langchain4j/langchain4j

Features

• JVM LLM Applications - A comprehensive library designed specifically for building LLM-powered applications on the JVM.
• Provider-Agnostic Model Interfaces - Provides a provider-agnostic abstraction layer that standardizes inputs and outputs across multiple LLM providers.
• Agentic Reasoning Loops - Implements orchestration patterns that allow models to iteratively plan, call tools, and refine answers.
• AI Agent Frameworks - Provides the necessary tools to define, configure, and deploy autonomous AI agents with tool-calling capabilities.
• Autonomous Agent Frameworks - Provides a framework for building autonomous agents that reason through complex tasks and execute external tools.
• Autonomous Agent Orchestration - Provides a framework for orchestrating autonomous agents that can reason through tasks and utilize external tools.
• Retrieval-Augmented Generation - Coordinates the full RAG pipeline from data ingestion to retrieval for grounding model responses.
• Conversational Models - Implements interfaces for interacting with chat-tuned language models to generate natural, human-like text responses.
• Tool Integrations - Enables AI agents to interact with external APIs and system environments through tool calling.
• Vector Memory Stores - Abstracts various embedding stores to provide a unified interface for semantic memory and retrieval.
• RAG Pipelines - Orchestrates end-to-end workflows for loading documents and retrieving context to augment language model prompts.
• LLM Application Frameworks - Serves as a foundational framework for building applications powered by large language models.
• Multi-Provider Abstractions - Implements a unified interface to abstract multiple LLM providers, allowing for easy model swapping.
• RAG Toolkits - Includes a toolkit for implementing retrieval augmented generation by coordinating LLMs, vector stores, and document loaders.
• Vector Database Abstractions - Provides a provider-neutral API for managing document indexing and similarity search across various vector stores.
• Vector Database Integrations - Offers a consistent integration layer for interacting with various high-dimensional vector database providers.
• Conversation History Management - Provides mechanisms to track and compress conversation history to maintain context in long-running AI sessions.
• Conversation Memory Managers - Manages stateful interaction history to maintain conversational context across multi-turn generative sessions.
• Document Loaders - Imports raw content from cloud storage, version control systems, and web pages for further AI processing.
• Enterprise AI Integration Tools - Provides integration capabilities for bringing generative AI into professional enterprise software stacks like Spring Boot.
• Local Model Execution - Enables the execution of large language models on local hardware for privacy and offline inference.
• Prompt Templates - Provides systems for defining reusable prompt structures with dynamic variables for consistent model inputs.
• Method Dispatchers - Maps model-generated function requests to actual Java methods for executing actions and returning results.
• Web Search Integrations - Integrates real-time web search capabilities to supplement language model knowledge with current information.
• Document Parsing Pipelines - Provides automated pipelines to parse diverse file formats into structured text chunks for RAG indexing.
• Vector Embedding Indexes - Implements mechanisms for creating specialized indexes to optimize retrieval of high-dimensional vector data.
• Vector Storage - Provides the capability to save and retrieve high-dimensional vector representations for efficient similarity searches.
• Multi-Format Document Parsing - Extracts text from diverse file formats including PDF and Office documents for AI indexing.
• Standardized Tool Execution - Connects models to external tools using standardized protocols to perform real-world actions.
• Artificial Intelligence - Unified APIs for integrating LLMs into applications.
• Frameworks and Ports - Java implementation of the framework.
• RAG Frameworks - Java-based framework for integrating LLMs into applications.

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