--- layout: default title: Platforms Supported parent: Getting Started nav_order: 5 permalink: /getting_started/platforms --- ___ # Platform Support ___ **Platform Supported** - **Python 3.9+** (note that we just added support for 3.12 starting in llmware version 0.2.12) - **System RAM**: recommended 16 GB RAM minimum (to run most local models on CPU) - **OS Supported**: Mac OS M1/M2/M3, Windows, Linux Ubuntu 20/22. We regularly build and test on Windows and Linux platforms with and without CUDA drivers. - **Deprecated OS**: Linux Aarch64 (0.2.6) and Mac x86 (0.2.10) - most features of llmware should work on these platforms, but new features integrated since those versions will not be available. If you have a particular need to work on one of these platforms, please raise an Issue, and we can work with you to try to find a solution. - **Linux**: we build to GLIBC 2.31+ - so Linux versions with older GLIBC drivers will generally not work (e.g., Ubuntu 18). To check the GLIBC version, you can use the command `ldd --version`. If it is 2.31 or any higher version, it should work. ___ ___ **Database** - LLMWare is an enterprise-grade data pipeline designed for persistent storage of key artifacts throughout the pipeline. We provide several options to parse 'in-memory' and write to jsonl files, but most of the functionality of LLMWare assumes that a persistent scalable data store will be used. - There are three different types of data storage used in LLMWare: 1. **Text Collection database** - all of the LLMWare parsers, by default, parse and text chunk unstructured content (and associated metadata) into one of three databases used for text collections, organized in Libraries - **MongoDB**, **Postgres** and **SQLite**. 2. **Vector database** - for storing and retrieving semantic embedding vectors, LLMWare supports the following vector databases - Milvus, PG Vector / Postgres, Qdrant, ChromaDB, Redis, Neo4J, Lance DB, Mongo-Atlas, Pinecone and FAISS. 3. **SQL Tables database** - for easily integrating table-based data into LLM workflows through the CustomTable class and for using in conjunction with a Text-2-SQL workflow - supported on Postgres and SQLite. - **Fast Start** option: you can start using SQLite locally without any separate installation by setting `LLMWareConfig.set_active_db("sqlite")` as shown in [configure_db_example](https://www.github.com/llmware-ai/llmware/blob/main/examples/Getting_Started/configure_db.py). For vector embedding examples, you can use ChromaDB, LanceDB or FAISS - all of which provide no-install options - just start using. - **Install DB dependencies**: we provide a number of Docker-Compose scripts which can be used, or follow install instructions provided by the database - generally easiest to install locally with Docker. **LLMWare File Storage** - llmware stores a variety of artifacts during its operation locally in the /llmware_data path, which can be found as follows: ```python from llmware.configs import LLMWareConfig llmware_fp = LLMWareConfig().get_llmware_path() print("llmware_data path: ", llmware_fp) ``` - to change the llmware path, we can change both the 'home' path, which is the main filepath, and the 'llmware_data' path name as follows: ```python from llmware.configs import LLMWareConfig # changing the llmware home path - change home + llmware_path_name LLMWareConfig().set_home("/my/new/local/home/path") LLMWareConfig().set_llmware_path_name("llmware_data2") # check the new llmware home path llmware_fp = LLMWareConfig().get_llmware_path() print("updated llmware path: ", llmware_fp) ``` ___ ___ **Local Models** - LLMWare treats open source and locally deployed models as "first class citizens" with all classes, methods and examples designed to work first with smaller, specialized, locally-deployed models. - By default, most models are pulled from public HuggingFace repositories, and cached locally. LLMWare will store all models locally at the /llmware_data/model_repo path, with all assets found in a folder tree with the models name. - If a Pytorch model is pulled from HuggingFace, then it will appear in the default HuggingFace /.cache path. - To view the local model path: ```python from llmware.configs import LLMWareConfig model_fp = LLMWareConfig().get_model_repo_path() print("model repo path: ", model_fp) ``` # More information about the project - [see main repository](https://www.github.com/llmware-ai/llmware.git) # About the project `llmware` is © 2023-{{ "now" | date: "%Y" }} by [AI Bloks](https://www.aibloks.com/home). ## Contributing Please first discuss any change you want to make publicly, for example on GitHub via raising an [issue](https://github.com/llmware-ai/llmware/issues) or starting a [new discussion](https://github.com/llmware-ai/llmware/discussions). You can also write an email or start a discussion on our Discrod channel. Read more about becoming a contributor in the [GitHub repo](https://github.com/llmware-ai/llmware/blob/main/CONTRIBUTING.md). ## Code of conduct We welcome everyone into the ``llmware`` community. [View our Code of Conduct](https://github.com/llmware-ai/llmware/blob/main/CODE_OF_CONDUCT.md) in our GitHub repository. ## ``llmware`` and [AI Bloks](https://www.aibloks.com/home) ``llmware`` is an open source project from [AI Bloks](https://www.aibloks.com/home) - the company behind ``llmware``. The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service. [AI Bloks](https://www.aibloks.com/home) was founded by [Namee Oberst](https://www.linkedin.com/in/nameeoberst/) and [Darren Oberst](https://www.linkedin.com/in/darren-oberst-34a4b54/) in October 2022. ## License `llmware` is distributed by an [Apache-2.0 license](https://www.github.com/llmware-ai/llmware/blob/main/LICENSE). ## Thank you to the contributors of ``llmware``! --- ---