* feat(skill): validate links, strip images, include openapi.json and changelog - Add post-processing step to rewrite Docusaurus site-root paths (e.g. /developer/foo) to proper relative .md paths within the skill - Strip markdown and HTML images from all generated files since assets are not bundled with the skill - Copy hindsight-docs/static/openapi.json into references/openapi.json and map /api-reference links to it - Include changelog.md from src/pages/ alongside faq and best-practices - Add final validation step that fails the build if any link still points outside the skill directory * ci: run generate-docs-skill in verify-generated-files job * fix(skill): strip unresolvable site-root links instead of leaving them broken * fix(skill): write file when images stripped but no links rewritten * chore(skill): regenerate with fixed links, stripped images, changelog and openapi * fix(skill): handle changelog as directory, add agno/hermes integrations, rebase on main
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3.2 KiB
Quick Start
Get up and running with Hindsight in 60 seconds.
{/* Import raw source files */}
Clients
Start the API Server
pip (API only)
pip install hindsight-api
export OPENAI_API_KEY=sk-xxx
export HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY
hindsight-api
API available at http://localhost:8888
Docker (Full Experience)
export OPENAI_API_KEY=sk-xxx
docker run --rm -it --pull always -p 8888:8888 -p 9999:9999 \
-e HINDSIGHT_API_LLM_API_KEY=$OPENAI_API_KEY \
-v $HOME/.hindsight-docker:/home/hindsight/.pg0 \
ghcr.io/vectorize-io/hindsight:latest
- API: http://localhost:8888
- Control Plane (Web UI): http://localhost:9999
💡 LLM Provider
Hindsight requires an LLM with structured output support. Recommended: Groq with gpt-oss-20b for fast, cost-effective inference.
See LLM Providers for more details.
Use the Client
Python
pip install hindsight-client
from hindsight_client import Hindsight
client = Hindsight(base_url="http://localhost:8888")
# Retain: Store information
client.retain(bank_id="my-bank", content="Alice works at Google as a software engineer")
# Recall: Search memories
client.recall(bank_id="my-bank", query="What does Alice do?")
# Reflect: Generate disposition-aware response
client.reflect(bank_id="my-bank", query="Tell me about Alice")
Node.js
npm install @vectorize-io/hindsight-client
import { HindsightClient } from '@vectorize-io/hindsight-client';
const client = new HindsightClient({ baseUrl: 'http://localhost:8888' });
// Retain: Store information
await client.retain('my-bank', 'Alice works at Google as a software engineer');
// Recall: Search memories
await client.recall('my-bank', 'What does Alice do?');
// Reflect: Generate response
await client.reflect('my-bank', 'Tell me about Alice');
CLI
curl -fsSL https://hindsight.vectorize.io/get-cli | bash
# Retain: Store information
hindsight memory retain my-bank "Alice works at Google as a software engineer"
# Recall: Search memories
hindsight memory recall my-bank "What does Alice do?"
# Reflect: Generate response
hindsight memory reflect my-bank "Tell me about Alice"
Go
go get github.com/vectorize-io/hindsight/hindsight-clients/go
# Section 'quickstart-full' not found in api/quickstart.go
What's Happening
| Operation | What it does |
|---|---|
| Retain | Content is processed, facts are extracted, entities are identified and linked in a knowledge graph |
| Recall | Four search strategies (semantic, keyword, graph, temporal) run in parallel to find relevant memories |
| Reflect | Retrieved memories are used to generate a disposition-aware response |
Integrations
Next Steps
- Retain — Advanced options for storing memories
- Recall — Search and retrieval strategies
- Reflect — Disposition-aware reasoning
- Memory Banks — Configure disposition and mission
- Server Deployment — Docker Compose, Helm, and production setup