Retrieval Augmented Generation (RAGs) is a common way to augment the generation of your LLM by retrieving a set of documents based on the user query and giving it to the LLM to use as context for answering, either by using a vector database, getting responses from an API, or integrated agent files and memory.

It can be challenging, however, to build a good quality RAG pipeline, making sure the right data was retrieved, preventing the LLM from hallucinating, monitor which documents are the most used and keep iterating to improve it, this is where integrating with LangWatch can help, by integrating your RAG you unlock a series of Guardrails, Measurements and Analytics for RAGs LangWatch.

To capture a RAG span, you can use the @langwatch.span(type="rag") decorator, along with a call to .update() to add the contexts to the span:

@langwatch.span(type="rag")
def rag_retrieval():
    # the documents you retrieved from your vector database
    search_results = ["France is a country in Europe.", "Paris is the capital of France."]

    # capture them on the span contexts before returning
    langwatch.get_current_span().update(contexts=search_results)

    return search_results

If you have document or chunk ids from the results, we recommend you can to capture them along with the id using RAGChunk, as this allows them to be grouped together and generate documents analytics on LangWatch dashboard:

from langwatch.types import RAGChunk

@langwatch.span(type="rag")
def rag_retrieval():
    # the documents you retrieved from your vector database
    search_results = [
        {
            "id": "doc-1",
            "content": "France is a country in Europe.",
        },
        {
            "id": "doc-2",
            "content": "Paris is the capital of France.",
        },
    ]

    # capture then on the span contexts with RAGChunk before returning
    langwatch.get_current_span().update(
        contexts=[
            RAGChunk(
                document_id=document["id"],
                content=document["content"],
            )
            for document in search_results
        ]
    )

    return search_results

Then you’ll be able to see the captured contexts that will also be used later on for evaluatios on LangWatch dashboard:

REST API IntegrationConcepts