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Fleak
Overview :
Fleak is a low-code, serverless API builder designed for data teams. It requires no infrastructure and allows for immediate embedding of API endpoints within existing modern AI and data technology stacks. By streamlining the integration of data components and creating a unified API, it enables seamless scalability, helping to simplify workflows so that teams can focus on extracting insights from data rather than managing data operations.
Target Users :
The target audience includes data scientists, data analysts, and software engineers who require a user-friendly, scalable platform for efficient AI transformation.
Use Cases
Embed and store data in Pinecone to enhance data retrieval and analysis efficiency.
Enhance LLM responses with RAG and Pinecone integration, suitable for information-rich applications.
Explore datasets and apply sentiment labels using SQL and LLM for content processing and tagging.
Create scalable personalized email workflows using SQL and LLM.
Features
Supports various data types including JSON, SQL, CSV, and plain text.
Offers flexible options to customize workflow steps for different data transformations.
Seamlessly integrates with large language models, databases, and other tools.
Publishes, manages, and monitors APIs with support for version control and performance tracking.
No infrastructure requirements for building and running applications, ensuring scalable and cost-effective AI workflows.
Coordinates multiple large language models to optimize the performance of AI workflows.
Integrates with any storage environment, including cloud data warehouses or data lakes.
How to Use
1. Visit the Fleak official website and register an account.
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2. Choose to create a new workflow or use a pre-built template.
3. Configure the basic components of the data workflow, such as data transformation and text embedding.
4. Add and configure workflow nodes to support various data types.
5. Test the workflow and preview the results to ensure accuracy.
6. Integrate AI models and databases to streamline connection requirements.
7. Publish the workflow, manage the API, and monitor performance.
8. Deploy the workflow to a staging or production environment as needed.
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