RAG and chat-with-docs

This project is a community-driven knowledge repository and technical learning resource focused on the field of generative artificial intelligence. It serves as a centralized hub for developers and practitioners to access curated research, tutorials, and foundational concepts necessary for building and deploying modern artificial intelligence applications. The platform distinguishes itself through a collaborative, distributed contribution model that aggregates diverse learning materials into a structured, searchable knowledge base. It covers a wide range of specialized topics, including retrieval-augmented generation, large language model training, fine-tuning techniques, and agentic workflows. Beyond technical skill development, the repository functions as a professional development hub, offering interview preparation resources and guidance for those pursuing careers in the artificial intelligence industry. The content is organized through a hierarchical taxonomy, allowing users to navigate complex subjects such as system evaluation, multimodal models, and security tools. The repository provides access to comprehensive code notebooks and structured tutorials, all maintained as static documentation within a version control system to ensure accessibility and ease of discovery.

GPT4All is a cross-platform runtime environment designed to execute large language models directly on local consumer hardware. By leveraging an optimized C++ inference backend, it enables private, offline AI interactions without requiring an internet connection or external cloud services. The project provides a comprehensive ecosystem for managing the entire model lifecycle, including discovery, downloading, and configuration of local weights. What distinguishes the platform is its integrated retrieval-augmented generation engine, which allows users to index local documents into semantic vector spaces. This capability enables context-aware chat sessions where the model can reference private files, notes, and spreadsheets to provide grounded, relevant responses. The system also features a local HTTP server that exposes an OpenAI-compatible API, allowing developers to integrate these private, self-hosted models into existing applications and workflows. Beyond its core inference and retrieval capabilities, the project includes a graphical desktop interface for end-user interaction and a Python software development kit for programmatic access. These tools support advanced configuration of model parameters, performance monitoring, and the management of local embedding pipelines for custom semantic search tasks. The software is distributed as a unified application package, with documentation available to guide users through installation and local environment setup.

This project is a technical curriculum and development guide focused on large language model prompt engineering, fine-tuning, and the creation of retrieval augmented generation applications. It serves as a comprehensive resource for developers to master crafting precise instructions and textual patterns to improve the quality and predictability of model outputs. The material covers the end-to-end workflow of adapting open-source models to specific datasets and integrating language models with vector databases to generate responses based on private information. It also provides a systematic approach to tracking and debugging generative AI systems through benchmarking and output evaluation. Beyond prompt design, the guides address AI application orchestration by chaining model calls and logic steps into complex workflows. The scope includes implementing semantic search and managing the full lifecycle of AI application development from initial prompt construction to final model evaluation. The project is implemented as a series of Jupyter Notebooks.

Quivr is a retrieval-augmented generation platform designed to transform raw documents into searchable knowledge bases. It functions as a centralized environment where users can ingest files, index them into vector databases, and interact with language models to receive contextually relevant, data-backed responses. The platform distinguishes itself through an agentic workflow orchestrator that sequences retrieval tasks, tool execution, and model interactions to resolve complex, multi-step queries. This engine is entirely configuration-driven, allowing users to define document ingestion, chunking parameters, and workflow node sequences through structured schemas. By maintaining a unified knowledge management interface, the system tracks chat history alongside file storage, ensuring that interactions remain context-aware across diverse local and remote backends. Beyond its core orchestration, the system provides a comprehensive pipeline for document processing, including parsing for various file formats and asynchronous task execution to maintain responsiveness during data ingestion. It supports the development of specialized chatbots, including voice-enabled interfaces, by integrating speech-to-text and text-to-speech capabilities with its underlying retrieval systems. The project utilizes strict base classes to enforce configuration integrity, ensuring consistent data processing across all application settings.

This repository is a comprehensive set of tutorials and examples for building software powered by large language models. It serves as an application development guide and a prompt engineering framework, providing instructional content for integrating model logic with user interfaces and external data sources. The project provides technical walkthroughs for specialized workflows, including the implementation of retrieval augmented generation using vector databases and semantic search. It includes guidance on adapting pre-trained model weights through fine-tuning with private datasets and the orchestration of autonomous agents that connect language models to external tools and APIs. The material covers a broad range of AI development capabilities, including prompt optimization for summarization and inference, the deployment of generative AI interfaces, and the systematic evaluation of model outputs for quality and consistency.

This project is a privacy-first backend service designed to facilitate retrieval-augmented generation by processing local documents into searchable vector representations. It provides a modular architecture that allows users to ingest diverse file formats, manage document metadata, and perform semantic searches to provide context-aware responses for chat and completion requests. The system distinguishes itself through a database-agnostic abstraction layer that supports various storage backends, ranging from local disk storage to enterprise-grade vector databases. It offers flexible deployment options, enabling users to run language models entirely on private hardware or connect to external cloud-based providers through a unified interface. To improve the quality of generated output, the engine incorporates reranking logic that refines retrieved document chunks before they are processed by the language model. The platform includes a comprehensive suite of tools for managing document intelligence pipelines, including automated parsing, text chunking, and embedding generation. Users can configure the system through environment-based profiles to match specific hardware capabilities, such as CPU or GPU-accelerated setups, and stream responses in real time to reduce latency. The application is configured via runtime settings files and environment variables, with support for building custom container images to suit specific deployment requirements.

This project is a collection of tutorials and guides for building large language model applications using the LangChain framework, written in Chinese. It serves as a learning resource for developing software that integrates language models with memory and chain-based logic. The resource provides specific walkthroughs for implementing retrieval augmented generation systems using vector stores and document loaders. It includes guides on creating autonomous agents that dynamically select and execute external tools, as well as tutorials for translating plain text queries into executable database commands. The guides cover a broad range of capabilities, including the construction of custom knowledge bases, the implementation of conversational memory, and the execution of natural language data querying. It also addresses data processing tasks such as loading documents from diverse sources, splitting text for token limits, and extracting structured data from the web.

LlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, it provides a flexible, event-driven architecture for composing modular pipelines, enabling developers to chain data ingestion, transformation, and retrieval steps into sophisticated, multi-agent systems that can coordinate tasks and hand off control between individual agents. The platform covers the entire lifecycle of language model applications, including advanced document processing for parsing and structuring complex file formats, and a diagnostic layer for observability that tracks execution traces and performance metrics. It also includes a suite of evaluation tools for measuring retrieval effectiveness and response quality, alongside mechanisms for query routing and custom post-processing to ensure high-precision information delivery.

Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, checkpoint-based state persistence for pausing and resuming workflows, and human-in-the-loop interrupt mechanisms for manual approvals. The project covers a wide range of capability areas, including RAG pipeline implementation with semantic tool retrieval and document processing. It provides standardized component abstractions for model integration, a middleware-based interception system for observability and tracing, and tool integration for filesystem and shell command execution. Agent runtimes can be exposed as external services using HTTP and Server-Sent Events for real-time streaming communication.

This repository serves as a comprehensive library of architectural blueprints and code examples for integrating large language models into software applications. It functions as a developer learning resource, providing structured tutorials and implementation patterns that demonstrate how to build intelligent features using advanced prompting and data processing techniques. The collection distinguishes itself by focusing on complex reasoning and data-grounding workflows. It provides practical guidance on implementing retrieval-augmented generation pipelines, which connect language models to private data sources for accurate, context-aware responses. Furthermore, it covers sophisticated techniques such as chain-of-thought prompting to improve logical reasoning, and model-driven entity extraction to transform unstructured text into structured knowledge graphs or database queries. Beyond these core patterns, the repository offers a wide range of automated text analysis capabilities, including document summarization and natural language data classification. These recipes are designed to help engineers streamline data processing tasks and build robust, production-ready workflows. Each guide is provided as a self-contained Jupyter Notebook, including the necessary code and data to execute the examples. Users can get started by navigating to a specific directory and following the instructions within the provided notebook files.

LangChainJS is an AI agent orchestrator and application framework designed for building autonomous systems that use large language models to plan and execute tasks. It serves as an integration library that connects language models with tools, memory, and external data sources to create context-aware logic and complex workflows. The project provides a provider-agnostic interface and model provider abstraction, allowing applications to switch between different language model providers without rewriting core logic. It includes a toolkit for retrieval augmented generation, utilizing retrievers to inject real-time external data and ground model generation in facts. The framework covers the orchestration of stateful agent trajectories, modular chain composition, and pluggable memory backends for persisting conversation history. It also includes observability tools for tracking, debugging, and monitoring model outputs and agent performance in production environments.

This platform serves as a comprehensive environment for managing private language models, document knowledge bases, and automated agent workflows within secure local infrastructure. It functions as a document-aware workspace that enables users to ingest diverse file formats into searchable repositories, ensuring that all data processing and model inference remain within private, local environments to maintain data sovereignty. The system distinguishes itself through a modular agentic engine that allows for the definition of custom skills and external tool execution. By utilizing a multi-model abstraction layer, it normalizes interactions across various local and cloud-based providers, while workspace-scoped management ensures that system prompts and knowledge bases remain isolated to meet specific operational requirements. Beyond core orchestration, the platform includes a document-parsing pipeline that converts files into structured text for semantic retrieval via local vector indexing. Users can further extend functionality through command-line triggers and persistent system instructions, standardizing how artificial intelligence behaves across different business contexts.

This is a framework for building and deploying customizable AI agent services using a standardized reference architecture. It provides the core infrastructure necessary to host multiple agents within a single service, supporting interactive chat interfaces and real-time response streaming. The project distinguishes itself with a human-in-the-loop mechanism that allows agent execution to be paused for manual approval or intervention. It also features path-based routing to direct requests to specific agents and a multi-stage content moderation system to filter outputs through safety guardrails. The toolkit covers broad capability areas including stateful memory management for persistent user context and retrieval-augmented generation using vector databases for external knowledge integration. Additional functionality includes voice interaction systems for speech-to-text and text-to-speech capabilities, as well as user feedback collection for performance monitoring. The service is designed for containerized deployment to ensure consistent hosting across different environments.

GraphRAG is a data processing pipeline and retrieval engine designed to transform unstructured text into interconnected knowledge graphs. By utilizing language models to extract entities and relationships, it builds structured representations of information that enable context-aware retrieval for downstream applications. The system distinguishes itself through hierarchical graph clustering and large-scale data synthesis, which organize massive document corpora into multi-level structures. This approach allows for both vector-based semantic searches and graph-based traversals, providing a comprehensive method for navigating complex datasets and identifying hidden connections between concepts. The platform includes a modular orchestration pipeline that manages the entire lifecycle of information, from initial ingestion and indexing to query execution. Users can refine the synthesis and retrieval processes by adjusting prompt templates and configuration arguments to align with specific data characteristics.

langchaingo is an LLM application framework for Go designed for building language model-powered applications and autonomous agents. It serves as an orchestration library and tool integration framework that allows developers to link prompt sequences and model calls into complex, multi-step workflows. The project provides a toolkit for implementing retrieval-augmented generation pipelines by processing unstructured documents and retrieving relevant context via vector search. It includes a dedicated integration layer for indexing high-dimensional embeddings and performing similarity searches across various vector database backends. Its broader capabilities cover AI workflow automation, the creation of autonomous agents that use reasoning to execute external tools, and the management of conversation state to maintain context across multi-turn dialogues. The framework also supports integrating external search tools, executing database queries, and triggering third-party workflows.

This project is a comprehensive retrieval-augmented generation platform designed for building, managing, and deploying knowledge-based AI applications. It provides a unified environment for organizing datasets, configuring conversational chat assistants, and developing autonomous agents that execute multi-step reasoning workflows. By integrating document intelligence with advanced retrieval pipelines, the platform enables the creation of grounded, verifiable responses supported by traceable citations. The platform distinguishes itself through deep document understanding and sophisticated knowledge orchestration. It supports complex document parsing, including the extraction of tables and images, and utilizes graph-based indexing to enhance reasoning over large document collections. Users can configure multiple recall strategies and fused re-ranking to optimize retrieval accuracy, while the system maintains context through multi-turn dialogue management and flexible tool-use frameworks. The architecture is built on a modular, containerized microservice foundation that supports both local inference engines and external language model APIs. It includes asynchronous task processing for document ingestion and indexing, ensuring system responsiveness during heavy workloads. The platform also provides a standardized interface for model abstraction, allowing for seamless integration with existing language model ecosystems. Developers can interact with the platform through a comprehensive suite of RESTful endpoints and Python client libraries, which cover the full lifecycle of agents, datasets, and knowledge graphs. The system is designed for flexible deployment, offering configurable environment settings and support for custom containerized environments to facilitate local development and infrastructure portability.

This project is a Java-based framework integration that provides an AI agent runtime, a graph-based AI workflow engine, and an LLM orchestration framework for Spring applications. It enables the development of stateful autonomous agents and the implementation of retrieval-augmented generation systems using document processing and vector databases. The framework distinguishes itself through a graph-based workflow runtime for designing complex AI pipelines with conditional routing and persistent state. It supports multi-agent orchestration via service-discovery coordination and provides human-in-the-loop mechanisms to mandate manual review or confirmation before automated workflows proceed. The system covers a broad range of capabilities, including structured AI output mapping to ensure type safety, conversational memory management for multi-turn dialogues, and tool-calling loops for executing external functions. It also includes monitoring and observability tools for visualizing agent reasoning and debugging workflow execution through a local interface. Users can bootstrap AI projects and generate source code through a visual configuration interface.

LightRAG is a graph-based retrieval framework designed to build retrieval-augmented generation pipelines. It structures unstructured text into knowledge graphs, enabling multi-hop reasoning and complex query synthesis across large document collections. By integrating dense vector embeddings with structured knowledge graphs, the system facilitates both similarity-based and relationship-aware information retrieval. The framework distinguishes itself through a dual-level retrieval strategy that combines low-level keyword matching with high-level semantic graph traversal to capture both specific facts and broad thematic context. It supports incremental knowledge management, allowing the underlying graph structure to be updated dynamically as new data arrives without requiring a full re-indexing of the dataset. Additionally, the system functions as a multimodal information extractor, processing both text and visual data to create unified, searchable knowledge bases. The platform provides modular, prompt-driven pipeline orchestration to coordinate document parsing, knowledge extraction, and language model generation. These automated workflows allow for the synthesis of information across interconnected documents to provide context-aware responses to nuanced, multi-step inquiries.

LangChain4j is a framework and library for building applications powered by large language models on the JVM. It provides a unified API for developing AI agents, implementing retrieval augmented generation, and integrating generative AI capabilities into professional software built with frameworks like Spring Boot or Quarkus. The project enables the creation of autonomous agents that can reason through tasks, manage memory, and execute external tools to achieve specific goals. It differentiates itself through a unified model interface that allows developers to switch between multiple model providers without changing application logic. The framework covers a broad surface of AI orchestration, including RAG pipeline coordination, vector store abstraction for high-dimensional embeddings, and document parsing for various file formats. It also includes capabilities for tool-calling dispatchers, agentic reasoning loops, and GPU accelerated inference.

This project serves as an educational resource and technical guide for building production-ready intelligent systems. It provides a collection of hands-on tutorials, blueprints, and documentation focused on the development of applications powered by large language models, autonomous agentic workflows, and retrieval-augmented generation. The repository distinguishes itself by offering structured implementations for multi-agent orchestration and standardized communication protocols. It enables developers to integrate external tools and data sources into their systems, ensuring interoperability and persistent memory access for autonomous agents. The content emphasizes practical engineering patterns, including vector-based retrieval and modular pipeline composition, to maintain context awareness and system scalability. Beyond core agentic and retrieval architectures, the project covers a broad range of engineering capabilities such as multimodal data processing, model performance evaluation, and fine-tuning techniques. It provides frameworks for observability-driven development, allowing for the monitoring and benchmarking of system outputs to ensure reliability in production environments. The materials are delivered through a literate programming environment, utilizing interactive notebooks to combine executable code, documentation, and visualization for technical experimentation.