Parshva Daftari
70 lines
2.2 KiB
Plaintext
70 lines
2.2 KiB
Plaintext
---
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title: Google AI
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---
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To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
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### Usage
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<CodeGroup>
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```python Python
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import os
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from mem0 import Memory
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os.environ["GOOGLE_API_KEY"] = "key"
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os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
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config = {
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"embedder": {
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"provider": "gemini",
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"config": {
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"model": "models/text-embedding-004",
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}
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}
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="john")
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```
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```typescript TypeScript
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import { Memory } from 'mem0ai/oss';
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const config = {
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embedder: {
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provider: 'google',
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config: {
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apiKey: process.env.GOOGLE_API_KEY || '',
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model: 'text-embedding-004',
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// The output dimensionality is fixed at 768 for Google AI embeddings
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},
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},
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};
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const memory = new Memory(config);
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const messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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await memory.add(messages, { userId: "john" });
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```
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</CodeGroup>
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### Config
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Here are the parameters available for configuring Gemini embedder:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `model` | The name of the embedding model to use | `models/text-embedding-004` |
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| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` |
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| `api_key` | The Google API key | `None` |
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