intelligence-layer-sdk
a unified framework for leveraging LLMs
Stars: 63
The Aleph Alpha Intelligence Layer️ offers a comprehensive suite of development tools for crafting solutions that harness the capabilities of large language models (LLMs). With a unified framework for LLM-based workflows, it facilitates seamless AI product development, from prototyping and prompt experimentation to result evaluation and deployment. The Intelligence Layer SDK provides features such as Composability, Evaluability, and Traceability, along with examples to get started. It supports local installation using poetry, integration with Docker, and access to LLM endpoints for tutorials and tasks like Summarization, Question Answering, Classification, Evaluation, and Parameter Optimization. The tool also offers pre-configured tasks for tasks like Classify, QA, Search, and Summarize, serving as a foundation for custom development.
README:
The Aleph Alpha Intelligence Layer️ offers a comprehensive suite of development tools for crafting solutions that harness the capabilities of large language models (LLMs). With a unified framework for LLM-based workflows, it facilitates seamless AI product development, from prototyping and prompt experimentation to result evaluation and deployment.
The key features of the Intelligence Layer are:
- Composability: Streamline your journey from prototyping to scalable deployment. The Intelligence Layer SDK offers seamless integration with diverse evaluation methods, manages concurrency, and orchestrates smaller tasks into complex workflows.
- Evaluability: Continuously evaluate your AI applications against your quantitative quality requirements. With the Intelligence Layer SDK you can quickly iterate on different solution strategies, ensuring confidence in the performance of your final product. Take inspiration from the provided evaluations for summary and search when building a custom evaluation logic for your own use case.
- Traceability: At the core of the Intelligence Layer is the belief that all AI processes must be auditable and traceable. We provide full observability by seamlessly logging each step of every workflow. This enhances your debugging capabilities and offers greater control post-deployment when examining model responses.
- Examples: Get started by following our hands-on examples, demonstrating how to use the Intelligence Layer SDK and interact with its API.
- Aleph Alpha Intelligence Layer
- Table of contents
- Installation
- Getting started
- Models
- Example index
- References
- License
- For Developers
Clone the Intelligence Layer repository from GitHub.
git clone [email protected]:Aleph-Alpha/intelligence-layer-sdk.gitThe Intelligence Layer uses poetry, which serves as the package manager and manages the virtual environments.
We recommend installing poetry globally, while still isolating it in a virtual environment, using pipx, following the official instructions.
Afterward, simply run poetry install to create a new virtual environment and install all project dependencies.
poetry installThe environment can be activated via poetry shell. See the official poetry documentation for more information.
To install the Aleph-Alpha Intelligence Layer from the JFrog artifactory in you project, you have to add this information to your poetry setup via the following four steps. First, add the artifactory as a source to your project via
poetry source add --priority=explicit artifactory https://alephalpha.jfrog.io/artifactory/api/pypi/python/simpleSecond, to install the poetry environment, export your JFrog credentials to the environment
export [email protected]
export POETRY_HTTP_BASIC_ARTIFACTORY_PASSWORD=your-token-hereThird, add the Intelligence Layer to the project
poetry add --source artifactory intelligence-layerFourth, execute
poetry installNow the Intelligence Layer should be available as a Python package and ready to use.
from intelligence_layer.core import TaskIn VSCode, to enable auto-import up to the second depth, where all symbols are exported, add the following entry to your ./.vscode/settings.json:
"python.analysis.packageIndexDepths": [
{
"name": "intelligence_layer",
"depth": 2
}
]To use the Intelligence Layer in Docker, a few settings are needed to not leak your GitHub token.
You will need your GitHub token set in your environment.
In order to modify the git config add the following to your docker container:
RUN apt-get -y update
RUN apt-get -y install git curl gcc python3-dev
RUN pip install poetry
RUN poetry install --no-dev --no-interaction --no-ansi \
&& rm -f ~/.gitconfig📘 Not sure where to start? Familiarize yourself with the Intelligence Layer using the below notebooks as interactive tutorials. If you prefer you can also read about the concepts first.
The tutorials aim to guide you through implementing several common use-cases with the Intelligence Layer. They introduce you to key concepts and enable you to create your own use-cases. In general the tutorials are build in a way that you can simply hop into the topic you are most interested in. However, for starters we recommend to read through the Summarization tutorial first. It explains the core concepts of the intelligence layer in more depth while for the other tutorials we assume that these concepts are known.
The tutorials require access to an LLM endpoint. You can choose between using the Aleph Alpha API (https://api.aleph-alpha.com) or an on-premise setup by configuring the appropriate environment variables. To configure the environment variables, create a .env file in the root directory of the project and copy the contents of the .env.example file into it.
To use the Aleph Alpha API, that is set as the default host URL, set the AA_TOKEN variable to your Aleph Alpha access token, and you are good to go.
To use an on-premises setup, set the CLIENT_URL variable to your host URL.
| Order | Topic | Description | Notebook 📓 |
|---|---|---|---|
| 1 | Summarization | Summarize a document | summarization.ipynb |
| 2 | Question Answering | Various approaches for QA | qa.ipynb |
| 3 | Classification | Learn about two methods of classification | classification.ipynb |
| 4 | Evaluation | Evaluate LLM-based methodologies | evaluation.ipynb |
| 5 | Parameter Optimization | Compare Task configuration for optimization | parameter_optimization.ipynb |
| 6 | Attention Manipulation | Use TextControls for Attention Manipulation (AtMan) |
attention_manipulation_with_text_controls.ipynb |
| 7 | Elo QA Evaluation | Evaluate QA tasks in an Elo ranking | elo_qa_eval.ipynb |
| 8 | Quickstart Task | Build a custom Task for your use case |
quickstart_task.ipynb |
| 9 | Document Index | Connect your proprietary knowledge base | document_index.ipynb |
| 10 | Human Evaluation | Connect to Argilla for manual evaluation | human_evaluation.ipynb |
| 11 | Performance tips | Contains some small tips for performance | performance_tips.ipynb |
| 12 | Deployment | Shows how to deploy a Task in a minimal FastAPI app. | fastapi_tutorial.ipynb |
| 13 | Issue Classification | Deploy a Task in Kubernetes to classify Jira issues | Found in adjacent repository |
| 14 | Evaluate with Studio | Shows how to evaluate your Task using Studio |
evaluate_with_studio.ipynb |
The how-tos are quick lookups about how to do things. Compared to the tutorials, they are shorter and do not explain the concepts they are using in-depth.
| Tutorial | Description |
|---|---|
| Tasks | |
| ...define a task | How to come up with a new task and formulate it |
| ...implement a task | Implement a formulated task and make it run with the Intelligence Layer |
| ...debug and log a task | Tools for logging and debugging in tasks |
| Analysis Pipeline | |
| ...implement a simple evaluation and aggregation logic | Basic examples of evaluation and aggregation logic |
| ...create a dataset | Create a dataset used for running a task |
| ...run a task on a dataset | Run a task on a whole dataset instead of single examples |
| ...resume a run after a crash | Resume a run after a crash or exception occurred |
| ...evaluate multiple runs | Evaluate (multiple) runs in a single evaluation |
| ...aggregate multiple evaluations | Aggregate (multiple) evaluations in a single aggregation |
| ...retrieve data for analysis | Retrieve experiment data in multiple different ways |
| ...implement a custom human evaluation | Necessary steps to create an evaluation with humans as a judge via Argilla |
| ...implement elo evaluations | Evaluate runs and create ELO ranking for them |
| ...implement incremental evaluation | Implement and run an incremental evaluation |
| Studio | |
| ...use Studio with traces | Submitting Traces to Studio for debugging |
| ...upload existing datasets | Upload Datasets to Studio |
| ...execute a benchmark | Execute a benchmark |
Currently, we support a bunch of models accessible via the Aleph Alpha API. Depending on your local setup, you may even have additional models available.
| Model | Description |
|---|---|
| LuminousControlModel | Any control-type model based on the first Luminous generation, specifically luminous-base-control, luminous-extended-control and luminous-supreme-control. |
| Pharia1ChatModel | Pharia-1 based models prompted for multi-turn interactions. Includes pharia-1-llm-7b-control and pharia-1-llm-7b-control-aligned. |
| Llama3InstructModel | Llama-3 based models prompted for one-turn instruction answering. Includes llama-3-8b-instruct, llama-3-70b-instruct, llama-3.1-8b-instruct and llama-3.1-70b-instruct. |
| Llama3ChatModel | Llama-3 based models prompted for multi-turn interactions. Includes llama-3-8b-instruct, llama-3-70b-instruct, llama-3.1-8b-instruct and llama-3.1-70b-instruct. |
To give you a starting point for using the Intelligence Layer, we provide some pre-configured Tasks that are ready to use out-of-the-box, as well as an accompanying "Getting started" guide in the form of Jupyter Notebooks.
| Type | Task | Description |
|---|---|---|
| Classify | EmbeddingBasedClassify | Classify a short text by computing its similarity with example texts for each class. |
| Classify | PromptBasedClassify | Classify a short text by assessing each class' probability using zero-shot prompting. |
| Classify | PromptBasedClassifyWithDefinitions | Classify a short text by assessing each class' probability using zero-shot prompting. Each class is defined by a natural language description. |
| Classify | KeywordExtract | Generate matching labels for a short text. |
| QA | MultipleChunkRetrieverQa | Answer a question based on an entire knowledge base. Recommended for most RAG-QA use-cases. |
| QA | LongContextQa | Answer a question based on one document of any length. |
| QA | MultipleChunkQa | Answer a question based on a list of short texts. |
| QA | SingleChunkQa | Answer a question based on a short text. |
| QA | RetrieverBasedQa (deprecated) | Answer a question based on a document base using a BaseRetriever implementation. |
| Search | Search | Search for texts in a document base using a BaseRetriever implementation. |
| Search | ExpandChunks | Expand chunks retrieved with a BaseRetriever implementation. |
| Summarize | SteerableLongContextSummarize | Condense a long text into a summary with a natural language instruction. |
| Summarize | SteerableSingleChunkSummarize | Condense a short text into a summary with a natural language instruction. |
| Summarize | RecursiveSummarize | Recursively condense a text into a summary. |
Note that we do not expect the above use cases to solve all of your issues. Instead, we encourage you to think of our pre-configured use cases as a foundation to fast-track your development process. By leveraging these tasks, you gain insights into the framework's capabilities and best practices.
We encourage you to copy and paste these use cases directly into your own project. From here, you can customize everything, including the prompt, model, and more intricate functional logic. For more information, check the tutorials and the how-tos
The full code documentation can be found in our read-the-docs here
This project can only be used after signing the agreement with Aleph Alpha®. Please refer to the LICENSE file for more details.
For further information check out our different guides and documentations:
- Concepts.md for an overview of what Intelligence Layer is and how it works.
- style_guide.md on how we write and document code.
- RELEASE.md for the release process of IL.
- CHANGELOG.md for the latest changes.
- Share the details of your problem with us.
- Write your code according to our style guide.
- Add doc strings to your code as described here.
- Write tests for new features (Executing Tests).
- Add an how_to and/or notebook as a documentation (check out this for guidance).
- Update the Changelog with your changes.
- Request a review for the MR, so that it can be merged.
If you want to execute all tests, you first need to spin up your docker container and execute the commands with your own GITLAB_TOKEN.
export GITLAB_TOKEN=...
echo $GITLAB_TOKEN | docker login registry.gitlab.aleph-alpha.de -u your_email@for_gitlab --password-stdin
docker compose pull to update containersAfterwards simply run docker compose up --build. You can then either run the tests in your IDE or via the terminal.
In VSCode
- Sidebar > Testing
- Select pytest as framework for the tests
- Select
intelligence_layer/testsas source of the tests
You can then run the tests from the sidebar.
In a terminal In order to run a local proxy of the CI pipeline (required to merge) you can run
scripts/all.sh
This will run linters and all tests.
The scripts to run single steps can also be found in the scripts folder.
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