Practical overview
When it fits
Use Llmware when a project needs a Python-based pipeline from document ingestion through retrieval and model inference, particularly where local or self-hosted processing and private knowledge bases are important.
Who it is for
- Developers building enterprise RAG applications.
- Teams working with private organizational documents.
- Builders targeting local, edge or self-hosted language-model deployments.
- Python users who need document parsing, embeddings, retrieval and model inference in one framework.
Documented uses
- Ingest and parse collections of business documents.
- Create libraries that organize searchable enterprise knowledge.
- Install embeddings and connect libraries to vector databases.
- Run text, semantic, hybrid or filtered retrieval queries.
- Ground language-model responses or agents in retrieved internal knowledge.
Requirements & limits
The project is publicly available as an Apache-2.0-licensed GitHub repository. The repository documents use with Windows, macOS and Linux, including CLI and self-hosted environments.
- Python is the primary language.
- The framework supports local deployment as well as major API-based models.
- Document ingestion includes a broad set of office, text, structured, image, audio and web-oriented file types.
- Multiple retrieval modes are documented, including text, semantic, metadata and document-filtered queries.
- The model and runtime options include GGUF, OpenVINO, ONNX Runtime, ONNX Runtime QNN, WindowsLocalFoundry and PyTorch.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Create and query a document library
Ingest local documents into a library, add an embedding and run text or semantic retrieval.
Before you start
- Python environment with llmware installed.
- A local folder containing supported documents.
- An embedding model and vector database choice, such as Milvus or ChromaDB.
Steps
Create a new library with Library().create_new_library().
Pass the document folder to add_files() for parsing, text chunking and indexing.
Install an embedding with install_new_embedding(), specifying an embedding model and vector database.
Load the library, create a Query object and run a text_query() or semantic_query().
Expected outcome
A library containing indexed document content and query results from text or semantic retrieval.
Guide limitations
- The documented steps cover library creation, ingestion, embedding installation and querying; they do not describe the complete answer-generation stage.
- The supplied examples use local file folders and named embedding and vector-database options.
Compare options for Retrieval-augmented applications
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Llmware | Ingest and parse collections of business documents. | CLI, Linux, macOS, Self-hosted | The project is publicly available as an Apache-2.0-licensed GitHub repository. The repository documents use with Windows, macOS and Linux, including CLI and self-hosted environments. |
| Langsearch | Crawl website or local data for a knowledge base. | CLI, Self-hosted, Server | |
| Hybrid Search Rag | Combine document retrieval and language-model generation in a configurable workflow. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- llmware-ai/llmware · Official homepage title · retained in its source language