Using `use_project` with LlamaTrace: Setup and Troubleshooting Guide

! pip install "llama-index-core>=0.10.43" "openinference-instrumentation-llama-index>=2" "opentelemetry-proto>=1.12.0"
! pip install "arize-phoenix[evals,llama-index]"
# Azure-specific dependencies
! pip install azure-search-documents==11.5.1
! pip install llama-index-vector-stores-azureaisearch
! pip install llama-index-embeddings-azure-openai
! pip install llama-index-llms-azure-openai

# Additional tools and utilities
! pip install python-dotenv

from tqdm import tqdm
import json
from openinference.instrumentation import using_metadata
from phoenix.trace import using_project

# Load all evaluation questions from queries.jsonl
eval_questions = []
with open("eval/queries.jsonl", "r") as file:
    for line in file:
        # Parse each line as JSON and extract the query
        json_line = json.loads(line.strip())
        eval_questions.append(json_line)

# List of query engines and their respective project names
query_engines = [
    (keyword_query_engine, "Keyword"),
    (hybrid_query_engine, "Hybrid"),
    (semantic_hybrid_query_engine, "Semantic_Hybrid"),
]

# Loop through each question and query it against each engine
for query_data in tqdm(eval_questions):
    query = query_data["query"]
    query_classification = query_data.get("query_classification", "undefined")  # Default to 'undefined' if not present
    
    for engine, project_name in query_engines:
        try:
            metadata = query_classification
            with using_project(project_name), using_metadata(metadata):
                # Assuming the query method expects a string query and returns results
                engine.query(query)
        except Exception as e:
            print(f"Error querying {project_name} for query '{query}': {e}")