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:

Table 1. Tasks you can do programmatically in watsonx.ai
Task Python Node.js REST API
Get details about the available foundation models Get model specs Example List the supported foundation models
Check the tokens a model calculates for a prompt Tokenize built-in foundation models Example Text tokenization
Inference a foundation model Generate text Example 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
Chat with a foundation model ModelInference.chat() Example Infer text
List all prompt templates List all prompt templates Get a prompt template
Inference a foundation model by using a prompt template Prompt Template Manager Example Infer text
Vectorize text Embed documents Example Text embedding
Extract text from documents Text Extractions Text extraction
Rerank document passages Rerank Generate rerank
Forecast future values TSModelInference timeSeriesForecast Time series forecast
Integrate with LangChain IBM extension in LangChain • Chat API
• Foundation models
• Embedding models
Integrate with LlamaIndex • IBM LLMs in LlamaIndex
• IBM embeddings in LlamaIndex

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