9 results found

1. Grounding

   Enable VertexAI web access for googleSearchRetrieval() and Grounding to work on the FirebaseVertexAI.

   8 votes

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   0 comments  ·  Vertex AI for Firebase  ·  Delete…  ·  Admin →
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2. Vertex AI only for authenticated users

   Currently, App Check is the only mechanism to control access to the Vertex API. This is not always practical and does not allow per-user access control.
   Please add a mechanism similar to Security Rules for Firestore, to enforce only authenticated Firebase users to access the AI APIs.

   6 votes

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   1 comment  ·  Vertex AI for Firebase  ·  Delete…  ·  Admin →
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3. การใช้งานแบตเตอรี่ออนไลน์
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   0 comments  ·  Vertex AI for Firebase  ·  Edit…  ·  Delete…  ·  Admin →
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4. Enhanced Genkit Integration in Firebase Studio for Context-Aware AI Development

   1. Introduction & Problem Statement: Firebase Studio aims to be a premier cloud-based IDE for building modern applications, including those powered by AI using Genkit and Vertex AI. Currently, while Genkit provides powerful tools for defining AI flows (data indexing, RAG, code generation), the workflow for developing, testing, and leveraging these flows locally within Firebase Studio presents significant friction. Users seeking to build sophisticated, project-aware AI assistants (that go beyond the built-in chat features) face challenges: Discoverability of Execution Mechanisms: The intended method for locally running custom Genkit flows (defined with defineFlow) or launching the Genkit Developer UI within the Studio environment is unclear and not easily discoverable via standard terminal commands (genkit, npx genkit start), Command Palette searches, or UI exploration. Standard CLI approaches often fail due to the managed nature of the Studio environment. Workflow Interruption: This lack of a clear local run/debug loop hinders rapid iteration on Genkit flows, forcing developers towards premature deployment or complex workarounds. Achieving Context-Aware AI: The ultimate goal for many users is to leverage Genkit/Vertex AI not just for generic tasks, but to create assistants deeply aware of their project's codebase, indexed documentation (via Vector Search), and structure. This requires seamless integration between the user's custom Genkit flows (handling indexing and RAG) and the IDE's development tools.
   2. Proposed Vision & Solution: Firebase Studio should provide a first-class, intuitive development experience for users building custom Genkit flows. This involves seamless integration points for local testing, debugging, and eventually leveraging these flows for context-aware code generation and project interaction. The vision is to empower developers to build their own project-specific "AI co-pilots" powered by Vertex AI and orchestrated by Genkit, directly within the IDE.
   3. Key Features & Implementation Suggestions: A. Clear Genkit Flow Execution: Command Palette Action: Introduce unambiguous Command Palette actions like Genkit: Run Flow... which allows selecting a defined flow and easily providing input (handling JSON encoding). UI Integration: Implement context-menu actions (right-click on flow definition files) or CodeLens links (buttons near defineFlow) to directly "Run" or "Debug" a specific Genkit flow. Output Integration: Ensure flow execution logs and results are clearly displayed within an integrated Studio terminal or output panel. B. Integrated Genkit Developer UI Launch: Command Palette Action: Provide a clear action like Genkit: Start Developer UI that correctly launches the Genkit UI (handling port forwarding and executable paths within the Studio environment). UI Button/Menu: Consider a dedicated button or menu item for launching the Dev UI. C. Enhanced Context Awareness Mechanisms: Flow Access to Project Structure: Document or provide utilities/patterns for how a Genkit flow (potentially running as a tool/agent) can understand the project's file structure to place generated code correctly. Seamless RAG Integration: Ensure flows designed for RAG (retrieving from Vector Search containing project docs/code) can be easily triggered, potentially via custom Command Palette actions or integrated tools, feeding context directly into generation prompts. (Advanced) Code Modification Tools: Explore Genkit tools or patterns specifically designed to safely apply LLM-generated code changes back into the workspace files, potentially leveraging existing IDE refactoring capabilities. D. Documentation & Discoverability: Dedicated Documentation: Create clear, prominent documentation specifically detailing the Firebase Studio workflow for local Genkit development, testing, and debugging. Explicitly show how to run flows and start the Dev UI within Studio. In-IDE Hints: Consider subtle hints or walkthroughs within Studio when Genkit configuration is detected, guiding users towards the correct execution mechanisms.
   4. Benefits: Improved Developer Experience: Drastically reduces friction for developers building AI features with Genkit in Studio. Increased Genkit/Vertex AI Adoption: Makes these powerful tools more accessible and practical within Firebase's flagship IDE. Stronger Firebase Studio Value Proposition: Positions Studio as a truly integrated environment for sophisticated AI application development. Enables Advanced Use Cases: Directly supports users aiming to build context-aware AI coding assistants tailored to their projects.
   5. Conclusion: By implementing clear execution mechanisms for custom Genkit flows and the Developer UI, and by providing pathways for deeper project context integration, Firebase Studio can unlock the full potential of Genkit/Vertex AI for developers, moving beyond generic AI assistance towards truly powerful, project-aware AI-augmented development. Addressing the discoverability and workflow gaps for local testing is the critical first step.

   1. Introduction & Problem Statement: Firebase Studio aims to be a premier cloud-based IDE for building modern applications, including those powered by AI using Genkit and Vertex AI. Currently, while Genkit provides powerful tools for defining AI flows (data indexing, RAG, code generation), the workflow for developing, testing, and leveraging these flows locally within Firebase Studio presents significant friction. Users seeking to build sophisticated, project-aware AI assistants (that go beyond the built-in chat features) face challenges: Discoverability of Execution Mechanisms: The intended method for locally running custom Genkit flows (defined with defineFlow) or launching the Genkit Developer UI within the Studio… more
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   0 comments  ·  Vertex AI for Firebase  ·  Edit…  ·  Delete…  ·  Admin →
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5. Luyjaz1712
   1 vote

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   0 comments  ·  Vertex AI for Firebase  ·  Edit…  ·  Delete…  ·  Admin →
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6. Allow access to Gemini preview models

   Currently the latest stable or other stable Gemini 1.5 models are available only (see https://firebase.google.com/docs/vertex-ai/gemini-models#available-model-names). These lag behind with features such as function calling ability (performance), structured output (according to the feature matrix Gemini 1.5 Flash does not support that but we know it has multiple versions already capable of). Why cannot we access the preview model we'd like?

   4 votes

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   2 comments  ·  Vertex AI for Firebase  ·  Delete…  ·  Admin →
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7. Train A Model Through API

   I don't know if this is already exisiting but i think it should be possible to train a custom model through the vertex AI API for firebase.

   3 votes

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   0 comments  ·  Vertex AI for Firebase  ·  Delete…  ·  Admin →
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8. Add input and output tokens used and model to the response object.

   One of the very useful things about the Open AI API is that the response includes the amount of input and output tokens used , as well as the model that was hit. I am recording this data per call in my firebase instance for my app along with time of processing to monitor performance. I also add the appropriate cost per model in there and so I dynamically calculate the costs per model call as well. This helps me make business case decisions on how I use the AI. 
   It would be great to have that  available in  the Vertex AI API calls as well. 

   ---

   Is the way to get that now to make a separate call?

   const { totalTokens, totalBillableCharacters } = await model.countTokens([prompt, imagePart]);

   One of the very useful things about the Open AI API is that the response includes the amount of input and output tokens used , as well as the model that was hit. I am recording this data per call in my firebase instance for my app along with time of processing to monitor performance. I also add the appropriate cost per model in there and so I dynamically calculate the costs per model call as well. This helps me make business case decisions on how I use the AI. 
   It would be great to have that  available in  the Vertex AI API… more

   2 votes

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9. Automated Function Calling for Kotlin

   Allow the SDK to call automatically the functions that the model requires before to proceed with the prompt

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