LISA is an infrastructure-as-code solution providing scalable, low latency access to customers’ generative LLMs and embedding language models. LISA accelerates and supports customers’ GenAI experimentation and adoption, particularly in regions where Amazon Bedrock is not available. LISA allows customers to move quickly rather than independently solve the undifferentiated heavy lifting of hosting and inference architecture. Customers deploy LISA into a single AWS account and integrate it with an identity provider. Customers bring their own models to LISA for self-hosting and inference supported by Amazon Elastic Container Service (ECS). Model configuration is managed through LISA’s model management APIs.
As use cases and model requirements grow, customers can configure LISA with external model providers. Through OpenAI's API spec via the LiteLLM proxy, LISA is compatible with 100+ models from various providers, including Amazon Bedrock and Amazon Jumpstart. LISA customers can centralize communication across many model providers via LiteLLM, leveraging LISA for model orchestration. Using LISA as a model orchestration layer allows customers to standardize integrations with externally hosted models in a single place. Without an orchestration layer, customers must individually manage unique API integrations with each provider.
- Self Host Models: Bring your own text generation and embedding models to LISA for hosting and inference.
- Model Orchestration: Centralize and standardize configuration with 100+ models from model providers via LiteLLM, including Amazon Bedrock models.
- Chatbot User Interface: Through the chatbot user interface, users can prompt LLMs, receive responses, modify prompt templates, change model arguments, and manage their session history. Administrators can control available features via the configuration page.
- Retrieval-augmented generation (RAG): RAG reduces the need for fine-tuning, an expensive and time-consuming undertaking, and delivers more contextually relevant outputs. LISA offers RAG through Amazon OpenSearch or PostgreSQL’s PGVector extension on Amazon RDS.
- Non-RAG Model Context: Users can upload documents to their chat sessions to enhance responses or support use cases like document summarization.
- Model Management: Administrators can add, remove, and update models configured with LISA through the model management configuration page or APIs.
- OpenAI API spec: LISA can be configured with compatible tooling. For example, customers can configure LISA as the model provider for the Continue plugin, an open-source AI code assistance for JetBrains and Visual Studio Code integrated development environments (IDEs). This allows users to select from any LISA-configured model to support LLM prompting directly in their IDE.
- Libraries: If your workflow includes libraries such as LangChain or OpenAI, then you can place LISA in your application by changing only the endpoint and headers for the client objects.
- FedRAMP: The AWS services that LISA leverages are FedRAMP High compliant.
- Ongoing Releases: We offer on-going release with new functionality. LISA’s roadmap is customer driven.
- Set up and have access to an AWS account with appropriate permissions
- All the resource creation that happens as part of CDK deployments expects Administrator or Administrator-like permissions with resource creation and mutation permissions. Installation will not succeed if this profile does not have permissions to create and edit arbitrary resources for the system. Note: This level of permissions is not required for the runtime of LISA. This is only necessary for deployment and subsequent updates.
- Familiarity with AWS Cloud Development Kit (CDK) and infrastructure-as-code principles
- Optional: If using the chat UI, Have your Identity Provider (IdP) information and access
- Optional: Have your VPC information available, if you are using an existing one for your deployment
- Note: CDK and Model Management both leverage AWS Systems Manager Agent (SSM) parameter store. Confirm that SSM is approved for use by your organization before beginning.
- AWS CLI installed and configured
- Python 3.9 or later
- Node.js 14 or later
- Docker installed and running
- Sufficient disk space for model downloads and conversions
For detailed instructions on setting up, configuring, and deploying LISA, please refer to our separate documentation on installation and usage.
Although this repository is released under the Apache 2.0 license, when configured to use PGVector as a RAG store it
uses
the third party psycopg2-binary library. The psycopg2-binary project's licensing includes
the LGPL with exceptions license.