feat: Implement Ollama embedding

Adds support for Ollama embedding, enabling the use of Ollama as an embedding model for RAG.

This allows users to leverage Ollama's advanced embedding capabilities for better document understanding and retrieval.

src/components/Sidepanel/Settings/body.tsx

@@ -11,7 +11,8 @@ import {
   defaultEmbeddingChunkOverlap,
   defaultEmbeddingChunkSize,
   defaultEmbeddingModelForRag,
-  saveForRag
+  saveForRag,
+  getEmbeddingModels
 } from "~/services/ollama"
 
 import {
@@ -77,7 +78,7 @@ export const SettingsBody = () => {
         getOllamaURL(),
         systemPromptForNonRag(),
         promptForRag(),
-        getAllModels({ returnEmpty: true }),
+        getEmbeddingModels({ returnEmpty: true }),
         defaultEmbeddingChunkOverlap(),
         defaultEmbeddingChunkSize(),
         defaultEmbeddingModelForRag(),

src/hooks/useMessage.tsx

@@ -539,7 +539,7 @@ export const useMessage = () => {
       if (selectedPrompt) {
         applicationChatHistory.unshift(
           new SystemMessage({
-            content:  selectedPrompt.content
+            content: selectedPrompt.content
           })
         )
       }

src/models/OAIEmbedding.ts

@@ -1,52 +1,21 @@
 import { type ClientOptions, OpenAI as OpenAIClient } from "openai"
-import { Embeddings, type EmbeddingsParams } from "@langchain/core/embeddings"
+import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings"
 import { chunkArray } from "@langchain/core/utils/chunk_array"
 import { OpenAICoreRequestOptions, LegacyOpenAIInput } from "./types"
 import { wrapOpenAIClientError } from "./utils/openai"
 
-/**
- * Interface for OpenAIEmbeddings parameters. Extends EmbeddingsParams and
- * defines additional parameters specific to the OpenAIEmbeddings class.
- */
 export interface OpenAIEmbeddingsParams extends EmbeddingsParams {
-    /**
-     * Model name to use
-     * Alias for `model`
-     */
     modelName: string
-    /** Model name to use */
     model: string
-
-    /**
-     * The number of dimensions the resulting output embeddings should have.
-     * Only supported in `text-embedding-3` and later models.
-     */
     dimensions?: number
-
-    /**
-     * Timeout to use when making requests to OpenAI.
-     */
     timeout?: number
-
-    /**
-     * The maximum number of documents to embed in a single request. This is
-     * limited by the OpenAI API to a maximum of 2048.
-     */
     batchSize?: number
-
-    /**
-     * Whether to strip new lines from the input text. This is recommended by
-     * OpenAI for older models, but may not be suitable for all use cases.
-     * See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
-     */
     stripNewLines?: boolean
-
     signal?: AbortSignal
 }
 
 export class OAIEmbedding
-    extends Embeddings
+    extends Embeddings {
-    implements OpenAIEmbeddingsParams {
     modelName = "text-embedding-ada-002"
 
     model = "text-embedding-ada-002"
@@ -107,7 +76,7 @@ export class OAIEmbedding
         this.modelName =
             fieldsWithDefaults?.model ?? fieldsWithDefaults?.modelName ?? this.model
         this.model = this.modelName
-        this.batchSize = fieldsWithDefaults?.batchSize
+        this.batchSize = fieldsWithDefaults?.batchSize || this.batchSize
         this.stripNewLines = fieldsWithDefaults?.stripNewLines ?? this.stripNewLines
         this.timeout = fieldsWithDefaults?.timeout
         this.dimensions = fieldsWithDefaults?.dimensions
@@ -127,15 +96,12 @@ export class OAIEmbedding
             ...configuration,
             ...fields?.configuration
         }
+
+
+        // initialize the client
+        this.client = new OpenAIClient(this.clientConfig)
     }
 
-    /**
-     * Method to generate embeddings for an array of documents. Splits the
-     * documents into batches and makes requests to the OpenAI API to generate
-     * embeddings.
-     * @param texts Array of documents to generate embeddings for.
-     * @returns Promise that resolves to a 2D array of embeddings for each document.
-     */
     async embedDocuments(texts: string[]): Promise<number[][]> {
         const batches = chunkArray(
             this.stripNewLines ? texts.map((t) => t.replace(/\n/g, " ")) : texts,
@@ -165,12 +131,6 @@ export class OAIEmbedding
         return embeddings
     }
 
-    /**
-     * Method to generate an embedding for a single document. Calls the
-     * embeddingWithRetry method with the document as the input.
-     * @param text Document to generate an embedding for.
-     * @returns Promise that resolves to an embedding for the document.
-     */
     async embedQuery(text: string): Promise<number[]> {
         const params: OpenAIClient.EmbeddingCreateParams = {
             model: this.model,
@@ -183,16 +143,19 @@ export class OAIEmbedding
         return data[0].embedding
     }
 
-    /**
+    async _embed(texts: string[]): Promise<number[][]> {
-     * Private method to make a request to the OpenAI API to generate
+        const embeddings: number[][] = await Promise.all(
-     * embeddings. Handles the retry logic and returns the response from the
+            texts.map((text) => this.caller.call(() => this.embedQuery(text)))
-     * API.
+        )
-     * @param request Request to send to the OpenAI API.
+
-     * @returns Promise that resolves to the response from the API.
+        return embeddings
-     */
+    }
+
+
     protected async embeddingWithRetry(
         request: OpenAIClient.EmbeddingCreateParams
     ) {
+
         const requestOptions: OpenAICoreRequestOptions = {}
         if (this.azureOpenAIApiKey) {
             requestOptions.headers = {

src/utils/memory-embeddings.ts

@@ -47,12 +47,11 @@ export const memoryEmbedding = async ({
   type: string
   pdf: { content: string; page: number }[]
   keepTrackOfEmbedding: Record<string, MemoryVectorStore>
-  ollamaEmbedding: OllamaEmbeddings
+  ollamaEmbedding: any
   setIsEmbedding: (value: boolean) => void
   setKeepTrackOfEmbedding: (value: Record<string, MemoryVectorStore>) => void
 }) => {
   setIsEmbedding(true)
-
   const loader = getLoader({ html, pdf, type, url })
   const docs = await loader.load()
   const chunkSize = await defaultEmbeddingChunkSize()