Search Facts

Retrieve memories using multi-strategy search.

Prerequisites

Make sure you've completed the Quick Start to install the client and start the server.

Basic Search

Python

from hindsight_client import Hindsight

client = Hindsight(base_url="http://localhost:8888")

client.recall(bank_id="my-bank", query="What does Alice do?")

Node.js

import { HindsightClient } from '@vectorize-io/hindsight-client';

const client = new HindsightClient({ baseUrl: 'http://localhost:8888' });

await client.recall('my-bank', 'What does Alice do?');

CLI

hindsight memory search my-bank "What does Alice do?"

Search Parameters

Parameter	Type	Default	Description
query	string	required	Natural language query
types	list	all	Filter: world, experience, opinion
budget	string	"mid"	Budget level: "low", "mid", "high"
max_tokens	int	4096	Token budget for results

Python

results = client.recall(
    bank_id="my-bank",
    query="What does Alice do?",
    types=["world", "experience"],
    budget="high",
    max_tokens=8000
)

Node.js

const results = await client.recall('my-bank', 'What does Alice do?', {
    budget: 'high',
    maxTokens: 8000
});

Full-Featured Search

For more control, use the full-featured recall method:

Python

# Full response with trace info
response = client.recall_memories(
    bank_id="my-bank",
    query="What does Alice do?",
    types=["world", "experience"],
    budget="high",
    max_tokens=8000,
    trace=True,
    include_entities=True,
    max_entity_tokens=500
)

# Access results
for r in response["results"]:
    print(f"{r['text']} (score: {r['weight']:.2f})")

# Access entity observations (if include_entities=True)
if "entities" in response:
    for entity in response["entities"]:
        print(f"Entity: {entity['name']}")

Node.js

// Full response with trace info
const response = await client.recallMemories('my-bank', {
    query: 'What does Alice do?',
    types: ['world', 'experience'],
    budget: 'high',
    maxTokens: 8000,
    trace: true
});

// Access results
for (const r of response.results) {
    console.log(`${r.text} (score: ${r.weight})`);
}

Temporal Queries

Hindsight automatically detects time expressions and activates temporal search:

Python

# These queries activate temporal-graph retrieval
results = client.recall(bank_id="my-bank", query="What did Alice do last spring?")
results = client.recall(bank_id="my-bank", query="What happened in June?")
results = client.recall(bank_id="my-bank", query="Events from last year")

CLI

hindsight memory search my-bank "What did Alice do last spring?"
hindsight memory search my-bank "What happened between March and May?"

Supported temporal expressions:

Expression	Parsed As
"last spring"	March 1 - May 31 (previous year)
"in June"	June 1-30 (current/nearest year)
"last year"	Jan 1 - Dec 31 (previous year)
"last week"	7 days ago - today
"between March and May"	March 1 - May 31

Filter by Fact Type

Search specific memory networks:

Python

# Only world facts (objective information)
world_facts = client.recall(
    bank_id="my-bank",
    query="Where does Alice work?",
    types=["world"]
)

# Only experience (conversations and events)
experience = client.recall(
    bank_id="my-bank",
    query="What have I recommended?",
    types=["experience"]
)

# Only opinions (formed beliefs)
opinions = client.recall(
    bank_id="my-bank",
    query="What do I think about Python?",
    types=["opinion"]
)

# World facts and experience (exclude opinions)
facts = client.recall(
    bank_id="my-bank",
    query="What happened?",
    types=["world", "experience"]
)

CLI

hindsight memory search my-bank "Python" --fact-type opinion
hindsight memory search my-bank "Alice" --fact-type world,experience

How Recall Works

Learn about the four search strategies (semantic, keyword, graph, temporal) and RRF fusion in the Recall Architecture guide.

Token Budget Management

Hindsight is built for AI agents, not humans. Traditional search systems return "top-k" results, but agents don't think in terms of result counts—they think in tokens. An agent's context window is measured in tokens, and that's exactly how Hindsight measures results.

The max_tokens parameter lets you control how much of your agent's context budget to spend on memories:

# Fill up to 4K tokens of context with relevant memories
results = client.recall(bank_id="my-bank", query="What do I know about Alice?", max_tokens=4096)

# Smaller budget for quick lookups
results = client.recall(bank_id="my-bank", query="Alice's email", max_tokens=500)

This design means you never have to guess whether 10 results or 50 results will fit your context. Just specify the token budget and Hindsight returns as many relevant memories as will fit.

Additional Context: Chunks and Entity Observations

For the most relevant memories, you can optionally retrieve additional context—each with its own token budget:

Option	Parameter	Description
Chunks	include_chunks, max_chunk_tokens	Raw text chunks that generated the memories
Entity Observations	include_entities, max_entity_tokens	Related observations about entities mentioned in results

response = client.recall_memories(
    bank_id="my-bank",
    query="What does Alice do?",
    max_tokens=4096,              # Budget for memories
    include_chunks=True,
    max_chunk_tokens=2000,        # Budget for raw chunks
    include_entities=True,
    max_entity_tokens=1000        # Budget for entity observations
)

# Access the additional context
chunks = response.get("chunks", {})
entities = response.get("entities", [])

This gives your agent richer context while maintaining precise control over total token consumption.

Budget Levels

The budget parameter controls graph traversal depth:

• "low": Fast, shallow search — good for simple lookups
• "mid": Balanced — default for most queries
• "high": Deep exploration — finds indirect connections

Python

# Quick lookup
results = client.recall(bank_id="my-bank", query="Alice's email", budget="low")

# Deep exploration
results = client.recall(bank_id="my-bank", query="How are Alice and Bob connected?", budget="high")

Node.js

// Quick lookup
const results = await client.recall('my-bank', "Alice's email", { budget: 'low' });

// Deep exploration
const deep = await client.recall('my-bank', 'How are Alice and Bob connected?', { budget: 'high' });