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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.

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Usage

import os
from zentry 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 for more information.

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Config

Here are the parameters available for configuring the Vertex AI embedder:
ParameterDescriptionDefault Value
modelThe name of the Vertex AI embedding model to usetext-embedding-004
vertex_credentials_jsonPath to the Google Cloud credentials JSON fileNone
embedding_dimsDimensions of the embedding model256
memory_add_embedding_typeThe type of embedding to use for the add memory actionRETRIEVAL_DOCUMENT
memory_update_embedding_typeThe type of embedding to use for the update memory actionRETRIEVAL_DOCUMENT
memory_search_embedding_typeThe type of embedding to use for the search memory actionRETRIEVAL_QUERY