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Getting Started with Hindsight Cloud ​

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Source: docs.hindsight.vectorize.io/getting-started

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Prerequisites ​

  • A Hindsight Cloud account (available at ui.hindsight.vectorize.io)
  • Basic REST API knowledge or familiarity with Python/TypeScript

Step 1: Sign Up and Log In ​

  1. Visit ui.hindsight.vectorize.io
  2. Select Sign Up and complete account creation
  3. Confirm your email address
  4. Log in to access the dashboard

Step 2: Create an Organization ​

Organizations are the top-level container for all your resources including memory banks, team members, API keys, and billing data. Enter an organization name and click Create.

Step 3: Create Your First Memory Bank ​

  1. From the dashboard, select Create Memory Bank
  2. Provide a descriptive name (e.g., "My AI Assistant")

Step 4: Get Your API Key ​

  1. Click the Connect button in the navigation
  2. Select Create API Key
  3. Enter a descriptive key name
  4. Choose an expiration setting
  5. Copy your API key immediately — it will not be shown again

Step 5: Make Your First API Call ​

Python ​

bash
pip install hindsight-client
python
from hindsight_client import Hindsight

client = Hindsight(
    base_url="https://api.hindsight.vectorize.io",
    api_key="your-api-key"
)

# Create a memory bank
bank = client.create_bank(
    bank_id="my-assistant",
    name="My Assistant"
)

# Store a memory
client.retain(
    bank_id="my-assistant",
    content="The user's name is Alice and she prefers Python."
)

# Retrieve relevant memories
result = client.recall(
    bank_id="my-assistant",
    query="What is the user's name?"
)

for memory in result.results:
    print(memory.text)

# Get an AI-powered answer
response = client.reflect(
    bank_id="my-assistant",
    query="What do we know about this user?"
)
print(response.text)

# Create a mental model
result = client.create_mental_model(
    bank_id="my-assistant",
    name="User Profile",
    source_query="What do we know about this user?"
)

print(f"Creating mental model (operation: {result.operation_id})")

# List mental models
models = client.list_mental_models(bank_id="my-assistant")
for m in models.items:
    print(f"{m.name}: {m.content}")

client.close()

TypeScript ​

bash
npm install @vectorize-io/hindsight-client
typescript
import { HindsightClient } from '@vectorize-io/hindsight-client';

const client = new HindsightClient({
  baseUrl: 'https://api.hindsight.vectorize.io',
  apiKey: 'your-api-key'
});

// Create a memory bank
const bank = await client.createBank('my-assistant', {
  name: 'My Assistant'
});

// Store a memory
await client.retain(
  'my-assistant',
  "The user's name is Alice and she prefers Python."
);

// Retrieve relevant memories
const result = await client.recall(
  'my-assistant',
  "What is the user's name?"
);

result.results.forEach(memory => {
  console.log(memory.text);
});

// Create a mental model
const mmResult = await client.createMentalModel(
  'my-assistant',
  'User Profile',
  'What do we know about this user?'
);

console.log(`Creating mental model (operation: ${mmResult.operation_id})`);

cURL ​

bash
# Store a memory
curl -X POST https://api.hindsight.vectorize.io/v1/default/banks/{bank_id}/memories \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"items": [{"content": "The user'\''s name is Alice and she prefers Python."}]}'

# Retrieve memories
curl -X POST https://api.hindsight.vectorize.io/v1/default/banks/{bank_id}/memories/recall \
  -H "Authorization: Bearer your-api-key" \
  -H "Content-Type: application/json" \
  -d '{"query": "What is the user'\''s name?"}'

Next Steps ​