Coding generative AI solutions
IBM watsonx.ai has REST APIs that support programmatic tasks for working with foundation models. These APIs are exercised in a Python library and Node.js package that you can use to leverage foundation models in your generative AI applications.
Tasks that you can do programmatically
You can use the watsonx.ai REST API, Python library, or Node.js SDK to do the following tasks programmatically:
| Task | Python library | REST API |
|---|---|---|
| Get details about the available foundation models | Get model specs | List the supported foundation models |
| Get a list of available custom foundation models | Get custom model specs helper | Retrieve the deployments Use the type=custom_foundation_model parameter. |
| Check the tokens a model calculates for a prompt | Tokenize deployed or built-in foundation models | Text tokenization |
| Inference a foundation model | Generate text | Text generation |
| Configure AI guardrails when inferencing a foundation model | Removing harmful content | Use the moderations field to apply filters to foundation model input and output. See Infer text |
| Prompt-tune a foundation model | See the documentation | See the documentation |
| Inference a tuned foundation model | Generate text | Infer text |
| List all prompt templates | List all prompt templates | |
| List the deployed prompt templates | List deployed prompt templates | List the deployments (type=prompt_template) |
| Inference a foundation model by using a prompt template | Prompt Template Manager | Infer text |
| Vectorize text | Embed documents | Text embedding |
| Integrate with LangChain | IBM extension in LangChain | |
| Integrate with LlamaIndex | IBM LLMs in LlamaIndex IBM embeddings in LlamaIndex |
Learn more
- Credentials for programmatic access
- Finding the project ID
- Foundation model IDs
- Python library
- Node.js SDK
- REST API
- Text embedding generation
Parent topic: Developing generative AI solutions