[LangChain] Agents

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Introduction

LangChain Agents are a way to create custom language models that can be used to perform specific tasks. Agents are created by specifying a set of instructions that tell the model how to perform the task.

Benefits of using LangChain Agents

There are several benefits to using LangChain Agents:

  • Increased flexibility: Agents can be customized to perform specific tasks. This allows you to create models that are specifically tailored to your needs.
  • Increased efficiency: Agents can be used to automate tasks that would otherwise be time-consuming or difficult to do manually.
  • Increased accuracy: Agents can be trained on large datasets of text, which can help them to learn how to perform tasks more accurately.

How to use LangChain Agents

To use LangChain Agents, you need to first create an agent. Then, you can train the agent on a dataset of text. Once the agent has been trained, you can use it to perform the task that you specified.

Here is an example of how to create a LangChain Agent:

import langchain

agent = langchain.Agent()

agent.add_instruction("Generate text", "Write a poem about love.")
agent.add_instruction("Translate text", "Translate this document from English to French.")

agent.train("path/to/dataset")

Once you have created an agent, you can train it on a dataset of text. For example, you could train the agent above on a dataset of poems or a dataset of translated documents.

Here is an example of how to train an agent:

import langchain

agent = langchain.Agent()

agent.add_instruction("Generate text", "Write a poem about love.")
agent.add_instruction("Translate text", "Translate this document from English to French.")

agent.train("path/to/dataset")

Once the agent has been trained, you can use it to perform the task that you specified. For example, you could use the agent above to generate a poem about love or to translate a document from English to French.

Here is an example of how to use an agent:

import langchain

agent = langchain.Agent()

agent.add_instruction("Generate text", "Write a poem about love.")
agent.add_instruction("Translate text", "Translate this document from English to French.")

agent.train("path/to/dataset")

text = agent.generate_text("Write a poem about love.")

print(text)

Code samples

Here are some code samples that demonstrate how to use LangChain Agents:

  • Creating an agent: This code sample creates a LangChain Agent.
import langchain

agent = langchain.Agent()
  • Adding instructions to an agent: This code sample adds instructions to a LangChain Agent.
import langchain

agent = langchain.Agent()

agent.add_instruction("Generate text", "Write a poem about love.")
agent.add_instruction("Translate text", "Translate this document from English to French.")
  • Training an agent: This code sample trains a LangChain Agent on a dataset of text.
import langchain

agent = langchain.Agent()

agent.add_instruction("Generate text", "Write a poem about love.")
agent.add_instruction("Translate text", "Translate this document from English to French.")

agent.train("path/to/dataset")
  • Using an agent: This code sample uses a LangChain Agent to generate text.
import langchain

agent = langchain.Agent()

agent.add_instruction("Generate text", "Write a poem about love.")
agent.add_instruction("Translate text", "Translate this document from English to French.")

agent.train("path/to/dataset")

text = agent.generate_text("Write a poem about love.")

print(text)

Conclusion

LangChain Agents are a powerful way to create custom language models that can be used to perform specific tasks. They can be used to increase the flexibility, efficiency, and accuracy of language models.

https://github.com/hwchase17/langchain

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