​
Node Details

  • Name: postApiChain
  • Type: POSTApiChain
  • Category: [[API Chain|Chains]]
  • Version: 1.0
  • Icon: post.svg
  • Description: Chain to run queries against POST API

​
Parameters

  1. Language Model (Required)
    • Type: BaseLanguageModel
    • Description: The language model used to generate API requests and process responses.
  2. API Documentation (Required)
    • Type: string
    • Description: Description of how the API works. Should include details about endpoints, request body structure, and response formats.
    • Rows: 4
  3. Headers (Optional)
    • Type: json
    • Description: Headers to be included in the API request.
    • Additional Params: true
  4. URL Prompt (Optional)
    • Type: string
    • Description: Prompt used to tell LLMs how to construct the URL and request body. Must contain and placeholders.
    • Default: A predefined prompt template for URL and request body construction.
    • Rows: 4
    • Additional Params: true
  5. Answer Prompt (Optional)
    • Type: string
    • Description: Prompt used to tell LLMs how to process the API response. Must contain , , and placeholders.
    • Default: A predefined prompt template for answer generation.
    • Rows: 4
    • Additional Params: true

​
Input

  • A string containing the user’s question or query about the API.

​
Output

  • A string containing the answer to the user’s question, based on the API response.

​
How It Works

  1. The chain receives a user question about the API.
  2. It uses the language model and the URL prompt to generate an appropriate API URL and request body based on the API documentation and the question.
  3. The chain makes a POST request to the constructed URL, including any specified headers and the generated request body.
  4. Upon receiving the API response, it uses the language model and the answer prompt to generate a human-readable answer to the original question.
  5. The final answer is returned as output.

​
Use Cases

  • Querying external POST APIs to answer user questions
  • Automating complex API interactions in chatbots or virtual assistants
  • Simplifying interactions with APIs that require structured request bodies
  • Creating natural language interfaces for data submission or retrieval from POST APIs
  • Integrating multiple POST API sources to answer complex queries or perform multi-step operations

​
Special Features

  • Dynamic Request Generation: Constructs both API URLs and request bodies based on natural language questions.
  • Flexible API Documentation: Can work with various POST APIs by providing appropriate documentation.
  • Customizable Prompts: Allows fine-tuning of URL/request body construction and answer generation processes.
  • Header Support: Enables authentication and other custom headers for API requests.
  • Language Model Integration: Leverages advanced language models for intelligent API interaction.

​
Notes

  • The quality of results depends significantly on the completeness and accuracy of the provided API documentation.
  • Custom URL and answer prompts can be used to optimize the chain for specific APIs or use cases.
  • The chain is designed specifically for POST requests and may not be suitable for APIs requiring other HTTP methods.
  • Proper error handling should be implemented when using this chain in production environments.
  • The effectiveness of the chain can vary depending on the complexity of the API and the nature of the user queries.
  • Care should be taken when constructing request bodies to ensure they meet the API’s requirements.
The POST API Chain node provides a powerful tool for creating natural language interfaces to POST APIs. By combining API documentation, language models, and customizable prompts, it enables developers to build sophisticated systems that can interact with external POST APIs based on user queries. This node is particularly valuable in scenarios where you want to provide a user-friendly interface to complex API systems that require structured data submission or where you need to integrate POST API-based operations into conversational AI applications.