137 lines
3.8 KiB
Markdown
137 lines
3.8 KiB
Markdown
# Hindsight API
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**Memory System for AI Agents** — Temporal + Semantic + Entity Memory Architecture using PostgreSQL with pgvector.
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Hindsight gives AI agents persistent memory that works like human memory: it stores facts, tracks entities and relationships, handles temporal reasoning ("what happened last spring?"), and forms opinions based on configurable disposition traits.
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## Installation
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```bash
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pip install hindsight-api
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```
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## Quick Start
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### Run the Server
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```bash
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# Set your LLM provider
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export HINDSIGHT_API_LLM_PROVIDER=openai
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export HINDSIGHT_API_LLM_API_KEY=sk-xxxxxxxxxxxx
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# Start the server (uses embedded PostgreSQL by default)
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hindsight-api
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```
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The server starts at http://localhost:8888 with:
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- REST API for memory operations
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- MCP server at `/mcp` for tool-use integration
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### Use the Python API
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```python
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from hindsight_api import MemoryEngine
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# Create and initialize the memory engine
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memory = MemoryEngine()
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await memory.initialize()
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# Create a memory bank for your agent
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bank = await memory.create_memory_bank(
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name="my-assistant",
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background="A helpful coding assistant"
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)
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# Store a memory
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await memory.retain(
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memory_bank_id=bank.id,
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content="The user prefers Python for data science projects"
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)
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# Recall memories
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results = await memory.recall(
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memory_bank_id=bank.id,
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query="What programming language does the user prefer?"
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)
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# Reflect with reasoning
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response = await memory.reflect(
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memory_bank_id=bank.id,
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query="Should I recommend Python or R for this ML project?"
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)
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```
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## CLI Options
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```bash
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hindsight-api --help
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# Common options
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hindsight-api --port 9000 # Custom port (default: 8888)
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hindsight-api --host 127.0.0.1 # Bind to localhost only
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hindsight-api --workers 4 # Multiple worker processes
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hindsight-api --log-level debug # Verbose logging
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```
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## Configuration
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Configure via environment variables:
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `HINDSIGHT_API_DATABASE_URL` | PostgreSQL connection string | `pg0` (embedded) |
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| `HINDSIGHT_API_LLM_PROVIDER` | `openai`, `groq`, `gemini`, `ollama` | `openai` |
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| `HINDSIGHT_API_LLM_API_KEY` | API key for LLM provider | - |
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| `HINDSIGHT_API_LLM_MODEL` | Model name | `gpt-4o-mini` |
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| `HINDSIGHT_API_HOST` | Server bind address | `0.0.0.0` |
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| `HINDSIGHT_API_PORT` | Server port | `8888` |
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### Example with External PostgreSQL
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```bash
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export HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@localhost:5432/hindsight
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export HINDSIGHT_API_LLM_PROVIDER=groq
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export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx
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hindsight-api
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```
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## Docker
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```bash
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docker run --rm -it -p 8888:8888 \
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-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
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-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
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ghcr.io/vectorize-io/hindsight:latest
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```
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## MCP Server
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For local MCP integration without running the full API server:
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```bash
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hindsight-local-mcp
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```
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This runs a stdio-based MCP server that can be used directly with MCP-compatible clients.
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## Key Features
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- **Multi-Strategy Retrieval (TEMPR)** — Semantic, keyword, graph, and temporal search combined with RRF fusion
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- **Entity Graph** — Automatic entity extraction and relationship tracking
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- **Temporal Reasoning** — Native support for time-based queries
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- **Disposition Traits** — Configurable skepticism, literalism, and empathy influence opinion formation
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- **Three Memory Types** — World facts, bank actions, and formed opinions with confidence scores
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## Documentation
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Full documentation: [https://hindsight.vectorize.io](https://hindsight.vectorize.io)
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- [Installation Guide](https://hindsight.vectorize.io/developer/installation)
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- [Configuration Reference](https://hindsight.vectorize.io/developer/configuration)
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- [API Reference](https://hindsight.vectorize.io/api-reference)
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- [Python SDK](https://hindsight.vectorize.io/sdks/python)
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## License
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Apache 2.0
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