Hybrid Search API - Adaptive Evidence Retrieval

Find relevant memories using adaptive dense and sparse retrieval, entity and graph evidence, temporal truth, facet coverage, and calibrated ranking metadata.

How Search Works

Hebbrix uses multiple independent signals to find the most relevant memories:

Vector Search: semantic similarity using embeddings. Understands meaning, not just keywords.
BM25 Keyword: classic keyword matching for exact terms and names.
Graph Traversal: finds related memories through entity relationships.
Temporal & Calibrated Ranking: distinguishes current from superseded facts, preserves multi-intent facet coverage, and exposes whether scores are calibrated or relative.

Endpoints

Code Examples

Basic Search

Python

import os
import requests

BASE = "https://api.hebbrix.com/v1"
H = {"Authorization": f"Bearer {os.environ['HEBBRIX_API_KEY']}"}

# Adaptive hybrid search with explicit confidence and coverage metadata
r = requests.post(
    f"{BASE}/search",
    headers=H,
    json={"query": "user preferences", "limit": 10},
)
for hit in r.json()["results"]:
    print(f"[{hit['score']:.2f}] {hit['content']}")

Search with Filters

Python

# Top-level SDK helper, scoped to a collection
results = client.search(
    query="project deadlines",
    collection_id="col_work",
    limit=20,
)

# Advanced search with date range and recency boost: call the endpoint directly
from datetime import datetime, timedelta

r = requests.post(
    f"{BASE}/search/advanced",
    headers=H,
    json={
        "query": "meetings",
        "date_range_start": (datetime.now() - timedelta(days=7)).isoformat(),
        "boost_recent": True,
    },
)
results = r.json()

Find Similar Memories

Python

# GET /v1/search/similar/{memory_id}: vector similarity search
r = requests.get(
    f"{BASE}/search/similar/mem_abc123",
    headers=H,
    params={"limit": 5},
)
for hit in r.json()["results"]:
    print(f"[{hit['score']:.2f}] {hit['content']}")

cURL Examples

POST/v1/search

curl -X POST "https://api.hebbrix.com/v1/search" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "query": "What are the user's preferences?",
  "limit": 10
}'

POST/v1/search/reason

curl -X POST "https://api.hebbrix.com/v1/search/reason" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "query": "What programming languages does the user know?",
  "include_steps": true
}'

Search Types

Field	Type	Description
hybrid	Fast (default)	General queries (default)
vector	Fast	Semantic/conceptual queries
bm25	Fastest	Exact keyword matching
graph	Moderate	Entity relationships