What is Langdock API used for?

It allows developers to programmatically access Langdock assistants, knowledge folders, and AI models to build custom applications like chatbots or automated document processors.

Does this skill support TypeScript?

Yes, it includes comprehensive implementation examples and interfaces for both Node.js/TypeScript and Python.

Can I use this for Retrieval-Augmented Generation (RAG)?

Absolutely. This skill includes specific logic for combining knowledge folder search with assistant completions to build robust RAG pipelines.

How do I authenticate with the Langdock API?

Authentication is handled via a Bearer token. You can retrieve your API key from the Langdock Dashboard under Settings -> API Keys.

Can I create assistants dynamically?

Yes, the skill demonstrates how to create temporary assistants on-the-fly with custom instructions without pre-configuring them in the Langdock UI.

Langdock API Integration

Name: Langdock API Integration
Author: mberto10

bymberto10

0•

API Development

Integrates Langdock's AI assistants, knowledge retrieval, and RAG pipelines into custom applications using Python or Node.js.

This skill provides comprehensive patterns and boilerplate for developers looking to leverage Langdock as a backend for AI-powered features. It offers specialized guidance for creating chatbots, managing knowledge folders, implementing Retrieval-Augmented Generation (RAG), and extracting structured data. By providing ready-to-use implementation patterns for both Python and TypeScript, it streamlines the process of embedding sophisticated LLM capabilities and document search functionality directly into existing software architectures.

Key Features

010 GitHub stars

02Advanced Knowledge Search and RAG pipeline implementation logic

03Dynamic creation of temporary specialist assistants for on-the-fly tasks

04Structured data extraction using JSON schemas and Pydantic models

05Multi-language support for Python and TypeScript API implementations

06Production-ready patterns for Chatbot backends and history management

Use Cases

01Implementing internal document Q&A systems using knowledge folders

02Automating structured data extraction from unstructured business text

03Building custom customer support chatbots powered by Langdock assistants

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mberto10/mberto-compound langdock-api

For use in Claude.ai and ChatGPT

Key Features

010 GitHub stars

02Advanced Knowledge Search and RAG pipeline implementation logic

03Dynamic creation of temporary specialist assistants for on-the-fly tasks

04Structured data extraction using JSON schemas and Pydantic models

05Multi-language support for Python and TypeScript API implementations

06Production-ready patterns for Chatbot backends and history management

Use Cases

01Implementing internal document Q&A systems using knowledge folders

02Automating structured data extraction from unstructured business text

03Building custom customer support chatbots powered by Langdock assistants

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mberto10/mberto-compound langdock-api

For use in Claude.ai and ChatGPT