可观测性#

from llama_index.core import set_global_handler

# general usage
set_global_handler("<handler_name>", **kwargs)

# Phoenix can display in real time the traces automatically
# collected from your LlamaIndex application.
# Run all of your LlamaIndex applications as usual and traces
# will be collected and displayed in Phoenix.

# setup Arize Phoenix for logging/observability
import llama_index.core
import os

PHOENIX_API_KEY = "<PHOENIX_API_KEY>"
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
llama_index.core.set_global_handler(
    "arize_phoenix", endpoint="https://llamatrace.com/v1/traces"
)

...

import mlflow

mlflow.llama_index.autolog()  # Enable mlflow tracing

from traceloop.sdk import Traceloop

Traceloop.init()

# Phoenix can display in real time the traces automatically
# collected from your LlamaIndex application.
# Run all of your LlamaIndex applications as usual and traces
# will be collected and displayed in Phoenix.

import phoenix as px

# Look for a URL in the output to open the App in a browser.
px.launch_app()
# The App is initially empty, but as you proceed with the steps below,
# traces will appear automatically as your LlamaIndex application runs.

import llama_index.core

llama_index.core.set_global_handler("arize_phoenix")
...

pip install llama-index langfuse

import os

# Get keys for your project from the project settings page: https://cloud.langfuse.com
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com"  # 🇪🇺 EU region
# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region

from langfuse.llama_index import LlamaIndexInstrumentor

# Get your keys from the Langfuse project settings page and set them as environment variables
# or pass them as arguments when initializing the instrumentor

instrumentor = LlamaIndexInstrumentor()

# Automatically trace all LlamaIndex operations
instrumentor.start()

# ... your LlamaIndex index creation ...
index.as_query_engine().query("What is the capital of France?")

# Flush events to langfuse
instrumentor.flush()

from llama_index.core import set_global_handler

# You should provide your Literal AI API key and base url using the following environment variables:
# LITERAL_API_KEY, LITERAL_API_URL
set_global_handler("literalai")

export OPIK_API_KEY="<OPIK_API_KEY>"
export OPIK_WORKSPACE="<OPIK_WORKSPACE - Often the same as your API key>"

# Optional
#export OPIK_URL_OVERRIDE="<OPIK_URL_OVERRIDE>"

from llama_index.core import Document, VectorStoreIndex, set_global_handler

# You should provide your OPIK API key and Workspace using the following environment variables:
# OPIK_API_KEY, OPIK_WORKSPACE
set_global_handler(
    "opik",
)

# This example uses OpenAI by default so don't forget to set an OPENAI_API_KEY
index = VectorStoreIndex.from_documents([Document.example()])
query_engine = index.as_query_engine()

questions = [
    "Tell me about LLMs",
    "How do you fine-tune a neural network ?",
    "What is RAG ?",
]

for question in questions:
    print(f"> \033[92m{question}\033[0m")
    response = query_engine.query(question)
    print(response)

from llama_index.core.instrumentation import get_dispatcher
from argilla_llama_index import ArgillaHandler

argilla_handler = ArgillaHandler(
    dataset_name="query_llama_index",
    api_url="http://localhost:6900",
    api_key="argilla.apikey",
    number_of_retrievals=2,
)
root_dispatcher = get_dispatcher()
root_dispatcher.add_span_handler(argilla_handler)
root_dispatcher.add_event_handler(argilla_handler)

from llama_index.core import set_global_handler

# Make sure you've installed the 'llama-index-callbacks-langfuse' integration package.

# NOTE: Set your environment variables 'LANGFUSE_SECRET_KEY', 'LANGFUSE_PUBLIC_KEY' and 'LANGFUSE_HOST'
# as shown in your langfuse.com project settings.

set_global_handler("langfuse")

from llama_index.core import set_global_handler

set_global_handler("deepeval")

# NOTE: Run 'deepeval login' in the CLI to log traces on Confident AI, DeepEval's hosted evaluation platform.
# Run all of your LlamaIndex applications as usual and traces
# will be collected and displayed on Confident AI whenever evaluations are ran.
...

from llama_index.core import set_global_handler

set_global_handler("wandb", run_args={"project": "llamaindex"})

# NOTE: No need to do the following
from llama_index.callbacks.wandb import WandbCallbackHandler
from llama_index.core.callbacks import CallbackManager
from llama_index.core import Settings

# wandb_callback = WandbCallbackHandler(run_args={"project": "llamaindex"})
# Settings.callback_manager = CallbackManager([wandb_callback])

# access additional methods on handler to persist index + load index
import llama_index.core

# persist index
llama_index.core.global_handler.persist_index(graph, index_name="my_index")
# load storage context
storage_context = llama_index.core.global_handler.load_storage_context(
    artifact_url="ayut/llamaindex/my_index:v0"
)

import llama_index.core

llama_index.core.set_global_handler("openinference")

# NOTE: No need to do the following
from llama_index.callbacks.openinference import OpenInferenceCallbackHandler
from llama_index.core.callbacks import CallbackManager
from llama_index.core import Settings

# callback_handler = OpenInferenceCallbackHandler()
# Settings.callback_manager = CallbackManager([callback_handler])

# Run your LlamaIndex application here...
for query in queries:
    query_engine.query(query)

# View your LLM app data as a dataframe in OpenInference format.
from llama_index.core.callbacks.open_inference_callback import as_dataframe

query_data_buffer = llama_index.core.global_handler.flush_query_data_buffer()
query_dataframe = as_dataframe(query_data_buffer)

# use trulens
from trulens_eval import TruLlama

tru_query_engine = TruLlama(query_engine)

# query
tru_query_engine.query("What did the author do growing up?")

from llama_index.core import set_global_handler

set_global_handler(
    "honeyhive",
    project="My HoneyHive Project",
    name="My LLM Workflow Name",
    api_key="MY HONEYHIVE API KEY",
)

# NOTE: No need to do the following
from llama_index.core.callbacks import CallbackManager

# from honeyhive.utils.llamaindex_tracer import HoneyHiveLlamaIndexTracer
from llama_index.core import Settings

# hh_tracer = HoneyHiveLlamaIndexTracer(
#     project="My HoneyHive Project",
#     name="My LLM Workflow Name",
#     api_key="MY HONEYHIVE API KEY",
# )
# Settings.callback_manager = CallbackManager([hh_tracer])

import os

os.environ["PROMPTLAYER_API_KEY"] = "pl_7db888a22d8171fb58aab3738aa525a7"

from llama_index.core import set_global_handler

# pl_tags are optional, to help you organize your prompts and apps
set_global_handler("promptlayer", pl_tags=["paul graham", "essay"])

pip install langtrace-python-sdk

from langtrace_python_sdk import (
    langtrace,
)  # Must precede any llm module imports

langtrace.init(api_key="<LANGTRACE_API_KEY>")

pip install openlit

import openlit

openlit.init()

pip install llama-index-instrumentation-agentops

from llama_index.core import set_global_handler

# NOTE: Feel free to set your AgentOps environment variables (e.g., 'AGENTOPS_API_KEY')
# as outlined in the AgentOps documentation, or pass the equivalent keyword arguments
# anticipated by AgentOps' AOClient as **eval_params in set_global_handler.

set_global_handler("agentops")

import llama_index.core

llama_index.core.set_global_handler("simple")