The Zyeta backend is a powerful, scalable system designed to provide AI-driven knowledge base and conversation capabilities. Built with modern asynchronous Python, the platform leverages FastAPI, PostgreSQL with vector extensions, and integrates with leading LLM providers.

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Core Features

Knowledge Base Management

Store, index, and retrieve documents with semantic search capabilities

Conversational AI

Interact with LLMs through persistent conversation contexts

Multi-Organization Support

Isolated environments with role-based access control

Custom Agents

Configurable AI agents with specialized capabilities

Tool Integration

Extend agent capabilities with custom tools

LLM Provider Flexibility

Connect to various LLM providers (OpenAI, Anthropic, etc.)

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System Architecture

Zyeta follows a modular, service-oriented architecture built with FastAPI:

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Key Components

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API Layer

The API layer provides RESTful endpoints for all platform functionality:

  • FastAPI Framework: Modern, high-performance web framework
  • Automatic Documentation: OpenAPI/Swagger docs for all endpoints
  • Type Validation: Pydantic models for request/response validation
  • Authentication: JWT-based auth with role-based access control

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Service Layer

Services encapsulate business logic for specific domains:

  • Authentication Service: User management, login, registration
  • Knowledge Base Service: Document storage, indexing, and retrieval
  • Organization Service: Multi-tenant organization management
  • Conversation Service: LLM interaction and message history
  • Agent Service: Configurable AI agent management
  • Tool Service: Custom tool creation for agents

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Data Layer

The data layer handles persistent storage and retrieval:

  • PostgreSQL: Primary relational database
  • pgvector Extension: Vector storage for embeddings
  • SQLAlchemy ORM: Async database access with transaction support
  • Alembic Migrations: Database schema version control

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Integration Layer

The backend connects to various external services:

  • LLM Providers: OpenAI, Anthropic, and others
  • File Storage: S3-compatible storage for documents
  • Email Services: User notifications and invitations

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Key Features In Depth

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Knowledge Base System

The knowledge base system allows storage and retrieval of documents with semantic search:

1

Document Upload

Users upload documents in various formats (PDF, DOCX, TXT)

2

Text Extraction

Text is extracted from documents using appropriate parsers

3

Chunking

Documents are split into manageable chunks for embedding

4

Embedding Generation

LLM-based embeddings are created for each chunk

5

Vector Storage

Embeddings are stored in pgvector for efficient similarity search

6

Semantic Search

User queries are converted to embeddings and matched against stored vectors

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Conversation System

The conversation system enables persistent interactions with LLMs:

  • Streaming Responses: Real-time streaming of LLM responses
  • History Management: Persistent conversation history
  • Context Window Management: Smart handling of token limits
  • RAG Integration: Augment conversations with knowledge base results

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Organization & RBAC

The multi-organization system with role-based access control:

  • Organization Isolation: Data separation between organizations
  • Role Hierarchy: Configurable role levels (Owner, Admin, Member, Guest)
  • Fine-grained Permissions: Resource:action permission model
  • Invitation Flow: Email-based user invitations

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Technology Stack

  • Language: Python 3.10+
  • Web Framework: FastAPI
  • Database: PostgreSQL 15+ with pgvector
  • ORM: SQLAlchemy 2.0 (async)
  • Authentication: JWT tokens
  • Validation: Pydantic
  • Testing: Pytest
  • Linting: Ruff, mypy
  • Deployment: Docker + Kubernetes

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Getting Started

To get started with the Zyeta backend:

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API Reference

For API documentation, see the API Reference section.

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