fleet-memory/hindsight-docs/docs/developer/installation.md

Installation

Hindsight can be deployed in several ways depending on your infrastructure and requirements.

:::tip Don't want to manage infrastructure? Hindsight Cloud is a fully managed service that handles all infrastructure, scaling, and maintenance. We're onboarding design partners now — request early access. :::

Prerequisites

PostgreSQL with pgvector

Hindsight requires PostgreSQL with the pgvector extension for vector similarity search.

By default, Hindsight uses pg0 — an embedded PostgreSQL that runs locally on your machine. This is convenient for development but not recommended for production.

For production, use an external PostgreSQL with pgvector:

• Supabase — Managed PostgreSQL with pgvector built-in
• Neon — Serverless PostgreSQL with pgvector
• AWS RDS / Cloud SQL / Azure — With pgvector extension enabled
• Self-hosted — PostgreSQL 14+ with pgvector installed

LLM Provider

You need an LLM API key for fact extraction, entity resolution, and answer generation:

• Groq (recommended): Fast inference with gpt-oss-20b
• OpenAI: GPT-4o, GPT-4o-mini
• Ollama: Run models locally

See Models for detailed comparison and configuration.

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Docker

Best for: Quick start, development, small deployments

Single Container (Quickest)

Run everything in one container with embedded PostgreSQL:

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 Server: http://localhost:8888
• Control Plane (Web UI): http://localhost:9999

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Helm / Kubernetes

Best for: Production deployments, auto-scaling, cloud environments

# Install with built-in PostgreSQL
helm install hindsight oci://ghcr.io/vectorize-io/charts/hindsight \
  --set api.llm.provider=groq \
  --set api.llm.apiKey=gsk_xxxxxxxxxxxx \
  --set postgresql.enabled=true

# Or use external PostgreSQL
helm install hindsight oci://ghcr.io/vectorize-io/charts/hindsight \
  --set api.llm.provider=groq \
  --set api.llm.apiKey=gsk_xxxxxxxxxxxx \
  --set postgresql.enabled=false \
  --set api.database.url=postgresql://user:pass@postgres.example.com:5432/hindsight

# Install a specific version
helm install hindsight oci://ghcr.io/vectorize-io/charts/hindsight --version 0.1.3

# Upgrade to latest
helm upgrade hindsight oci://ghcr.io/vectorize-io/charts/hindsight

Requirements:

• Kubernetes cluster (GKE, EKS, AKS, or self-hosted)
• Helm 3.8+

See the Helm chart documentation for advanced configuration.

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Bare Metal (pip)

Best for: Custom deployments, integration into existing Python applications

Install

pip install hindsight-all

Run with Embedded Database

For development and testing, Hindsight can run with an embedded PostgreSQL (pg0):

export HINDSIGHT_API_LLM_PROVIDER=groq
export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx

hindsight-api

This creates a database in ~/.hindsight/data/ and starts the API on http://localhost:8888.

Run with External PostgreSQL

For production, connect to your own PostgreSQL instance:

export HINDSIGHT_API_DATABASE_URL=postgresql://user:pass@localhost:5432/hindsight
export HINDSIGHT_API_LLM_PROVIDER=groq
export HINDSIGHT_API_LLM_API_KEY=gsk_xxxxxxxxxxxx

hindsight-api

Note: The database must exist and have pgvector enabled (CREATE EXTENSION vector;).

CLI 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 --log-level debug    # Verbose logging

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Next Steps

• Configuration — Environment variables and settings
• Models — ML models and providers
• Metrics — Monitoring and observability