Vectorize is a powerful tool designed to help engineers build efficient and scalable AI pipelines, including Retrieval Augmented Generation (RAG) pipelines. Here's how you can leverage Vectorize to build a production-ready RAG pipeline:

1. Create a RAG Pipeline: Start by creating a new RAG pipeline in Vectorize. You can configure the pipeline by selecting your vector database type and integrating with an AI platform like OpenAI for text embedding.

2. Connect Data Sources: Vectorize offers a variety of options to connect your data sources, including web crawlers, file uploads, and cloud storage integrations. You can add multiple data sources to your pipeline.

3. Schedule and Backfill: Once your data sources are connected, you can schedule your RAG pipeline to run on a regular basis or trigger it manually. Vectorize will then backfill your vector database with the processed data.

4. Test and Optimize: Vectorize provides a RAG sandbox where you can test your pipeline's performance by asking questions and evaluating the results. The RAG evaluation tool helps you analyze, debug, and optimize your pipeline's retrieval performance.

5. Advanced Document Extraction: Vectorize's Iris feature enables advanced document parsing, allowing you to extract text, images, and tables from a wide range of file types, including complex PDFs and invoices. This ensures your data is optimized for AI-powered search and retrieval.

By leveraging Vectorize's comprehensive suite of tools, you can build a scalable and production-ready RAG pipeline that streamlines your data processing, improves retrieval performance, and simplifies the deployment of your AI applications.

Vectorize is a powerful tool designed to streamline the process of building scalable and production-ready retrieval-augmented generation (RAG) pipelines. It offers a comprehensive suite of features to help engineers like us build efficient AI pipelines without hassle.

One of the standout features of Vectorize is its AI document parsing capabilities. With the new Vectorize Iris tool, you can effortlessly extract text, images, and tables from a wide range of file types, including PDFs, Word documents, and PowerPoint presentations. This feature ensures that your unstructured data is optimized for AI-powered search and retrieval.

Vectorize also simplifies the process of building RAG pipelines. The platform provides a user-friendly drag-and-drop interface that allows you to easily configure your data sources, select the appropriate AI models (such as OpenAI, Amazon Bedrock, or Google Vertex AI), and connect to your preferred vector database. This streamlined approach helps you quickly deploy real-time RAG pipelines for your unstructured data.

Another valuable feature of Vectorize is the RAG evaluation tool. This tool helps you analyze, debug, and optimize the performance of your retrieval pipelines by tracking query accuracy, ranking relevancy, and detecting failures. You can test different embedding models and see the key metrics, such as precision, recall, and latency, to refine your RAG pipeline for better AI responses.

Vectorize's sandbox environment further enhances the development and testing of your RAG applications. You can explore the performance of various language models, including LLaMA 3.3 and DeepSeeR 170B, and assess how they perform with the data you've provided.

Overall, Vectorize is a comprehensive platform that simplifies the process of building scalable and production-ready RAG pipelines. Its AI document parsing, embedding integration, vector search capabilities, and evaluation tools make it a valuable asset for engineers looking to streamline their AI application development.

To set up a RAG pipeline with Vectorize, follow these steps:

1. Create an Account: Go to the Vectorize website and create a free account.

2. Create an Organization: After logging in, create an organization to invite your team members.

3. Build a New RAG Pipeline: Click on the "New Pipeline" button and give your pipeline a name, such as "RAG Pipeline".

4. Select Vector Database Type: Choose the vector database type where you want to store your data.

5. Add an AI Platform: Select an AI platform, such as OpenAI, to generate text embeddings.

6. Connect Data Source: Connect your data source, whether it's a web crawler, file upload, or integration with services like Dropbox.

7. Schedule the Pipeline: Set the pipeline to run manually, on a schedule, or in real-time to populate your vector database.

8. Test the RAG Sandbox: Once the pipeline is set up, you can test it in the RAG sandbox by asking questions and evaluating the results.

9. Analyze and Optimize: Use the RAG Evaluation tool to track query accuracy, ranking relevancy, and detect failures to refine your pipeline.

10. Leverage Vectorize Iris: Utilize the Vectorize Iris feature to extract text, images, and tables from various file types, such as PDFs and invoices, to optimize your data for AI-powered search and retrieval.

By following these steps, you can efficiently build scalable and production-ready RAG pipelines with Vectorize, streamlining your data processing and enabling powerful AI-driven search and retrieval capabilities.

Vectorize provides powerful tools to help you test and optimize your Retrieval Augmented Generation (RAG) pipelines. The RAG Evaluation tool allows you to analyze, debug, and optimize your retrieval performance by tracking query accuracy, ranking relevancy, and detecting failures. You can see key metrics like precision, recall, and latency to refine your RAG pipeline for better AI responses.

The RAG Sandbox lets you test your RAG pipelines with different models, from LLaMA 3.3 to DeepSeeR 170B, to find the best fit for your use case. You can ask questions and see how the system retrieves and combines relevant context from the provided documents.

In addition, Vectorize's Extraction Tester enables you to test the extraction of data from various file types, including complex PDFs, invoices, and more. The fine-tuned Vectorize Iris vision model can accurately extract text, tables, and other structured data, making it easier to prepare your data for AI-powered search and retrieval.

These tools empower you to build scalable and production-ready RAG pipelines, ensuring your AI applications deliver high-quality responses based on the most relevant information.

Vectorize's Extraction Tester is a powerful tool that allows you to effortlessly extract data from a wide range of document types, including complex PDFs, invoices, and more. This feature is powered by Vectorize's fine-tuned vision model, Iris, which is specifically designed for advanced extraction tasks.

With the Extraction Tester, you can simply upload any file, whether it's text, Markdown, images, or HTML, and the tool will accurately extract the relevant data. It preserves the original layout and formatting, providing you with the extracted text in both plain text and Markdown formats.

The true power of the Extraction Tester shines when dealing with challenging documents, such as invoices. Even for these complex layouts, the tool is able to accurately identify and extract the numerical values, textual information, and maintain the original formatting.

By leveraging the Extraction Tester, you can streamline your document processing workflows, saving time and effort. This feature seamlessly integrates with Vectorize's broader suite of tools, allowing you to efficiently build scalable and production-ready RAG pipelines for your unstructured data.

Vectorize is a powerful tool that simplifies the process of building scalable and production-ready RAG (Retrieval Augmented Generation) pipelines. It automates data extraction, finds the best vectorization strategy, and allows you to quickly deploy real-time RAG pipelines for your unstructured data.

The platform offers several key features that make it a game-changer for building efficient AI pipelines:

1. Drag-and-Drop Pipeline Editor: Vectorize's intuitive pipeline editor enables you to easily build your RAG pipeline using a drag-and-drop interface, streamlining the process of connecting data sources, extracting and chunking data, and integrating with state-of-the-art language models.

2. Automated Data Extraction: Vectorize's Iris feature provides advanced document parsing capabilities, allowing you to extract text, images, and tables from a wide range of file types, including PDFs, Word documents, and PowerPoint presentations, with high accuracy.

3. RAG Evaluation Tool: Vectorize's RAG evaluation tool helps you analyze, debug, and optimize the performance of your retrieval pipelines by tracking query accuracy, ranking relevancy, and detecting failures, enabling you to refine your pipelines for better AI responses.

4. Scalable Vector Database Integration: Vectorize seamlessly integrates with various vector databases, allowing you to store and query your data efficiently, ensuring your RAG pipelines can handle large-scale data processing.

Overall, Vectorize is a comprehensive platform that simplifies the development of AI applications by automating the complex tasks involved in building scalable and production-ready RAG pipelines. Its intuitive tools and features empower engineers to focus on building innovative solutions rather than getting bogged down in the technical details of data processing and model integration.