RAG Pipelines
Last updated
Last updated
Retrieval-Augmented Generation (RAG) pipelines are an essential component of systems that aim to deliver accurate, contextually relevant responses by leveraging both retrieval mechanisms and generation models. These pipelines bridge the gap between unstructured data sources, retrieval systems, and language models (LLMs), ensuring that generated content is based on factual and up-to-date information.
Unstructured data refers to information that does not have a predefined data model, making it difficult to process and analyze using traditional methods. This includes data like text documents, PDFs, images, audio, and more. The challenge with unstructured data is the large variety of forms it takes and the potentially complex nature of extracting information out of unstructured data sources. Unstructured data lacks the well-defined format found in relational databases and spreadsheets, so extracting meaningful information from it requires more advanced techniques, like vectorization, text embeddings, and other machine learning models.
Unstructured data is everywhere—customer feedback, research papers, technical documents, knowledge bases, and more. However, unlocking the value within these data types can be tricky because:
It’s hard to search through: Traditional keyword-based search methods struggle with unstructured data, as context, relationships, and semantics are often missed.
Hard to keep up-to-date: Unlike structured data systems, updating or removing outdated or irrelevant content from unstructured data sources can be time-consuming.
Difficult to scale: With large volumes of unstructured data, processing and making it available for real-time queries is a major challenge.
RAG Pipelines allow organizations to turn this unstructured data into a format that can be easily searched and retrieved. They do this by transforming unstructured data into vector embeddings, which represent the semantic meaning of the data. These vectors are then indexed into vector databases, making them easily retrievable by the RAG system when a user submits a query.
A RAG Pipeline integrates several components: data connectors to retrieve and ingest unstructured data, vectorization models to convert the data into embeddings, vector databases to store and index these embeddings, and retrieval mechanisms to fetch relevant vectors when a query is made.
Source Connectors: RAG pipelines support various connectors to ingest data from different sources. These could be Amazon S3 buckets, Google Cloud Storage, or other knowledge bases, file systems, or SaaS platforms. These connectors allow unstructured data to be brought into the pipeline for processing.
Vectorization: Once data is ingested, the unstructured content must be extracted, chunked and transformed into vector embeddings. These embeddings capture the semantic meaning of the data, making it searchable in a more contextual manner compared to keyword search.
There are many ways to vectorize your unstructured data. You can chunk the documents into large chunks, smaller chunks. You can separate the chunks based on sentences, paragraphs, and other techniques. Once you have your chunks isolated you can use different text embedding models such as models from OpenAI, Voyage AI, Mistral, and others to generate your text embedding vectors.
Vector Databases: After vectorization, the embeddings are stored in a vector database like Pinecone, DataStax Astra, or Elastic. These databases are designed to handle vector-based searches efficiently and allow the RAG system to retrieve the most relevant data when queries are made.
Retrieval and Query: When a user submits a query, your RAG application fetches relevant vectors from the database based on the query’s content. This data is then passed to a language model (LLM) like OpenAI or LLaMA to generate a final, contextually relevant response.
One of the critical aspects of a successful RAG pipeline is ensuring that the vector search indexes are regularly updated with the latest information. If indexes are not kept up-to-date, users might receive outdated responses, leading to hallucinations (incorrect or irrelevant responses from the LLM).
For instance, if a company’s knowledge base is updated frequently, but the vector index isn’t refreshed, the RAG system may pull from old, irrelevant data, causing the LLM to generate inaccurate answers. This is why regularly updating vector indexes is crucial to maintaining an accurate and reliable RAG pipeline.
In a well-configured RAG pipeline, data ingestion and vectorization processes can be automated to ensure the vector index is continuously updated with fresh data. By scheduling the pipeline to run at regular intervals (e.g., hourly or daily), new documents are processed, vectorized, and written into the vector database, ensuring users always get the most up-to-date responses.
The RAG Pipelines in Vectorize provide a comprehensive solution for organizations dealing with large amounts of unstructured data. The platform supports multiple connectors, making it easy to integrate various data sources and storage solutions. Additionally, the ability to regularly update vector indexes ensures that your retrieval and generation models always work with the most current and relevant information.
Integration with RAG Evaluation: Vectorize is unique in its combination of RAG Evaluation to first identify the best performing vectorization strategy, then allowing you to implement that strategy in a real time RAG pipeline.
Flexible Connectors: Ingest data from multiple sources like Amazon S3, Google Cloud, or databases such as DataStax Astra.
Customizable Vectorization: Choose from various embedding models and configure chunk sizes, overlaps, and vectorization strategies to suit your data needs.
Automation: Automate the ingestion and vectorization process, ensuring your vector indexes are always current.
Scalable: Handle large volumes of unstructured data, transforming it into searchable, semantically meaningful vectors.
Reduced Hallucination Risk: Keep vector indexes up-to-date to prevent outdated or incorrect information from creeping into the generated responses.
By configuring and maintaining RAG pipelines through Vectorize, you can ensure your system continues to deliver accurate, reliable, and contextually aware responses to user queries, even as your data changes over time.
