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https://github.com/hwchase17/langchain
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22603d19e0
# WandbTracer This PR adds the `WandbTracer` and deprecates the existing `WandbCallbackHandler`. Added an example notebook under the docs section alongside the `LangchainTracer` Here's an example [colab](https://colab.research.google.com/drive/1pY13ym8ENEZ8Fh7nA99ILk2GcdUQu0jR?usp=sharing) with the same notebook and the [trace](https://wandb.ai/parambharat/langchain-tracing/runs/8i45cst6) generated from the colab run Co-authored-by: Bharat Ramanathan <ramanathan.parameshwaran@gohuddl.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
118 lines
3.3 KiB
Python
118 lines
3.3 KiB
Python
"""Integration tests for the langchain tracer module."""
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import asyncio
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import os
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import pytest
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from aiohttp import ClientSession
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from langchain.agents import AgentType, initialize_agent, load_tools
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from langchain.callbacks.manager import wandb_tracing_enabled
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from langchain.llms import OpenAI
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questions = [
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(
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"Who won the US Open men's final in 2019? "
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"What is his age raised to the 0.334 power?"
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),
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(
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"Who is Olivia Wilde's boyfriend? "
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"What is his current age raised to the 0.23 power?"
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),
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(
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"Who won the most recent formula 1 grand prix? "
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"What is their age raised to the 0.23 power?"
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),
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(
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"Who won the US Open women's final in 2019? "
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"What is her age raised to the 0.34 power?"
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),
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("Who is Beyonce's husband? " "What is his age raised to the 0.19 power?"),
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]
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def test_tracing_sequential() -> None:
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os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
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os.environ["WANDB_PROJECT"] = "langchain-tracing"
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for q in questions[:3]:
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llm = OpenAI(temperature=0)
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tools = load_tools(
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["llm-math", "serpapi"],
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llm=llm,
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)
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agent = initialize_agent(
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tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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agent.run(q)
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def test_tracing_session_env_var() -> None:
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os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
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llm = OpenAI(temperature=0)
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tools = load_tools(
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["llm-math", "serpapi"],
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llm=llm,
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)
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agent = initialize_agent(
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tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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agent.run(questions[0])
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@pytest.mark.asyncio
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async def test_tracing_concurrent() -> None:
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os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
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aiosession = ClientSession()
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llm = OpenAI(temperature=0)
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async_tools = load_tools(
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["llm-math", "serpapi"],
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llm=llm,
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aiosession=aiosession,
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)
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agent = initialize_agent(
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async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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tasks = [agent.arun(q) for q in questions[:3]]
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await asyncio.gather(*tasks)
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await aiosession.close()
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def test_tracing_context_manager() -> None:
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llm = OpenAI(temperature=0)
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tools = load_tools(
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["llm-math", "serpapi"],
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llm=llm,
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)
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agent = initialize_agent(
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tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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if "LANGCHAIN_WANDB_TRACING" in os.environ:
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del os.environ["LANGCHAIN_WANDB_TRACING"]
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with wandb_tracing_enabled():
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agent.run(questions[0]) # this should be traced
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agent.run(questions[0]) # this should not be traced
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@pytest.mark.asyncio
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async def test_tracing_context_manager_async() -> None:
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llm = OpenAI(temperature=0)
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async_tools = load_tools(
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["llm-math", "serpapi"],
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llm=llm,
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)
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agent = initialize_agent(
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async_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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if "LANGCHAIN_WANDB_TRACING" in os.environ:
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del os.environ["LANGCHAIN_TRACING"]
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# start a background task
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task = asyncio.create_task(agent.arun(questions[0])) # this should not be traced
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with wandb_tracing_enabled():
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tasks = [agent.arun(q) for q in questions[1:4]] # these should be traced
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await asyncio.gather(*tasks)
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await task
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