Here’s a step-by-step guide to creating a Custom API Chain using LangChain’s APIChain.from_llm_and_api_docs method. This method allows you to define API chains for different endpoints based on their Swagger or OpenAPI documentation.

Step 1: Install LangChain

If you haven't already, you'll need to install LangChain. You can do this using pip:

Step 2: Prepare API Documentation (Swagger/OpenAPI)

For each API you want to integrate, you will need the Swagger or OpenAPI documentation. These documents describe the API endpoints, request formats, and response structures. Ensure you have access to the documentation for the APIs you want to use.

For example, an API's OpenAPI documentation might look like this (a simplified example):

You’ll need the full documentation for each API you plan to integrate.

Step 3: Create an API Chain

Use LangChain’s APIChain to create a chain for handling API queries. The APIChain is designed to handle multiple APIs and select the right one based on user input. Here’s how you can set it up:

3.1 Define API Documentation for Each API

For each API, you need to define the API's Swagger/OpenAPI documentation in a format that LangChain can understand. This can be done using the APIChain.from_llm_and_api_docs method, where you'll pass the documentation.

Here’s a basic structure for setting up the API chain:

3.2 Provide API Endpoints and Documentation

In the api_docs argument, provide all the necessary Swagger/OpenAPI documentation for each API endpoint you want to integrate. You can provide multiple APIs in this list if needed.

3.3 Define a Prompt Template for API Querying

LangChain uses prompt templates to manage how the LLM interacts with the APIs. Define a prompt that describes how the API should be queried and processed.

Example prompt template for querying the Weather API:

This template defines how the LLM will process user input (e.g., “city name”) and convert it into a request to the correct API.

Step 4: Define Routing Logic

LangChain automatically handles routing logic based on the input query and API documentation. You can, however, add a custom logic if needed. For example, if you're querying multiple APIs, you could implement custom functions to analyze the user input and select the right API dynamically.

For instance, the routing function could look like this:

Step 5: Use the API Chain

Once the API chain is set up, you can use it to handle queries. Pass the user input to the chain, and it will route the request to the correct API based on the defined logic.

Step 6: Test and Iterate

Test the API chain with various queries to ensure that the routing works as expected and the correct API is being queried. You can refine the routing logic, prompt templates, or API documentation based on the results.

Optional: Combine Responses from Multiple APIs

If you need to query multiple APIs, you can use LangChain's AgentExecutor to combine results from different sources. This is useful if your system needs to aggregate data from multiple endpoints for a single query.

Conclusion

By following these steps, you'll be able to build a custom API chain using LangChain’s APIChain.from_llm_and_api_docs method. This system allows for seamless integration of multiple APIs with an LLM, enabling efficient API selection based on the user’s query.

How to integrate a Large Language Models (LLMs) with APIs