mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
f0d5b59962
Removes required usage of `requests` from `langchain-core`, all of which has been deprecated. - removes Tracer V1 implementations - removes old `try_load_from_hub` github-based hub implementations Removal done in a way where imports will still succeed, and usage will fail with a `RuntimeError`.
261 lines
9.4 KiB
Python
261 lines
9.4 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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from aiohttp import ClientSession
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from langchain_core.callbacks.manager import atrace_as_chain_group, trace_as_chain_group
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from langchain_core.prompts import PromptTemplate
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from langchain_core.tracers.context import tracing_v2_enabled
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from langchain_community.chat_models import ChatOpenAI
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from langchain_community.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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from langchain.agents import AgentType, initialize_agent, load_tools
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os.environ["LANGCHAIN_TRACING"] = "true"
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for q in questions[:3]:
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llm = OpenAI(temperature=0)
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tools = load_tools(["llm-math", "serpapi"], llm=llm)
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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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from langchain.agents import AgentType, initialize_agent, load_tools
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os.environ["LANGCHAIN_TRACING"] = "true"
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os.environ["LANGCHAIN_SESSION"] = "my_session"
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llm = OpenAI(temperature=0)
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tools = load_tools(["llm-math", "serpapi"], llm=llm)
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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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if "LANGCHAIN_SESSION" in os.environ:
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del os.environ["LANGCHAIN_SESSION"]
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async def test_tracing_concurrent() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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os.environ["LANGCHAIN_TRACING"] = "true"
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aiosession = ClientSession()
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llm = OpenAI(temperature=0)
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async_tools = load_tools(["llm-math", "serpapi"], llm=llm, aiosession=aiosession)
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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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async def test_tracing_concurrent_bw_compat_environ() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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os.environ["LANGCHAIN_HANDLER"] = "langchain"
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if "LANGCHAIN_TRACING" in os.environ:
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del os.environ["LANGCHAIN_TRACING"]
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aiosession = ClientSession()
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llm = OpenAI(temperature=0)
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async_tools = load_tools(["llm-math", "serpapi"], llm=llm, aiosession=aiosession)
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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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if "LANGCHAIN_HANDLER" in os.environ:
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del os.environ["LANGCHAIN_HANDLER"]
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async def test_tracing_v2_environment_variable() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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os.environ["LANGCHAIN_TRACING_V2"] = "true"
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aiosession = ClientSession()
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llm = OpenAI(temperature=0)
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async_tools = load_tools(["llm-math", "serpapi"], llm=llm, aiosession=aiosession)
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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_v2_context_manager() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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llm = ChatOpenAI(temperature=0)
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tools = load_tools(["llm-math", "serpapi"], llm=llm)
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agent = initialize_agent(
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tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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if "LANGCHAIN_TRACING_V2" in os.environ:
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del os.environ["LANGCHAIN_TRACING_V2"]
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with tracing_v2_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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def test_tracing_v2_chain_with_tags() -> None:
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from langchain.chains.constitutional_ai.base import ConstitutionalChain
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from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
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from langchain.chains.llm import LLMChain
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llm = OpenAI(temperature=0)
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chain = ConstitutionalChain.from_llm(
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llm,
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chain=LLMChain.from_string(llm, "Q: {question} A:"),
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tags=["only-root"],
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constitutional_principles=[
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ConstitutionalPrinciple(
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critique_request="Tell if this answer is good.",
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revision_request="Give a better answer.",
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)
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],
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)
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if "LANGCHAIN_TRACING_V2" in os.environ:
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del os.environ["LANGCHAIN_TRACING_V2"]
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with tracing_v2_enabled():
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chain.run("what is the meaning of life", tags=["a-tag"])
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def test_tracing_v2_agent_with_metadata() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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os.environ["LANGCHAIN_TRACING_V2"] = "true"
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llm = OpenAI(temperature=0)
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chat = ChatOpenAI(temperature=0)
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tools = load_tools(["llm-math", "serpapi"], llm=llm)
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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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chat_agent = initialize_agent(
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tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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agent.run(questions[0], tags=["a-tag"], metadata={"a": "b", "c": "d"})
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chat_agent.run(questions[0], tags=["a-tag"], metadata={"a": "b", "c": "d"})
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async def test_tracing_v2_async_agent_with_metadata() -> None:
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from langchain.agents import AgentType, initialize_agent, load_tools
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os.environ["LANGCHAIN_TRACING_V2"] = "true"
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llm = OpenAI(temperature=0, metadata={"f": "g", "h": "i"})
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chat = ChatOpenAI(temperature=0, metadata={"f": "g", "h": "i"})
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async_tools = load_tools(["llm-math", "serpapi"], llm=llm)
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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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chat_agent = initialize_agent(
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async_tools,
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chat,
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agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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verbose=True,
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)
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await agent.arun(questions[0], tags=["a-tag"], metadata={"a": "b", "c": "d"})
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await chat_agent.arun(questions[0], tags=["a-tag"], metadata={"a": "b", "c": "d"})
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def test_trace_as_group() -> None:
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from langchain.chains.llm import LLMChain
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llm = OpenAI(temperature=0.9)
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prompt = PromptTemplate(
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input_variables=["product"],
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template="What is a good name for a company that makes {product}?",
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)
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chain = LLMChain(llm=llm, prompt=prompt)
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with trace_as_chain_group("my_group", inputs={"input": "cars"}) as group_manager:
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chain.run(product="cars", callbacks=group_manager)
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chain.run(product="computers", callbacks=group_manager)
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final_res = chain.run(product="toys", callbacks=group_manager)
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group_manager.on_chain_end({"output": final_res})
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with trace_as_chain_group("my_group_2", inputs={"input": "toys"}) as group_manager:
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final_res = chain.run(product="toys", callbacks=group_manager)
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group_manager.on_chain_end({"output": final_res})
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def test_trace_as_group_with_env_set() -> None:
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from langchain.chains.llm import LLMChain
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os.environ["LANGCHAIN_TRACING_V2"] = "true"
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llm = OpenAI(temperature=0.9)
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prompt = PromptTemplate(
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input_variables=["product"],
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template="What is a good name for a company that makes {product}?",
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)
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chain = LLMChain(llm=llm, prompt=prompt)
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with trace_as_chain_group(
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"my_group_env_set", inputs={"input": "cars"}
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) as group_manager:
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chain.run(product="cars", callbacks=group_manager)
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chain.run(product="computers", callbacks=group_manager)
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final_res = chain.run(product="toys", callbacks=group_manager)
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group_manager.on_chain_end({"output": final_res})
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with trace_as_chain_group(
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"my_group_2_env_set", inputs={"input": "toys"}
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) as group_manager:
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final_res = chain.run(product="toys", callbacks=group_manager)
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group_manager.on_chain_end({"output": final_res})
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async def test_trace_as_group_async() -> None:
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from langchain.chains.llm import LLMChain
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llm = OpenAI(temperature=0.9)
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prompt = PromptTemplate(
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input_variables=["product"],
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template="What is a good name for a company that makes {product}?",
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)
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chain = LLMChain(llm=llm, prompt=prompt)
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async with atrace_as_chain_group("my_async_group") as group_manager:
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await chain.arun(product="cars", callbacks=group_manager)
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await chain.arun(product="computers", callbacks=group_manager)
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await chain.arun(product="toys", callbacks=group_manager)
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async with atrace_as_chain_group(
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"my_async_group_2", inputs={"input": "toys"}
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) as group_manager:
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res = await asyncio.gather(
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*[
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chain.arun(product="toys", callbacks=group_manager),
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chain.arun(product="computers", callbacks=group_manager),
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chain.arun(product="cars", callbacks=group_manager),
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]
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)
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await group_manager.on_chain_end({"output": res})
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