Evaluation Query Engine Tool

In this section we will show you how you can use an EvalQueryEngineTool with an agent. Some reasons you may want to use a EvalQueryEngineTool:

1. Use specific kind of evaluation for a tool, and not just the agent’s reasoning
2. Use a different LLM for evaluating tool responses than the agent LLM

An EvalQueryEngineTool is built on top of the QueryEngineTool. Along with wrapping an existing query engine, it also must be given an existing evaluator to evaluate the responses of that query engine.

Install Dependencies

%pip install llama-index-embeddings-huggingface
%pip install llama-index-llms-openai
%pip install llama-index-agents-openai

import os

os.environ["OPENAI_API_KEY"] = "sk-..."

Initialize and Set LLM and Local Embedding Model

from llama_index.core.settings import Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.openai import OpenAI

Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5"
)
Settings.llm = OpenAI()

Download and Index Data

This is something we are donig for the sake of this demo. In production environments, data stores and indexes should already exist and not be created on the fly.

Create Storage Contexts

from llama_index.core import (
    StorageContext,
    load_index_from_storage,
)

try:
    storage_context = StorageContext.from_defaults(
        persist_dir="./storage/lyft",
    )
    lyft_index = load_index_from_storage(storage_context)

    storage_context = StorageContext.from_defaults(
        persist_dir="./storage/uber"
    )
    uber_index = load_index_from_storage(storage_context)

    index_loaded = True
except:
    index_loaded = False

Download Data

!mkdir -p 'data/10k/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'

Load Data

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex

if not index_loaded:
    # load data
    lyft_docs = SimpleDirectoryReader(
        input_files=["./data/10k/lyft_2021.pdf"]
    ).load_data()
    uber_docs = SimpleDirectoryReader(
        input_files=["./data/10k/uber_2021.pdf"]
    ).load_data()

    # build index
    lyft_index = VectorStoreIndex.from_documents(lyft_docs)
    uber_index = VectorStoreIndex.from_documents(uber_docs)

    # persist index
    lyft_index.storage_context.persist(persist_dir="./storage/lyft")
    uber_index.storage_context.persist(persist_dir="./storage/uber")

Create Query Engines

lyft_engine = lyft_index.as_query_engine(similarity_top_k=5)
uber_engine = uber_index.as_query_engine(similarity_top_k=5)

Create Evaluator

from llama_index.core.evaluation import RelevancyEvaluator

evaluator = RelevancyEvaluator()

Create Query Engine Tools

from llama_index.core.tools import ToolMetadata
from llama_index.core.tools.eval_query_engine import EvalQueryEngineTool

query_engine_tools = [
    EvalQueryEngineTool(
        evaluator=evaluator,
        query_engine=lyft_engine,
        metadata=ToolMetadata(
            name="lyft",
            description=(
                "Provides information about Lyft's financials for year 2021. "
                "Use a detailed plain text question as input to the tool."
            ),
        ),
    ),
    EvalQueryEngineTool(
        evaluator=evaluator,
        query_engine=uber_engine,
        metadata=ToolMetadata(
            name="uber",
            description=(
                "Provides information about Uber's financials for year 2021. "
                "Use a detailed plain text question as input to the tool."
            ),
        ),
    ),
]

Setup OpenAI Agent

from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

agent = FunctionAgent(tools=query_engine_tools, llm=OpenAI(model="gpt-4.1"))

Query Engine Passes Evaluation

Here we are asking a question about Lyft’s financials. This is what we should expect to happen:

1. The agent will use the lyftk tool first
2. The EvalQueryEngineTool will evaluate the response of the query engine using its evaluator
3. The output of the query engine will pass evaluation because it contains Lyft’s financials

response = await agent.run("What was Lyft's revenue growth in 2021?")
print(str(response))

Added user message to memory: What was Lyft's revenue growth in 2021?
=== Calling Function ===
Calling function: lyft with args: {"input": "What was Lyft's revenue growth in 2021?"}
Got output: Lyft's revenue growth in 2021 was $3,208,323, which increased compared to the revenue in 2020 and 2019.
========================

=== Calling Function ===
Calling function: uber with args: {"input": "What was Lyft's revenue growth in 2021?"}
Got output: Could not use tool uber because it failed evaluation.
Reason: NO
========================

Lyft's revenue grew by $3,208,323 in 2021, which increased compared to the revenue in 2020 and 2019.