Chroma Vector Database Java Client

This is a very basic/naive implementation in Java of the Chroma Vector Database API.

This client works with Chroma Versions 0.4.3+

Features

Embeddings Support

✅ Default Embedding Function (all-mini-lm model)
✅ OpenAI Embedding Function
✅ Cohere Embedding Function
✅ HuggingFace Embedding Function (Inference API)
✅ Ollama Embedding Function
✅ Hugging Face Text Embedding Inference (HFEI) API
[ ] Sentence Transformers
[ ] PaLM API
[ ] Custom Embedding Function
[ ] Cloudflare Workers AI

Feature Parity with ChromaDB API

[x] Reset
[x] Heartbeat
[x] List Collections
[x] Get Version
[x] Create Collection
[x] Delete Collection
[x] Collection Add
[x] Collection Get (partial without additional parameters)
[x] Collection Count
[x] Collection Query
[x] Collection Modify
[x] Collection Update
[x] Collection Upsert
[x] Collection Create Index
[x] Collection Delete - delete documents in collection

TODO

[x] Push the package to Maven Central - https://docs.github.com/en/actions/publishing-packages/publishing-java-packages-with-maven
⚒️ Fluent API - make it easier for users to make use of the library
[ ] Support for PaLM API
[x] Support for Sentence Transformers with Hugging Face API
⚒️ Authentication ⚒️

Usage

Add Maven dependency:


<dependency>
    <groupId>io.github.amikos-tech</groupId>
    <artifactId>chromadb-java-client</artifactId>
    <version>0.1.7</version>
</dependency>

Ensure you have a running instance of Chroma running. We recommend one of the two following options:

Official documentation - https://docs.trychroma.com/usage-guide#running-chroma-in-clientserver-mode
If you are a fan of Kubernetes, you can use the Helm chart - https://github.com/amikos-tech/chromadb-chart (Note: You will need Docker, minikube and kubectl installed)

Default Embedding Function

Since version 0.1.6 the library also offers a built-in default embedding function which does not rely on any external API to generate embeddings and works in the same way it works in core Chroma Python package.

package tech.amikos;

import tech.amikos.chromadb.*;
import tech.amikos.chromadb.Collection;
import tech.amikos.chromadb.embeddings.DefaultEmbeddingFunction;

import java.util.*;

public class Main {
    public static void main(String[] args) {
        try {
            Client client = new Client(System.getenv("CHROMA_URL"));
            client.reset();
            EmbeddingFunction ef = new DefaultEmbeddingFunction();
            Collection collection = client.createCollection("test-collection", null, true, ef);
            List<Map<String, String>> metadata = new ArrayList<>();
            metadata.add(new HashMap<String, String>() {{
                put("type", "scientist");
            }});
            metadata.add(new HashMap<String, String>() {{
                put("type", "spy");
            }});
            collection.add(null, metadata, Arrays.asList("Hello, my name is John. I am a Data Scientist.", "Hello, my name is Bond. I am a Spy."), Arrays.asList("1", "2"));
            Collection.QueryResponse qr = collection.query(Arrays.asList("Who is the spy"), 10, null, null, null);
            System.out.println(qr);
        } catch (Exception e) {
            System.out.println(e);
        }
    }
}

Example OpenAI Embedding Function

In this example we rely on tech.amikos.chromadb.embeddings.openai.OpenAIEmbeddingFunction to generate embeddings for our documents.

| Important: Ensure you have OPENAI_API_KEY environment variable set

package tech.amikos;

import tech.amikos.chromadb.Client;
import tech.amikos.chromadb.Collection;
import tech.amikos.chromadb.EmbeddingFunction;
import tech.amikos.chromadb.embeddings.openai.OpenAIEmbeddingFunction;

import java.util.*;

public class Main {
    public static void main(String[] args) {
        try {
            Client client = new Client(System.getenv("CHROMA_URL"));
            String apiKey = System.getenv("OPENAI_API_KEY");
            EmbeddingFunction ef = new OpenAIEmbeddingFunction(apiKey, "text-embedding-3-small");
            Collection collection = client.createCollection("test-collection", null, true, ef);
            List<Map<String, String>> metadata = new ArrayList<>();
            metadata.add(new HashMap<String, String>() {{
                put("type", "scientist");
            }});
            metadata.add(new HashMap<String, String>() {{
                put("type", "spy");
            }});
            collection.add(null, metadata, Arrays.asList("Hello, my name is John. I am a Data Scientist.", "Hello, my name is Bond. I am a Spy."), Arrays.asList("1", "2"));
            Collection.QueryResponse qr = collection.query(Arrays.asList("Who is the spy"), 10, null, null, null);
            System.out.println(qr);
        } catch (Exception e) {
            e.printStackTrace();
            System.out.println(e);
        }
    }
}

The above should output:

{"documents":[["Hello, my name is Bond. I am a Spy.","Hello, my name is John. I am a Data Scientist."]],"ids":[["2","1"]],"metadatas":[[{"type":"spy"},{"type":"scientist"}]],"distances":[[0.28461432,0.50961685]]}

Custom OpenAI Endpoint

For endpoints compatible with OpenAI Embeddings API (e.g. ollama), you can use the following:

Note: We have added a builder to help with the configuration of the OpenAIEmbeddingFunction

EmbeddingFunction ef = OpenAIEmbeddingFunction.Instance()
        .withOpenAIAPIKey(apiKey)
        .withModelName("llama2")
        .withApiEndpoint("http://localhost:11434/api/embedding") // not really custom, but just to test the method
        .build();

Quick Start Guide with Ollama:

docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
docker exec -it ollama ollama run llama2 # press Ctrl+D to exit after model downloads successfully
# test it
curl http://localhost:11434/api/embeddings -d '{\n  "model": "llama2",\n  "prompt": "Here is an article about llamas..."\n}'

Example Cohere Embedding Function

In this example we rely on tech.amikos.chromadb.embeddings.cohere.CohereEmbeddingFunction to generate embeddings for our documents.

| Important: Ensure you have COHERE_API_KEY environment variable set

package tech.amikos;

import tech.amikos.chromadb.*;
import tech.amikos.chromadb.Collection;
import tech.amikos.chromadb.embeddings.cohere.CohereEmbeddingFunction;

import java.util.*;

public class Main {
    public static void main(String[] args) {
        try {
            Client client = new Client(System.getenv("CHROMA_URL"));
            client.reset();
            String apiKey = System.getenv("COHERE_API_KEY");
            EmbeddingFunction ef = new CohereEmbeddingFunction(apiKey);
            Collection collection = client.createCollection("test-collection", null, true, ef);
            List<Map<String, String>> metadata = new ArrayList<>();
            metadata.add(new HashMap<String, String>() {{
                put("type", "scientist");
            }});
            metadata.add(new HashMap<String, String>() {{
                put("type", "spy");
            }});
            collection.add(null, metadata, Arrays.asList("Hello, my name is John. I am a Data Scientist.", "Hello, my name is Bond. I am a Spy."), Arrays.asList("1", "2"));
            Collection.QueryResponse qr = collection.query(Arrays.asList("Who is the spy"), 10, null, null, null);
            System.out.println(qr);
        } catch (Exception e) {
            e.printStackTrace();
            System.out.println(e);
        }
    }
}

The above should output:

{"documents":[["Hello, my name is Bond. I am a Spy.","Hello, my name is John. I am a Data Scientist."]],"ids":[["2","1"]],"metadatas":[[{"type":"spy"},{"type":"scientist"}]],"distances":[[5112.614,10974.804]]}

Example Hugging Face Sentence Transformers Embedding Function

Hugging Face Inference API

In this example we rely on tech.amikos.chromadb.embeddings.hf.HuggingFaceEmbeddingFunction to generate embeddings for our documents using HuggingFace cloud-based inference API.

| Important: Ensure you have HF_API_KEY environment variable set

package tech.amikos;

import tech.amikos.chromadb.*;
import tech.amikos.chromadb.Collection;
import tech.amikos.chromadb.embeddings.hf.HuggingFaceEmbeddingFunction;

import java.util.*;

public class Main {
    public static void main(String[] args) {
        try {
            Client client = new Client("http://localhost:8000");
            String apiKey = System.getenv("HF_API_KEY");
            EmbeddingFunction ef = new HuggingFaceEmbeddingFunction(apiKey);
            Collection collection = client.createCollection("test-collection", null, true, ef);
            List<Map<String, String>> metadata = new ArrayList<>();
            metadata.add(new HashMap<String, String>() {{
                put("type", "scientist");
            }});
            metadata.add(new HashMap<String, String>() {{
                put("type", "spy");
            }});
            collection.add(null, metadata, Arrays.asList("Hello, my name is John. I am a Data Scientist.", "Hello, my name is Bond. I am a Spy."), Arrays.asList("1", "2"));
            Collection.QueryResponse qr = collection.query(Arrays.asList("Who is the spy"), 10, null, null, null);
            System.out.println(qr);
        } catch (Exception e) {
            System.out.println(e);
        }
    }
}

The above should output:

{"documents":[["Hello, my name is Bond. I am a Spy.","Hello, my name is John. I am a Data Scientist."]],"ids":[["2","1"]],"metadatas":[[{"type":"spy"},{"type":"scientist"}]],"distances":[[0.9073759,1.6440368]]}

Hugging Face Text Embedding Inference (HFEI) API

In this example we'll use a local Docker based server to generate the embeddings with Snowflake/snowflake-arctic-embed-s mode.

First let's start the HFEI server:

docker run -d -p 8008:80 --platform linux/amd64 --name hfei ghcr.io/huggingface/text-embeddings-inference:cpu-1.5.0 --model-id Snowflake/snowflake-arctic-embed-s --revision main

Note: Check the official documentation for more details - https://github.com/huggingface/text-embeddings-inference

Then we can use the following code to generate embeddings. Note the use of new HuggingFaceEmbeddingFunction.WithAPIType(HuggingFaceEmbeddingFunction.APIType.HFEI_API)); to define the API type, this will ensure the client uses the correct endpoint.

package tech.amikos;

import tech.amikos.chromadb.*;
import tech.amikos.chromadb.Collection;
import tech.amikos.chromadb.embeddings.hf.HuggingFaceEmbeddingFunction;

import java.util.*;

public class Main {
    public static void main(String[] args) {
        try {
            Client client = new Client("http://localhost:8000");
            EmbeddingFunction ef = new HuggingFaceEmbeddingFunction(
                    WithParam.baseAPI("http://localhost:8008"),
                    new HuggingFaceEmbeddingFunction.WithAPIType(HuggingFaceEmbeddingFunction.APIType.HFEI_API));
            Collection collection = client.createCollection("test-collection", null, true, ef);
            List<Map<String, String>> metadata = new ArrayList<>();
            metadata.add(new HashMap<String, String>() {{
                put("type", "scientist");
            }});
            metadata.add(new HashMap<String, String>() {{
                put("type", "spy");
            }});
            collection.add(null, metadata, Arrays.asList("Hello, my name is John. I am a Data Scientist.", "Hello, my name is Bond. I am a Spy."), Arrays.asList("1", "2"));
            Collection.QueryResponse qr = collection.query(Arrays.asList("Who is the spy"), 10, null, null, null);
            System.out.println(qr);
        } catch (Exception e) {
            System.out.println(e);
        }
    }
}

The above should similar to the following output:

{"documents":[["Hello, my name is Bond. I am a Spy.","Hello, my name is John. I am a Data Scientist."]],"ids":[["2","1"]],"metadatas":[[{"type":"spy"},{"type":"scientist"}]],"distances":[[0.19665092,0.42433012]]}

Ollama Embedding Function

In this example we rely on tech.amikos.chromadb.embeddings.ollama.OllamaEmbeddingFunction to generate embeddings for our documents.

package tech.amikos;

import tech.amikos.chromadb.*;
import tech.amikos.chromadb.embeddings.ollama.OllamaEmbeddingFunction;
import tech.amikos.chromadb.Collection;

import java.util.*;

public class Main {
    public static void main(String[] args) {
        try {
            Client client = new Client(System.getenv("CHROMA_URL"));
            client.reset();
            EmbeddingFunction ef = new OllamaEmbeddingFunction();
            Collection collection = client.createCollection("test-collection", null, true, ef);
            List<Map<String, String>> metadata = new ArrayList<>();
            metadata.add(new HashMap<String, String>() {{
                put("type", "scientist");
            }});
            metadata.add(new HashMap<String, String>() {{
                put("type", "spy");
            }});
            collection.add(null, metadata, Arrays.asList("Hello, my name is John. I am a Data Scientist.", "Hello, my name is Bond. I am a Spy."), Arrays.asList("1", "2"));
            Collection.QueryResponse qr = collection.query(Arrays.asList("Who is the spy"), 10, null, null, null);
            System.out.println(qr);
        } catch (Exception e) {
            System.out.println(e);
        }
    }
}

Example Auth

Note: This is a workaround until the client overhaul is completed

Basic Auth:

package tech.amikos;

import tech.amikos.chromadb.*;
import tech.amikos.chromadb.Collection;

import java.util.*;

public class Main {
    public static void main(String[] args) {
        try {
            Client client = new Client(System.getenv("CHROMA_URL"));
            String encodedString = Base64.getEncoder().encodeToString("admin:admin".getBytes());
            client.setDefaultHeaders(new HashMap<>() {{
                put("Authorization", "Basic " + encodedString);
            }});
            // your code here
        } catch (Exception e) {
            System.out.println(e);
        }
    }
}

Static Auth - Authorization:

package tech.amikos;

import tech.amikos.chromadb.*;
import tech.amikos.chromadb.Collection;

import java.util.*;

public class Main {
    public static void main(String[] args) {
        try {
            Client client = new Client(System.getenv("CHROMA_URL"));
            String encodedString = Base64.getEncoder().encodeToString("admin:admin".getBytes());
            client.setDefaultHeaders(new HashMap<>() {{
                put("Authorization", "Bearer test-token");
            }});
            // your code here
        } catch (Exception e) {
            System.out.println(e);
        }
    }
}

Static Auth - X-Chroma-Token:

package tech.amikos;

import tech.amikos.chromadb.*;
import tech.amikos.chromadb.Collection;

import java.util.*;

public class Main {
    public static void main(String[] args) {
        try {
            Client client = new Client(System.getenv("CHROMA_URL"));
            String encodedString = Base64.getEncoder().encodeToString("admin:admin".getBytes());
            client.setDefaultHeaders(new HashMap<>() {{
                put("X-Chroma-Token", "test-token");
            }});
            // your code here
        } catch (Exception e) {
            System.out.println(e);
        }
    }
}

Development Notes

We have made some minor changes on top of the ChromaDB API (src/main/resources/openapi/api.yaml) so that the API can work with Java and Swagger Codegen. The reason is that statically type languages like Java don't like the anyOf and oneOf keywords (This also is the reason why we don't use the generated java client for OpenAI API).

Contributing

Pull requests are welcome.

References

https://docs.trychroma.com/ - Official Chroma documentation
https://github.com/amikos-tech/chromadb-chart - Chroma Helm chart for cloud-native deployments
https://github.com/openai/openai-openapi - OpenAI OpenAPI specification (While we don't use it to generate a client for Java, it helps us understand the API better)

amikos-tech / chromadb-java-client

readme

Chroma Vector Database Java Client

Features

Embeddings Support

Feature Parity with ChromaDB API

TODO

Usage

Default Embedding Function

Example OpenAI Embedding Function

Custom OpenAI Endpoint

Example Cohere Embedding Function

Example Hugging Face Sentence Transformers Embedding Function

Hugging Face Inference API

Hugging Face Text Embedding Inference (HFEI) API

Ollama Embedding Function

Example Auth

Development Notes

Contributing

References