Add embedding providers

README.md

@@ -78,11 +78,13 @@ Initially, only a minimal subset of all of Chroma's interface is implemented or
 - [X] Zero dependencies on third party libraries
 - [X] Concurrent processing (when adding and querying documents)
 - Embedding creators:
-  - [X] [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings/embedding-models) (default)
+  - [X] [OpenAI](https://platform.openai.com/docs/guides/embeddings/embedding-models) (default)
+  - [X] [Mistral](https://docs.mistral.ai/platform/endpoints/#embedding-models)
+  - [X] [Jina](https://jina.ai/embeddings)
+  - [X] [mixedbread.ai](https://www.mixedbread.ai/)
+  - [X] [LocalAI](https://github.com/mudler/LocalAI)
   - [X] Bring your own
-  - [ ] [Mistral (API)](https://docs.mistral.ai/api/#operation/createEmbedding)
-  - [ ] [ollama](https://ollama.ai/)
-  - [ ] [LocalAI](https://github.com/mudler/LocalAI)
+  - [ ] [ollama](https://ollama.ai/) (As of 2024-02-10 their OpenAI compatible API doesn't support embeddings yet, but they have a custom API which does)
 - Similarity search:
   - [X] Exact nearest neighbor search using cosine similarity
   - [ ] Approximate nearest neighbor search with index

embed_jina.go

@@ -0,0 +1,18 @@
+package chromem
+
+const baseURLJina = "https://api.jina.ai/v1"
+
+type EmbeddingModelJina string
+
+const (
+	EmbeddingModelJina2BaseEN   EmbeddingModelJina = "jina-embeddings-v2-base-en"
+	EmbeddingModelJina2BaseDE   EmbeddingModelJina = "jina-embeddings-v2-base-de"
+	EmbeddingModelJina2BaseCode EmbeddingModelJina = "jina-embeddings-v2-base-code"
+	EmbeddingModelJina2BaseZH   EmbeddingModelJina = "jina-embeddings-v2-base-zh"
+)
+
+// NewEmbeddingFuncJina returns a function that creates embeddings for a document
+// using the Jina API.
+func NewEmbeddingFuncJina(apiKey string, model EmbeddingModelJina) EmbeddingFunc {
+	return NewEmbeddingFuncOpenAICompat(baseURLJina, apiKey, string(model))
+}

embed_localai.go

@@ -0,0 +1,16 @@
+package chromem
+
+const baseURLLocalAI = "http://localhost:8080/v1"
+
+// NewEmbeddingFuncLocalAI returns a function that creates embeddings for a document
+// using the LocalAI API.
+// You can start a LocalAI instance like this:
+//
+//	docker run -it -p 127.0.0.1:8080:8080 localai/localai:v2.7.0-ffmpeg-core bert-cpp
+//
+// And then call this constructor with model "bert-cpp-minilm-v6".
+// But other embedding models are supported as well. See the LocalAI documentation
+// for details.
+func NewEmbeddingFuncLocalAI(model string) EmbeddingFunc {
+	return NewEmbeddingFuncOpenAICompat(baseURLLocalAI, "", model)
+}

embed_mistral.go

@@ -0,0 +1,15 @@
+package chromem
+
+const (
+	baseURLMistral = "https://api.mistral.ai/v1"
+	// Currently there's only one. Let's turn this into a pseudo-enum as soon as there are more.
+	embeddingModelMistral = "mistral-embed"
+)
+
+// NewEmbeddingFuncMistral returns a function that creates embeddings for a document
+// using the Mistral API.
+func NewEmbeddingFuncMistral(apiKey string) EmbeddingFunc {
+	// The Mistral API docs don't mention the `encoding_format` as optional,
+	// but it seems to be, just like OpenAI. So we reuse the OpenAI function.
+	return NewEmbeddingFuncOpenAICompat(baseURLMistral, apiKey, embeddingModelMistral)
+}

embed_mixedbread.go

@@ -0,0 +1,22 @@
+package chromem
+
+const baseURLMixedbread = "https://api.mixedbread.ai"
+
+type EmbeddingModelMixedbread string
+
+const (
+	EmbeddingModelMixedbreadUAELargeV1          EmbeddingModelMixedbread = "UAE-Large-V1"
+	EmbeddingModelMixedbreadBGELargeENV15       EmbeddingModelMixedbread = "bge-large-en-v1.5"
+	EmbeddingModelMixedbreadGTELarge            EmbeddingModelMixedbread = "gte-large"
+	EmbeddingModelMixedbreadE5LargeV2           EmbeddingModelMixedbread = "e5-large-v2"
+	EmbeddingModelMixedbreadMultilingualE5Large EmbeddingModelMixedbread = "multilingual-e5-large"
+	EmbeddingModelMixedbreadMultilingualE5Base  EmbeddingModelMixedbread = "multilingual-e5-base"
+	EmbeddingModelMixedbreadAllMiniLML6V2       EmbeddingModelMixedbread = "all-MiniLM-L6-v2"
+	EmbeddingModelMixedbreadGTELargeZh          EmbeddingModelMixedbread = "gte-large-zh"
+)
+
+// NewEmbeddingFuncMixedbread returns a function that creates embeddings for a document
+// using the mixedbread.ai API.
+func NewEmbeddingFuncMixedbread(apiKey string, model EmbeddingModelMixedbread) EmbeddingFunc {
+	return NewEmbeddingFuncOpenAICompat(baseURLMixedbread, apiKey, string(model))
+}

embedding.go → embed_openai.go

@@ -11,9 +11,7 @@ import (
 	"os"
 )
 
-const (
-	BaseURLOpenAI = "https://api.openai.com/v1"
-)
+const BaseURLOpenAI = "https://api.openai.com/v1"
 
 type EmbeddingModelOpenAI string
 
@@ -60,7 +58,7 @@ func NewEmbeddingFuncOpenAICompat(baseURL, apiKey, model string) EmbeddingFunc {
 		// Prepare the request body.
 		reqBody, err := json.Marshal(map[string]string{
 			"input": document,
-			"model": string(model),
+			"model": model,
 		})
 		if err != nil {
 			return nil, fmt.Errorf("couldn't marshal request body: %w", err)
@@ -84,12 +82,7 @@ func NewEmbeddingFuncOpenAICompat(baseURL, apiKey, model string) EmbeddingFunc {
 
 		// Check the response status.
 		if resp.StatusCode != http.StatusOK {
-			body, err := io.ReadAll(resp.Body)
-			if err != nil {
-				panic(err)
-			}
-			fmt.Println("========", string(body))
-			return nil, errors.New("error response from the OpenAI API: " + resp.Status)
+			return nil, errors.New("error response from the embedding API: " + resp.Status)
 		}
 
 		// Read and decode the response body.