Langchain Tools and Agents use cases with examples

These 2 articles will give you some context

• What is LangChain?
• Vector Database & Langchain?

What is LangChain?

A revolutionary framework designed to simplify and enhance the development of language-based AI applications. In this blog post, we’ll explore the core components of LangChain, specifically focusing on its powerful tools and agents that make it a game-changer for developers and businesses alike.

Tools

Tools are interfaces that an agent, chain, or LLM can use to interact with the world. They combine a few things:

1. The name of the tool
2. A description of what the tool is
3. JSON schema of what the inputs to the tool are
4. The function to call
5. Whether the result of a tool should be returned directly to the user

View the large collection of built-in Tools.

An example of a Wikipedia Tool that is available

You can view the output of the code in this link

Chains

Chains let you create a pre-defined sequence of tool usage(s).

For Reference

Agents

The core idea of agents is to use a language model to choose a sequence of actions to take. In chains, a sequence of actions is hardcoded (in code). In agents, a language model is used as a reasoning engine to determine which actions to take and in which order.

Agents let the model use tools in a loop so that it can decide how many times to use tools.

Review the various loaders available to load different types of documents here.

An example that creates a web base loader, loads it into 'docs', and Splits the text into 'documents'. OpenAIEmbeddings()initializes the?embedding model?using your OpenAI API key to convert the text chunks into vector embeddings which are essential for similarity searches. Create an index using FAISS based on the documents and embedding model to enable fast similarity searches within your dataset.

Create a retriever tool

Using the retriever, along with its name ("WebMD") and description One can create a retriever tool.

Aggregating the {tool+prompt } that is pulled from the LangChain hub you can create your agent and invoke it. Here is how you can do it.

For detailed posts visit here

I have tried my best to break these concepts as simple as possible, Do let me know what you think!