### Vertex AI 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/). ### Usage ```python import os from mem0 import Memory # Set the path to your Google Cloud credentials JSON file os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/your/credentials.json" os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM config = { "embedder": { "provider": "vertexai", "config": { "model": "text-embedding-004", "memory_add_embedding_type": "RETRIEVAL_DOCUMENT", "memory_update_embedding_type": "RETRIEVAL_DOCUMENT", "memory_search_embedding_type": "RETRIEVAL_QUERY" } } } m = Memory.from_config(config) messages = [ {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"}, {"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."}, {"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."}, {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."} ] m.add(messages, user_id="john") ``` The embedding types can be one of the following: - SEMANTIC_SIMILARITY - CLASSIFICATION - CLUSTERING - RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, QUESTION_ANSWERING, FACT_VERIFICATION - CODE_RETRIEVAL_QUERY Check out the [Vertex AI documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/task-types#supported_task_types) for more information. ### Config Here are the parameters available for configuring the Vertex AI embedder: | Parameter | Description | Default Value | | ------------------------- | ------------------------------------------------ | -------------------- | | `model` | The name of the Vertex AI embedding model to use | `text-embedding-004` | | `vertex_credentials_json` | Path to the Google Cloud credentials JSON file | `None` | | `embedding_dims` | Dimensions of the embedding model | `256` | | `memory_add_embedding_type` | The type of embedding to use for the add memory action | `RETRIEVAL_DOCUMENT` | | `memory_update_embedding_type` | The type of embedding to use for the update memory action | `RETRIEVAL_DOCUMENT` | | `memory_search_embedding_type` | The type of embedding to use for the search memory action | `RETRIEVAL_QUERY` |