LlamaIndex (tracing)

1
from respan import Respan
2
from respan_instrumentation_llama_index import LlamaIndexInstrumentor
3
4
respan = Respan(
5
    instrumentations=[LlamaIndexInstrumentor()],
6
    customer_identifier="user_123",
7
    metadata={"service": "rag-api", "version": "1.0.0"},
8
)

1
from llama_index.core import Document, SummaryIndex
2
from respan import Respan, propagate_attributes, workflow
3
from respan_instrumentation_llama_index import LlamaIndexInstrumentor
4
5
respan = Respan(instrumentations=[LlamaIndexInstrumentor()])
6
7
@workflow(name="rag_request")
8
def run_query(question: str):
9
    index = SummaryIndex.from_documents([
10
        Document(text="Respan captures traces from LlamaIndex applications.")
11
    ])
12
    return index.as_query_engine().query(question)
13
14
def handle_request(user_id: str, question: str):
15
    with propagate_attributes(
16
        customer_identifier=user_id,
17
        thread_identifier="conv_abc_123",
18
        metadata={"plan": "pro"},
19
    ):
20
        response = run_query(question)
21
        print(response)

1
from llama_index.core import Document, SummaryIndex
2
from respan import Respan, task, workflow
3
from respan_instrumentation_llama_index import LlamaIndexInstrumentor
4
5
respan = Respan(instrumentations=[LlamaIndexInstrumentor()])
6
7
@task(name="build_index")
8
def build_index():
9
    return SummaryIndex.from_documents([
10
        Document(text="Respan traces LlamaIndex index and query operations."),
11
        Document(text="LlamaIndex combines retrievers, query engines, and agents."),
12
    ])
13
14
@workflow(name="rag_pipeline")
15
def rag_pipeline(question: str):
16
    index = build_index()
17
    return index.as_query_engine().query(question)
18
19
print(rag_pipeline("Summarize the documents."))
20
respan.flush()

1
from llama_index.core import Document, SummaryIndex
2
3
index = SummaryIndex.from_documents([
4
    Document(text="Respan captures traces for LLM calls and workflow steps."),
5
    Document(text="LlamaIndex can combine query engines, retrievers, and tools."),
6
])
7
query_engine = index.as_query_engine()
8
9
response = query_engine.query("What do the documents say?")
10
print(response)

1
from llama_index.embeddings.openai import OpenAIEmbedding
2
3
embedding_model = OpenAIEmbedding(model="text-embedding-3-small")
4
embedding = embedding_model.get_text_embedding(
5
    "LlamaIndex uses embeddings to retrieve relevant document chunks."
6
)
7
print(len(embedding))

1
import asyncio
2
from llama_index.core.agent.workflow import ReActAgent
3
from llama_index.core.tools import FunctionTool
4
5
def multiply_numbers(a: int, b: int) -> int:
6
    return a * b
7
8
multiply_tool = FunctionTool.from_defaults(
9
    fn=multiply_numbers,
10
    name="multiply_numbers",
11
    description="Multiply two integers and return the product.",
12
)
13
agent = ReActAgent(
14
    tools=[multiply_tool],
15
    system_prompt="Use tools when arithmetic is required.",
16
    streaming=False,
17
)
18
19
async def main():
20
    response = await agent.run(
21
        user_msg="Use the multiply_numbers tool to calculate 7 multiplied by 6."
22
    )
23
    print(response)
24
25
asyncio.run(main())