Weights & Biases 跟踪
本笔记本介绍如何将您的LangChain实验跟踪到一个集中的Weights and Biases仪表板中。
要了解更多关于提示工程和回调的信息，请参考此笔记本，其中解释了这两者以及您可以预期的结果仪表板：
查看详细描述和示例，请访问W&B文章。
注意: 正在弃用WandbCallbackHandler，转而使用WandbTracer。未来请使用WandbTracer，因为它更灵活，允许更细粒度的日志记录。
要了解更多关于WandbTracer的信息，请参考agent_with_wandb_tracing笔记本或使用以下colab笔记本。
要了解更多关于Weights & Biases Prompts的信息，请参考以下prompts文档。
%pip install --upgrade --quiet wandb
%pip install --upgrade --quiet pandas
%pip install --upgrade --quiet textstat
%pip install --upgrade --quiet spacy
!python -m spacy download en_core_web_sm
import os
os.environ["WANDB_API_KEY"] = ""
# os.environ["OPENAI_API_KEY"] = ""
# os.environ["SERPAPI_API_KEY"] = ""
from datetime import datetime
from langchain_community.callbacks import WandbCallbackHandler
from langchain_core.callbacks import StdOutCallbackHandler
from langchain_openai import OpenAI
Callback Handler that logs to Weights and Biases.
Parameters:
job_type (str): The type of job.
project (str): The project to log to.
entity (str): The entity to log to.
tags (list): The tags to log.
group (str): The group to log to.
name (str): The name of the run.
notes (str): The notes to log.
visualize (bool): Whether to visualize the run.
complexity_metrics (bool): Whether to log complexity metrics.
stream_logs (bool): Whether to stream callback actions to W&B
Default values for WandbCallbackHandler(...)
visualize: bool = False,
complexity_metrics: bool = False,
stream_logs: bool = False,
注意：对于测试版工作流程，我们已将默认分析基于textstat，并将可视化基于spacy
"""Main function.
This function is used to try the callback handler.
Scenarios:
1. OpenAI LLM
2. Chain with multiple SubChains on multiple generations
3. Agent with Tools
"""
session_group = datetime.now().strftime("%m.%d.%Y_%H.%M.%S")
wandb_callback = WandbCallbackHandler(
job_type="inference",
project="langchain_callback_demo",
group=f"minimal_{session_group}",
name="llm",
tags=["test"],
)
callbacks = [StdOutCallbackHandler(), wandb_callback]
llm = OpenAI(temperature=0, callbacks=callbacks)
[34m[1mwandb[0m: Currently logged in as: [33mharrison-chase[0m. Use [1m`wandb login --relogin`[0m to force relogin
Tracking run with wandb version 0.14.0
Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150408-e47j1914</code>
Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target="_blank">llm</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">Weights & Biases</a> (<a href='https://wandb.me/run' target="_blank">docs</a>)<br/>
View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo</a>
View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a>
[34m[1mwandb[0m: [33mWARNING[0m The wandb callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/wandb/wandb/issues with the tag `langchain`.
# Defaults for WandbCallbackHandler.flush_tracker(...)
reset: bool = True,
finish: bool = False,
flush_tracker 函数用于将 LangChain 会话记录到 Weights & Biases。它接收 LangChain 模块或代理，并至少记录提示和生成内容以及 LangChain 模块的序列化形式到指定的 Weights & Biases 项目中。默认情况下，我们会重置会话而不是直接结束会话。
使用场景
使用LLM
# SCENARIO 1 - LLM
llm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 3)
wandb_callback.flush_tracker(llm, name="simple_sequential")
Waiting for W&B process to finish... <strong style="color:green">(success).</strong>
View run <strong style="color:#cdcd00">llm</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a><br/>Synced 5 W&B file(s), 2 media file(s), 5 artifact file(s) and 0 other file(s)
Find logs at: <code>./wandb/run-20230318_150408-e47j1914/logs</code>
VBox(children=(Label(value='Waiting for wandb.init()...\r'), FloatProgress(value=0.016745895149999985, max=1.0…
Tracking run with wandb version 0.14.0
Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150534-jyxma7hu</code>
Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target="_blank">simple_sequential</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">Weights & Biases</a> (<a href='https://wandb.me/run' target="_blank">docs</a>)<br/>
View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo</a>
View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a>
链内
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
API Reference:LLMChain | PromptTemplate
# SCENARIO 2 - Chain
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)
test_prompts = [
{
"title": "documentary about good video games that push the boundary of game design"
},
{"title": "cocaine bear vs heroin wolf"},
{"title": "the best in class mlops tooling"},
]
synopsis_chain.apply(test_prompts)
wandb_callback.flush_tracker(synopsis_chain, name="agent")
Waiting for W&B process to finish... <strong style="color:green">(success).</strong>
View run <strong style="color:#cdcd00">simple_sequential</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a><br/>Synced 4 W&B file(s), 2 media file(s), 6 artifact file(s) and 0 other file(s)
Find logs at: <code>./wandb/run-20230318_150534-jyxma7hu/logs</code>
VBox(children=(Label(value='Waiting for wandb.init()...\r'), FloatProgress(value=0.016736786816666675, max=1.0…
Tracking run with wandb version 0.14.0
Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150550-wzy59zjq</code>
Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target="_blank">agent</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">Weights & Biases</a> (<a href='https://wandb.me/run' target="_blank">docs</a>)<br/>
View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo</a>
View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a>
使用代理
from langchain.agents import AgentType, initialize_agent, load_tools
# SCENARIO 3 - Agent with Tools
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)
agent.run(
"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",
callbacks=callbacks,
)
wandb_callback.flush_tracker(agent, reset=False, finish=True)
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.
Action: Search
Action Input: "Leo DiCaprio girlfriend"[0m
Observation: [36;1m[1;3mDiCaprio had a steady girlfriend in Camila Morrone. He had been with the model turned actress for nearly five years, as they were first said to be dating at the end of 2017. And the now 26-year-old Morrone is no stranger to Hollywood.[0m
Thought:[32;1m[1;3m I need to calculate her age raised to the 0.43 power.
Action: Calculator
Action Input: 26^0.43[0m
Observation: [33;1m[1;3mAnswer: 4.059182145592686
[0m
Thought:[32;1m[1;3m I now know the final answer.
Final Answer: Leo DiCaprio's girlfriend is Camila Morrone and her current age raised to the 0.43 power is 4.059182145592686.[0m
[1m> Finished chain.[0m
Waiting for W&B process to finish... <strong style="color:green">(success).</strong>
View run <strong style="color:#cdcd00">agent</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target="_blank">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a><br/>Synced 5 W&B file(s), 2 media file(s), 7 artifact file(s) and 0 other file(s)
Find logs at: <code>./wandb/run-20230318_150550-wzy59zjq/logs</code>