cf5803e44c
# Add ToolException that a tool can throw
This is an optional exception that tool throws when execution error
occurs.
When this exception is thrown, the agent will not stop working,but will
handle the exception according to the handle_tool_error variable of the
tool,and the processing result will be returned to the agent as
observation,and printed in pink on the console.It can be used like this:
```python
from langchain.schema import ToolException
from langchain import LLMMathChain, SerpAPIWrapper, OpenAI
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0)
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
class Error_tool:
def run(self, s: str):
raise ToolException('The current search tool is not available.')
def handle_tool_error(error) -> str:
return "The following errors occurred during tool execution:"+str(error)
search_tool1 = Error_tool()
search_tool2 = SerpAPIWrapper()
tools = [
Tool.from_function(
func=search_tool1.run,
name="Search_tool1",
description="useful for when you need to answer questions about current events.You should give priority to using it.",
handle_tool_error=handle_tool_error,
),
Tool.from_function(
func=search_tool2.run,
name="Search_tool2",
description="useful for when you need to answer questions about current events",
return_direct=True,
)
]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,
handle_tool_errors=handle_tool_error)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
```
## Who can review?
- @vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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| tests | ||
| .dockerignore | ||
| .flake8 | ||
| .gitignore | ||
| .readthedocs.yaml | ||
| CITATION.cff | ||
| Dockerfile | ||
| LICENSE | ||
| Makefile | ||
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| README.md | ||
🦜️🔗 LangChain
⚡ Building applications with LLMs through composability ⚡
Looking for the JS/TS version? Check out LangChain.js.
Production Support: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.
Quick Install
pip install langchain
or
conda install langchain -c conda-forge
🤔 What is this?
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
This library aims to assist in the development of those types of applications. Common examples of these applications include:
❓ Question Answering over specific documents
- Documentation
- End-to-end Example: Question Answering over Notion Database
💬 Chatbots
- Documentation
- End-to-end Example: Chat-LangChain
🤖 Agents
- Documentation
- End-to-end Example: GPT+WolframAlpha
📖 Documentation
Please see here for full documentation on:
- Getting started (installation, setting up the environment, simple examples)
- How-To examples (demos, integrations, helper functions)
- Reference (full API docs)
- Resources (high-level explanation of core concepts)
🚀 What can this help with?
There are six main areas that LangChain is designed to help with. These are, in increasing order of complexity:
📃 LLMs and Prompts:
This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
🔗 Chains:
Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
📚 Data Augmented Generation:
Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
🤖 Agents:
Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
🧠 Memory:
Memory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
🧐 Evaluation:
[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
For more information on these concepts, please see our full documentation.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see here.