Manifest Project Introduction
The Manifest project is a tool designed to simplify prompt programming with Foundation Models, which are advanced AI models capable of handling a variety of tasks. It provides a streamlined way to work with these models, making the process more accessible and efficient for users. Below is an overview of its features and functionality.
Installation
Installing Manifest is straightforward and can be done using Python's package manager, pip. Depending on the needs, users can install the basic package, or extend it with support for diffusion or local HuggingFace models. There is also an option for developers to install the package directly from its GitHub repository for development purposes.
Getting Started
To get started with Manifest, you only need to perform a few simple steps. When using OpenAI services, users need to set an API key and establish a session. Here's a sample code snippet to illustrate this:
from manifest import Manifest
# Start a session with OpenAI
manifest = Manifest(client_name="openai")
manifest.run("Why is the grass green?")
Manifest Components
The design philosophy of Manifest centers around ease of use, iteration, and integration with existing AI systems. Here are its core components:
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Models: Manifest supports various AI models from providers such as OpenAI, AI21, Cohere, and locally hosted HuggingFace models. Users can switch between models by changing configuration parameters.
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Global Cache: To facilitate the iterative process and ensure reproducibility, Manifest offers a global caching system. This system can save and share inputs and outputs across different users using SQLite or Redis databases.
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Running Queries: Users can execute queries both synchronously and asynchronously. This allows for efficient use of resources and faster response times in applications.
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Streaming Queries: If supported by the model, Manifest can stream responses. This feature is particularly useful for applications where real-time data or incremental updates are necessary.
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Model Pools: Manifest can handle multiple models simultaneously, which can be beneficial for load balancing and redundancy. It can distribute queries across these models efficiently with options for round-robin or random selection.
Other Model Types
Manifest extends its capabilities to support various model types:
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Local HuggingFace Models: Users can host their own models using a dedicated API, with support for large transformer models distributed across GPUs.
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Chat Models: Manifest provides tools to facilitate conversations using models optimized for chat-based interactions.
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Embedding Models: Embedding models help translate human language into computer-readable data. Manifest's framework allows easy integration of these models for applications requiring semantic understanding.
Road Map
Manifest is continually evolving to add more features and functionalities. The current roadmap includes enhancements such as support for more AI clients, diffusion models, and improved orchestration features.
Development
For those interested in developing with Manifest, the project supports a robust development workflow. Contributors are encouraged to set up a Redis instance for testing purposes and to follow the testing suite structure before submitting changes.
Citation
For academic and professional use, users are encouraged to cite Manifest in their publications. Comprehensive citations help in tracing the impact and usage of the tool in various research domains.
Manifest is a powerful yet simple tool that embodies a collaborative approach to leveraging advanced AI models. With a focus on usability, integration, and efficiency, it brings foundational AI capabilities within easy reach of developers and researchers alike.