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fixed hardcoded embeddingDims (#3537)

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This commit is contained in:
Mrinank Bhowmick
2025-10-09 11:09:20 -07:00
committed by GitHub
parent 9000576173
commit 64571002f5
11 changed files with 85 additions and 29 deletions
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docs/components/embedders/models/azure_openai.mdx
+12 -1
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@@ -117,9 +117,20 @@ Refer to [Azure Identity troubleshooting tips](https://github.com/Azure/azure-sd
### Config ### Config
Here are the parameters available for configuring Azure OpenAI embedder: Here are the parameters available for configuring Azure OpenAI embedder:
<Tabs>
<Tab title="Python">
| Parameter | Description | Default Value | | Parameter | Description | Default Value |
| --- | --- | --- | | --- | --- | --- |
| `model` | The name of the embedding model to use | `text-embedding-3-small` | | `model` | The name of the embedding model to use | `text-embedding-3-small` |
| `embedding_dims` | Dimensions of the embedding model | `1536` | | `embedding_dims` | Dimensions of the embedding model | `1536` |
| `azure_kwargs` | The Azure OpenAI configs | `config_keys` | | `azure_kwargs` | The Azure OpenAI configs | `config_keys` |
</Tab>
<Tab title="TypeScript">
| Parameter | Description | Default Value |
| ----------------- | --------------------------------------------- | -------------------------- |
| `model` | The name of the embedding model to use | `text-embedding-3-small` |
| `embeddingDims` | Dimensions of the embedding model | `1536` |
| `apiKey` | Azure OpenAI API key | `None` |
| `modelProperties` | Object containing endpoint and other settings | `{ endpoint: "",...rest }`|
</Tab>
</Tabs>
docs/components/embedders/models/google_AI.mdx
+22 -12
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@@ -38,13 +38,13 @@ import { Memory } from 'mem0ai/oss';
const config = { const config = {
embedder: { embedder: {
provider: 'google', provider: "google",
config: { config: {
apiKey: process.env.GOOGLE_API_KEY || '', apiKey: process.env["GOOGLE_API_KEY"],
model: 'text-embedding-004', model: "gemini-embedding-001",
// The output dimensionality is fixed at 768 for Google AI embeddings embeddingDims: 1536,
},
}, },
},
}; };
const memory = new Memory(config); const memory = new Memory(config);
@@ -61,9 +61,19 @@ await memory.add(messages, { userId: "john" });
### Config ### Config
Here are the parameters available for configuring Gemini embedder: Here are the parameters available for configuring Gemini embedder:
<Tabs>
| Parameter | Description | Default Value | <Tab title="Python">
| --- | --- | --- | | Parameter | Description | Default Value |
| `model` | The name of the embedding model to use | `models/text-embedding-004` | | ---------------- | ------------------------------------ | ----------------------- |
| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` | | `model` | The name of the embedding model to use| `models/text-embedding-004` |
| `api_key` | The Google API key | `None` | | `embedding_dims` | Dimensions of the embedding model | `1536` |
| `api_key` | The Google API key | `None` |
</Tab>
<Tab title="TypeScript">
| Parameter | Description | Default Value |
| ----------------- | --------------------------------------------- | -------------------------- |
| `model` | The name of the embedding model to use | `gemini-embedding-001` |
| `embeddingDims` | Dimensions of the embedding model | `1536` |
| `apiKey` | Google API key | `None` |
</Tab>
</Tabs>
docs/components/embedders/models/ollama.mdx
+1
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@@ -69,5 +69,6 @@ Here are the parameters available for configuring Ollama embedder:
| --- | --- | --- | | --- | --- | --- |
| `model` | The name of the Ollama model to use | `nomic-embed-text:latest` | | `model` | The name of the Ollama model to use | `nomic-embed-text:latest` |
| `url` | Base URL for Ollama server | `http://localhost:11434` | | `url` | Base URL for Ollama server | `http://localhost:11434` |
| `embeddingDims` | Dimensions of the embedding model | 768
</Tab> </Tab>
</Tabs> </Tabs>
docs/v0x/components/embedders/models/azure_openai.mdx
+13 -2
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@@ -23,7 +23,7 @@ config = {
"embedder": { "embedder": {
"provider": "azure_openai", "provider": "azure_openai",
"config": { "config": {
"model": "text-embedding-3-large" "model": "text-embedding-3-large",
"azure_kwargs": { "azure_kwargs": {
"api_version": "", "api_version": "",
"azure_deployment": "", "azure_deployment": "",
@@ -117,9 +117,20 @@ Refer to [Azure Identity troubleshooting tips](https://github.com/Azure/azure-sd
### Config ### Config
Here are the parameters available for configuring Azure OpenAI embedder: Here are the parameters available for configuring Azure OpenAI embedder:
<Tabs>
<Tab title="Python">
| Parameter | Description | Default Value | | Parameter | Description | Default Value |
| --- | --- | --- | | --- | --- | --- |
| `model` | The name of the embedding model to use | `text-embedding-3-small` | | `model` | The name of the embedding model to use | `text-embedding-3-small` |
| `embedding_dims` | Dimensions of the embedding model | `1536` | | `embedding_dims` | Dimensions of the embedding model | `1536` |
| `azure_kwargs` | The Azure OpenAI configs | `config_keys` | | `azure_kwargs` | The Azure OpenAI configs | `config_keys` |
</Tab>
<Tab title="TypeScript">
| Parameter | Description | Default Value |
| ----------------- | --------------------------------------------- | -------------------------- |
| `model` | The name of the embedding model to use | `text-embedding-3-small` |
| `embeddingDims` | Dimensions of the embedding model | `1536` |
| `apiKey` | Azure OpenAI API key | `None` |
| `modelProperties` | Object containing endpoint and other settings | `{ endpoint: "",...rest }`|
</Tab>
</Tabs>
docs/v0x/components/embedders/models/google_AI.mdx
+22 -11
View File
@@ -38,13 +38,13 @@ import { Memory } from 'mem0ai/oss';
const config = { const config = {
embedder: { embedder: {
provider: 'google', provider: "google",
config: { config: {
apiKey: process.env.GOOGLE_API_KEY || '', apiKey: process.env["GOOGLE_API_KEY"],
model: 'text-embedding-004', model: "gemini-embedding-001",
// The output dimensionality is fixed at 768 for Google AI embeddings embeddingDims: 1536,
},
}, },
},
}; };
const memory = new Memory(config); const memory = new Memory(config);
@@ -62,8 +62,19 @@ await memory.add(messages, { userId: "john" });
Here are the parameters available for configuring Gemini embedder: Here are the parameters available for configuring Gemini embedder:
| Parameter | Description | Default Value | <Tabs>
| --- | --- | --- | <Tab title="Python">
| `model` | The name of the embedding model to use | `models/text-embedding-004` | | Parameter | Description | Default Value |
| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` | | ---------------- | ------------------------------------ | ----------------------- |
| `api_key` | The Google API key | `None` | | `model` | The name of the embedding model to use| `models/text-embedding-004` |
| `embedding_dims` | Dimensions of the embedding model | `1536` |
| `api_key` | The Google API key | `None` |
</Tab>
<Tab title="TypeScript">
| Parameter | Description | Default Value |
| ----------------- | --------------------------------------------- | -------------------------- |
| `model` | The name of the embedding model to use | `gemini-embedding-001` |
| `embeddingDims` | Dimensions of the embedding model | `1536` |
| `apiKey` | Google API key | `None` |
</Tab>
</Tabs>
docs/v0x/components/embedders/models/ollama.mdx
+1
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@@ -69,5 +69,6 @@ Here are the parameters available for configuring Ollama embedder:
| --- | --- | --- | | --- | --- | --- |
| `model` | The name of the Ollama model to use | `nomic-embed-text:latest` | | `model` | The name of the Ollama model to use | `nomic-embed-text:latest` |
| `url` | Base URL for Ollama server | `http://localhost:11434` | | `url` | Base URL for Ollama server | `http://localhost:11434` |
| `embeddingDims` | Dimensions of the embedding model | 768 |
</Tab> </Tab>
</Tabs> </Tabs>
mem0-ts/src/oss/src/embeddings/azure.ts
+2
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@@ -5,6 +5,7 @@ import { EmbeddingConfig } from "../types";
export class AzureOpenAIEmbedder implements Embedder { export class AzureOpenAIEmbedder implements Embedder {
private client: AzureOpenAI; private client: AzureOpenAI;
private model: string; private model: string;
private embeddingDims?: number;
constructor(config: EmbeddingConfig) { constructor(config: EmbeddingConfig) {
if (!config.apiKey || !config.modelProperties?.endpoint) { if (!config.apiKey || !config.modelProperties?.endpoint) {
@@ -19,6 +20,7 @@ export class AzureOpenAIEmbedder implements Embedder {
...rest, ...rest,
}); });
this.model = config.model || "text-embedding-3-small"; this.model = config.model || "text-embedding-3-small";
this.embeddingDims = config.embeddingDims || 1536;
} }
async embed(text: string): Promise<number[]> { async embed(text: string): Promise<number[]> {
mem0-ts/src/oss/src/embeddings/google.ts
+7 -3
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@@ -5,17 +5,21 @@ import { EmbeddingConfig } from "../types";
export class GoogleEmbedder implements Embedder { export class GoogleEmbedder implements Embedder {
private google: GoogleGenAI; private google: GoogleGenAI;
private model: string; private model: string;
private embeddingDims?: number;
constructor(config: EmbeddingConfig) { constructor(config: EmbeddingConfig) {
this.google = new GoogleGenAI({ apiKey: config.apiKey }); this.google = new GoogleGenAI({
this.model = config.model || "text-embedding-004"; apiKey: config.apiKey || process.env.GOOGLE_API_KEY,
});
this.model = config.model || "gemini-embedding-001";
this.embeddingDims = config.embeddingDims || 1536;
} }
async embed(text: string): Promise<number[]> { async embed(text: string): Promise<number[]> {
const response = await this.google.models.embedContent({ const response = await this.google.models.embedContent({
model: this.model, model: this.model,
contents: text, contents: text,
config: { outputDimensionality: 768 }, config: { outputDimensionality: this.embeddingDims },
}); });
return response.embeddings![0].values!; return response.embeddings![0].values!;
} }
mem0-ts/src/oss/src/embeddings/ollama.ts
+2
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@@ -6,6 +6,7 @@ import { logger } from "../utils/logger";
export class OllamaEmbedder implements Embedder { export class OllamaEmbedder implements Embedder {
private ollama: Ollama; private ollama: Ollama;
private model: string; private model: string;
private embeddingDims?: number;
// Using this variable to avoid calling the Ollama server multiple times // Using this variable to avoid calling the Ollama server multiple times
private initialized: boolean = false; private initialized: boolean = false;
@@ -14,6 +15,7 @@ export class OllamaEmbedder implements Embedder {
host: config.url || "http://localhost:11434", host: config.url || "http://localhost:11434",
}); });
this.model = config.model || "nomic-embed-text:latest"; this.model = config.model || "nomic-embed-text:latest";
this.embeddingDims = config.embeddingDims || 768;
this.ensureModelExists().catch((err) => { this.ensureModelExists().catch((err) => {
logger.error(`Error ensuring model exists: ${err}`); logger.error(`Error ensuring model exists: ${err}`);
}); });
mem0-ts/src/oss/src/embeddings/openai.ts
+2
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@@ -5,10 +5,12 @@ import { EmbeddingConfig } from "../types";
export class OpenAIEmbedder implements Embedder { export class OpenAIEmbedder implements Embedder {
private openai: OpenAI; private openai: OpenAI;
private model: string; private model: string;
private embeddingDims?: number;
constructor(config: EmbeddingConfig) { constructor(config: EmbeddingConfig) {
this.openai = new OpenAI({ apiKey: config.apiKey }); this.openai = new OpenAI({ apiKey: config.apiKey });
this.model = config.model || "text-embedding-3-small"; this.model = config.model || "text-embedding-3-small";
this.embeddingDims = config.embeddingDims || 1536;
} }
async embed(text: string): Promise<number[]> { async embed(text: string): Promise<number[]> {
mem0-ts/src/oss/src/types/index.ts
+1
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@@ -16,6 +16,7 @@ export interface EmbeddingConfig {
apiKey?: string; apiKey?: string;
model?: string | any; model?: string | any;
url?: string; url?: string;
embeddingDims?: number;
modelProperties?: Record<string, any>; modelProperties?: Record<string, any>;
} }