Article

3 ways Airbyte 1.0 helps you optimize your Gen AI workflows

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September 24, 2024

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With the rise of GenAI and LLMs, data engineering is evolving fast. AI models must handle vast amounts of structured and unstructured data from various sources. Managing these pipelines—especially when incorporating vector databases and retrieval-augmented generation (RAG) workflows—has become more complex. Ensuring data is reliable, accurate, and AI-ready is critical for businesses looking to stay competitive. With our 1.0 release, Airbyte helps data teams streamline AI workflows, optimize pipelines, and fully leverage modern GenAI technologies.

This article will detail three ways you can supercharge your GenAI workflows with Airbyte. But here's a first quick demo if you're curious how it looks like:

1. Delivering data into Vector stores: brain of AI workflows

Data engineers face a fundamental problem: connecting, syncing, and maintaining data from a myriad of sources. Whether it's SaaS applications, databases, or data warehouses, keeping data pipelines stable and up-to-date is a heavy lift. Imagine you’re tasked with feeding data to your AI models and pulling these into a unified flow, which can be exhausting.

This is where Airbyte stands out. Rather than manually building and managing your home-grown connectors, you can now use the new AI Assist and Marketplace. Connectors in the Marketplace make it easy to collect data from hundreds of sources, including vector databases like Pinecone, Weaviate, Milvus, and Chroma. Once you have the data, AI workflows that depend on embeddings provide smooth data movement into AI-enabled warehouses like Snowflake Cortex and BigQuery Vertex AI.

{
      "id": "599d75c8-517c-4f37-88df-ff16576bd607",
      "values": [0.0076571689, ..., 0.0138477711],
      "metadata": {
        "_airbyte_stream": "issues",
        "_record_id": 1556650122,
        "author_association": "CONTRIBUTOR",
        "comments": 3,
        "created_at": "2023-01-25T13:21:50Z",
        // ...
        "text": "...The acceptance-test-config.yml file is in a legacy format. Please migrate to the latest format...",
        "updated_at": "2023-07-17T09:20:56Z",
      }
}

Each vector in Airbyte is linked to a metadata object. This metadata is combined and sent to the LLM, allowing you to create prompts specific to your use case. For more details, check out this tutorial.

Loading data into Vector stores is only part of the task. Challenges can arise in the deduplication and incremental processing of that data. With 1.0, Airbyte addresses these challenges by leveraging key features built into the Airbyte protocol:

Primary keys ensure each document or record is uniquely identified, enabling accurate deduplication in our vector store destinations, even when the number of document chunks changes between versions.
Incremental processing uses a cursor value (like a timestamp) to delineate which data was pulled and which is new. Knowing a cursor value also allows the Airbyte system to automatically keep a history of changes to records in the destination.
Processing the changed records only lowers embedding costs. Thanks to incremental processing, users avoid the expense and time required to recalculate vector embeddings, which are often the highest costs in the process.

Airbyte supports composite primary keys, which can be used for incremental data sync tasks

By creating sync processes that utilize one or all of the features above, users can ensure reliable and efficient data handling by eliminating much of the complexity associated with data processing.

2. Handling unstructured data: the next frontier

Handling unstructured data—such as PDFs, emails, images, and documents—has long been a complex and time-consuming task for data engineers. Airbyte addresses this challenge by integrating with popular open-source libraries, making it easier to convert these diverse file types into structured formats for analysis. Here’s how Airbyte streamlines the process:

Extracts structured and unstructured data from documents stored in S3, Google Drive, and Azure Blob Storage, emitting the content as markdown while preserving key elements like headings and lists.
Using OCR technology to process scanned documents, making them usable for analysis. It also supports multiple formats, such as PDFs, Word, PowerPoint, and Google Docs, treating them like structured data sources.
Performs incremental syncs to process only new or updated files, saving time and reducing costs, especially in expensive vector embedding operations.

Unstructured data can become a key ingredient in providing information to LLMs. By moving unstructured data into LLM sources, app developers can leverage LLM's natural language processing capabilities to categorize, summarize, and translate unstructured text to identify patterns and sentiments within diverse data sources, powering AI agents, chatbots, and much more.


  import textwrap
  from rich.console import Console

  # convert incoming stream data into documents
  docs = list(read_result["issues"].to_documents(
      title_property="title",
      content_properties=["body"],
      metadata_properties=["state", "url", "number"],
      render_metadata=True,
  ))

  # print a doc comprising github issue
  console = Console()
  console.print(str(docs[10]))

Using PyAirbyte to read unstructured documents from GitHub.

3. Powering LLMs and RAG workflows with reliable data

Using trusted training data and integrating first-party data from enterprise systems gives LLM and RAG models better control and more effective training. Large-scale AI models risk delays, redundancies, and unnecessary resource consumption without an optimized pipeline.

Airbyte tackles this with incremental processing and deduplication, pulling only what’s needed and cutting out redundant data. It also supports embedding generation and integration across popular LLM frameworks like LangChain, LlamaIndex, Cohere, and Anthropic. Here’s what it offers:

LangChain and LlamaIndex integrations: Integrate Airbyte sources like Salesforce, HubSpot, and Stripe into your AI workflows—no hassle, just quick and reliable data access.
Automated embedding management: Airbyte does the heavy lifting by chunking and indexing your raw data so your vector databases stay tidy and ready for action.
PyAirbyte: Love flexibility? PyAirbyte gives you programmatic control over data transformation, caching, and merging—all from a Python library, letting you tweak your AI workflows precisely how you want.


  from langchain_openai import ChatOpenAI
  from langchain import hub
  from langchain_core.output_parsers import StrOutputParser
  from langchain_core.runnables import RunnablePassthrough

  retriever = vectorstore.as_retriever()
  prompt = hub.pull("rlm/rag-prompt")
  llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)


  def format_docs(docs):
      return "\n\n".join(doc.page_content for doc in docs)

  rag_chain = (
      {"context": retriever | format_docs, "question": RunnablePassthrough()}
      | prompt
      | llm
      | StrOutputParser()
  )
  print("Langchain RAG pipeline set up successfully.")

By syncing unstructured data into LLMs and leveraging PyAirbyte, a rich set of Python libraries, data engineers can quickly set up a RAG application using Langchain. For full code examples, check out the following Jupyter notebook.

Wrapping up

At Airbyte, we’re committed to empowering data engineers by simplifying data availability for AI workflows. Our platform provides the tools to consolidate data from diverse sources, process unstructured data quickly and easily, and optimize RAG workflows for large language models.

By automating data movement, syncing only the necessary information, and enabling seamless integrations with vector databases and LLM providers, Airbyte allows you to focus on building innovative AI solutions. As your AI projects grow in complexity, we’re here to ensure that your data pipelines stay efficient, scalable, and ready to support the most advanced AI-driven applications.

If you’re ready to power your GenAI or RAG workflows with Airbyte 1.0, get started today, try the code from this post or join our upcoming webinar “Building Data Pipelines for Generative AI” to learn more about the exciting features powering this release. You can also check the other announcements of Airbyte 1.0: