Capturing RAG

Retrieval Augmented Generation (RAG) is a common pattern in LLM applications where you first retrieve relevant context from a knowledge base and then use that context to generate a response. LangWatch provides specific ways to capture RAG data, enabling better observability and evaluation of your RAG pipelines. By capturing the contexts (retrieved documents) used by the LLM, you unlock several benefits in LangWatch:

Specialized RAG evaluators (e.g., Faithfulness, Context Relevancy).
Analytics on document usage (e.g., which documents are retrieved most often, which ones lead to better responses).
Deeper insights into the retrieval step of your pipeline.

There are two main ways to capture RAG spans: manually creating a RAG span or using framework-specific integrations like the one for LangChain.

Manual RAG Span Creation

You can manually create a RAG span by using tracer.withActiveSpan() with type: "rag". Inside this span, you should perform the retrieval and then update the span with the retrieved contexts. The contexts should be a list of LangWatchSpanRAGContext objects. The LangWatchSpanRAGContext object allows you to provide more metadata about each retrieved chunk, such as document_id, chunk_id, and content. Here’s an example:

import { setupObservability } from "langwatch/observability/node";
import { getLangWatchTracer } from "langwatch";
import type { LangWatchSpanRAGContext } from "langwatch/observability";

// Setup observability
setupObservability();

const tracer = getLangWatchTracer("rag-example");

async function generateAnswerFromContext(contexts: string[], userQuery: string): Promise<string> {
  return await tracer.withActiveSpan("GenerateAnswerFromContext", async (span) => {
    span.setType("llm");
    span.setRequestModel("gpt-5-mini");
    
    // Simulate LLM call using the contexts
    await new Promise(resolve => setTimeout(resolve, 500));
    const response = `Based on the context, the answer to '${userQuery}' is...`;
    
    // You can update the LLM span with model details, token counts, etc.
    span.setInput("text", `Contexts: ${contexts.join(", ")}\nQuery: ${userQuery}`);
    span.setOutput("text", response);
    
    return response;
  });
}

async function performRAG(userQuery: string): Promise<string> {
  return await tracer.withActiveSpan("My Custom RAG Process", async (span) => {
    span.setType("rag");
    
    // 1. Retrieve contexts
    // Simulate retrieval from a vector store or other source
    await new Promise(resolve => setTimeout(resolve, 300));
    const retrievedDocs = [
      "LangWatch helps monitor LLM applications.",
      "RAG combines retrieval with generation for better answers.",
      "TypeScript is a popular language for AI development."
    ];

    // Update the current RAG span with the retrieved contexts
    // You can pass a list of strings directly
    const ragContexts: LangWatchSpanRAGContext[] = retrievedDocs.map((content, index) => ({
      document_id: `doc${index + 1}`,
      chunk_id: `chunk${index + 1}`,
      content
    }));
    
    span.setRAGContexts(ragContexts);

    // Alternatively, for simpler context information:
    // span.setRAGContexts(retrievedDocs.map(content => ({
    //   document_id: "unknown",
    //   chunk_id: "unknown", 
    //   content
    // })));

    // 2. Generate answer using the contexts
    const finalAnswer = await generateAnswerFromContext(contexts: retrievedDocs, userQuery: userQuery);

    // The RAG span automatically captures its input (userQuery) and output (finalAnswer)
    // if dataCapture is not set to "none".
    return finalAnswer;
  });
}

async function handleUserQuestion(question: string): Promise<string> {
  return await tracer.withActiveSpan("User Question Handler", async (span) => {
    span.setInput("text", question);
    span.setAttributes({ "user_id": "example_user_123" });

    const answer = await performRAG(userQuery: question);

    span.setOutput("text", answer);
    return answer;
  });
}

// Example usage
async function main() {
  const userQuestion = "What is LangWatch used for?";
  const response = await handleUserQuestion(userQuestion);
  console.log(`Question: ${userQuestion}`);
  console.log(`Answer: ${response}`);
}

main().catch(console.error);

In this example:

performRAG uses tracer.withActiveSpan() with type: "rag".
Inside performRAG, we simulate a retrieval step.
span.setRAGContexts(ragContexts) is called to explicitly log the retrieved documents.
The generation step (generateAnswerFromContext) is called, which itself can be another span (e.g., an LLM span).

Advanced RAG Patterns

Multiple Retrieval Sources

You can capture RAG contexts from multiple sources in a single span:

async function multiSourceRAG(query: string): Promise<string> {
  return await tracer.withActiveSpan("Multi-Source RAG", async (span) => {
    span.setType("rag");
    
    // Simulate retrieval from multiple sources
    const vectorStoreContexts: LangWatchSpanRAGContext[] = [
      {
        document_id: "vector_doc_1",
        chunk_id: "vector_chunk_1",
        content: "Information from vector store"
      }
    ];
    
    const databaseContexts: LangWatchSpanRAGContext[] = [
      {
        document_id: "db_doc_1", 
        chunk_id: "db_chunk_1",
        content: "Information from database"
      }
    ];
    
    const apiContexts: LangWatchSpanRAGContext[] = [
      {
        document_id: "api_doc_1",
        chunk_id: "api_chunk_1", 
        content: "Information from API"
      }
    ];
    
    // Combine all contexts
    const allContexts = [
      ...vectorStoreContexts,
      ...databaseContexts,
      ...apiContexts
    ];
    
    span.setRAGContexts(allContexts);
    
    // Generate response using all contexts
    const response = `Based on ${allContexts.length} sources: ${query}`;
    return response;
  });
}

RAG with Metadata

You can include additional metadata in your RAG contexts:

async function ragWithMetadata(query: string): Promise<string> {
  return await tracer.withActiveSpan("RAG with Metadata", async (span) => {
    span.setType("rag");
    
    const contexts: LangWatchSpanRAGContext[] = [
      {
        document_id: "doc_123",
        chunk_id: "chunk_456",
        content: "Relevant content here"
      }
    ];
    
    // Add additional metadata to the span
    span.setAttributes({
      "rag.source": "vector_store",
      "rag.retrieval_method": "semantic_search",
      "rag.top_k": 5,
      "rag.threshold": 0.7
    });
    
    span.setRAGContexts(contexts);
    
    const response = `Based on the retrieved context: ${query}`;
    return response;
  });
}

Error Handling

When working with RAG operations, it’s important to handle errors gracefully and capture error information in your spans:

async function robustRAGRetrieval(query: string): Promise<LangWatchSpanRAGContext[]> {
  return await tracer.withActiveSpan("Robust RAG Retrieval", async (span) => {
    span.setType("rag");
    span.setInput("text", query);
    
    try {
      // Simulate retrieval that might fail
      const retrievedContexts: LangWatchSpanRAGContext[] = [
        {
          document_id: "doc_123",
          chunk_id: "chunk_456",
          content: "Relevant information from document 123"
        }
      ];
      
      span.setRAGContexts(retrievedContexts);
      span.setOutput("json", { status: "success", count: retrievedContexts.length });
      
      return retrievedContexts;
    } catch (error) {
      // Capture error information in the span
      span.setOutput("json", { 
        status: "error", 
        error_message: error instanceof Error ? error.message : String(error),
        error_type: error instanceof Error ? error.constructor.name : typeof error
      });
      
      // Re-throw the error (withActiveSpan will automatically mark the span as ERROR)
      throw error;
    }
  });
}

Best Practices

Use Descriptive Span Names: Name your RAG spans clearly to identify the retrieval method or source.
Include Metadata: Add relevant attributes like retrieval method, thresholds, or source information.
Handle Errors Gracefully: Wrap RAG operations in try-catch blocks and capture error information.
Optimize Context Size: Be mindful of the size of context content to avoid performance issues.
Use Consistent Document IDs: Use consistent naming conventions for document and chunk IDs.
Control Data Capture: Use data capture configuration to manage what gets captured in sensitive operations.

By effectively capturing RAG spans, you gain much richer data in LangWatch, enabling more powerful analysis and evaluation of your RAG systems. Refer to the SDK examples for more detailed implementations. For more advanced RAG patterns and framework-specific implementations:

Integration Guide - Basic setup and core concepts
Manual Instrumentation - Advanced span management for RAG pipelines
Semantic Conventions - RAG-specific attributes and naming conventions
LangChain Integration - Automatic RAG instrumentation with LangChain
Capturing Metadata - Adding custom metadata to RAG contexts

For production RAG applications, combine manual RAG spans with Semantic Conventions for consistent observability and better analytics.

Get Started

Agent Simulations

LLM Observability

LLM Evaluation

Prompt Management

LLM Development

API Endpoints

Use Cases

Support

Manual RAG Span Creation

Advanced RAG Patterns

Multiple Retrieval Sources

RAG with Metadata

Error Handling

Best Practices

Get Started

Agent Simulations

LLM Observability

LLM Evaluation

Prompt Management

LLM Development

API Endpoints

Use Cases

Support

​Manual RAG Span Creation

​Advanced RAG Patterns

​Multiple Retrieval Sources

​RAG with Metadata

​Error Handling

​Best Practices

​Related Documentation

Manual RAG Span Creation

Advanced RAG Patterns

Multiple Retrieval Sources

RAG with Metadata

Error Handling

Best Practices

Related Documentation