3615667f05
- Extend the VectorStore with similaritySearch using metadata filters using internal DSL and external DSL using Antlr - Metdata support for Pinecone, Milvus, and pgvector vector stores - PGVectorStore uses explict ::jsonpath casting for the pgvector filter expression to avoid injections - Add unit tests for the filter converters, parser and DSL. - Add ITs for the 3 vector stores Resolves: #75
HuggingFace Inference Endpoints with Spring AI
HuggingFace Inference Endpoints allow you to deploy and serve machine learning models in the cloud, making them accessible via an API. Further details on HuggingFace Inference Endpoints can be found here.
Prerequisites
You should get your HuggingFace API key and set it as an environment variable
export HUGGINGFACE_API_KEY=your_api_key_here
Note, there is not yet a Spring Boot Starter for this client implementation.
Obtain the endpoint URL of the Inference Endpoint. You can find this on the Inference Endpoint's UI here.
Making a call to the model
HuggingfaceAiClient client = new HuggingfaceAiClient(apiKey, basePath);
Prompt prompt = new Prompt("Your text here...");
AiResponse response = client.generate(prompt);
System.out.println(response.getGeneration().getText());
Example
Using the example found here
String mistral7bInstruct = """
[INST] You are a helpful code assistant. Your task is to generate a valid JSON object based on the given information:
name: John
lastname: Smith
address: #1 Samuel St.
Just generate the JSON object without explanations:
[/INST]""";
Prompt prompt = new Prompt(mistral7bInstruct);
AiResponse aiResponse = huggingfaceAiClient.generate(prompt);
System.out.println(response.getGeneration().getText());
Will produce the output
```json
{
"name": "John",
"lastname": "Smith",
"address": "#1 Samuel St."
}
```
Note the response itself is in Markdown format.