) if context else 'none'
+
     # Build user message with metadata and chunk content in a clear format
     # Format event_date with day of week for better temporal reasoning
     event_date_formatted = event_date.strftime('%A, %B %d, %Y')  # e.g., "Monday, June 10, 2024"
@@ -477,10 +499,10 @@ WHAT TO EXTRACT vs SKIP
 
 Chunk: {chunk_index + 1}/{total_chunks}
 Event Date: {event_date_formatted} ({event_date.isoformat()})
-Context: {context if context else 'none'}
+Context: {sanitized_context}
 
 Text:
-{chunk}"""
+{sanitized_chunk}"""
 
     for attempt in range(max_retries):
         try:

hindsight-clients/python/README.md

@@ -36,4 +36,4 @@ response = client.reflect(
 
 ## Documentation
 
-For full documentation, visit [hindsight.dev](https://hindsight.dev).
+For full documentation, visit [hindsight](https://github.com/vectorize-io/hindsight).

hindsight-clients/typescript/README.md

@@ -1,57 +1,89 @@
-# @hindsight/client
+# Hindsight TypeScript Client
 
-TypeScript client for Hindsight - Semantic memory system with personality-driven thinking.
+TypeScript client library for the Hindsight API.
-
-**Auto-generated from OpenAPI spec** - provides type-safe access to all Hindsight API endpoints.
 
 ## Installation
 
 ```bash
-npm install @hindsight/client
+npm install @vectorize-io/hindsight-client
 # or
-yarn add @hindsight/client
+yarn add @vectorize-io/hindsight-client
 ```
 
-## Quick Start
+## Usage
 
 ```typescript
-import { OpenAPI, MemoryStorageService, ReasoningService } from '@hindsight/client';
+import { HindsightClient } from '@vectorize-io/hindsight-client';
 
-// Configure API base URL
+const client = new HindsightClient({ baseUrl: 'http://localhost:8888' });
-OpenAPI.BASE = 'http://localhost:8888';
 
-// Store memory
+// Retain information
-await MemoryStorageService.putApiPutPost({
+await client.retain('my-bank', 'Alice works at Google in Mountain View.');
-  agent_id: 'user123',
+
-  content: 'Alice loves machine learning'
+// Recall memories
+const results = await client.recall('my-bank', 'Where does Alice work?');
+
+// Reflect and get an opinion
+const response = await client.reflect('my-bank', 'What do you think about Alice\'s career?');
+```
+
+## API Reference
+
+### `retain(bankId, content, options?)`
+
+Store a single memory.
+
+```typescript
+await client.retain('my-bank', 'User prefers dark mode', {
+  timestamp: new Date(),
+  context: 'Settings conversation',
+  metadata: { source: 'chat' }
 });
+```
 
-// Think (generate answer with personality)
+### `retainBatch(bankId, items, options?)`
-const response = await ReasoningService.thinkApiThinkPost({
+
-  agent_id: 'user123',
+Store multiple memories in batch.
-  query: 'What does Alice think about AI?',
+
-  thinking_budget: 50
+```typescript
+await client.retainBatch('my-bank', [
+  { content: 'Alice loves hiking' },
+  { content: 'Alice visited Paris last summer' }
+], { async: true });
+```
+
+### `recall(bankId, query, options?)`
+
+Recall memories matching a query.
+
+```typescript
+const results = await client.recall('my-bank', 'What are Alice\'s hobbies?', {
+  budget: 'mid'
 });
+```
 
+### `reflect(bankId, query, options?)`
+
+Generate a contextual answer using the bank's identity and memories.
+
+```typescript
+const response = await client.reflect('my-bank', 'What should I do this weekend?', {
+  budget: 'low'
+});
 console.log(response.text);
 ```
 
-## Available Services
+### `createBank(bankId, options)`
 
-- `MemoryStorageService` - Store and retrieve facts
+Create or update a memory bank with personality.
-- `SearchService` - Semantic and temporal search
-- `ReasoningService` - Personality-driven thinking
-- `VisualizationService` - Memory graphs and statistics
-- `ManagementService` - Agent profiles and configuration
-- `DocumentsService` - Document tracking
 
-All services are fully typed with TypeScript interfaces.
+```typescript
+await client.createBank('my-bank', {
+  name: 'My Assistant',
+  background: 'A helpful assistant that remembers everything.'
+});
+```
 
-## Development
+## Documentation
 
-Auto-generated from `openapi.json`. See [RELEASE.md](../../RELEASE.md) for regeneration instructions.
+For full documentation, visit [hindsight](https://github.com/vectorize-io/hindsight).
-
-## Links
-
-- [GitHub Repository](https://github.com/vectorize-io/hindsight)
-- [Full Documentation](https://github.com/vectorize-io/hindsight/blob/main/README.md)

hindsight-docs/docs/developer/api/installation.md

@@ -39,7 +39,7 @@ pip install hindsight-client
 npm install @hindsight/client
 ```
 
-**Requires:** A running Hindsight server (see [Server Deployment](/developer/server) for setup).
+**Requires:** A running Hindsight server (see [Server Deployment](/developer/installation) for setup).
 
 </TabItem>
 <TabItem value="cli" label="CLI">
@@ -120,4 +120,4 @@ hindsight --version
 ## Next Steps
 
 - [**Quick Start**](./quickstart) — Get running in 60 seconds
-- [**Server Deployment**](/developer/server) — Production setup options
+- [**Server Deployment**](/developer/installation) — Production setup options

hindsight-docs/docs/developer/api/quickstart.md

@@ -123,4 +123,4 @@ hindsight reflect my-bank "Tell me about Alice"
 - [**Recall**](./recall) — Search and retrieval strategies
 - [**Reflect**](./reflect) — Personality-aware reasoning
 - [**Memory Banks**](./memory-banks) — Configure personality and background
-- [**Server Options**](/developer/server) — Production deployment
+- [**Server Options**](/developer/installation) — Production deployment

hindsight-docs/docs/developer/index.md

@@ -121,4 +121,4 @@ The `bias_strength` parameter (0-1) controls how much personality influences opi
 - [**Operations**](/developer/api/operations) — Monitor async tasks
 
 ### Deployment
-- [**Server Setup**](/developer/server) — Deploy with Docker Compose, Helm, or pip
+- [**Server Setup**](/developer/installation) — Deploy with Docker Compose, Helm, or pip

hindsight-docs/docs/developer/installation.md

@@ -1,56 +1,33 @@
 # Installation
 
-Hindsight can be deployed in multiple ways depending on your infrastructure and requirements. This guide covers all installation methods and explains the core dependencies.
+Hindsight can be deployed in three ways depending on your infrastructure and requirements.
 
-## Dependencies
+## Prerequisites
 
-Hindsight has two core dependencies that you need to provide:
+### PostgreSQL with pgvector
 
-### 1. PostgreSQL Database
+Hindsight requires PostgreSQL with the **pgvector** extension for vector similarity search:
 
-**Why PostgreSQL?**
-
-Hindsight uses PostgreSQL with the **pgvector** extension to store and query semantic memories efficiently:
-
-- **Vector search**: pgvector enables fast approximate nearest neighbor (ANN) search using HNSW indexes
-- **Full-text search**: PostgreSQL's GIN indexes provide BM25-ranked text search
-- **Graph storage**: Entity relationships are stored using relational tables
-- **ACID compliance**: Ensures data consistency for memory operations
-- **Temporal queries**: Native date/time support for temporal reasoning
-
-**Requirements**:
 - PostgreSQL 14+ (recommended: 16+)
 - pgvector extension installed
-- ~2GB+ RAM for small deployments, 4GB+ for production
+- ~2GB+ RAM for small deployments
 
-### 2. LLM Provider
+### LLM Provider
 
-**Why an LLM?**
+You need an LLM API key for fact extraction, entity resolution, and answer generation:
 
-Hindsight uses Large Language Models for several critical operations:
+- **Groq** (recommended): Fast inference, high throughput
-
-- **Fact extraction**: Converting raw text into structured semantic facts during retention
-- **Entity resolution**: Identifying and linking entities across memories
-- **Temporal parsing**: Understanding time references in natural language
-- **Opinion generation**: Creating personality-based opinions during reflection
-- **Answer generation**: Synthesizing responses from retrieved memories
-
-**Performance Impact**: The LLM is the primary bottleneck for **write operations (retention)**. See [Performance](./performance.md) for details on optimizing throughput.
-
-**Supported Providers**:
-- **Groq**: Fast inference, high throughput (recommended for production)
 - **OpenAI**: GPT-4, GPT-4o, GPT-4 Mini
 - **Anthropic**: Claude 3.5 Sonnet, Haiku
-- **Ollama**: Run models locally (llama3.1, mixtral, etc.)
+- **Ollama**: Run models locally
-- **Any OpenAI-compatible API**: Custom endpoints
 
-## Installation Methods
+---
 
-### Docker Compose (Recommended)
+## Docker
 
 **Best for**: Quick start, development, small deployments
 
-**Why use this?**: Bundles all dependencies (PostgreSQL with pgvector, API server, optional Control Plane) in a single command.
+Docker Compose bundles all dependencies (PostgreSQL with pgvector, API server, Control Plane) in a single command.
 
 ```bash
 # Clone the repository
@@ -68,43 +45,35 @@ cd docker
 ./start.sh
 ```
 
-**What you get**:
+**Services started**:
 - **API Server**: http://localhost:8888
 - **Control Plane** (Web UI): http://localhost:3000
 - **Swagger UI**: http://localhost:8888/docs
-- **PostgreSQL**: Runs in container with pgvector extension
 
 **Management**:
 ```bash
-# Stop services
+./stop.sh   # Stop services
-cd docker && ./stop.sh
+./clean.sh  # Delete all data
-
-# Clean all data (WARNING: deletes all memories)
-cd docker && ./clean.sh
-
-# View logs
-docker-compose logs -f api
-docker-compose logs -f postgres
 ```
 
-### Helm Chart (Kubernetes)
+---
+
+## Helm / Kubernetes
 
 **Best for**: Production deployments, auto-scaling, cloud environments
 
-**Why use this?**: Kubernetes-native deployment with proper resource management, health checks, and auto-scaling capabilities.
-
 ```bash
 # Add Hindsight Helm repository
 helm repo add hindsight https://vectorize-io.github.io/hindsight
 helm repo update
 
-# Install with basic configuration
+# Install with built-in PostgreSQL
 helm install hindsight hindsight/hindsight \
   --set api.llm.provider=groq \
   --set api.llm.apiKey=gsk_xxxxxxxxxxxx \
   --set postgresql.enabled=true
 
-# Or use your own PostgreSQL
+# Or use external PostgreSQL
 helm install hindsight hindsight/hindsight \
   --set api.llm.provider=groq \
   --set api.llm.apiKey=gsk_xxxxxxxxxxxx \
@@ -112,248 +81,65 @@ helm install hindsight hindsight/hindsight \
   --set api.database.url=postgresql://user:pass@postgres.example.com:5432/hindsight
 ```
 
-**What you need**:
-- Kubernetes cluster (GKE, EKS, AKS, or self-hosted)
-- kubectl configured
-- Helm 3+
-- External PostgreSQL with pgvector (recommended) or use built-in PostgreSQL
-
-See the [Helm chart documentation](https://github.com/vectorize-io/hindsight/tree/main/deploy/helm) for advanced configuration.
-
-### pip install (Python Package)
-
-**Best for**: Custom deployments, development, integration into existing Python applications
-
-**Why use this?**: Maximum flexibility. Runs as a Python application with embedded PostgreSQL (pg0) by default, or connects to your own database.
-
-#### Install
-
-```bash
-# Install the all-in-one package
-pip install hindsight-all
-
-# Verify installation
-hindsight-api --version
-```
-
-#### Run with Embedded Database (pg0)
-
-**Best for**: Development, testing, single-machine deployments
-
-```bash
-# Configure LLM provider
-export HINDSIGHT_API_LLM_PROVIDER=groq
-export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx
-
-# Start the server - uses embedded pg0
-hindsight-api
-```
-
-**What happens**:
-- Creates `~/.hindsight/data/` directory for database storage
-- Downloads ML models on first run (~500MB)
-- Starts API server on http://localhost:8888
-- Ready to use - no external dependencies needed!
-
-**Limitations**:
-- Single process only (no horizontal scaling)
-- Lower performance than dedicated PostgreSQL
-- Not recommended for production
-
-#### Run with External PostgreSQL
-
-**Best for**: Production, high-performance deployments
-
-```bash
-# Configure database
-export HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@localhost:5432/hindsight
-
-# Configure LLM
-export HINDSIGHT_API_LLM_PROVIDER=groq
-export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx
-
-# Start the server
-hindsight-api
-```
-
 **Requirements**:
-- PostgreSQL 14+ with pgvector extension
+- Kubernetes cluster (GKE, EKS, AKS, or self-hosted)
-- Database must already exist
+- Helm 3+
-- pgvector extension must be enabled: `CREATE EXTENSION vector;`
 
-#### CLI Options
+See the [Helm chart documentation](https://github.com/vectorize-io/hindsight/tree/main/helm) for advanced configuration.
+
+---
+
+## Bare Metal (pip)
+
+**Best for**: Custom deployments, integration into existing Python applications
+
+### Install
 
 ```bash
-hindsight-api --help
+pip install hindsight-all
-
-# Common options
-hindsight-api --port 9000              # Custom port (default: 8888)
-hindsight-api --host 127.0.0.1         # Bind to localhost only
-hindsight-api --workers 4              # Multiple worker processes
-hindsight-api --mcp                    # Enable MCP server
-hindsight-api --log-level debug        # Verbose logging
-hindsight-api --reload                 # Auto-reload on code changes (dev)
 ```
 
-### Cloud Managed Services
+### Run with Embedded Database
 
-**Best for**: Production with minimal ops overhead
+For development and testing, Hindsight can run with an embedded PostgreSQL (pg0):
-
-You can deploy Hindsight to cloud platforms using their managed services:
-
-#### AWS
 
 ```bash
-# Use RDS PostgreSQL with pgvector
-# Deploy via ECS, EKS, or EC2
-# Example: ECS with Fargate
-docker build -t hindsight-api .
-aws ecr get-login-password | docker login --username AWS
-docker push your-ecr-repo/hindsight-api
-# Deploy via ECS task definition
-```
-
-**Required AWS Services**:
-- **RDS PostgreSQL** with pgvector extension
-- **ECS/EKS** for container orchestration
-- **Secrets Manager** for API keys
-- **ALB** for load balancing (optional)
-
-#### Google Cloud
-
-```bash
-# Use Cloud SQL PostgreSQL with pgvector
-# Deploy via Cloud Run or GKE
-gcloud run deploy hindsight \
-  --image gcr.io/your-project/hindsight-api \
-  --set-env-vars HINDSIGHT_API_DATABASE_URL=... \
-  --set-secrets HINDSIGHT_API_LLM_API_KEY=...
-```
-
-**Required GCP Services**:
-- **Cloud SQL PostgreSQL** with pgvector
-- **Cloud Run** or **GKE** for deployment
-- **Secret Manager** for API keys
-
-#### Supabase
-
-**Simplest cloud deployment** - Supabase provides PostgreSQL with pgvector built-in:
-
-```bash
-# 1. Create a Supabase project at supabase.com
-# 2. Get your database URL from Settings > Database
-# 3. Deploy API server with DATABASE_URL
-
-export HINDSIGHT_API_DATABASE_URL=postgresql://postgres:password@db.xxxxxxxxxxxx.supabase.co:5432/postgres
 export HINDSIGHT_API_LLM_PROVIDER=groq
 export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx
 
 hindsight-api
 ```
 
-## Choosing an Installation Method
+This creates a database in `~/.hindsight/data/` and starts the API on http://localhost:8888.
 
-| Method | Best For | Pros | Cons |
+### Run with External PostgreSQL
-|--------|----------|------|------|
-| **Docker Compose** | Development, small deployments | Easy setup, all dependencies included | Not scalable, single host |
-| **Helm/Kubernetes** | Production, auto-scaling | Scalable, cloud-native, resilient | Complex setup, K8s knowledge required |
-| **pip install** | Development, custom integration | Flexible, Python-native, embedded DB option | Manual dependency management |
-| **Cloud Services** | Production with managed infrastructure | Minimal ops, auto-scaling, managed DB | Higher cost, cloud lock-in |
 
-## Post-Installation
+For production, connect to your own PostgreSQL instance:
-
-### Verify Installation
 
 ```bash
-# Check API server health
+export HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@localhost:5432/hindsight
-curl http://localhost:8888/health
+export HINDSIGHT_API_LLM_PROVIDER=groq
+export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx
 
-# List banks (should return empty array initially)
+hindsight-api
-curl http://localhost:8888/api/v1/banks
-
-# View API documentation
-open http://localhost:8888/docs
 ```
 
-### First Steps
+**Note**: The database must exist and have pgvector enabled (`CREATE EXTENSION vector;`).
 
-1. **Create your first bank**:
+### CLI Options
-   ```bash
-   curl -X POST http://localhost:8888/api/v1/banks/my-first-bank \
-     -H "Content-Type: application/json" \
-     -d '{"name": "My First Bank"}'
-   ```
-
-2. **Retain your first memory**:
-   ```bash
-   curl -X POST http://localhost:8888/api/v1/banks/my-first-bank/retain \
-     -H "Content-Type: application/json" \
-     -d '{"items": [{"content": "The Eiffel Tower is in Paris."}]}'
-   ```
-
-3. **Recall the memory**:
-   ```bash
-   curl -X POST http://localhost:8888/api/v1/banks/my-first-bank/recall \
-     -H "Content-Type: application/json" \
-     -d '{"query": "Where is the Eiffel Tower?"}'
-   ```
-
-### Next Steps
-
-- **Configure** your deployment: [Configuration](./configuration.md)
-- **Understand ML models**: [Models](./models.md)
-- **Monitor performance**: [Metrics](./metrics.md)
-- **Optimize for production**: [Performance](./performance.md)
-
-## Troubleshooting
-
-### PostgreSQL Connection Issues
 
 ```bash
-# Test database connection
+hindsight-api --port 9000          # Custom port (default: 8888)
-psql "$HINDSIGHT_API_DATABASE_URL"
+hindsight-api --host 127.0.0.1     # Bind to localhost only
-
+hindsight-api --workers 4          # Multiple worker processes
-# Verify pgvector extension
+hindsight-api --mcp                # Enable MCP server
-psql -c "SELECT * FROM pg_extension WHERE extname = 'vector';"
+hindsight-api --log-level debug    # Verbose logging
-
-# Enable pgvector if missing
-psql -c "CREATE EXTENSION IF NOT EXISTS vector;"
-```
-
-### LLM Provider Issues
-
-```bash
-# Test Groq API key
-curl https://api.groq.com/openai/v1/models \
-  -H "Authorization: Bearer $HINDSIGHT_API_LLM_API_KEY"
-
-# Test OpenAI API key
-curl https://api.openai.com/v1/models \
-  -H "Authorization: Bearer $HINDSIGHT_API_LLM_API_KEY"
-```
-
-### Port Already in Use
-
-```bash
-# Find process using port 8888
-lsof -i :8888
-
-# Kill the process
-kill -9 <PID>
-
-# Or use a different port
-hindsight-api --port 9000
-```
-
-### Model Download Issues
-
-```bash
-# Models are downloaded to ~/.cache/huggingface/
-# Clear cache and retry
-rm -rf ~/.cache/huggingface/
-hindsight-api  # Will re-download models
 ```
 
 ---
 
-For installation issues not covered here, please [open an issue](https://github.com/your-repo/hindsight/issues) on GitHub.
+## Next Steps
+
+- [Configuration](./configuration.md) — Environment variables and settings
+- [Models](./models.md) — ML models and providers
+- [Metrics](./metrics.md) — Monitoring and observability

hindsight-docs/docs/developer/performance.md

@@ -309,7 +309,7 @@ Hindsight has been evaluated on the LoComo (Long Context Memory) benchmark:
 - **Average recall latency**: 400-600ms (mid budget)
 - **Average reflect latency**: 1500-2500ms (end-to-end)
 
-See [benchmarks README](../../benchmarks/README.md) for detailed results.
+See the [GitHub repository](https://github.com/vectorize-io/hindsight/tree/main/hindsight-dev/benchmarks) for detailed benchmark results.
 
 ### Performance Metrics
 

uv.lock

@@ -1141,7 +1141,7 @@ provides-extras = ["test"]
 
 [[package]]
 name = "hindsight-api"
-version = "0.0.12"
+version = "0.0.14"
 source = { editable = "hindsight-api" }
 dependencies = [
     { name = "alembic" },
@@ -1243,7 +1243,7 @@ dev = [
 
 [[package]]
 name = "hindsight-client"
-version = "0.0.12"
+version = "0.0.14"
 source = { editable = "hindsight-clients/python" }
 dependencies = [
     { name = "aiohttp" },
@@ -1275,7 +1275,7 @@ provides-extras = ["test"]
 
 [[package]]
 name = "hindsight-dev"
-version = "0.0.12"
+version = "0.0.14"
 source = { editable = "hindsight-dev" }
 dependencies = [
     { name = "hindsight-api" },