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Vedaya API

The Vedaya API provides comprehensive functionality for data ingestion, knowledge graph management and retrieval operations. This API allows you to build intelligent applications that can process, analyze, and retrieve information from various document sources.

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API Capabilities

The API is organized into several functional areas:

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Data Ingestion

  • Upload files for processing and embedding
  • Connect to cloud storage providers (Google Drive, Dropbox, OneDrive, S3, Azure)
  • Ingest documents from URLs
  • Monitor ingestion status
  • Configure chunking and embedding settings

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Knowledge Graph

  • Entity detection and relationship extraction
  • Graph visualization and exploration
  • Concept clustering
  • Neo4j integration
  • Entity management (merging, querying)

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Retrieval and RAG

  • Query the knowledge base
  • Retrieve relevant document chunks
  • RAG (Retrieval Augmented Generation) pipeline support
  • Chatbot functionality with RAG capabilities

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Authentication

Most API endpoints work without authentication. For deployments that require authentication, you can configure it with environment variables: 
import os

# Configure API (prefer environment variables)
API_BASE_URL = os.getenv("VEDAYA_API_BASE_URL", "https://vedaya-kg.fly.dev")
API_KEY = os.getenv("VEDAYA_API_KEY", "")  # leave empty if no auth

# Build headers
headers = {"Content-Type": "application/json"}
if API_KEY and API_KEY.strip() and API_KEY != "sk-mock-dummy-key":
    headers["Authorization"] = f"Bearer {API_KEY}"
See the Authentication Guide for complete details.

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Base URL

https://vedaya-kg.fly.dev
Note: Use vedaya-kg.fly.dev (without trailing slash) for the latest deployment. For detailed endpoint documentation, please refer to the specific API sections.