Deepthought Technical Documentation Overview
Deepthought is a graph-based reasoning engine that ingests arbitrary JSON, models each atomic value as a datum, links them with edges, and continuously refines the strength of those links through a reinforcement-learning loop. There is no required schema and no domain-specific assumptions—you send JSON; Deepthought turns it into a living, weighted knowledge graph.
Getting Started
- API Access
You’ll need:
Base API URL
Authentication token or key (if your deployment requires one)
- Prepare Your Data
Send any valid JSON. Deepthought will:
Parse every primitive or composite value into datums.
Infer relationships (edges) from JSON structure (nesting, arrays, key/value pairs).
Example payload:
{ "user": { "name": "Ada Lovelace", "age": 36, "interests": ["mathematics", "computing", "poetry"] }, "metadata": { "source": "imported-profile" } }
- Send Your Request
curl -X POST https://api.deepthought.io/v1/analyze
-H "Content-Type: application/json"
-H "Authorization: Bearer YOUR_TOKEN"
-d @your-data.json
Making API Requests POST /v1/analyze
Submit JSON for ingestion and graph updates.
Field Description Body Any JSON object Auth header Authorization: Bearer (if required) Example Response { "graphId": "run-20250610-ab12", "summary": { "datumsProcessed": 25, "newDatums": 7, "edgesCreated": 42, "reinforcedEdges": 18 } }
GET /v1/health
Simple liveness check:
curl https://api.deepthought.io/v1/health
Core Concepts Concept Description Datum Atomic unit derived from any JSON key, value, or array element. Stored with a unique ID. Edge Directed relationship between two datums, inferred from JSON structure. Carries a weight ∈ [0, 1]. Graph The evolving store of all datums and edges. Each ingest run mutates this graph. Graph ID Returned per request for traceability (graphId). Edge Weights
New edges → initialized with a random weight 0 – 1.
Existing edges → adjusted by a reinforcement-learning algorithm that strengthens frequently co-occurring relationships.
How It Works (Pipeline)
Parsing – Recursively walk JSON, emit datums, infer edges.
Graph Lookup – Match datums against the graph; create any that are unknown.
Weight Adjustment –
Reinforce existing edges.
Add new edges with random weights.
Persistence – Store new/updated nodes and edges in the backing graph database.
Typical Use Cases Use Case What Deepthought Provides Knowledge-graph construction Feed semi-structured data to build a continuously evolving graph. Relationship inference Surface emergent links for recommendations, clustering, or analytics. ML feature engineering Export weighted edges as high-signal relational features. Data-quality gates Detect disconnected or weakly connected datums before ETL loads. Exploratory analysis Traverse the graph to uncover patterns or anomalies in large datasets. Submitting Bug Reports
Where
Email support@deepthought.io
Enterprise users: internal ticketing portal.
Include
Clear title
Full input JSON
graphId, timestamp, and response body
Expected vs. actual behavior
Logs or request IDs (if available)
Template
Title: New datums not reported in summary
Input: { "test": { "value": "unseen-datum" } }
Expected: summary.newDatums == 1 Actual: summary.newDatums == 0
GraphId: run-20250611-cdef