Galactic Streamhub

The AI that understands your world.

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Galactic StreamHub 🚀✨

Inspiration: "This technology wasn't built for me." ♿️❤️

Our journey began with a fundamental and often-overlooked truth in the digital age: much of our technology is implicitly designed for users without disabilities, creating significant and frustrating barriers for others. This realization sparked a more profound question: What if an AI assistant could be engineered from the ground up to dismantle these barriers? What if it could become a cognitive partner dedicated to leveling the playing field?

This is the core mission of Galactic StreamHub. We rejected the idea of accessibility as an afterthought and instead made it the central pillar of our architecture.

We envisioned an AI that doesn't just respond to commands, but actively bridges the gap between the digital and physical worlds for those who need it most. This led us to design a multi-faceted accessibility suite powered by a team of specialist agents:

  • For visually impaired users: We tackled the challenge of environmental awareness head-on. The SceneDescriberAgent provides a rich, narrative understanding of a user's surroundings ("I see a coffee mug to your right and a laptop in front of you"), while the TextReaderAgent uses high-precision OCR to read medicine labels, mail, or street signs aloud. These aren't novelties; they are tools for daily navigation and independence.

  • For users with auditory challenges: To enhance situational awareness, the SoundRecognitionAgent was designed to identify and announce important environmental sounds, like a doorbell ringing, a smoke alarm, or a baby crying, providing a critical layer of perception.

  • For users with cognitive challenges: We recognized that access to information is not enough; comprehension is key. The TextSimplificationAgent was created to transform complex text—from dense medical reports to bureaucratic documents—into clear, simple language, making vital information accessible to everyone.

By creating specialist agents for these distinct tasks, we are not just adding features; we are building a tool that empowers users who are too often left behind by technology. Galactic StreamHub is our answer to the pain of digital exclusion, a secure, scalable system on Google Cloud designed to prove that with thoughtful architecture, AI can become a true force for accessibility and independence.

  • Secure & Personalized Accessibility: 🔐 Recognizing that assistive technology must be private and personal, every user interaction is protected by Firebase Authentication

We aimed to build an AI that could not only hear you 👂 and see your environment 👀 but could also bridge the gap between abstract data and visual reality. We envisioned a secure, scalable system on Google Cloud that could orchestrate a team of specialist agents 🤖🤖🤖 to query multiple databases, connect published papers to ongoing clinical trials, generate data visualizations, and, most ambitiously, correlate textual findings with actual medical imagery from a multimodal database powered by MongoDB Atlas.

Privacy & Security by Design

AVA's architecture was built with user privacy as a core principle. We believe that for a personal AI companion to be truly trusted, it must handle sensitive data responsibly.

  • Local-First Emotional Analysis: The most sensitive biometric data—your facial expressions and vocal tone—are processed locally on the device running the server. The EmotionalSynthesizerAgent uses local libraries (DeepFace, transformers) for this analysis. This means your raw video and audio data for emotional context are not sent to external cloud services, ensuring a high degree of privacy.

  • Secure Authenticated Sessions: All interactions with AVA are protected by Firebase Authentication. The backend verifies a secure token for every WebSocket connection, ensuring that only the authenticated user can access their session. The user's unique Firebase ID (uid) is used to namespace all session data and memory, preventing data crossover.

  • Purpose-Driven Cloud Interaction: Data is only sent to external cloud APIs when a specific, user-initiated tool requires it. For example, an image frame is sent to the Google Vision API only when the user explicitly asks AVA to read text via the OCR tool. The system avoids continuous, indiscriminate data streaming to third-party services.

  • Decoupled Transient Data: Real-time data, like the most recent audio chunk for vocal analysis, is held in a transient, in-memory store (shared_state.py) that is cleared when the session ends. This minimizes the data footprint and separates ephemeral data from long-term conversational memory.

  • Secure by Design: Integrates Google Cloud's Model Armor to proactively sanitize user prompts against injection, jailbreaking, and other potential attacks.

What it does 🛠️⚕️📊

Galactic StreamHub is an interactive, secure, and deeply multimodal AI assistant that operates with a sophisticated multi-agent architecture, featuring dual operational modes behind a secure authentication layer.

  • Secure & Personalized Access: 🔐 Every user interaction is protected by Firebase Authentication with Google Sign-In. The backend verifies a user's ID token before establishing a WebSocket connection, ensuring each session is secure and personalized, linking securely to the user's Firebase UID.

  • Live Multimodal Interaction: 👁️‍🗨️ The agent can process a live video feed to understand the user's environment and can engage in real-time, bidirectional audio conversations for a seamless, hands-free experience.

  • Dedicated Accessibility Suite for Visual Assistance: 👓 To better serve visually impaired users, AVA includes a dedicated accessibility workflow. It can:

    • Describe the Scene: Provide a natural, narrative description of the user's surroundings.
    • Read Text Aloud: Accurately read text from objects in the real world (like labels or documents) using Google's Cloud Vision API for high-precision Optical Character Recognition (OCR).

  • Auditory Accessibility 👂: To enhance auditory awareness and provide valuable feedback, AVA includes a dedicated auditory accessibility workflow. It can:

  • Analyze Speech Sentiment: Provide real-time feedback on vocal tone by analyzing transcribed speech with the Google Cloud Natural Language API.

  • Recognize Ambient Sounds: Identify and report significant background noises (like a doorbell or alarm) to improve the user's situational awareness.

  • Proactive Environmental Assistance: 💡 Based on what it "sees," AVA can anticipate needs. If it sees cocktail ingredients, it offers recipes using its CocktailDB tool 🍸. If it sees you're missing something, it can use its Google Maps tool 🗺️ to find the nearest store.

  • The "Triple-Stream" Biomedical Research Engine: 🔬 This is the core of our project. When a query is biomedical, AVA delegates to a master InsightSynthesisAgent that initiates a powerful, multi-stage RAG pipeline:

    1. Parallelized Multi-Source Fetch: 🌐 Simultaneously queries multiple data sources in parallel:
      • PubMed Knowledge Base on MongoDB Atlas (using Vector Search).
      • Clinical Trials Knowledge Base on MongoDB Atlas (using Vector Search).
      • A Multimodal Medical Image Database on MongoDB Atlas (using Vector Search on vision-language embeddings).
      • Live Google Search, ClinicalTrials.gov API, and the OpenFDA drug database.
    2. Autonomous Insight Generation: 🧠 An "assembly line" of agents works to find hidden connections, linking key findings in PubMed papers to specific ongoing clinical trials.
    3. Multimodal Synthesis: The system then synthesizes its findings in three parallel streams:
      • Text Report: A full narrative summary is written.
      • Data Visualization: Quantifiable data is automatically found and plotted into charts 📈.
      • Visual Evidence: Key textual descriptions (e.g., "lung nodule") are used to retrieve and display corresponding medical scan images 🖼️ from the multimodal database.
    4. Persistent Knowledge Growth: 💡 If new information is found online, AVA asks for permission to save it. Upon confirmation, the new data is processed, embedded, and stored in the appropriate MongoDB Atlas collection, permanently expanding the AI's knowledge.

How we built it 🧑‍💻🔧

Galactic StreamHub is a cloud-native, full-stack application leveraging a state-of-the-art AI and data stack.

Backend (Python, FastAPI, Google Cloud):

  • Google ADK (Agent Development Kit) v1.1.1: The core of our agentic logic. We used LlmAgent for specialized tasks and the root agent, ParallelAgent for concurrent data fetching and synthesis (crucial for the ResearchOrchestratorAgent), and SequentialAgent to build our main "assembly line" for insight generation. AgentTool was used extensively to wrap agents, making them callable tools.
  • MongoDB Atlas: The absolute backbone of our RAG system. We maintain three distinct collections: one for PubMed articles, one for clinical trials, and one for our multimodal medical images. Each collection is indexed with MongoDB Atlas Vector Search, enabling lightning-fast and semantically relevant retrieval across text, numbers, and images.
  • Vertex AI: We use Google's text-embedding-005 model for our text-based collections and the powerful multimodalembedding@001 model to generate rich, 1408-dimension vectors that combine visual data from CT scans with AI-generated textual descriptions.
  • Firebase Authentication: The backend's WebSocket endpoint (/ws) is protected. It requires an ID token from the client, which is verified using the Firebase Admin SDK. A connection is only established if the token is valid, linking the session securely to the user's Firebase UID. It's also secured with Firebase App Check.
  • Tool Integration (MCP): Leverages external tools via the Model Context Protocol, including a pre-built server for Google Maps to handle geocoding and place searches.

Frontend (HTML, CSS, JavaScript):

  • Firebase JS SDK: Manages the entire client-side Google Sign-In flow, retrieves the secure ID token, and passes it to the backend to establish the secure WebSocket connection.
  • Dynamic UI: A custom-designed, futuristic interface with a dark space theme 🌠 and glassmorphic panels that dynamically renders the agent's complex, multi-part responses, including streaming text, audio playback 🔊, data charts generated by the VisualizationAgent, and the retrieved medical scan images.
  • Vanta.js: Creates the dynamic, animated starfield background ✨.

Deployment on Google Cloud Run:

  • Containerization & Serverless Deployment: The entire FastAPI application is containerized using a Dockerfile and deployed as a service on Google Cloud Run. This provides a fully managed, auto-scaling, and cost-effective serverless platform that automatically scales based on traffic.
  • CI/CD & Security: We use gcloud run deploy for streamlined deployments and securely manage all our API keys and database URIs using Google Cloud Secret Manager. Fine-tuning included allocating sufficient memory (4Gi) and CPU, and configuring service account permissions for stable deployment.

Of course. This is a crucial step to ensure your project's documentation is accurate, transparent, and reflects the excellent debugging work you've done. It's important to document not just the successes, but also the challenges and the current state of the system.

You should update the "Knative to Kubernetes: Migration Learnings & Paradigm Shifts" section, as it's the most relevant place. I will provide a rewritten version of that section that tells the full story accurately.

Knative to Kubernetes: A Migration Journey and Overcoming Platform Challenges 🔄

Our initial deployment on Google Cloud Run benefited from its underlying Knative architecture, which provides a serverless, declarative API. Migrating to Google Kubernetes Engine (GKE) Autopilot was a strategic move to gain more granular control. This journey involved translating Knative concepts into Kubernetes resources and navigating a significant platform-level challenge with GKE networking.

  • From Knative Service to Kubernetes Deployment & Service:

    • The Cloud Run YAML (kind: Service, apiVersion: serving.knative.dev/v1) encapsulates both the application's state and its network exposure.
    • In Kubernetes, this is split:
      • kind: Deployment (apiVersion: apps/v1): Manages the application pods, image updates, replica counts, and container specifications.
      • kind: Service (apiVersion: v1): Provides stable internal network addressing for the pods managed by the Deployment.

  • Autoscaling: Knative Annotations to Kubernetes HorizontalPodAutoscaler (HPA):

    • Cloud Run's simple autoscaling.knative.dev/maxScale annotation was replaced with a Kubernetes HorizontalPodAutoscaler (apiVersion: autoscaling/v2). This HPA was configured to scale pods based on CPU utilization, mirroring our previous scaling logic while GKE Autopilot manages the underlying node resources.

  • Secrets Management:

    • We transitioned from Cloud Run's direct integration with Google Cloud Secret Manager to using Kubernetes Secret objects. These secrets are populated with base64 encoded values and securely referenced in the Deployment's environment variables (valueFrom.secretKeyRef). Workload Identity remains the cornerstone of our security, allowing pods to access other Google Cloud services without stored keys.

Conversational Memory Management

This application implements conversational memory to provide context for ongoing interactions, enabling agents to understand follow-up questions, recall previous statements, and maintain a more natural conversational flow. The memory is persisted in a MongoDB database and integrated into the agent lifecycle using the ADK's callback mechanism.

Core Components

  1. Storage (mongo_memory.py):

    • Database: MongoDB is used as the backend to store conversation history.
    • Connection: The MongoMemory class handles the connection to MongoDB, retrieving the URI from environment variables (MONGODB_URI) or Google Cloud Secret Manager (MULTIMODAL_MONGODB_URI).
    • Service: An instance mongo_memory_service = MongoMemory(...) provides a global access point for memory operations.
    • Key Operations:
      • add_interaction(user_id, session_id, user_input, agent_response, turn_sequence): Saves a single turn of conversation (user query and agent reply) to the database, associated with a user and session.
      • get_recent_interactions(user_id, session_id, limit): Retrieves a specified number of the most recent interactions for a given user and session.

  2. Integration via Callbacks (callbacks.py):

    • The ADK's callback system is leveraged to hook into the lifecycle of LlmAgent instances.
    • save_interaction_after_model_callback(callback_context, llm_response):
      • Trigger: This function is registered as an after_model_callback. It executes after an LLM has processed a request and generated a response.
      • Action:
        1. Retrieves the user_id and session_id from the callback_context.
        2. Extracts the last user input from the session's event history (callback_context._invocation_context.session.events).
        3. Gets the agent's current response directly from the llm_response object passed to the callback.
        4. Calls mongo_memory_service.add_interaction() to persist this turn.
      • Outcome: The conversation turn is saved to MongoDB for future reference. It returns None to indicate it's not modifying the LLM's response at this stage.
    • load_memory_before_model_callback(callback_context, llm_request):
      • Trigger: This function is registered as a before_model_callback. It executes before a request is sent to an LLM.
      • Action:
        1. Retrieves user_id and session_id.
        2. Calls mongo_memory_service.get_recent_interactions() to fetch the recent conversation history (up to HISTORY_LIMIT).
        3. Formats this history into a list of google.genai.types.Content objects (alternating user and model roles).
        4. Prepends this list of historical Content objects to the llm_request.contents. This effectively adds the conversation history to the prompt that the LLM will receive.
      • Outcome: The LLM is provided with the context of recent interactions, allowing it to generate more relevant and context-aware responses. It returns None to allow the (now modified) llm_request to proceed to the model.

  3. Activation in Agent Configuration (agent_config.py):

    • The two callback functions (load_memory_before_model_callback and save_interaction_after_model_callback) are imported.
    • A dictionary memory_callbacks is defined to conveniently group these: python memory_callbacks = { "before_model_callback": load_memory_before_model_callback, "after_model_callback": save_interaction_after_model_callback, }
    • When LlmAgent instances (such as the root_agent, environmental_monitor_agent, text_synthesizer_agent, etc.) are created, these callbacks are registered with them using dictionary unpacking: python example_agent = LlmAgent( model=MODEL_ID_STREAMING, name="ExampleAgentWithMemory", instruction="...", tools=[...], **memory_callbacks # This applies the before_model and after_model callbacks )
    • This registration ensures that for every LLM call made by these specific agents, the conversation history is first loaded into the prompt, and then the resulting interaction is saved.

Flow of Memory Usage

  1. User sends a query to an agent configured with memory callbacks.
  2. Before LLM Call: load_memory_before_model_callback is triggered.
    • It fetches recent interactions for the user/session from MongoDB.
    • This history is added to the beginning of the prompt sent to the LLM.
  3. The LLM processes the combined input (history + current query) and generates a response.
  4. After LLM Call: save_interaction_after_model_callback is triggered.
    • It takes the user's current query (retrieved from session events) and the LLM's fresh response.
    • This new turn is saved to MongoDB.
  5. The agent returns the LLM's response to the user.

This cycle ensures that each interaction benefits from past context and contributes to the memory for future turns, specifically for the agents where the memory_callbacks have been applied.

Gemini Model Usage

This project leverages various Google Gemini models through the Vertex AI SDK to power its core functionalities, from conversational AI and text processing to advanced multimodal capabilities like image understanding and semantic search.

1. Core Conversational AI and Task Orchestration

  • Model: gemini-2.0-flash (referred to as GEMINI_PRO_MODEL_ID in agent_config.py)
  • Purpose: This model serves as the primary engine for most of the text-based Large Language Model (LLM) operations within the various specialized agents.
  • Usage:
    • Instruction Following: Agents like the EnvironmentalMonitorAgent, ContextualPrecomputationAgent, ReactiveTaskDelegatorAgent, various research agents (LocalPubMedSearchAgent, KeyInsightExtractorAgent, etc.), and synthesizer agents (TextSynthesizerAgent, FinalReportAggregatorAgent) use gemini-2.0-flash to understand their specific instructions and execute their roles.
    • Tool Use Decisions: These agents decide when and how to use their configured tools (including other agents or functions like query_pubmed_articles) based on the input and their instructions, powered by this model.
    • Text Generation: Generating reports, summaries, and internal state values as defined by each agent's specialized prompt.
    • Routing and Delegation: Agents like IntentRouterAgent and IngestionRouterAgent use it to parse user queries or data snippets and make decisions on how to delegate tasks to other specialized agents or tools.

2. Streaming Multimodal Interaction (AVA - Advanced Visual Assistant)

  • Model: gemini-2.0-flash-live-preview-04-09 (streaming-compatible model, referred to as MODEL_ID_STREAMING and GEMINI_MULTIMODAL_MODEL_ID in agent_config.py)
  • Purpose: This model powers the main user-facing "AVA" (Advanced Visual Assistant) agent (root_agent), enabling real-time, bidirectional interaction involving text, and implicitly, vision (and potentially audio if configured with MCP tools that handle audio streams).
  • Usage:
    • Visual Scene Analysis: As per its instructions, AVA is designed to "analyze incoming video frames," "identify relevant objects ('seen_items')," and infer "initial_context_keywords" from the visual scene combined with the user's query. This functionality is enabled by the multimodal capabilities of the Gemini live/streaming model.
    • Real-time Response: Handles user requests that may refer to or depend on the visual context being streamed to the agent.

3. Advanced Image Understanding and Processing

This functionality is primarily located in the multimodal ingestion and search script (e.g., ingest_multimodal_data.py ).

  • a. AI-Powered Image Captioning:

    • Model: gemini-2.5-flash-preview-05-20 (initialized as generative_model in your multimodal processing script).
    • Purpose: To generate rich, contextually relevant text descriptions for medical images (e.g., CT scans).
    • Usage (generate_image_caption function):
      1. Detailed Description: The model first receives the image data (Part.from_data) and a detailed prompt ("You are a radiology assistant. Describe the key anatomical structures and any potential anomalies...") to generate a comprehensive, long-form caption of the medical image.
      2. Concise Summarization: The same model then takes this long-form caption and a summarization prompt ("Summarize it into a very concise, single paragraph...") to produce a shorter, more focused summary suitable for metadata or embedding.

  • b. Multimodal Embeddings for Semantic Search:

    • Model: multimodalembedding@001 (initialized as embedding_model from MultiModalEmbeddingModel in your multimodal processing script).
    • Purpose: To create dense vector representations (embeddings) that capture the semantic meaning of both images and text, enabling powerful similarity searches.
    • Usage:
      1. Image Ingestion (generate_multimodal_embedding function): When ingesting medical images, this model takes the image bytes and its AI-generated text description to produce a 1408-dimension multimodal embedding. This embedding (specifically embeddings.image_embedding) is then stored in MongoDB alongside the image metadata.
      2. Similarity Search (find_similar_images function): When a user provides a text query to find similar images, the multimodalembedding@001 model is used again to generate an embedding for the input text query (query_embeddings.text_embedding). This text query vector is then used in a vector search operation against the pre-computed image embeddings stored in MongoDB to find the most visually and semantically similar images.

Initialization and Configuration

  • All Gemini models are accessed via the Vertex AI SDK.

  • Initialization typically involves:

    import vertexai
from vertexai.generative_models import GenerativeModel # For Flash, Pro
from vertexai.vision_models import MultiModalEmbeddingModel # For multimodal embeddings

vertexai.init(project=GCP_PROJECT_ID, location=GCP_LOCATION)

### Example initializations:
llm_flash_agent = GenerativeModel("gemini-2.0-flash")
streaming_vision_agent = GenerativeModel("gemini-2.0-flash-live-preview-04-09") # (or similar, typically wrapped by ADK LlmAgent)
captioning_model = GenerativeModel("gemini-2.5-flash-preview-05-20")
embedding_model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding@001")

  • Within the ADK framework (agent_config.py), models are typically assigned to LlmAgent instances via the model parameter.

This multi-model approach allows the application to use the most suitable Gemini variant for each specific task, optimizing for performance, capability, and cost.

Agent Evaluation

This project uses the ADK's evaluation framework (adk eval) to test the performance and correctness of the agents against predefined conversation scenarios (eval sets). This ensures that as the agent's logic evolves, its behavior remains consistent and correct.

Running Evaluations

This project contains evaluation sets for multiple agents to test specific capabilities in isolation.

To evaluate the primary main_agent's conversational and tool-use abilities:

adk eval main_agent main_agent/cocktailsEval.evalset.json

This command will:

  1. Load the root_agent defined in the main_agent module.
  2. Run the conversation turns defined in main_agent/cocktailsEval.evalset.json.
  3. Compare the agent's actual tool calls and final responses against the expected "golden" responses defined in the eval set.
  4. Generate a detailed result file in main_agent/.adk/eval_history/.

The evaluation results help track metrics like tool_trajectory_avg_score (did the agent call the right tools?) and response_match_score (how similar was the agent's text response to the expected one?).

Challenges we ran into 🤯🐛

  • Building the Multimodal Pipeline: The process was complex: downloading DICOM files, converting them to a usable format, using Gemini to generate descriptive captions, handling API character limits with a summarization layer, generating the multimodal embedding, and finally storing it all in MongoDB was a significant engineering challenge.
  • AgentTool Invocation Quirks: We discovered that ADK AgentTool instances required a .func attribute to be callable in streaming flows, which we patched manually. This critical fix was documented in a GitHub issue we filed. 🩹
  • Designing a Robust Multi-Agent Workflow: Our initial single-agent approach struggled with complex, multi-step tasks. We iteratively re-architected the system into the final "Triple-Stream Synthesizer" with a dispatcher and aggregator. This iterative process was challenging but resulted in a far more robust and scalable system.
  • Secure WebSocket Authentication: Implementing a bulletproof auth flow where a Firebase ID token is generated on the client, passed via a query parameter, and verified on the backend before the WebSocket connection is accepted required careful implementation and testing.
  • Orchestrating a Parallel RAG Pipeline: Designing the ResearchOrchestratorAgent to run multiple data fetches concurrently and then reliably collect and pass all results to the synthesis agent was a complex asynchronous challenge.

Accomplishments that we're proud of 🎉🏆

  • A Truly Multimodal AI: We successfully built a system that doesn't just process text but correlates it with real visual data. Demonstrating the agent finding a paper on lung cancer and then displaying an actual CT scan of a lung nodule is the accomplishment we are most proud of.
  • Advanced Multi-Agent Architecture: We didn't just use agents; we built a sophisticated "assembly line" for insight. The final "Dispatcher and Triple-Stream Synthesizer" pattern is a robust, scalable, and elegant solution to a very complex problem.
  • Meaningful Accessibility Features: We went beyond a proof-of-concept and built a practical accessibility suite that leverages the power of Gemini's scene understanding and the accuracy of the Google Cloud Vision API to provide real-world assistance to visually impaired users, making the digital and physical world more accessible.
  • End-to-End Secure, Scalable Deployment: Building and deploying a complete system that integrates Firebase Auth, a complex multi-agent backend using MongoDB for its core knowledge, and a dynamic frontend, all running as a serverless app on Google Cloud Run, is a massive achievement.
  • AI-Powered Data Labeling: We used Gemini not just as the agent's "brain," but also to bootstrap our knowledge base by having it generate the descriptive captions for our medical images, creating a virtuous cycle of AI improving its own data.
  • Dynamic Knowledge Base: The ability to find new information on the web and, with the user's permission, ingest it into our MongoDB database makes our AI a living, learning system.
  • Automated Data Visualization: Our VisualizationAgent can autonomously identify and chart data from synthesized research, turning complex findings into easy-to-understand visuals.
  • **We've identified our Kubernetes load balancer setup and created a migration plan to move 2 backend services and 2 forwarding rules from Google Cloud's classic Application Load Balancer to the new global external Application Load Balancer infrastructure.

What we learned 🧠📚

  • The Power of Specialized Agents: A complex task is best solved by breaking it down and giving each piece to a small, hyper-focused agent. The "assembly line" pattern is far more robust than a single, monolithic agent.
  • Deep Dive into ADK 1.x: Gained significant experience with the newer ADK, especially its async nature, ParallelAgent for concurrency, and AgentTool intricacies.
  • MongoDB is a Multimodal Powerhouse: We learned that MongoDB Atlas isn't just for storing text. By combining its ability to store complex documents with the power of Atlas Vector Search, it can serve as the foundation for a powerful knowledge base that seamlessly blends text, numbers, and visual embedding data.
  • Full-Stack AI Security: We mastered the end-to-end pattern of using Firebase for client-side authentication and the Admin SDK for backend verification, a crucial skill for building real-world, secure AI applications.
  • Secure, Serverless Agent Deployment: Gained practical experience containerizing a complex Python application with Docker and deploying it on Google Cloud Run. We learned how to manage environment variables and service account permissions for a seamless cloud deployment.

What's next for Galactic StreamHub 🚀🌠

  • ** Zero Knowledge Proofs**: We would securely store user data by encrypting it.
  • Expand the Multimodal Knowledge Base: Ingest more varied medical imaging datasets (MRIs, Pathology Slides) to broaden the agent's visual expertise.
  • Interactive Visuals: Allow users to click on parts of a medical image and have the agent explain what it sees in that specific region.
  • Expand RAG Data Sources: Integrate more APIs and databases into our ResearchOrchestratorAgent to provide even more comprehensive answers.
  • More Advanced Visualizations: Move beyond simple charts to generate more complex graphs, heatmaps, or interactive diagrams.
  • Contribute Back: Continue sharing our findings and issue reports with the ADK community to help improve the framework for everyone. 🤝
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