Overview

Three additional evaluations for Llamaindex have been introduced, complementing existing ones. These evaluations run automatically, with results displayed in the output. More details on UpTrain’s evaluations can be found here.

Selected operators from the LlamaIndex pipeline are highlighted for demonstration:

1. RAG Query Engine Evaluations: The RAG query engine plays a crucial role in retrieving context and generating responses. To ensure its performance and response quality, we conduct the following evaluations:

   • Context Relevance: Determines if the context extracted from the query is relevant to the response.
   • Factual Accuracy: Assesses if the LLM is hallcuinating or providing incorrect information.
   • Response Completeness: Checks if the response contains all the information requested by the query.
     You can check out the complete list of evaluations UpTrain supports here

2. Sub-Question Query Generation Evaluation: The SubQuestionQueryGeneration operator decomposes a question into sub-questions, generating responses for each using a RAG query engine. Given the complexity, we include the previous evaluations and add:

   • Sub Query Completeness: Assures that the sub-questions accurately and comprehensively cover the original query.

3. Re-Ranking Evaluations: Re-ranking involves reordering nodes based on relevance to the query and choosing top n nodes. Different evaluations are performed based on the number of nodes returned after re-ranking.

   • Context Reranking (For same number of nodes): Checks if the order of re-ranked nodes is more relevant to the query than the original order.
   • Context Conciseness (For different number of nodes): Examines whether the reduced number of nodes still provides all the required information.

These evaluations collectively ensure the robustness and effectiveness of the RAG query engine, SubQuestionQueryGeneration operator, and the re-ranking process in the LlamaIndex pipeline.

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How to do it?

pip install -q html2text llama-index pandas tqdm uptrain cohere

from llama_index import (
    ServiceContext,
    VectorStoreIndex,
)
from llama_index.node_parser import SentenceSplitter
from llama_index.readers import SimpleWebPageReader
from llama_index.callbacks import CallbackManager, UpTrainCallbackHandler
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.service_context import set_global_service_context
from llama_index.query_engine.sub_question_query_engine import (
    SubQuestionQueryEngine,
)
from llama_index.tools.query_engine import QueryEngineTool
from llama_index.tools.types import ToolMetadata

callback_handler = UpTrainCallbackHandler(
    key_type="openai",
    api_key="sk-...",  # Replace with your OpenAI API key
    project_name_prefix="llama",
)
Settings.callback_manager = CallbackManager([callback_handler])

documents = SimpleWebPageReader().load_data(
  [
      "https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt"
  ]
)

parser = SentenceSplitter()
nodes = parser.get_nodes_from_documents(documents)

We have completed the basic steps, now let’s look at the demontration for using LlamaIndex for the following 3 cases:

• RAG Query Engine Evaluations
• Sub-Question Query Generation Evaluation
• Re-Ranking Evaluations