56 lines
2.6 KiB
Plaintext
56 lines
2.6 KiB
Plaintext
### Vertex AI
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To use Google Cloud's Vertex AI for text embedding models, set the `GOOGLE_APPLICATION_CREDENTIALS` environment variable to point to the path of your service account's credentials JSON file. These credentials can be created in the [Google Cloud Console](https://console.cloud.google.com/).
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### Usage
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```python
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import os
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from mem0 import Memory
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# Set the path to your Google Cloud credentials JSON file
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os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/your/credentials.json"
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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": "vertexai",
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"config": {
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"model": "text-embedding-004",
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"memory_add_embedding_type": "RETRIEVAL_DOCUMENT",
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"memory_update_embedding_type": "RETRIEVAL_DOCUMENT",
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"memory_search_embedding_type": "RETRIEVAL_QUERY"
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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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The embedding types can be one of the following:
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- SEMANTIC_SIMILARITY
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- CLASSIFICATION
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- CLUSTERING
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- RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION
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- CODE_RETRIEVAL_QUERY
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Check out the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/task-types#supported_task_types) for more information.
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### Config
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Here are the parameters available for configuring the Vertex AI embedder:
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| Parameter | Description | Default Value |
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| ------------------------- | ------------------------------------------------ | -------------------- |
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| `model` | The name of the Vertex AI embedding model to use | `text-embedding-004` |
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| `vertex_credentials_json` | Path to the Google Cloud credentials JSON file | `None` |
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| `embedding_dims` | Dimensions of the embedding model | `256` |
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| `memory_add_embedding_type` | The type of embedding to use for the add memory action | `RETRIEVAL_DOCUMENT` |
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| `memory_update_embedding_type` | The type of embedding to use for the update memory action | `RETRIEVAL_DOCUMENT` |
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| `memory_search_embedding_type` | The type of embedding to use for the search memory action | `RETRIEVAL_QUERY` |
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