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This PR introduces a significant update to the Toolbox configuration file format, which is one of the primary **breaking changes** required for the implementation of the Advanced Control Plane. # Summary of Changes The configuration schema has been updated to enforce resource isolation and facilitate atomic, incremental updates. * Resource Isolation: Resource definitions are now separated into individual blocks, using a distinct structure for each resource type (Source, Tool, Toolset, etc.). This improves readability, management, and auditing of configuration files. * Field Name Modification: Internal field names have been modified to align with declarative methodologies. Specifically, the configuration now separates kind (general resource type, e.g., Source) from type (specific implementation, e.g., Postgres). # User Impact Existing tools.yaml configuration files are now in an outdated format. Users must eventually update their files to the new YAML format. # Mitigation & Compatibility Backward compatibility is maintained during this transition to ensure no immediate user action is required for existing files. * Immediate Backward Compatibility: The source code includes a pre-processing layer that automatically detects outdated configuration files (v1 format) and converts them to the new v2 format under the hood. * [COMING SOON] Migration Support: The new toolbox migrate subcommand will be introduced to allow users to automatically convert their old configuration files to the latest format. # Example Example for config file v2: ``` kind: sources name: my-pg-instance type: cloud-sql-postgres project: my-project region: my-region instance: my-instance database: my_db user: my_user password: my_pass --- kind: authServices name: my-google-auth type: google clientId: testing-id --- kind: tools name: example_tool type: postgres-sql source: my-pg-instance description: some description statement: SELECT * FROM SQL_STATEMENT; parameters: - name: country type: string description: some description --- kind: tools name: example_tool_2 type: postgres-sql source: my-pg-instance description: returning the number one statement: SELECT 1; --- kind: toolsets name: example_toolset tools: - example_tool ``` --------- Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Averi Kitsch <akitsch@google.com>
74 lines
2.5 KiB
Markdown
74 lines
2.5 KiB
Markdown
---
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title: "Gemini Embedding"
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type: docs
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weight: 1
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description: >
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Use Google's Gemini models to generate high-performance text embeddings for vector databases.
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---
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## About
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Google Gemini provides state-of-the-art embedding models that convert text into
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high-dimensional vectors.
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### Authentication
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Toolbox uses your [Application Default Credentials
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(ADC)][adc] to authorize with the
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Gemini API client.
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Optionally, you can use an [API key][api-key] obtain an API
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Key from the [Google AI Studio][ai-studio].
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We recommend using an API key for testing and using application default
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credentials for production.
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[adc]: https://cloud.google.com/docs/authentication#adc
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[api-key]: https://ai.google.dev/gemini-api/docs/api-key#api-keys
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[ai-studio]: https://aistudio.google.com/app/apikey
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## Behavior
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### Automatic Vectorization
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When a tool parameter is configured with `embeddedBy: <your-gemini-model-name>`,
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the Toolbox intercepts the raw text input from the client and sends it to the
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Gemini API. The resulting numerical array is then formatted before being passed
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to your database source.
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### Dimension Matching
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The `dimension` field must match the expected size of your database column
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(e.g., a `vector(768)` column in PostgreSQL). This setting is supported by newer
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models since 2024 only. You cannot set this value if using the earlier model
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(`models/embedding-001`). Check out [available Gemini models][modellist] for more
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information.
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[modellist]:
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https://docs.cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings#supported-models
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## Example
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```yaml
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kind: embeddingModels
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name: gemini-model
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type: gemini
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model: gemini-embedding-001
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apiKey: ${GOOGLE_API_KEY}
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dimension: 768
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```
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{{< notice tip >}}
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Use environment variable replacement with the format ${ENV_NAME}
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instead of hardcoding your secrets into the configuration file.
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{{< /notice >}}
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## Reference
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| **field** | **type** | **required** | **description** |
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|-----------|:--------:|:------------:|--------------------------------------------------------------|
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| type | string | true | Must be `gemini`. |
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| model | string | true | The Gemini model ID to use (e.g., `gemini-embedding-001`). |
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| apiKey | string | false | Your API Key from Google AI Studio. |
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| dimension | integer | false | The number of dimensions in the output vector (e.g., `768`). |
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