Problem Statement
⚙️ Integration Nightmare
Enterprises and development teams spend weeks building and maintaining fragmented pipelines for data ingestion, content cleaning, embedding generation, and vector storage.
Each stage requires a different API, data format, and tool—creating redundant code, fragile dependencies, and constant maintenance work.
The result is slower delivery, inconsistent output, and teams locked in repetitive integration cycles instead of focusing on product logic.
💸 Visibility & Flexibility Crisis
Once these pipelines are built, they’re expensive and difficult to modify.
Teams lack visibility into how their data moves, what it costs, and where bottlenecks occur.
When models or providers evolve, everything must be rebuilt—forcing rework, re-embedding, and redeployment rather than seamless adaptation.
Solution & Features
Gyana is a universal server that automates the creation and management of vector knowledge bases.
It converts raw content such as links, documents, or datasets into structured, retrieval-ready knowledge that is standardised, explainable, and instantly usable across AI systems. Through a single API, Gyana fetches and cleans source material, segments text into chunks, generates embeddings, and exports a compressed, Base64-encoded vector knowledge base in a standard format. All processing—ingestion, extraction, embedding, and export—is handled automatically to ensure consistency, compatibility, and auditability at scale.
In short, Gyana turns unstructured data into ready-to-use, verifiable vector knowledge bases.
Core Functionality
Universal Vector Access: Create, search, and export vector KBs via one WebSocket endpoint.
MCP Protocol Native: Full compliance for seamless integration with MCP clients.
Model-Agnostic Embedding: Uses OpenAI’s text-embedding-3-small; ready for Gemini/Anthropic.
Built-in Management
Access Key Authentication.
Per-user usage tracking.
Tier-based rate limiting.
Access Methods
MCP Direct: Claude Desktop, Postman MCP.
WebSocket (WSS): Real-time JSON-RPC 2.0.
REST Wrapper: For traditional HTTP clients.
Future-Proof Architecture
Provider-agnostic.
Versioned APIs.
Backwards compatible model switching.
Infrastructure
Production-Ready: TLS encryption, uptime monitoring, error logging.
Auto-Scaling: Handles high load automatically.
Zero-Downtime Updates: No service breaks when models or APIs change.
GYANA Universal VectorKB MCP SERVER
WHY?
AI products need structured context, but current pipelines are fragmented and complex to maintain.
Gyana delivers a single, universal layer for building and serving vector knowledge bases that plug into any AI stack.
1️⃣ Multiple Tools, One Problem
Teams spend weeks stitching together scripts for crawling, embedding, and vector storage to make data searchable.
Gyana replaces multiple disconnected scripts and tools with a single API that handles ingestion, embedding, storage, and retrieval.
2️⃣ Embedding Model Changes
Embedding models and providers evolve frequently—new versions, dimensions, and APIs appear every few months.
Gyana manages these transitions behind the scenes, keeping your existing knowledge bases compatible and ensuring new VectorKB requests continue to run transparently—without disruption or code changes.
5️⃣ Scaling Bottlenecks
Home-grown setups stall under real workloads.
Gyana’s managed infrastructure scales automatically, with built-in rate-limiting and performance monitoring.
3️⃣ Hidden Maintenance Costs
Custom pipelines are fragile—every API update or dependency change means re-work.
Gyana automatically maintains compatibility, versioning, and monitoring, so your setup remains stable as APIs evolve.
4️⃣ Lack of Traceability
Most DIY pipelines lose the link between an answer and its source.
Gyana preserves citations and metadata for every record, enabling explainable, auditable responses.
Get in Touch
Begin your journey towards leveraging GYANA MCP Server: Universal VectorKB without vendor lock-in!