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Basic Information

llmware is an open framework for building LLM-based enterprise applications, with a strong focus on Retrieval-Augmented Generation (RAG) pipelines and multi-model agent workflows. It bundles a Model Catalog of small, specialized models (SLIM, BLING, DRAGON and Industry BERT series) and provides integrated components to ingest, parse, chunk, embed and index documents into libraries. The repo exposes primitives for querying libraries, packaging retrieved evidence into prompts, running local or API-backed models, and orchestrating multi-step analyses with agents and function calls. It targets developers and teams who need to deploy private, cost-effective knowledge-centric LLM applications, offering examples that run on laptops without GPUs as well as deployment patterns for servers and clusters. The project also documents supported vector and text databases, optional native libraries for OCR and audio, and end-to-end examples for contract analysis, chatbots, lecture tools and financial research.

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App Details

Features
Model Catalog that standardizes access to 150+ models and provides unified loading and inference methods for HF, GGUF, ONNX, OpenVino and API models. Library and ingestion utilities to parse mixed file types, text-chunk, embed and manage multiple libraries. Retrieval APIs supporting text, semantic, hybrid and metadata filters with multi-embedding support. Prompt utilities that attach sources, run evidence checks and maintain prompt history. RAG-optimized small models and SLIM tools tuned for function calling and multi-model agents. Built-in support for many vector DBs and text stores, easy fast-start defaults (SQLite and ChromaDB), and docker-compose scripts for production databases. Examples, tutorials and quickstart scripts, voice transcription integration, OCR document parsing, model benchmarks and an inference server for agent deployments.
Use Cases
llmware accelerates building knowledge-driven LLM applications by combining model management, data ingestion, retrieval and prompt orchestration in one library. Teams can rapidly prototype RAG workflows and multi-step agent processes using small, optimized models that run on CPUs, lowering cost and easing private deployments. The integrated Library and Query components simplify parsing and indexing documents at scale and let developers mix embeddings and vector databases for better retrieval. Prompt-with-sources and evidence-check features support explainability and fact checking. Prebuilt examples, quickstart scripts, and an inference server help move prototypes to production. Support for many DB backends, GGUF quantization, ONNX/OpenVino options, and agent function-calling models makes it practical to deploy secure, enterprise-grade solutions for contract analysis, BI chatbots, voice Q&A and other document-centric use cases.

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