Observability for LlamaIndex with Opik

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import os
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import getpass
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if "OPENAI_API_KEY" not in os.environ:
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    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")

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from llama_index.core import global_handler, set_global_handler
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set_global_handler("opik")
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opik_callback_handler = global_handler

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from llama_index.core import Settings
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from llama_index.core.callbacks import CallbackManager
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from opik.integrations.llama_index import LlamaIndexCallbackHandler
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# Basic setup
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opik_callback = LlamaIndexCallbackHandler()
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# Or with optional parameters
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opik_callback = LlamaIndexCallbackHandler(
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    project_name="my-llamaindex-project",  # Set custom project name
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    skip_index_construction_trace=True     # Skip tracking index construction
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)
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Settings.callback_manager = CallbackManager([opik_callback])

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import os
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from llama_index.core import global_handler, set_global_handler
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# Set project name for better organization
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os.environ["OPIK_PROJECT_NAME"] = "llamaindex-integration-demo"
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set_global_handler("opik")
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opik_callback_handler = global_handler

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import os
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import requests
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# Create directory if it doesn't exist
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os.makedirs('./data/paul_graham/', exist_ok=True)
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# Download the file using requests
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url = 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt'
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response = requests.get(url)
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with open('./data/paul_graham/paul_graham_essay.txt', 'wb') as f:
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    f.write(response.content)

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import os
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import getpass
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if "OPENAI_API_KEY" not in os.environ:
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    os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API key: ")

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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
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documents = SimpleDirectoryReader("./data/paul_graham").load_data()
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index = VectorStoreIndex.from_documents(documents)
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query_engine = index.as_query_engine()
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response = query_engine.query("What did the author do growing up?")
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print(response)

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import opik
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from llama_index.core import global_handler, set_global_handler
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from llama_index.llms.openai import OpenAI
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from llama_index.core.llms import ChatMessage
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# Configure Opik integration
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set_global_handler("opik")
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opik_callback_handler = global_handler
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@opik.track()
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def my_llm_application(user_query: str):
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    """Process user query with LlamaIndex"""
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    llm = OpenAI(model="gpt-3.5-turbo")
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    messages = [
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        ChatMessage(role="system", content="You are a helpful assistant."),
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        ChatMessage(role="user", content=user_query),
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    ]
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    response = llm.chat(messages)
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    return response.message.content
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# Call the tracked function
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result = my_llm_application("What is the capital of France?")
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print(result)

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import opik
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from llama_index.core import global_handler, set_global_handler
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from llama_index.llms.openai import OpenAI
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from llama_index.core.llms import ChatMessage
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# Configure Opik integration
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set_global_handler("opik")
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opik_callback_handler = global_handler
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# Create a manual trace
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with opik.start_as_current_trace(name="user_query_processing"):
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    llm = OpenAI(model="gpt-3.5-turbo")
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    messages = [
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        ChatMessage(role="user", content="Explain quantum computing in simple terms"),
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    ]
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    response = llm.chat(messages)
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    print(response.message.content)

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import opik
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from llama_index.core.workflow import Workflow, StartEvent, StopEvent, step, Event
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from llama_index.core import Settings
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from llama_index.core.callbacks import CallbackManager
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from llama_index.core import global_handler, set_global_handler
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# Configure Opik integration for LLM calls within steps
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set_global_handler("opik")
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class QueryEvent(Event):
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    """Event for passing query through workflow."""
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    query: str
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class MyRAGWorkflow(Workflow):
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    """Simple RAG workflow with tracked steps."""
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    @step
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    @opik.track()
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    async def retrieve_context(self, ev: StartEvent) -> QueryEvent:
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        """Retrieve relevant context for the query."""
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        query = ev.get("query", "")
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        # Your retrieval logic here
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        context = f"Context for: {query}"
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        return QueryEvent(query=f"{context} | {query}")
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    @step
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    @opik.track()
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    async def generate_response(self, ev: QueryEvent) -> StopEvent:
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        """Generate final response using the context."""
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        # Your generation logic here
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        result = f"Response based on: {ev.query}"
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        return StopEvent(result=result)
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# Create workflow instance
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workflow = MyRAGWorkflow()
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# Use start_as_current_span to track workflow execution
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with opik.start_as_current_span(
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    name="rag_workflow_execution",
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    input={"query": "What are the key features?"},
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    project_name="llama-index-workflows"
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) as span:
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    result = await workflow.run(query="What are the key features?")
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    span.update(output={"result": result})
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print(result)
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opik.flush_tracker()  # Ensure all traces are sent

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from llama_index.llms.openai import OpenAI
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from llama_index.core.llms import ChatMessage
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from llama_index.core import global_handler, set_global_handler
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# Configure Opik integration
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set_global_handler("opik")
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# Configure OpenAI LLM with stream_options to include usage information
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llm = OpenAI(
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    model="gpt-3.5-turbo",
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    additional_kwargs={
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        "stream_options": {"include_usage": True}
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    }
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)
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messages = [
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    ChatMessage(role="user", content="Tell me a short joke")
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]
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# Token usage will now be tracked in streaming responses
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response = llm.stream_chat(messages)
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for chunk in response:
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    print(chunk.delta, end="", flush=True)