138 lines
3.3 KiB
Markdown
138 lines
3.3 KiB
Markdown
---
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sidebar_position: 0
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---
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# Quick Start
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Get up and running with Hindsight in 60 seconds.
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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## Start the API Server
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<Tabs>
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<TabItem value="pip" label="pip (API only)">
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```bash
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pip install hindsight-api
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export OPENAI_API_KEY=sk-xxx
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export HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY
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hindsight-api
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```
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API available at http://localhost:8888
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</TabItem>
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<TabItem value="docker" label="Docker (Full Experience)">
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```bash
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export OPENAI_API_KEY=sk-xxx
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docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
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-e HINDSIGHT_API_LLM_API_KEY=$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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- **API**: http://localhost:8888
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- **Control Plane** (Web UI): http://localhost:9999
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</TabItem>
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</Tabs>
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:::tip LLM Provider
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Hindsight requires an LLM with structured output support. Recommended: **Groq** with `gpt-oss-20b` for fast, cost-effective inference.
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See [LLM Providers](/developer/models#llm) for more details.
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:::
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---
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## Use the Client
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<Tabs>
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<TabItem value="python" label="Python">
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```bash
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pip install hindsight-client
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```
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```python
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from hindsight_client import Hindsight
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client = Hindsight(base_url="http://localhost:8888")
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# Retain: Store information
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client.retain(bank_id="my-bank", content="Alice works at Google as a software engineer")
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# Recall: Search memories
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client.recall(bank_id="my-bank", query="What does Alice do?")
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# Reflect: Generate disposition-aware response
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client.reflect(bank_id="my-bank", query="Tell me about Alice")
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```
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</TabItem>
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<TabItem value="node" label="Node.js">
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```bash
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npm install @vectorize-io/hindsight-client
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```
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```javascript
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const { HindsightClient } = require('@vectorize-io/hindsight-client');
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const client = new HindsightClient({ baseUrl: 'http://localhost:8888' });
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// Retain: Store information
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await client.retain('my-bank', 'Alice works at Google as a software engineer');
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// Recall: Search memories
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await client.recall('my-bank', 'What does Alice do?');
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// Reflect: Generate response
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await client.reflect('my-bank', 'Tell me about Alice');
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```
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</TabItem>
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<TabItem value="cli" label="CLI">
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```bash
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curl -fsSL https://hindsight.vectorize.io/get-cli | bash
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```
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```bash
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# Retain: Store information
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hindsight memory retain my-bank "Alice works at Google as a software engineer"
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# Recall: Search memories
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hindsight memory recall my-bank "What does Alice do?"
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# Reflect: Generate response
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hindsight memory reflect my-bank "Tell me about Alice"
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```
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</TabItem>
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</Tabs>
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---
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## What's Happening
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| Operation | What it does |
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|-----------|--------------|
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| **Retain** | Content is processed, facts are extracted, entities are identified and linked in a knowledge graph |
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| **Recall** | Four search strategies (semantic, keyword, graph, temporal) run in parallel to find relevant memories |
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| **Reflect** | Retrieved memories are used to generate a disposition-aware response |
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---
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## Next Steps
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- [**Retain**](./retain) — Advanced options for storing memories
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- [**Recall**](./recall) — Search and retrieval strategies
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- [**Reflect**](./reflect) — Disposition-aware reasoning
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- [**Memory Banks**](./memory-banks) — Configure disposition and background
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- [**Server Deployment**](/developer/installation) — Docker Compose, Helm, and production setup
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