0 Fork 0

mirror of https://github.com/langchain-ai/langchain.git synced 2026-10-05 01:15:09 +03:00

Files

T

standby24x7 16e64d889a docs: Update function "run" to "invoke" in fake_llm.ipynb (#19570 )

This patch updates function "run" to "invoke" in fake_llm.ipynb. Without
this patch, you see following warning.

LangChainDeprecationWarning: The function `run` was deprecated in
LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.

Signed-off-by: Masanari Iida <standby24x7@gmail.com>

2024-03-26 09:54:31 -04:00

2.9 KiB

Raw Blame History

Fake LLM

LangChain provides a fake LLM class that can be used for testing. This allows you to mock out calls to the LLM and simulate what would happen if the LLM responded in a certain way.

In this notebook we go over how to use this.

We start this with using the FakeLLM in an agent.

In [1]:

from langchain_community.llms.fake import FakeListLLM

In [2]:

from langchain.agents import AgentType, initialize_agent, load_tools

In [3]:

tools = load_tools(["python_repl"])

In [16]:

responses = ["Action: Python REPL\nAction Input: print(2 + 2)", "Final Answer: 4"]
llm = FakeListLLM(responses=responses)

In [17]:

agent = initialize_agent(
    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)

In [18]:

agent.invoke("whats 2 + 2")

Out [18]:


[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3mAction: Python REPL
Action Input: print(2 + 2)[0m
Observation: [36;1m[1;3m4
[0m
Thought:[32;1m[1;3mFinal Answer: 4[0m

[1m> Finished chain.[0m

'4'

In [ ]:

2.9 KiB Raw Blame History

Fake LLM

2.9 KiB

Raw Blame History