# Llmware

> description: community resources, getting help and sharing ideas

## Pages

- [Community](community-community.md): Welcome to the llmware community! We are on a mission to pioneer the use of small language models as transformation...
- [FAQ](community-faq.md): You can set the chunk size with the``chunk_size``parameter of the``add_files``method.
- [Join Our Community](community-join-our-community.md): ___
- [Need Hep](community-need-help.md): ___
- [Troubleshooting](community-troubleshooting.md): ___
- [Agent Inference Server](components-agent-inference-server.md): LLMWare supports multiple deployment options, including the use of REST APIs to implement most model invocations.
- [Agents](components-agents.md): Agents with Function Calls and SLIM Models 🔥
- [Components](components-components.md): llmware is characterized by a logically integrated set of data pipelines involved in building LLM-based workflows, ce...
- [Data Stores](components-data-stores.md): Simple-to-Scale Database Options - integrated data stores from laptop to parallelized cluster.
- [Embedding Models](components-embedding-models.md): llmware supports 30+ embedding models out of the box in the default ModelCatalog, with easy extensibility to add other
- [GGUF](components-gguf.md): llmware packages its own build of the llama.cpp backend engine to enable running quantized models in GGUF format, whi...
- [Library](components-library.md): Library is the main organizing construct for unstructured information in LLMWare. Users can create one large librar...
- [Model Catalog](components-model-catalog.md): Access all models the same way with easy lookup, regardless of underlying implementation.
- [Prompt with Sources](components-prompt-with-sources.md): Prompt with Sources: the easiest way to combine knowledge retrieval with a LLM inference, and provides several high-l...
- [Query](components-query.md): Query libraries with mix of text, semantic, hybrid, metadata, and custom filters. The retrieval.py module implements...
- [RAG Optimized Models](components-rag-optimized-models.md): RAG-Optimized Models - 1-7B parameter models designed for RAG workflow integration and running locally.
- [Release History](components-release-history.md): Release History
- [SLIM Models](components-slim-models.md): Generally, function-calling is a specialized capability of frontier language models, such as OpenAI GPT4.
- [Vector Databases](components-vector-databases.md): llmware supports the following vector databases:
- [Whisper CPP](components-whisper-cpp.md): llmware has an integrated WhisperCPP backend which enables fast, easy local voice-to-text processing.
- [Code contributions](contributing-code.md): One way to contribute to``llmware``is by contributing to the code base.
- [Contributing](contributing-contributing.md): {: .note}
- [Documentation contributions](contributing-documentation.md): One way to contribute to``llmware``is by contributing documentation.
- [Agents](examples-agents.md): 🚀 Start Building Multi-Model Agents Locally on a Laptop 🚀
- [Datasets](examples-datasets.md): llmware provides powerful capabilities to transform raw unstructured information into various model-ready datasets.
- [Embedding](examples-embedding.md): We introduce``llmware``through self-contained examples.
- [Examples](examples-examples.md): llmware offers a wide range of examples to cover the lifecycle of building RAG and Agent based applications using
- [Introduction by Examples](examples-getting-started.md): We introduce``llmware``through self-contained examples.
- [Models](examples-models.md): We introduce``llmware``through self-contained examples.
- [Notebooks](examples-notebooks.md): We introduce``llmware``through self-contained examples.
- [Parsing](examples-parsing.md): We introduce``llmware``through self-contained examples.
- [Prompts](examples-prompts.md): We introduce``llmware``through self-contained examples.
- [Retrieval](examples-retrieval.md): We introduce``llmware``through self-contained examples.
- [Structured Tables](examples-structured-tables.md): We introduce``llmware``through self-contained examples.
- [UI](examples-ui.md): We introduce``llmware``through self-contained examples.
- [Use Cases](examples-use-cases.md): 🚀 Use Cases Examples 🚀
- [Clone Repo](getting-started-clone-repo.md): The llmware repo can be pulled locally to get access to all the examples, or to work directly with the latest version...
- [Fast Start](getting-started-fast-start.md): Fast Start: Learning RAG with llmware through 6 examples
- [Getting Started](getting-started-getting-started.md): From quickly building POCs to scalable LLM Apps for the enterprise, LLMWare is packed with all the tools you need.
- [Installation](getting-started-installation.md): Set up
- [Overview](getting-started-overview.md): `llmware`provides a unified framework for building LLM-based applications (e.g, RAG, Agents), using small, specializ...
- [Platforms Supported](getting-started-platforms.md): ___
- [Working with Docker](getting-started-working-with-docker.md): This section is a short guide on setting up a Linux environment with Docker and running LLMWare examples with differe...
- [Home | llmware](index.md): From quickly building POCs to scalable LLM Apps for the enterprise, LLMWare is packed with all the tools you need.
- [Advanced RAG](learn-advanced-techniques-for-rag.md): llmware Youtube Video Channel
- [Core RAG Scenarios Running Locally](learn-core-rag-scenarios-running-locally.md): Core RAG Scenarios Run Locally
- [Voice Transcription with Whisper CPP](learn-integrated-voice-transcription-with-whisper-cpp.md): Integrated Voice Transcription with Whisper CPP
- [Learn](learn-learn.md): Learn: Youtube Video Series
- [Other Topics](learn-other-topics.md): Other Notable Videos and Topics
- [Parsing Embedding and Data Extraction](learn-parsing-embedding-data-extraction.md): Parsing, Embedding, and Data Extraction
- [Using Agents & Function Calls with SLIM Models](learn-using-agents-functions-slim-models.md): Using Agents, Function Calls and SLIM Models
- [Using Quantized GGUF Models](learn-using-quantized-gguf-models.md): Using Quantized GGUF Models