Observability for LlamaIndex with Highlight

Step 1

Install and initialize the SDK in your code. It will automatically detect the workflow structure in build traces.

import asyncio

import chromadb
import os

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from traceloop.sdk import Traceloop

os.environ["TOKENIZERS_PARALLELISM"] = "false"

Traceloop.init(app_name="llama_index_example")

chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")

# define embedding function
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")

# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

# set up ChromaVectorStore and load in data
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)


async def main():
    # Query Data
    query_engine = index.as_query_engine()
    response = await query_engine.aquery("What did the author do growing up?")

    print(response)


if __name__ == "__main__":
    asyncio.run(main())

import * as traceloop from "@traceloop/node-server-sdk";
import {
  OpenAIEmbedding,
  SimpleDirectoryReader,
  SimpleVectorStore,
  VectorStoreIndex,
  serviceContextFromDefaults,
  storageContextFromDefaults,
} from "llamaindex";

traceloop.initialize({
  appName: "sample_llamaindex",
  apiKey: process.env.TRACELOOP_API_KEY,
  disableBatch: true,
});


const documents = await new SimpleDirectoryReader().loadData({
  // Download from:
  // https://github.com/traceloop/openllmetry-js/tree/main/packages/sample-app/data/paul_graham
  directoryPath: "data/paul_graham", 
});

const serviceContext = serviceContextFromDefaults({ embedModel });
const storageContext = await storageContextFromDefaults({ vectorStore });

const index = await VectorStoreIndex.fromDocuments(documents, {
  storageContext,
  serviceContext,
});

const queryEngine = index.asQueryEngine();

const res = await queryEngine.query("What did the author do growing up?");
console.log(res)

Step 2

Route traces to Highlights’s OTLP endpoint and set the Highlight project ID in the headers.

TRACELOOP_BASE_URL=https://otel.highlight.io:4318
TRACELOOP_HEADERS="x-highlight-project=<YOUR_HIGHLIGHT_PROJECT_ID>"

Docs

Observability for LlamaIndex with Highlight

Step 1

Step 2

Discover use cases

Trace a LlamaIndex-based RAG pipeline

Trace a LlamaIndex-based Agent

More things you can connect to {platform}

Chroma

OpenAI

Cohere

Ollama

Gemini

Watsonx

Bedrock

Anthropic

LangChain

Mistral AI

together.ai