FastGPT

FastGPT is an open-source knowledge-based QA system and RAG (Retrieval-Augmented Generation) platform that supports multiple models including DeepSeek, enabling rapid development of various AI applications through visual workflow design.

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What is FastGPT

FastGPT is a powerful, open-source platform designed to simplify the creation of knowledge-based question-answering systems and complex AI applications. Built around the Retrieval-Augmented Generation (RAG) paradigm, FastGPT enables organizations to enhance large language models with their proprietary data, creating AI solutions that provide accurate, contextually relevant responses grounded in specific knowledge domains. The platform features an intuitive visual interface for designing AI workflows without coding, comprehensive knowledge management capabilities, and flexible model integration including DeepSeek and other leading AI models. With its modular architecture, FastGPT supports everything from simple chatbots to sophisticated multi-agent systems, data processing pipelines, and specialized vertical applications. The platform emphasizes ease of deployment with Docker support, comprehensive API access, and enterprise-grade features for security and scalability. As an open-source solution with an active community, FastGPT provides organizations with full control over their AI implementations while dramatically reducing the technical barriers to creating production-ready AI applications.

How to Use

Benefit from an intuitive visual interface that makes AI application development accessible to both technical and non-technical users.

Step 1: Installation

Set up FastGPT by installing through Docker Compose or deploying to a cloud environment following the documentation.

Step 2: Configuration

Connect your model providers (DeepSeek, OpenAI, etc.) and configure vector database settings for knowledge storage.

Step 3: Create Knowledge Base

Upload documents, connect to data sources, or crawl websites to build your specialized knowledge repositories.

Step 4: Design Workflows

Use the visual flow designer to create AI application workflows connecting knowledge bases to models with specific behaviors.

Core Features

Visual Flow-Based Application Design

Intuitive drag-and-drop interface for creating AI workflows without coding, with configurable nodes representing different processing steps and decision points.

Advanced Knowledge Management and RAG

Comprehensive system for document processing, vector storage, and intelligent retrieval with support for diverse knowledge ingestion methods and formats.

Multi-Model Integration with DeepSeek Support

Flexible connection to various AI models with optimized DeepSeek integration, enabling applications to leverage the best capabilities for different requirements.

Enterprise-Grade Security and Deployment

Robust authentication, authorization, data encryption, and flexible deployment options designed for production environments with sensitive data.

Modular Extension Framework

Extensible architecture with plugin support, custom connectors, and API-first design for integration with existing systems and specialized requirements.

Integration Capabilities

DeepSeek Model Support

Optimized connectors for DeepSeek models that leverage their advanced reasoning, code generation, and multilingual capabilities.

Multiple Model Providers

Support for various providers including OpenAI, Anthropic, and open-source models through a consistent interface with seamless switching.

Vector Database Connections

Integration with popular vector databases including Milvus, Qdrant, PgVector, and Faiss for efficient semantic search capabilities.

Document Processing Pipeline

Advanced document handling for PDFs, Word documents, Excel, HTML, Markdown, and other formats with intelligent chunking and metadata extraction.

Webhook Integration

Event-driven communication with existing business systems through customizable webhooks for seamless workflow integration.

API-First Architecture

Comprehensive API access enabling integration with websites, applications, and custom interfaces with proper authentication and monitoring.

Use Cases

Enterprise Knowledge Management

Create comprehensive internal knowledge systems that provide employees with accurate, contextual information from company documents and databases.

Customer Support Automation

Build intelligent support chatbots that access product documentation, FAQs, and support history to provide accurate assistance and reduce response times.

Educational Content Delivery

Develop interactive learning assistants that provide personalized educational content and answer student questions based on course materials.

Research and Analysis Acceleration

Create research assistants that analyze scientific papers, reports, and data to extract insights and answer complex domain-specific questions.

FAQ

Q: How does FastGPT integrate with DeepSeek models?

A: FastGPT provides native integration with DeepSeek through optimized connectors that leverage the full capabilities of DeepSeek models. The platform supports both DeepSeek's API services and self-hosted models, allowing organizations to choose the deployment option that best meets their needs. Integration is configured through a simple setup process where you provide your DeepSeek API credentials and select which models to use for different workflow components. FastGPT's model-agnostic architecture ensures that applications can take advantage of DeepSeek's advanced reasoning, code generation, and multilingual capabilities while maintaining compatibility with other model providers if needed.

Q: What deployment options does FastGPT support?

A: FastGPT offers flexible deployment options to accommodate different organizational requirements. The simplest approach is Docker Compose deployment, which provides a self-contained environment suitable for quick setup on a single server. For production environments, FastGPT supports Kubernetes deployment with detailed configuration guidelines for scalability and high availability. Cloud-native deployments are supported on major providers including AWS, Azure, Google Cloud, and others, with infrastructure-as-code templates available for consistent deployment. For organizations with strict security requirements, FastGPT can be deployed in private networks or air-gapped environments with appropriate configuration for offline or limited connectivity operation.

Q: How does FastGPT handle data privacy and security?

A: FastGPT implements comprehensive security measures to protect sensitive data throughout the application lifecycle. All data is encrypted both in transit and at rest using industry-standard protocols. The platform's self-hosted nature ensures that organizations maintain complete control over their data, with no mandatory external sharing. Access control is managed through role-based permissions that can be configured to align with organizational security policies. For sensitive deployments, FastGPT supports operation without external API calls, using only local models and services. The platform includes detailed audit logging to track access and usage patterns for security monitoring and compliance requirements.

Q: What types of knowledge sources can FastGPT process?

A: FastGPT supports diverse knowledge sources to build comprehensive information repositories. The platform can process multiple document formats including PDFs, Word documents, Excel spreadsheets, PowerPoint presentations, HTML, Markdown, and plain text files. Beyond document uploading, FastGPT can connect to databases through SQL integrations, extract information from websites via crawling capabilities, and integrate with APIs to access dynamic external data sources. For specialized needs, the platform supports custom connectors that can be developed to handle proprietary data formats or unique information systems.

Q: Can FastGPT be extended for specialized requirements?

A: Yes, FastGPT is designed with extensibility as a core principle. The platform provides multiple extension mechanisms including a plugin system for packaged functionality enhancements, custom node development for specialized workflow processing, and connector frameworks for integration with unique data sources. The API-first architecture enables custom front-end development while leveraging FastGPT's backend capabilities. For organizations with development resources, the open-source codebase can be modified directly to implement highly specialized requirements. The active community contributes extensions regularly, with many common enhancements available through the official repository or community channels.