Refine tool call execution logic in chat-stream-provider to prevent duplicates, enhance retries for agentic-rag, and improve incremental processing, including test updates.

- Implemented intelligent retrieval-augmented generation system (`agentic_rag`) for dynamic decision-making on knowledge retrieval.
- Uses Milvus with a large dataset
- Added comprehensive test cases for query analysis, storage, retrieval, and error handling.
- Integrated `AgenticRAGTools` into `chat-stream-provider` enabling tool-based responses.
- Updated dependencies with `@zilliz/milvus2-sdk-node` for Milvus integration.
- Updated lander hero title.

.dockerignore

@@ -0,0 +1,3 @@
+/.wrangler/
+/.open-gsio/
+/node_modules/

.gitignore

@@ -7,11 +7,22 @@
 **/.idea/
 **/html/
 **/.env
-packages/client/public/static/fonts/*
 
 **/secrets.json
 **/.dev.vars
-packages/client/public/sitemap.xml
-packages/client/public/robots.txt
 wrangler.dev.jsonc
 /packages/client/public/static/fonts/
+/packages/client/public/robots.txt
+/packages/client/public/sitemap.xml
+/packages/client/public/yachtpit.html
+/packages/client/public/yachtpit.js
+/packages/client/public/yachtpit_bg.wasm
+/packages/client/public/assets/
+/packages/client/public/apple-touch-icon-180x180.png
+/packages/client/public/icon.ico
+/packages/client/public/maskable-icon-512x512.png
+/packages/client/public/pwa-64x64.png
+/packages/client/public/pwa-192x192.png
+/packages/client/public/pwa-512x512.png
+packages/client/public/yachtpit_bg*
+/project/

Dockerfile

@@ -0,0 +1,51 @@
+FROM oven/bun:latest
+
+WORKDIR /app
+
+# Copy package files first for better caching
+COPY package.json bun.lock ./
+
+# Create directory structure for all packages
+RUN mkdir -p packages/ai packages/ai/src/types packages/client packages/coordinators packages/env packages/router packages/schema packages/scripts packages/server packages/services packages/cloudflare-workers/analytics packages/cloudflare-workers/open-gsio
+
+# Copy package.json files for all packages
+COPY packages/ai/package.json ./packages/ai/
+COPY packages/ai/src/types/package.json ./packages/ai/src/types/
+COPY packages/client/package.json ./packages/client/
+COPY packages/coordinators/package.json ./packages/coordinators/
+COPY packages/env/package.json ./packages/env/
+COPY packages/router/package.json ./packages/router/
+COPY packages/schema/package.json ./packages/schema/
+COPY packages/scripts/package.json ./packages/scripts/
+COPY packages/server/package.json ./packages/server/
+COPY packages/services/package.json ./packages/services/
+COPY packages/cloudflare-workers/analytics/package.json ./packages/cloudflare-workers/analytics/
+COPY packages/cloudflare-workers/open-gsio/package.json ./packages/cloudflare-workers/open-gsio/
+
+# Install dependencies
+RUN bun install
+
+# Copy the rest of the application
+COPY . .
+
+# Create .env file if it doesn't exist
+RUN touch ./packages/server/.env
+
+# Build client and server
+RUN bun build:client && bun build:server
+
+# Ensure the client directories exist
+RUN mkdir -p ./client/public ./client/dist/client
+
+# Copy client files to the expected locations
+RUN cp -r ./packages/client/public/* ./client/public/ || true
+RUN cp -r ./packages/client/dist/* ./client/dist/ || true
+
+EXPOSE 3003
+
+# Verify server.js exists
+RUN test -f ./packages/server/dist/server.js || (echo "Error: server.js not found" && exit 1)
+
+
+
+CMD ["bun", "./packages/server/dist/server.js"]

README.md

@@ -14,6 +14,7 @@ This is a full-stack Conversational AI.
 
 - [Installation](#installation)
 - [Deployment](#deployment)
+- [Docker](#docker)
 - [Local Inference](#local-inference)
   - [mlx-omni-server (default)](#mlx-omni-server)
     - [Adding models](#adding-models-for-local-inference-apple-silicon)
@@ -40,6 +41,59 @@ This is a full-stack Conversational AI.
 
 > Note: Subsequent deployments should omit `bun run deploy:secrets`
 
+## Docker
+
+You can run the server using Docker. The image is large but will be slimmed down in future commits.
+
+### Building the Docker Image
+
+```bash
+docker compose build
+# OR
+docker build -t open-gsio .
+```
+
+### Running the Docker Container
+
+```bash
+docker run -p 3003:3003 \
+  -e GROQ_API_KEY=your_groq_api_key \
+  -e FIREWORKS_API_KEY=your_fireworks_api_key \
+  open-gsio
+```
+
+You can omit any environment variables that you don't need. The server will be available at http://localhost:3003.
+
+### Using Docker Compose
+
+A `docker-compose.yml` file is provided in the repository. You can edit it to add your API keys:
+
+```yaml
+version: '3'
+services:
+  open-gsio:
+    build: .
+    ports:
+      - "3003:3003"
+    environment:
+      - GROQ_API_KEY=your_groq_api_key
+      - FIREWORKS_API_KEY=your_fireworks_api_key
+      # Other environment variables are included in the file
+    restart: unless-stopped
+```
+
+Then run:
+
+```bash
+docker compose up
+```
+
+Or to run in detached mode:
+
+```bash
+docker compose up -d
+```
+
 ## Local Inference
 
 > Local inference is supported for Ollama and mlx-omni-server. OpenAI compatible servers can be used by overriding OPENAI_API_KEY and OPENAI_API_ENDPOINT.
@@ -116,6 +170,8 @@ I would like to express gratitude to the following projects, libraries, and indi
 - [Vike](https://vike.dev/) - Framework for server-side rendering and routing
 - [Cloudflare Workers](https://developers.cloudflare.com/workers/) - Serverless execution environment
 - [Bun](https://bun.sh/) - JavaScript runtime and toolkit
+- [Marked.js](https://github.com/markedjs/marked) - Markdown Rendering
+- [Shiki](https://github.com/shikijs/shiki) - Syntax Highlighting
 - [itty-router](https://github.com/kwhitley/itty-router) - Lightweight router for serverless environments
 - [MobX-State-Tree](https://mobx-state-tree.js.org/) - State management solution
 - [OpenAI SDK](https://github.com/openai/openai-node) - Client for AI model integration

docker-compose.yml

@@ -0,0 +1,13 @@
+version: '3'
+services:
+  open-gsio:
+    image: open-gsio:latest
+    build:
+      pull: false
+      context: .
+      dockerfile: Dockerfile
+    ports:
+      - "3003:3003"
+    env_file:
+      - ./packages/server/.env
+    restart: unless-stopped

package.json

@@ -15,6 +15,7 @@
     "server:dev": "bun build:client && (cd packages/server && bun run dev)",
     "build": "(cd packages/cloudflare-workers/open-gsio && bun run deploy:dry-run)",
     "build:client": "(cd packages/client && bun run vite build)",
+    "build:server": "bun --filter=@open-gsio/server run build",
     "deploy": "(cd packages/cloudflare-workers/open-gsio && bun run deploy)",
     "deploy:secrets": "wrangler secret bulk secrets.json -c packages/cloudflare-workers/open-gsio/wrangler.jsonc",
     "openai:local:mlx": "packages/scripts/start_inference_server.sh mlx-omni-server",
@@ -30,7 +31,7 @@
     "@types/bun": "^1.2.17",
     "@typescript-eslint/eslint-plugin": "^8.35.0",
     "@typescript-eslint/parser": "^8.35.0",
-    "eslint": "^9.29.0",
+    "eslint": "^8",
     "eslint-config-prettier": "^10.1.5",
     "eslint-plugin-import": "^2.32.0",
     "eslint-plugin-prettier": "^5.5.1",
@@ -39,5 +40,9 @@
   },
   "peerDependencies": {
     "typescript": "^5.8.3"
-  }
+  },
+  "dependencies": {
+    "@chakra-ui/icons": "^2.2.4"
+  },
+  "packageManager": "pnpm@10.10.0+sha512.d615db246fe70f25dcfea6d8d73dee782ce23e2245e3c4f6f888249fb568149318637dca73c2c5c8ef2a4ca0d5657fb9567188bfab47f566d1ee6ce987815c39"
 }

packages/ai/package.json

@@ -40,6 +40,7 @@
     "@open-gsio/env": "workspace:*",
     "@open-gsio/schema": "workspace:*",
     "@anthropic-ai/sdk": "^0.55.0",
+    "@zilliz/milvus2-sdk-node": "^2.6.0",
     "openai": "^5.0.1",
     "wrangler": "^4.18.0",
     "vitest": "^3.1.4",

packages/ai/src/__tests__/assistant-sdk.test.ts

@@ -46,7 +46,6 @@ describe('AssistantSdk', () => {
 
       expect(prompt).toContain('# Assistant Knowledge');
       expect(prompt).toContain('### Date: ');
-      expect(prompt).toContain('### Web Host: ');
       expect(prompt).toContain('### User Location: ');
       expect(prompt).toContain('### Timezone: ');
     });

packages/ai/src/assistant-sdk/assistant-sdk.ts

@@ -22,9 +22,10 @@ export class AssistantSdk {
     const currentTime = `${now.getHours()}:${formattedMinutes} ${now.getSeconds()}s`;
 
     return `# Assistant Knowledge
+## Assistant Name
+### open-gsio
 ## Current Context
 ### Date: ${currentDate} ${currentTime}
-### Web Host: open-gsio.seemueller.workers.dev
 ${maxTokens ? `### Max Response Length: ${maxTokens} tokens (maximum)` : ''}
 ### Lexicographical Format: Markdown
 ### User Location: ${userLocation || 'Unknown'}

packages/ai/src/chat-sdk/chat-sdk.ts

@@ -2,6 +2,7 @@ import { Schema } from '@open-gsio/schema';
 import type { Instance } from 'mobx-state-tree';
 import { OpenAI } from 'openai';
 
+import type Message from '../../../schema/src/models/Message.ts';
 import { AssistantSdk } from '../assistant-sdk';
 import { ProviderRepository } from '../providers/_ProviderRepository.ts';
 import type {

packages/ai/src/providers/__tests__/chat-stream-provider.test.ts

@@ -1,5 +1,5 @@
 import { OpenAI } from 'openai';
-import { describe, it, expect, vi } from 'vitest';
+import { describe, it, expect, vi, beforeEach } from 'vitest';
 
 import {
   BaseChatProvider,
@@ -29,7 +29,7 @@ class TestChatProvider extends BaseChatProvider {
 }
 
 // Mock dependencies
-vi.mock('../../lib/chat-sdk', () => ({
+vi.mock('../../chat-sdk/chat-sdk.ts', () => ({
   default: {
     buildAssistantPrompt: vi.fn().mockReturnValue('Assistant prompt'),
     buildMessageChain: vi.fn().mockReturnValue([
@@ -39,6 +39,26 @@ vi.mock('../../lib/chat-sdk', () => ({
   },
 }));
 
+vi.mock('../../tools/agentic-rag.ts', () => ({
+  agenticRAG: vi.fn(),
+  AgenticRAGTools: {
+    type: 'function',
+    function: {
+      name: 'agentic_rag',
+      description: 'Test agentic RAG tool',
+      parameters: {
+        type: 'object',
+        properties: {
+          action: { type: 'string', enum: ['search_knowledge'] },
+          query: { type: 'string' },
+          collection_name: { type: 'string' },
+        },
+        required: ['action', 'collection_name'],
+      },
+    },
+  },
+}));
+
 describe('ChatStreamProvider', () => {
   it('should define the required interface', () => {
     // Verify the interface has the required method
@@ -50,26 +70,616 @@ describe('ChatStreamProvider', () => {
   });
 });
 
-describe('BaseChatProvider', () => {
+describe('BaseChatProvider - Model Tool Calling', () => {
-  it('should implement the ChatStreamProvider interface', () => {
+  let provider: TestChatProvider;
-    // Create a concrete implementation
+  let mockOpenAI: any;
-    const provider = new TestChatProvider();
+  let dataCallback: any;
+  let commonParams: CommonProviderParams;
 
-    // Verify it implements the interface
+  beforeEach(() => {
+    vi.clearAllMocks();
+
+    provider = new TestChatProvider();
+    dataCallback = vi.fn();
+
+    mockOpenAI = {
+      chat: {
+        completions: {
+          create: vi.fn(),
+        },
+      },
+    };
+
+    commonParams = {
+      openai: mockOpenAI,
+      systemPrompt: 'Test system prompt',
+      preprocessedContext: {},
+      maxTokens: 1000,
+      messages: [{ role: 'user', content: 'Test message' }],
+      model: 'gpt-4',
+      env: {} as any,
+    };
+  });
+
+  it('should implement the ChatStreamProvider interface', () => {
     expect(provider.handleStream).toBeInstanceOf(Function);
     expect(provider.getOpenAIClient).toBeInstanceOf(Function);
     expect(provider.getStreamParams).toBeInstanceOf(Function);
     expect(provider.processChunk).toBeInstanceOf(Function);
   });
 
-  it('should have abstract methods that need to be implemented', () => {
+  it('should handle regular text streaming without tool calls', async () => {
-    // This test verifies that the abstract methods exist
+    // Mock stream chunks for regular text response
-    // We can't instantiate BaseChatProvider directly, so we use the concrete implementation
+    const chunks = [
-    const provider = new TestChatProvider();
+      {
+        choices: [
+          {
+            delta: { content: 'Hello ' },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: { content: 'world!' },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'stop',
+          },
+        ],
+      },
+    ];
 
-    // Verify the abstract methods are implemented
+    mockOpenAI.chat.completions.create.mockResolvedValue({
-    expect(provider.getOpenAIClient).toBeDefined();
+      async *[Symbol.asyncIterator]() {
-    expect(provider.getStreamParams).toBeDefined();
+        for (const chunk of chunks) {
-    expect(provider.processChunk).toBeDefined();
+          yield chunk;
+        }
+      },
+    });
+
+    await provider.handleStream(commonParams, dataCallback);
+
+    expect(mockOpenAI.chat.completions.create).toHaveBeenCalledWith(
+      expect.objectContaining({
+        tools: expect.arrayContaining([
+          expect.objectContaining({
+            type: 'function',
+            function: expect.objectContaining({
+              name: 'agentic_rag',
+            }),
+          }),
+        ]),
+      }),
+    );
+  });
+
+  it('should handle tool calls in streaming response', async () => {
+    const { agenticRAG } = await import('../../tools/agentic-rag.ts');
+    vi.mocked(agenticRAG).mockResolvedValue({
+      success: true,
+      data: {
+        results: ['Test result'],
+        analysis: { needsRetrieval: false },
+      },
+    });
+
+    // Mock stream chunks for tool call response
+    const chunks = [
+      {
+        choices: [
+          {
+            delta: {
+              tool_calls: [
+                {
+                  index: 0,
+                  id: 'call_123',
+                  type: 'function',
+                  function: {
+                    name: 'agentic_rag',
+                    arguments:
+                      '{"action": "search_knowledge", "query": "test query", "collection_name": "test_collection"}',
+                  },
+                },
+              ],
+            },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'tool_calls',
+          },
+        ],
+      },
+    ];
+
+    // Second stream for response after tool execution
+    const secondStreamChunks = [
+      {
+        choices: [
+          {
+            delta: { content: 'Based on the search results: Test result' },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'stop',
+          },
+        ],
+      },
+    ];
+
+    let callCount = 0;
+    mockOpenAI.chat.completions.create.mockImplementation(() => {
+      callCount++;
+      if (callCount === 1) {
+        return Promise.resolve({
+          async *[Symbol.asyncIterator]() {
+            for (const chunk of chunks) {
+              yield chunk;
+            }
+          },
+        });
+      } else {
+        return Promise.resolve({
+          async *[Symbol.asyncIterator]() {
+            for (const chunk of secondStreamChunks) {
+              yield chunk;
+            }
+          },
+        });
+      }
+    });
+
+    await provider.handleStream(commonParams, dataCallback);
+
+    // Verify tool was called
+    expect(agenticRAG).toHaveBeenCalledWith({
+      action: 'search_knowledge',
+      query: 'test query',
+      collection_name: 'test_collection',
+    });
+
+    // Verify feedback messages were sent
+    expect(dataCallback).toHaveBeenCalledWith(
+      expect.objectContaining({
+        type: 'chat',
+        data: expect.objectContaining({
+          choices: expect.arrayContaining([
+            expect.objectContaining({
+              delta: expect.objectContaining({
+                content: expect.stringContaining('🔧 Invoking'),
+              }),
+            }),
+          ]),
+        }),
+      }),
+    );
+
+    expect(dataCallback).toHaveBeenCalledWith(
+      expect.objectContaining({
+        type: 'chat',
+        data: expect.objectContaining({
+          choices: expect.arrayContaining([
+            expect.objectContaining({
+              delta: expect.objectContaining({
+                content: expect.stringContaining('📞 Calling agentic_rag'),
+              }),
+            }),
+          ]),
+        }),
+      }),
+    );
+  });
+
+  it('should handle tool call streaming with incremental arguments', async () => {
+    const { agenticRAG } = await import('../../tools/agentic-rag.ts');
+    vi.mocked(agenticRAG).mockResolvedValue({
+      success: true,
+      data: { results: ['Test result'] },
+    });
+
+    // Mock stream chunks with incremental tool call arguments
+    const chunks = [
+      {
+        choices: [
+          {
+            delta: {
+              tool_calls: [
+                {
+                  index: 0,
+                  id: 'call_',
+                  type: 'function',
+                  function: { name: 'agentic_rag', arguments: '{"action": "search_' },
+                },
+              ],
+            },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {
+              tool_calls: [
+                {
+                  index: 0,
+                  id: '123',
+                  function: { arguments: 'knowledge", "query": "test", ' },
+                },
+              ],
+            },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {
+              tool_calls: [
+                {
+                  index: 0,
+                  function: { arguments: '"collection_name": "test_collection"}' },
+                },
+              ],
+            },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'tool_calls',
+          },
+        ],
+      },
+    ];
+
+    const secondStreamChunks = [
+      {
+        choices: [
+          {
+            delta: { content: 'Response after tool call' },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'stop',
+          },
+        ],
+      },
+    ];
+
+    let callCount = 0;
+    mockOpenAI.chat.completions.create.mockImplementation(() => {
+      callCount++;
+      if (callCount === 1) {
+        return Promise.resolve({
+          async *[Symbol.asyncIterator]() {
+            for (const chunk of chunks) {
+              yield chunk;
+            }
+          },
+        });
+      } else {
+        return Promise.resolve({
+          async *[Symbol.asyncIterator]() {
+            for (const chunk of secondStreamChunks) {
+              yield chunk;
+            }
+          },
+        });
+      }
+    });
+
+    await provider.handleStream(commonParams, dataCallback);
+
+    // Verify the complete tool call was assembled and executed
+    expect(agenticRAG).toHaveBeenCalledWith({
+      action: 'search_knowledge',
+      query: 'test',
+      collection_name: 'test_collection',
+    });
+  });
+
+  it('should prevent infinite tool call loops', async () => {
+    const { agenticRAG } = await import('../../tools/agentic-rag.ts');
+    vi.mocked(agenticRAG).mockResolvedValue({
+      success: true,
+      data: {
+        results: [],
+        analysis: { needsRetrieval: true },
+        retrieved_documents: [],
+      },
+    });
+
+    // Mock stream that always returns tool calls
+    const toolCallChunks = [
+      {
+        choices: [
+          {
+            delta: {
+              tool_calls: [
+                {
+                  index: 0,
+                  id: 'call_123',
+                  type: 'function',
+                  function: {
+                    name: 'agentic_rag',
+                    arguments:
+                      '{"action": "search_knowledge", "query": "test", "collection_name": "test_collection"}',
+                  },
+                },
+              ],
+            },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'tool_calls',
+          },
+        ],
+      },
+    ];
+
+    mockOpenAI.chat.completions.create.mockResolvedValue({
+      async *[Symbol.asyncIterator]() {
+        for (const chunk of toolCallChunks) {
+          yield chunk;
+        }
+      },
+    });
+
+    await provider.handleStream(commonParams, dataCallback);
+
+    // Should detect duplicate tool calls and force completion (up to 5 iterations based on maxToolCallIterations)
+    // In this case, it should stop after 2 calls due to duplicate detection, but could go up to 5
+    expect(mockOpenAI.chat.completions.create).toHaveBeenCalledTimes(2);
+  });
+
+  it('should handle tool call errors gracefully', async () => {
+    const { agenticRAG } = await import('../../tools/agentic-rag.ts');
+    vi.mocked(agenticRAG).mockRejectedValue(new Error('Tool execution failed'));
+
+    const chunks = [
+      {
+        choices: [
+          {
+            delta: {
+              tool_calls: [
+                {
+                  index: 0,
+                  id: 'call_123',
+                  type: 'function',
+                  function: {
+                    name: 'agentic_rag',
+                    arguments:
+                      '{"action": "search_knowledge", "query": "test", "collection_name": "test_collection"}',
+                  },
+                },
+              ],
+            },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'tool_calls',
+          },
+        ],
+      },
+    ];
+
+    const secondStreamChunks = [
+      {
+        choices: [
+          {
+            delta: { content: 'I apologize, but I encountered an error.' },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'stop',
+          },
+        ],
+      },
+    ];
+
+    let callCount = 0;
+    mockOpenAI.chat.completions.create.mockImplementation(() => {
+      callCount++;
+      if (callCount === 1) {
+        return Promise.resolve({
+          async *[Symbol.asyncIterator]() {
+            for (const chunk of chunks) {
+              yield chunk;
+            }
+          },
+        });
+      } else {
+        return Promise.resolve({
+          async *[Symbol.asyncIterator]() {
+            for (const chunk of secondStreamChunks) {
+              yield chunk;
+            }
+          },
+        });
+      }
+    });
+
+    await provider.handleStream(commonParams, dataCallback);
+
+    // Should still complete without throwing
+    expect(mockOpenAI.chat.completions.create).toHaveBeenCalledTimes(2);
+  });
+
+  it('should prevent duplicate tool calls', async () => {
+    const { agenticRAG } = await import('../../tools/agentic-rag.ts');
+    vi.mocked(agenticRAG).mockResolvedValue({
+      success: true,
+      data: { results: ['Test result'] },
+    });
+
+    // Mock the same tool call twice
+    const chunks = [
+      {
+        choices: [
+          {
+            delta: {
+              tool_calls: [
+                {
+                  index: 0,
+                  id: 'call_123',
+                  type: 'function',
+                  function: {
+                    name: 'agentic_rag',
+                    arguments:
+                      '{"action": "search_knowledge", "query": "test", "collection_name": "test_collection"}',
+                  },
+                },
+              ],
+            },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'tool_calls',
+          },
+        ],
+      },
+    ];
+
+    // Second iteration with same tool call
+    let callCount = 0;
+    mockOpenAI.chat.completions.create.mockImplementation(() => {
+      callCount++;
+      return Promise.resolve({
+        async *[Symbol.asyncIterator]() {
+          for (const chunk of chunks) {
+            yield chunk;
+          }
+        },
+      });
+    });
+
+    await provider.handleStream(commonParams, dataCallback);
+
+    // Should only execute the tool once, then force completion
+    expect(agenticRAG).toHaveBeenCalledTimes(1);
+  });
+
+  it('should handle invalid JSON in tool call arguments', async () => {
+    const chunks = [
+      {
+        choices: [
+          {
+            delta: {
+              tool_calls: [
+                {
+                  index: 0,
+                  id: 'call_123',
+                  type: 'function',
+                  function: {
+                    name: 'agentic_rag',
+                    arguments: '{"action": "search_knowledge", "invalid": json}', // Invalid JSON
+                  },
+                },
+              ],
+            },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'tool_calls',
+          },
+        ],
+      },
+    ];
+
+    const secondStreamChunks = [
+      {
+        choices: [
+          {
+            delta: { content: 'I encountered an error parsing the tool arguments.' },
+            finish_reason: null,
+          },
+        ],
+      },
+      {
+        choices: [
+          {
+            delta: {},
+            finish_reason: 'stop',
+          },
+        ],
+      },
+    ];
+
+    let callCount = 0;
+    mockOpenAI.chat.completions.create.mockImplementation(() => {
+      callCount++;
+      if (callCount === 1) {
+        return Promise.resolve({
+          async *[Symbol.asyncIterator]() {
+            for (const chunk of chunks) {
+              yield chunk;
+            }
+          },
+        });
+      } else {
+        return Promise.resolve({
+          async *[Symbol.asyncIterator]() {
+            for (const chunk of secondStreamChunks) {
+              yield chunk;
+            }
+          },
+        });
+      }
+    });
+
+    // Should not throw, should handle gracefully
+    await expect(provider.handleStream(commonParams, dataCallback)).resolves.not.toThrow();
   });
 });

packages/ai/src/providers/chat-stream-provider.ts

@@ -1,6 +1,7 @@
 import { OpenAI } from 'openai';
 
 import ChatSdk from '../chat-sdk/chat-sdk.ts';
+import { agenticRAG, AgenticRAGTools } from '../tools/agentic-rag.ts';
 import type { GenericEnv } from '../types';
 
 export interface CommonProviderParams {
@@ -35,12 +36,296 @@ export abstract class BaseChatProvider implements ChatStreamProvider {
     });
 
     const client = this.getOpenAIClient(param);
+
+    const tools = [AgenticRAGTools];
+
+    const callFunction = async (name, args) => {
+      if (name === 'agentic_rag') {
+        return agenticRAG(args);
+      }
+    };
+
+    // Main conversation loop - handle tool calls properly
+    let conversationComplete = false;
+    let toolCallIterations = 0;
+    const maxToolCallIterations = 5; // Prevent infinite loops
+    let toolsExecuted = false; // Track if we've executed tools
+    const attemptedToolCalls = new Set<string>(); // Track attempted tool calls to prevent duplicates
+
+    while (!conversationComplete && toolCallIterations < maxToolCallIterations) {
       const streamParams = this.getStreamParams(param, safeMessages);
-    const stream = await client.chat.completions.create(streamParams);
+      // Only provide tools on the first call, after that force text response
+      const currentTools = toolsExecuted ? undefined : tools;
+
+      const stream = await client.chat.completions.create({ ...streamParams, tools: currentTools });
+
+      let assistantMessage = '';
+      const toolCalls: any[] = [];
 
       for await (const chunk of stream as unknown as AsyncIterable<any>) {
+        // console.log('chunk', chunk);
+
+        // Handle tool calls
+        if (chunk.choices[0]?.delta?.tool_calls) {
+          const deltaToolCalls = chunk.choices[0].delta.tool_calls;
+
+          for (const deltaToolCall of deltaToolCalls) {
+            if (deltaToolCall.index !== undefined) {
+              // Initialize or get existing tool call
+              if (!toolCalls[deltaToolCall.index]) {
+                toolCalls[deltaToolCall.index] = {
+                  id: deltaToolCall.id || '',
+                  type: deltaToolCall.type || 'function',
+                  function: {
+                    name: deltaToolCall.function?.name || '',
+                    arguments: deltaToolCall.function?.arguments || '',
+                  },
+                };
+              } else {
+                // Append to existing tool call
+                if (deltaToolCall.function?.arguments) {
+                  toolCalls[deltaToolCall.index].function.arguments +=
+                    deltaToolCall.function.arguments;
+                }
+                if (deltaToolCall.function?.name) {
+                  toolCalls[deltaToolCall.index].function.name += deltaToolCall.function.name;
+                }
+                if (deltaToolCall.id) {
+                  toolCalls[deltaToolCall.index].id += deltaToolCall.id;
+                }
+              }
+            }
+          }
+        }
+
+        // Handle regular content
+        if (chunk.choices[0]?.delta?.content) {
+          assistantMessage += chunk.choices[0].delta.content;
+        }
+
+        // Check if stream is finished
+        if (chunk.choices[0]?.finish_reason) {
+          if (chunk.choices[0].finish_reason === 'tool_calls' && toolCalls.length > 0) {
+            // Increment tool call iterations counter
+            toolCallIterations++;
+            console.log(`Tool call iteration ${toolCallIterations}/${maxToolCallIterations}`);
+
+            // Execute tool calls and add results to conversation
+            console.log('Executing tool calls:', toolCalls);
+
+            // Limit to one tool call per iteration to prevent concurrent execution issues
+            // Also filter out duplicate tool calls
+            const uniqueToolCalls = toolCalls.filter(toolCall => {
+              const toolCallKey = `${toolCall.function.name}:${toolCall.function.arguments}`;
+              return !attemptedToolCalls.has(toolCallKey);
+            });
+            const toolCallsToExecute = uniqueToolCalls.slice(0, 1);
+
+            if (toolCallsToExecute.length === 0) {
+              console.log('All tool calls have been attempted already, forcing completion');
+              toolsExecuted = true;
+              conversationComplete = true;
+              break;
+            }
+
+            // Send feedback to user about tool invocation
+            dataCallback({
+              type: 'chat',
+              data: {
+                choices: [
+                  {
+                    delta: {
+                      content: `\n\n🔧 Invoking ${toolCallsToExecute.length} tool${toolCallsToExecute.length > 1 ? 's' : ''}...\n`,
+                    },
+                  },
+                ],
+              },
+            });
+
+            // Add assistant message with tool calls to conversation
+            safeMessages.push({
+              role: 'assistant',
+              content: assistantMessage || null,
+              tool_calls: toolCallsToExecute,
+            });
+
+            // Execute each tool call and add results
+            let needsMoreRetrieval = false;
+            for (const toolCall of toolCallsToExecute) {
+              if (toolCall.type === 'function') {
+                const name = toolCall.function.name;
+                console.log(`Calling function: ${name}`);
+
+                // Track this tool call attempt
+                const toolCallKey = `${toolCall.function.name}:${toolCall.function.arguments}`;
+                attemptedToolCalls.add(toolCallKey);
+
+                // Send feedback about specific tool being called
+                dataCallback({
+                  type: 'chat',
+                  data: {
+                    choices: [
+                      {
+                        delta: {
+                          content: `📞 Calling ${name}...`,
+                        },
+                      },
+                    ],
+                  },
+                });
+
+                try {
+                  const args = JSON.parse(toolCall.function.arguments);
+                  console.log(`Function arguments:`, args);
+
+                  const result = await callFunction(name, args);
+                  console.log(`Function result:`, result);
+
+                  // Check if agentic-rag indicates more retrieval is needed
+                  if (
+                    name === 'agentic_rag' &&
+                    result?.data?.analysis?.needsRetrieval === true &&
+                    (!result?.data?.retrieved_documents ||
+                      result.data.retrieved_documents.length === 0)
+                  ) {
+                    needsMoreRetrieval = true;
+                    console.log('Agentic RAG indicates more retrieval needed');
+
+                    // Add context about previous attempts to help LLM make better decisions
+                    const attemptedActions = Array.from(attemptedToolCalls)
+                      .filter(key => key.startsWith('agentic_rag:'))
+                      .map(key => {
+                        try {
+                          const args = JSON.parse(key.split(':', 2)[1]);
+                          return `${args.action} with query: "${args.query}"`;
+                        } catch {
+                          return 'unknown action';
+                        }
+                      });
+
+                    if (attemptedActions.length > 0) {
+                      safeMessages.push({
+                        role: 'system',
+                        content: `Previous retrieval attempts: ${attemptedActions.join(', ')}. Consider trying a different approach or more specific query.`,
+                      });
+                    }
+                  }
+
+                  // Send feedback about tool completion
+                  dataCallback({
+                    type: 'chat',
+                    data: {
+                      choices: [
+                        {
+                          delta: {
+                            content: ` ✅\n ${JSON.stringify(result)}`,
+                          },
+                        },
+                      ],
+                    },
+                  });
+
+                  // Add tool result to conversation
+                  safeMessages.push({
+                    role: 'tool',
+                    tool_call_id: toolCall.id,
+                    content: JSON.stringify(result),
+                  });
+                } catch (error) {
+                  console.error(`Error executing tool ${name}:`, error);
+
+                  // Send feedback about tool error
+                  dataCallback({
+                    type: 'chat',
+                    data: {
+                      choices: [
+                        {
+                          delta: {
+                            content: ` ❌ Error\n`,
+                          },
+                        },
+                      ],
+                    },
+                  });
+
+                  safeMessages.push({
+                    role: 'tool',
+                    tool_call_id: toolCall.id,
+                    content: `Error: ${error.message}`,
+                  });
+                }
+              }
+            }
+
+            // Only mark tools as executed if we don't need more retrieval
+            if (!needsMoreRetrieval) {
+              toolsExecuted = true;
+            }
+
+            // Send feedback that tool execution is complete
+            dataCallback({
+              type: 'chat',
+              data: {
+                choices: [
+                  {
+                    delta: {
+                      content: `\n🎯 Tool execution complete. Generating response...\n\n`,
+                    },
+                  },
+                ],
+              },
+            });
+
+            // Continue conversation with tool results
+            break;
+          } else {
+            // Regular completion - send final response
+            conversationComplete = true;
+          }
+        }
+
+        // Process chunk normally for non-tool-call responses
+        if (!chunk.choices[0]?.delta?.tool_calls) {
+          // console.log('after-tool-call-chunk', chunk);
+          const shouldBreak = await this.processChunk(chunk, dataCallback);
+          if (shouldBreak) {
+            conversationComplete = true;
+            break;
+          }
+        }
+      }
+    }
+
+    // Handle case where we hit maximum tool call iterations
+    if (toolCallIterations >= maxToolCallIterations && !conversationComplete) {
+      console.log('Maximum tool call iterations reached, forcing completion');
+
+      // Send a message indicating we've hit the limit and provide available information
+      dataCallback({
+        type: 'chat',
+        data: {
+          choices: [
+            {
+              delta: {
+                content:
+                  '\n\n⚠️ Maximum tool execution limit reached. Based on the available information, I can provide the following response:\n\n',
+              },
+            },
+          ],
+        },
+      });
+
+      // Make one final call without tools to get a response based on the tool results
+      const finalStreamParams = this.getStreamParams(param, safeMessages);
+      const finalStream = await client.chat.completions.create({
+        ...finalStreamParams,
+        tools: undefined, // Remove tools to force a text response
+      });
+
+      for await (const chunk of finalStream as unknown as AsyncIterable<any>) {
         const shouldBreak = await this.processChunk(chunk, dataCallback);
         if (shouldBreak) break;
       }
     }
   }
+}

packages/ai/src/providers/fireworks.ts

@@ -15,10 +15,21 @@ export class FireworksAiChatProvider extends BaseChatProvider {
     let modelPrefix = 'accounts/fireworks/models/';
     if (param.model.toLowerCase().includes('yi-')) {
       modelPrefix = 'accounts/yi-01-ai/models/';
+    } else if (param.model.toLowerCase().includes('/perplexity/')) {
+      modelPrefix = 'accounts/perplexity/models/';
+    } else if (param.model.toLowerCase().includes('/sentientfoundation/')) {
+      modelPrefix = 'accounts/sentientfoundation/models/';
+    } else if (param.model.toLowerCase().includes('/sentientfoundation-serverless/')) {
+      modelPrefix = 'accounts/sentientfoundation-serverless/models/';
+    } else if (param.model.toLowerCase().includes('/instacart/')) {
+      modelPrefix = 'accounts/instacart/models/';
     }
-
+    const finalModelIdentifier = param.model.includes(modelPrefix)
+      ? param.model
+      : `${modelPrefix}${param.model}`;
+    console.log('using fireworks model', finalModelIdentifier);
     return {
-      model: `${modelPrefix}${param.model}`,
+      model: finalModelIdentifier,
       messages: safeMessages,
       stream: true,
     };

packages/ai/src/tools/__tests__/agentic-rag.test.ts

@@ -0,0 +1,259 @@
+import { describe, it, expect, vi, beforeEach } from 'vitest';
+
+import { agenticRAG, AgenticRAGTools } from '../agentic-rag';
+
+// Mock the dependencies
+vi.mock('@zilliz/milvus2-sdk-node', () => ({
+  MilvusClient: vi.fn().mockImplementation(() => ({
+    listCollections: vi.fn().mockResolvedValue({
+      collection_names: ['family_domestic', 'business_corporate'],
+      data: [{ name: 'family_domestic' }, { name: 'business_corporate' }],
+    }),
+    search: vi.fn().mockResolvedValue({
+      results: [
+        {
+          content: 'Test document about AI and machine learning',
+          score: 0.85,
+          metadata: '{"category": "AI", "author": "Test Author"}',
+        },
+        {
+          content: 'Another document about neural networks',
+          score: 0.75,
+          metadata: '{"category": "ML", "author": "Another Author"}',
+        },
+      ],
+    }),
+    insert: vi.fn().mockResolvedValue({ success: true }),
+    createCollection: vi.fn().mockResolvedValue({ success: true }),
+    createIndex: vi.fn().mockResolvedValue({ success: true }),
+  })),
+  DataType: {
+    VarChar: 'VarChar',
+    FloatVector: 'FloatVector',
+  },
+}));
+
+vi.mock('openai', () => ({
+  OpenAI: vi.fn().mockImplementation(() => ({
+    embeddings: {
+      create: vi.fn().mockResolvedValue({
+        data: [{ embedding: new Array(768).fill(0.1) }],
+      }),
+    },
+  })),
+}));
+
+// Mock environment variables
+vi.stubEnv('FIREWORKS_API_KEY', 'test-api-key');
+
+describe('Agentic RAG System', () => {
+  beforeEach(() => {
+    vi.clearAllMocks();
+  });
+
+  it('should analyze queries correctly', async () => {
+    // Test factual query
+    const factualResult = await agenticRAG({
+      action: 'analyze_query',
+      query: 'What is artificial intelligence?',
+      collection_name: 'family_domestic',
+    });
+
+    expect(factualResult.status).toBe('success');
+    expect(factualResult.data.needsRetrieval).toBe(true);
+    expect(factualResult.data.queryType).toBe('factual');
+
+    // Test conversational query with multiple conversational keywords
+    const conversationalResult = await agenticRAG({
+      action: 'analyze_query',
+      query: 'Hello, how are you doing today?',
+      collection_name: 'family_domestic',
+    });
+
+    expect(conversationalResult.status).toBe('success');
+    expect(conversationalResult.data.needsRetrieval).toBe(false);
+    expect(conversationalResult.data.queryType).toBe('conversational');
+
+    // Test creative query with multiple creative keywords
+    const creativeResult = await agenticRAG({
+      action: 'analyze_query',
+      query: 'Write a story and compose a poem',
+      collection_name: 'family_domestic',
+    });
+
+    expect(creativeResult.status).toBe('success');
+    expect(creativeResult.data.needsRetrieval).toBe(false);
+    expect(creativeResult.data.queryType).toBe('creative');
+  });
+
+  it('should search knowledge base for factual queries', async () => {
+    const result = await agenticRAG({
+      action: 'search_knowledge',
+      query: 'What is machine learning?',
+      collection_name: 'family_domestic',
+      top_k: 2,
+      similarity_threshold: 0.1,
+    });
+
+    expect(result.status).toBe('success');
+    expect(result.context).toBeDefined();
+    expect(Array.isArray(result.context)).toBe(true);
+    expect(result.data.retrieved_documents).toBeDefined();
+    expect(result.data.analysis.needsRetrieval).toBe(true);
+  });
+
+  it('should not search for conversational queries', async () => {
+    const result = await agenticRAG({
+      action: 'search_knowledge',
+      query: 'Hello there! How are you?',
+      collection_name: 'family_domestic',
+    });
+
+    expect(result.status).toBe('success');
+    expect(result.data.analysis.needsRetrieval).toBe(false);
+    expect(result.data.retrieved_documents).toHaveLength(0);
+  });
+
+  it('should store documents successfully', async () => {
+    const result = await agenticRAG({
+      action: 'store_document',
+      document: {
+        id: 'test-doc-1',
+        content: 'This is a test document about neural networks and deep learning.',
+        metadata: { category: 'AI', author: 'Test Author' },
+      },
+      collection_name: 'family_domestic',
+    });
+
+    expect(result.status).toBe('success');
+    expect(result.data.document_id).toBe('test-doc-1');
+    expect(result.data.content_length).toBeGreaterThan(0);
+  });
+
+  it('should get context for factual queries', async () => {
+    const result = await agenticRAG({
+      action: 'get_context',
+      query: 'Tell me about vector databases',
+      collection_name: 'family_domestic',
+      top_k: 2,
+    });
+
+    expect(result.status).toBe('success');
+    expect(result.data.analysis.needsRetrieval).toBe(true);
+    expect(result.context).toBeDefined();
+    expect(result.data.context_summary).toBeDefined();
+  });
+
+  it('should handle semantic search', async () => {
+    const result = await agenticRAG({
+      action: 'semantic_search',
+      query: 'artificial intelligence concepts',
+      collection_name: 'family_domestic',
+      top_k: 3,
+    });
+
+    expect(result.status).toBe('success');
+    expect(result.data.results).toBeDefined();
+    expect(Array.isArray(result.data.results)).toBe(true);
+  });
+
+  it('should list collections', async () => {
+    const result = await agenticRAG({
+      action: 'list_collections',
+      collection_name: 'family_domestic',
+    });
+
+    expect(result.status).toBe('success');
+    expect(result.message).toContain('family_domestic');
+  });
+
+  it('should handle errors gracefully', async () => {
+    const result = await agenticRAG({
+      action: 'analyze_query',
+      collection_name: 'family_domestic',
+      // Missing query parameter
+    });
+
+    expect(result.status).toBe('error');
+    expect(result.message).toContain('Query is required');
+  });
+
+  it('should handle invalid actions', async () => {
+    const result = await agenticRAG({
+      action: 'invalid_action',
+      collection_name: 'family_domestic',
+    });
+
+    expect(result.status).toBe('error');
+    expect(result.message).toContain('Invalid action');
+  });
+
+  it('should have correct tool definition structure', () => {
+    expect(AgenticRAGTools.type).toBe('function');
+    expect(AgenticRAGTools.function.name).toBe('agentic_rag');
+    expect(AgenticRAGTools.function.description).toBeDefined();
+    expect(AgenticRAGTools.function.parameters.type).toBe('object');
+    expect(AgenticRAGTools.function.parameters.properties.action).toBeDefined();
+    expect(AgenticRAGTools.function.parameters.required).toContain('action');
+    expect(AgenticRAGTools.function.parameters.required).toContain('collection_name');
+  });
+
+  it('should demonstrate intelligent retrieval decision making', async () => {
+    // Test various query types to show intelligent decision making
+    const queries = [
+      { query: 'What is AI?', expectedRetrieval: true },
+      { query: 'Hello world how are you', expectedRetrieval: false },
+      { query: 'Write a poem and create a story', expectedRetrieval: false },
+      { query: 'Explain machine learning', expectedRetrieval: true },
+      { query: 'How are you doing today?', expectedRetrieval: true },
+      { query: 'Tell me about neural networks', expectedRetrieval: true },
+    ];
+
+    for (const testCase of queries) {
+      const result = await agenticRAG({
+        action: 'search_knowledge',
+        query: testCase.query,
+        collection_name: 'family_domestic',
+      });
+
+      expect(result.status).toBe('success');
+      expect(result.data.analysis.needsRetrieval).toBe(testCase.expectedRetrieval);
+
+      console.log(
+        `[DEBUG_LOG] Query: "${testCase.query}" - Retrieval needed: ${result.data.analysis.needsRetrieval}`,
+      );
+    }
+  });
+
+  it('should filter results by similarity threshold', async () => {
+    const result = await agenticRAG({
+      action: 'search_knowledge',
+      query: 'What is machine learning?',
+      collection_name: 'family_domestic',
+      similarity_threshold: 0.8, // High threshold
+    });
+
+    expect(result.status).toBe('success');
+    if (result.data.analysis.needsRetrieval) {
+      // Should only return results above threshold
+      result.data.retrieved_documents.forEach((doc: any) => {
+        expect(doc.score).toBeGreaterThanOrEqual(0.8);
+      });
+    }
+  });
+
+  it('should handle context window limits', async () => {
+    const result = await agenticRAG({
+      action: 'get_context',
+      query: 'Tell me about artificial intelligence',
+      collection_name: 'family_domestic',
+      context_window: 1000,
+    });
+
+    expect(result.status).toBe('success');
+    if (result.data.analysis.needsRetrieval && result.data.context_summary) {
+      // Context should respect the window limit (approximate check)
+      expect(result.data.context_summary.length).toBeLessThanOrEqual(2000); // Allow some flexibility
+    }
+  });
+});

packages/ai/src/tools/agentic-rag.ts

@@ -0,0 +1,530 @@
+import { MilvusClient, DataType } from '@zilliz/milvus2-sdk-node';
+import { OpenAI } from 'openai';
+
+import { ProviderRepository } from '../providers/_ProviderRepository.ts';
+
+/**
+ * Configuration for the Agentic RAG system
+ */
+export interface AgenticRAGConfig {
+  milvusAddress?: string;
+  collectionName?: string;
+  embeddingDimension?: number;
+  topK?: number;
+  similarityThreshold?: number;
+}
+
+/**
+ * Result structure for Agentic RAG operations
+ */
+export interface AgenticRAGResult {
+  message: string;
+  status: 'success' | 'error';
+  data?: any;
+  context?: string[];
+  relevanceScore?: number;
+}
+
+/**
+ * Document structure for knowledge base
+ */
+export interface Document {
+  id: string;
+  content: string;
+  metadata?: Record<string, any>;
+  embedding?: number[];
+}
+
+/**
+ * Agentic RAG Tools for intelligent retrieval-augmented generation
+ * This system makes intelligent decisions about when and how to retrieve information
+ */
+export const AgenticRAGTools = {
+  type: 'function',
+  function: {
+    name: 'agentic_rag',
+    description:
+      'Intelligent retrieval-augmented generation system that can store documents, search knowledge base, and provide contextual information based on user queries. The system intelligently decides when retrieval is needed.',
+    parameters: {
+      type: 'object',
+      properties: {
+        action: {
+          type: 'string',
+          enum: [
+            'list_collections',
+            'report_status',
+            'semantic_search',
+            'search_knowledge',
+            'analyze_query',
+            'get_context',
+          ],
+          description: 'Action to perform with the agentic RAG system.',
+        },
+        query: {
+          type: 'string',
+          description: 'User query or search term for knowledge retrieval.',
+        },
+        // document: {
+        //   type: 'object',
+        //   properties: {
+        //     content: { type: 'string', description: 'Document content to store' },
+        //     metadata: { type: 'object', description: 'Additional metadata for the document' },
+        //     id: { type: 'string', description: 'Unique identifier for the document' },
+        //   },
+        //   description: 'Document to store in the knowledge base.',
+        // },
+        collection_name: {
+          type: 'string',
+          // todo: make this fancy w/ dynamic collection
+          enum: [
+            'business_corporate',
+            'civil_procedure',
+            'criminal_justice',
+            'education_professions',
+            'environmental_infrastructure',
+            'family_domestic',
+            'foundational_law',
+            'government_administration',
+            'health_social_services',
+            'miscellaneous',
+            'property_real_estate',
+            'special_documents',
+            'taxation_finance',
+            'transportation_motor_vehicles',
+          ],
+          description: 'Name of the collection to work with.',
+        },
+        top_k: {
+          type: 'number',
+          description: 'Number of similar documents to retrieve (default: 5).',
+        },
+        similarity_threshold: {
+          type: 'number',
+          description: 'Minimum similarity score for relevant results (0-1, default: 0.7).',
+        },
+        context_window: {
+          type: 'number',
+          description: 'Maximum number of context tokens to include (default: 2000).',
+        },
+      },
+      required: ['action', 'collection_name'],
+      additionalProperties: false,
+    },
+    strict: true,
+  },
+};
+
+/**
+ * Default configuration for the Agentic RAG system
+ */
+const DEFAULT_CONFIG: AgenticRAGConfig = {
+  milvusAddress: 'localhost:19530',
+  collectionName: 'family_domestic',
+  embeddingDimension: 768,
+  topK: 5,
+  similarityThreshold: 0.5,
+};
+
+/**
+ * Simple embedding function using a mock implementation
+ * In production, this should use a real embedding service like OpenAI, Cohere, etc.
+ */
+async function generateEmbedding(text: string): Promise<number[] | undefined> {
+  const embeddingsClient = new OpenAI({
+    apiKey: process.env.FIREWORKS_API_KEY,
+    baseURL: ProviderRepository.OPENAI_COMPAT_ENDPOINTS.fireworks,
+  }).embeddings;
+
+  const embeddings = await embeddingsClient.create({
+    input: [text],
+    model: 'nomic-ai/nomic-embed-text-v1.5',
+    dimensions: 768,
+  });
+  return embeddings.data.at(0)?.embedding;
+}
+
+/**
+ * Analyze query to determine if retrieval is needed
+ */
+function analyzeQueryForRetrieval(query: string): {
+  needsRetrieval: boolean;
+  confidence: number;
+  reasoning: string;
+  queryType: 'factual' | 'conversational' | 'creative' | 'analytical';
+} {
+  const lowerQuery = query.toLowerCase();
+
+  // Keywords that suggest factual information is needed
+  const factualKeywords = [
+    'what is',
+    'who is',
+    'when did',
+    'where is',
+    'how does',
+    'explain',
+    'define',
+    'describe',
+    'tell me about',
+    'information about',
+    'details on',
+    'facts about',
+    'history of',
+    'background on',
+  ];
+
+  // Keywords that suggest conversational/creative responses
+  const conversationalKeywords = [
+    'hello',
+    'hi',
+    'how are you',
+    'thank you',
+    'please help',
+    'i think',
+    'in my opinion',
+    'what do you think',
+    'can you help',
+  ];
+
+  // Keywords that suggest creative tasks
+  const creativeKeywords = [
+    'write a',
+    'create a',
+    'generate',
+    'compose',
+    'draft',
+    'story',
+    'poem',
+    'essay',
+    'letter',
+    'email',
+  ];
+
+  let factualScore = 0;
+  let conversationalScore = 0;
+  let creativeScore = 0;
+
+  factualKeywords.forEach(keyword => {
+    if (lowerQuery.includes(keyword)) factualScore += 1;
+  });
+
+  conversationalKeywords.forEach(keyword => {
+    if (lowerQuery.includes(keyword)) conversationalScore += 1;
+  });
+
+  creativeKeywords.forEach(keyword => {
+    if (lowerQuery.includes(keyword)) creativeScore += 1;
+  });
+
+  // Determine query type and retrieval need
+  if (factualScore > conversationalScore && factualScore > creativeScore) {
+    return {
+      needsRetrieval: true,
+      confidence: Math.min(factualScore * 0.3, 0.9),
+      reasoning:
+        'Query appears to be asking for factual information that may benefit from knowledge retrieval.',
+      queryType: 'factual',
+    };
+  } else if (creativeScore > conversationalScore && creativeScore > 1) {
+    // Only skip retrieval for clearly creative tasks with multiple creative keywords
+    return {
+      needsRetrieval: false,
+      confidence: 0.8,
+      reasoning: 'Query appears to be requesting creative content generation.',
+      queryType: 'creative',
+    };
+  } else if (conversationalScore > 1 && conversationalScore > factualScore) {
+    // Only skip retrieval for clearly conversational queries with multiple conversational keywords
+    return {
+      needsRetrieval: false,
+      confidence: 0.7,
+      reasoning: 'Query appears to be conversational in nature.',
+      queryType: 'conversational',
+    };
+  } else {
+    // Default to retrieval for most cases to ensure comprehensive responses
+    return {
+      needsRetrieval: true,
+      confidence: 0.8,
+      reasoning: 'Defaulting to retrieval to provide comprehensive and accurate information.',
+      queryType: 'analytical',
+    };
+  }
+}
+
+/**
+ * Main Agentic RAG function that handles intelligent retrieval decisions
+ */
+export async function agenticRAG(args: {
+  action: string;
+  query?: string;
+  document?: Document;
+  collection_name?: string;
+  top_k?: number;
+  similarity_threshold?: number;
+  context_window?: number;
+  user_confirmed?: boolean;
+}): Promise<AgenticRAGResult> {
+  const config = { ...DEFAULT_CONFIG };
+  const collectionName = args.collection_name || config.collectionName!;
+  const topK = args.top_k || config.topK!;
+  const similarityThreshold = args.similarity_threshold || config.similarityThreshold!;
+
+  const milvusClient = new MilvusClient({ address: config.milvusAddress! });
+
+  try {
+    switch (args.action) {
+      case 'analyze_query':
+        if (!args.query) {
+          return { status: 'error', message: 'Query is required for analysis.' };
+        }
+
+        // eslint-disable-next-line no-case-declarations
+        const analysis = analyzeQueryForRetrieval(args.query);
+        return {
+          status: 'success',
+          message: `Query analysis complete. Retrieval ${analysis.needsRetrieval ? 'recommended' : 'not needed'}.`,
+          data: analysis,
+        };
+
+      case 'list_collections':
+        // eslint-disable-next-line no-case-declarations
+        const { collection_names } = (await milvusClient.listCollections()) as any as {
+          collection_names: string[];
+        };
+        return {
+          status: 'success',
+          message: JSON.stringify(collection_names),
+        };
+      case 'search_knowledge':
+        if (!args.query) {
+          return { status: 'error', message: 'Query is required for knowledge search.' };
+        }
+
+        // First, analyze if retrieval is needed
+        // eslint-disable-next-line no-case-declarations
+        const queryAnalysis = analyzeQueryForRetrieval(args.query);
+
+        if (!queryAnalysis.needsRetrieval) {
+          return {
+            status: 'success',
+            message: 'Query analysis suggests retrieval is not needed for this type of query.',
+            data: {
+              analysis: queryAnalysis,
+              retrieved_documents: [],
+              context: [],
+            },
+          };
+        }
+
+        // Generate embedding for the query
+        // eslint-disable-next-line no-case-declarations
+        const queryEmbedding = await generateEmbedding(args.query);
+
+        // Search for similar documents
+        // eslint-disable-next-line no-case-declarations
+        const searchResult = await milvusClient.search({
+          collection_name: collectionName,
+          vector: queryEmbedding,
+          topk: topK,
+          params: { nprobe: 8 },
+          output_fields: ['content', 'metadata'],
+        });
+
+        // Filter results by similarity threshold
+        // eslint-disable-next-line no-case-declarations
+        const relevantResults = searchResult.results.filter(
+          (result: any) => result.score >= similarityThreshold,
+        );
+
+        // eslint-disable-next-line no-case-declarations
+        const contextDocuments = relevantResults.map((result: any) => ({
+          content: result.content,
+          score: result.score,
+          metadata: result.metadata,
+        }));
+
+        return {
+          status: 'success',
+          message: `Found ${relevantResults.length} relevant documents for query.`,
+          data: {
+            analysis: queryAnalysis,
+            retrieved_documents: contextDocuments,
+            context: contextDocuments.map((doc: any) => doc.content),
+          },
+          context: contextDocuments.map((doc: any) => doc.content),
+          relevanceScore: relevantResults.length > 0 ? relevantResults.at(0)?.score : 0,
+        };
+
+      case 'store_document':
+        if (!args.document || !args.document.content) {
+          return { status: 'error', message: 'Document with content is required for storage.' };
+        }
+
+        // Generate embedding for the document
+        // eslint-disable-next-line no-case-declarations
+        const docEmbedding = await generateEmbedding(args.document.content);
+        // eslint-disable-next-line no-case-declarations
+        const docId =
+          args.document.id || `doc_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
+
+        // Store document in Milvus
+        await milvusClient.insert({
+          collection_name: collectionName,
+          fields_data: [
+            { name: 'id', values: [docId] },
+            { name: 'embedding', values: [docEmbedding] },
+            { name: 'content', values: [args.document.content] },
+            { name: 'metadata', values: [JSON.stringify(args.document.metadata || {})] },
+          ],
+        });
+
+        return {
+          status: 'success',
+          message: `Document stored successfully with ID: ${docId}`,
+          data: { document_id: docId, content_length: args.document.content.length },
+        };
+
+      case 'manage_collection':
+        try {
+          // Check if collection exists
+          const collections = await milvusClient.listCollections();
+          const collectionExists =
+            collections.data.filter(c => c.name.includes(collectionName)).length > 0;
+
+          if (!collectionExists) {
+            // Create collection with proper schema for RAG
+            const collectionSchema = {
+              collection_name: collectionName,
+              fields: [
+                {
+                  name: 'id',
+                  type: DataType.VarChar,
+                  params: { max_length: 100 },
+                  is_primary_key: true,
+                },
+                {
+                  name: 'embedding',
+                  type: DataType.FloatVector,
+                  params: { dim: config.embeddingDimension },
+                },
+                { name: 'content', type: DataType.VarChar, params: { max_length: 65535 } },
+                { name: 'metadata', type: DataType.VarChar, params: { max_length: 1000 } },
+              ],
+            };
+
+            await milvusClient.createCollection(collectionSchema as any);
+
+            // Create index for efficient similarity search
+            await milvusClient.createIndex({
+              collection_name: collectionName,
+              field_name: 'embedding',
+              index_type: 'IVF_FLAT',
+              params: { nlist: 1024 },
+              metric_type: 'COSINE',
+            });
+
+            return {
+              status: 'success',
+              message: `Collection '${collectionName}' created successfully with RAG schema.`,
+              data: { collection_name: collectionName, action: 'created' },
+            };
+          } else {
+            return {
+              status: 'success',
+              message: `Collection '${collectionName}' already exists.`,
+              data: { collection_name: collectionName, action: 'exists' },
+            };
+          }
+        } catch (error: any) {
+          return {
+            status: 'error',
+            message: `Error managing collection: ${error.message}`,
+          };
+        }
+
+      case 'semantic_search':
+        if (!args.query) {
+          return { status: 'error', message: 'Query is required for semantic search.' };
+        }
+
+        // eslint-disable-next-line no-case-declarations
+        const semanticEmbedding = await generateEmbedding(args.query);
+        // eslint-disable-next-line no-case-declarations
+        const semanticResults = await milvusClient.search({
+          collection_name: collectionName,
+          vector: semanticEmbedding,
+          topk: topK,
+          params: { nprobe: 8 },
+          output_fields: ['content', 'metadata'],
+        });
+
+        return {
+          status: 'success',
+          message: `Semantic search completed. Found ${semanticResults.results.length} results.`,
+          data: {
+            results: semanticResults.results.map((result: any) => ({
+              content: result.content,
+              score: result.score,
+              metadata: JSON.parse(result.metadata || '{}'),
+            })),
+          },
+        };
+
+      case 'get_context':
+        if (!args.query) {
+          return { status: 'error', message: 'Query is required to get context.' };
+        }
+
+        // This is a comprehensive context retrieval that combines analysis and search
+        // eslint-disable-next-line no-case-declarations
+        const contextAnalysis = analyzeQueryForRetrieval(args.query);
+        if (contextAnalysis.needsRetrieval) {
+          const contextEmbedding = await generateEmbedding(args.query);
+          const contextSearch = await milvusClient.search({
+            collection_name: collectionName,
+            vector: contextEmbedding,
+            topk: topK,
+            params: { nprobe: 8 },
+            output_fields: ['content', 'metadata'],
+          });
+
+          const contextResults = contextSearch.results
+            .filter((result: any) => result.score >= similarityThreshold)
+            .map((result: any) => ({
+              content: result.content,
+              score: result.score,
+              metadata: JSON.parse(result.metadata || '{}'),
+            }));
+
+          return {
+            status: 'success',
+            message: `Context retrieved successfully. Found ${contextResults.length} relevant documents.`,
+            data: {
+              analysis: contextAnalysis,
+              context_documents: contextResults,
+              context_summary: contextResults.map((doc: any) => doc.content).join('\n\n'),
+            },
+            context: contextResults.map((doc: any) => doc.content),
+          };
+        } else {
+          return {
+            status: 'success',
+            message: 'No context retrieval needed for this query type.',
+            data: {
+              analysis: contextAnalysis,
+              context_documents: [],
+              context_summary: '',
+            },
+          };
+        }
+
+      default:
+        return { status: 'error', message: 'Invalid action specified.' };
+    }
+  } catch (error: any) {
+    return {
+      status: 'error',
+      message: `Integration error: ${error.message}`,
+    };
+  }
+}

packages/ai/src/tools/basic.ts

@@ -0,0 +1,21 @@
+// tools/basicValue.ts
+export interface BasicValueResult {
+  value: string;
+}
+
+export const BasicValueTool = {
+  name: 'basicValue',
+  type: 'function',
+  description: 'Returns a basic value (timestamp-based) for testing',
+  parameters: {
+    type: 'object',
+    properties: {},
+    required: [],
+  },
+  function: async (): Promise<BasicValueResult> => {
+    // generate something obviously basic
+    const basic = `tool-called-${Date.now()}`;
+    console.log('[BasicValueTool] returning:', basic);
+    return { value: basic };
+  },
+};

packages/ai/src/tools/weather.ts

@@ -0,0 +1,25 @@
+export async function getWeather(latitude: any, longitude: any) {
+  const response = await fetch(
+    `https://api.open-meteo.com/v1/forecast?latitude=${latitude}&longitude=${longitude}&current=temperature_2m,wind_speed_10m&hourly=temperature_2m,relative_humidity_2m,wind_speed_10m`,
+  );
+  const data = await response.json();
+  return data.current.temperature_2m;
+}
+
+export const WeatherTool = {
+  type: 'function',
+  function: {
+    name: 'get_weather',
+    description: 'Get current temperature for provided coordinates in celsius.',
+    parameters: {
+      type: 'object',
+      properties: {
+        latitude: { type: 'number' },
+        longitude: { type: 'number' },
+      },
+      required: ['latitude', 'longitude'],
+      additionalProperties: false,
+    },
+    strict: true,
+  },
+};

packages/ai/src/tools/yachtpit.ts

@@ -0,0 +1,68 @@
+export interface ShipControlResult {
+  message: string;
+  status: 'success' | 'error';
+  data?: any;
+}
+
+/**
+ * A mock interface for controlling a ship.
+ */
+export const YachtpitTools = {
+  type: 'function',
+  description: 'Interface for controlling a ship: set speed, change heading, report status, etc.',
+
+  /**
+   * Mock implementation of a ship control command.
+   */
+  function: {
+    name: 'ship_control',
+    parameters: {
+      type: 'object',
+      properties: {
+        action: {
+          type: 'string',
+          enum: ['set_speed', 'change_heading', 'report_status', 'stop'],
+          description: 'Action to perform on the ship.',
+        },
+        value: {
+          type: 'number',
+          description:
+            'Numeric value for the action, such as speed (knots) or heading (degrees). Only required for set_speed and change_heading.',
+        },
+      },
+      required: ['action'],
+      additionalProperties: false,
+    },
+  },
+};
+
+export function yachtpitAi(args: { action: string; value?: number }): Promise<ShipControlResult> {
+  switch (args.action) {
+    case 'set_speed':
+      if (typeof args.value !== 'number') {
+        return { status: 'error', message: 'Missing speed value.' };
+      }
+      return { status: 'success', message: `Speed set to ${args.value} knots.` };
+    case 'change_heading':
+      if (typeof args.value !== 'number') {
+        return { status: 'error', message: 'Missing heading value.' };
+      }
+      return { status: 'success', message: `Heading changed to ${args.value} degrees.` };
+    case 'report_status':
+      // Return a simulated ship status
+      return {
+        status: 'success',
+        message: 'Ship status reported.',
+        data: {
+          speed: 12,
+          heading: 87,
+          engine: 'nominal',
+          position: { lat: 42.35, lon: -70.88 },
+        },
+      };
+    case 'stop':
+      return { status: 'success', message: 'Ship stopped.' };
+    default:
+      return { status: 'error', message: 'Invalid action.' };
+  }
+}

packages/client/package.json

@@ -8,7 +8,8 @@
     "tests:coverage": "vitest run --coverage.enabled=true",
     "generate:sitemap": "bun ./scripts/generate_sitemap.js open-gsio.seemueller.workers.dev",
     "generate:robotstxt": "bun ./scripts/generate_robots_txt.js open-gsio.seemueller.workers.dev",
-    "generate:fonts": "cp -r ../../node_modules/katex/dist/fonts public/static"
+    "generate:fonts": "cp -r ../../node_modules/katex/dist/fonts public/static",
+    "generate:pwa:assets": "test ! -f public/pwa-64x64.png && pwa-assets-generator --preset minimal-2023  public/logo.png || echo 'PWA assets already exist'"
   },
   "exports": {
     "./server/index.ts": {
@@ -17,19 +18,23 @@
     }
   },
   "devDependencies": {
-    "@open-gsio/env": "workspace:*",
+    "@chakra-ui/icons": "^2.2.4",
-    "@open-gsio/scripts": "workspace:*",
     "@chakra-ui/react": "^2.10.6",
     "@cloudflare/workers-types": "^4.20241205.0",
     "@emotion/react": "^11.13.5",
     "@emotion/styled": "^11.13.5",
+    "@open-gsio/env": "workspace:*",
+    "@open-gsio/scripts": "workspace:*",
     "@testing-library/jest-dom": "^6.4.2",
     "@testing-library/react": "^16.3.0",
     "@testing-library/user-event": "^14.5.2",
+    "@types/bun": "^1.2.17",
     "@types/marked": "^6.0.0",
+    "@vite-pwa/assets-generator": "^1.0.0",
     "@vitejs/plugin-react": "^4.3.4",
     "@vitest/coverage-v8": "^3.1.4",
     "@vitest/ui": "^3.1.4",
+    "bun": "^1.2.17",
     "chokidar": "^4.0.1",
     "framer-motion": "^11.13.1",
     "isomorphic-dompurify": "^2.19.0",
@@ -37,6 +42,7 @@
     "jsdom": "^24.0.0",
     "katex": "^0.16.20",
     "lucide-react": "^0.436.0",
+    "mapbox-gl": "^3.13.0",
     "marked": "^15.0.4",
     "marked-extended-latex": "^1.1.0",
     "marked-footnote": "^1.2.4",
@@ -44,20 +50,19 @@
     "mobx": "^6.13.5",
     "mobx-react-lite": "^4.0.7",
     "mobx-state-tree": "^6.0.1",
-    "moo": "^0.5.2",
     "qrcode.react": "^4.1.0",
     "react": "^19.1.0",
     "react-dom": "^19.1.0",
     "react-icons": "^5.4.0",
-    "react-streaming": "^0.3.44",
+    "react-map-gl": "^8.0.4",
+    "react-streaming": "^0.4.2",
     "react-textarea-autosize": "^8.5.5",
     "shiki": "^1.24.0",
+    "tslog": "^4.9.3",
     "typescript": "^5.7.2",
     "vike": "^0.4.235",
     "vite": "^7.0.0",
-    "vite-plugin-pwa": "^1.0.0",
+    "vite-plugin-pwa": "^1.0.1",
-    "vitest": "^3.1.4",
+    "vitest": "^3.1.4"
-    "bun": "^1.2.17",
-    "@types/bun": "^1.2.17"
   }
 }

packages/client/public/site.webmanifest

@@ -1,19 +0,0 @@
-{
-  "name": "",
-  "short_name": "",
-  "icons": [
-    {
-      "src": "/android-chrome-192x192.png",
-      "sizes": "192x192",
-      "type": "image/png"
-    },
-    {
-      "src": "/android-chrome-512x512.png",
-      "sizes": "512x512",
-      "type": "image/png"
-    }
-  ],
-  "theme_color": "#fffff0",
-  "background_color": "#000000",
-  "display": "standalone"
-}

packages/client/src/components/chat/Chat.tsx

@@ -28,7 +28,7 @@ const Chat = observer(({ height, width }) => {
       <GridItem
         overflow="auto"
         width="100%"
-        maxH="100%"
+        maxH="100vh"
         ref={scrollRef}
         // If there are attachments, use "100px". Otherwise, use "128px" on Android, "73px" elsewhere.
         pb={isAndroid ? '128px' : '73px'}

packages/client/src/components/chat/input-menu/InputMenu.tsx

@@ -171,7 +171,7 @@ const InputMenu: React.FC<{ isDisabled?: boolean }> = observer(({ isDisabled })
           bg="background.tertiary"
           color="text.primary"
           onClick={() => {
-            clientChatStore.setActiveConversation('conversation:new');
+            clientChatStore.reset();
             onClose();
           }}
           _hover={{ bg: 'rgba(0, 0, 0, 0.05)' }}

packages/client/src/components/chat/input/ChatInput.tsx

@@ -22,7 +22,7 @@ const ChatInput = observer(() => {
   const [shouldFollow, setShouldFollow] = useState<boolean>(userOptionsStore.followModeEnabled);
   const [couldFollow, setCouldFollow] = useState<boolean>(chatStore.isLoading);
 
-  const [inputWidth, setInputWidth] = useState<string>('50%');
+  const [inputWidth, setInputWidth] = useState<string>('40%');
 
   useEffect(() => {
     setShouldFollow(chatStore.isLoading && userOptionsStore.followModeEnabled);
@@ -64,10 +64,10 @@ const ChatInput = observer(() => {
   };
 
   const inputMaxWidth = useBreakpointValue(
-    { base: '50rem', lg: '50rem', md: '80%', sm: '100vw' },
+    { base: '30rem', lg: '50rem', md: '80%', sm: '100vw' },
     { ssr: true },
   );
-  const inputMinWidth = useBreakpointValue({ lg: '40rem' }, { ssr: true });
+  const inputMinWidth = useBreakpointValue({ lg: '40rem', md: '30rem' }, { ssr: true });
 
   useEffect(() => {
     setInputWidth('100%');
@@ -75,9 +75,7 @@ const ChatInput = observer(() => {
 
   return (
     <Box
-      width={inputWidth}
+      width={inputMinWidth}
-      maxW={inputMaxWidth}
-      minWidth={inputMinWidth}
       mx="auto"
       p={2}
       pl={2}

packages/client/src/components/chat/input/ChatInputTextArea.tsx

@@ -1,4 +1,4 @@
-import { Box, chakra, InputGroup } from '@chakra-ui/react';
+import { Box, chakra, InputGroup, useBreakpointValue } from '@chakra-ui/react';
 import { observer } from 'mobx-react-lite';
 import React, { useEffect, useRef, useState } from 'react';
 import AutoResize from 'react-textarea-autosize';
@@ -19,7 +19,7 @@ const InputTextArea: React.FC<InputTextAreaProps> = observer(
 
     useEffect(() => {
       if (value.length > 10) {
-        setHeightConstraint();
+        setHeightConstraint(parseInt(value));
       }
     }, [value]);
 
@@ -38,6 +38,7 @@ const InputTextArea: React.FC<InputTextAreaProps> = observer(
             ref={inputRef}
             value={value}
             height={heightConstraint}
+            maxH={heightConstraint}
             autoFocus
             onChange={e => onChange(e.target.value)}
             onKeyDown={onKeyDown}
@@ -49,7 +50,13 @@ const InputTextArea: React.FC<InputTextAreaProps> = observer(
             borderRadius="20px"
             border="none"
             placeholder="Free my mind..."
-            _placeholder={{ color: 'gray.400' }}
+            _placeholder={{
+              color: 'gray.400',
+              textWrap: 'nowrap',
+              textOverflow: 'ellipsis',
+              overflow: 'hidden',
+              width: '90%',
+            }}
             _focus={{
               outline: 'none',
             }}

packages/client/src/components/chat/lib/exportConversationAsMarkdown.ts

@@ -9,7 +9,7 @@ export function formatConversationMarkdown(messages: Instance<typeof IMessage>[]
       if (message.role === 'user') {
         return `**You**: ${message.content}`;
       } else if (message.role === 'assistant') {
-        return `**Geoff's AI**: ${message.content}`;
+        return `**open-gsio**: ${message.content}`;
       }
       return '';
     })

packages/client/src/components/chat/messages/MessageBubble.tsx

@@ -51,7 +51,7 @@ const MessageBubble = observer(({ msg, scrollRef }) => {
   const [isEditing, setIsEditing] = useState(false);
   const [isHovered, setIsHovered] = useState(false);
   const isUser = msg.role === 'user';
-  const senderName = isUser ? 'You' : "Geoff's AI";
+  const senderName = isUser ? 'You' : 'open-gsio';
   const isLoading = !msg.content || !(msg.content.trim().length > 0);
   const messageRef = useRef();
 

packages/client/src/components/chat/messages/__tests__/MessageBubble.test.tsx

@@ -104,7 +104,7 @@ describe('MessageBubble', () => {
   it('should render assistant message correctly', () => {
     render(<MessageBubble msg={mockAssistantMessage} scrollRef={mockScrollRef} />);
 
-    expect(screen.getByText("Geoff's AI")).toBeInTheDocument();
+    expect(screen.getByText('open-gsio')).toBeInTheDocument();
     expect(screen.getByTestId('message-content')).toHaveTextContent('Assistant response');
   });
 

packages/client/src/components/contexts/ComponentContext.tsx

@@ -0,0 +1,25 @@
+import React, { createContext, useContext, useState } from 'react';
+
+type ComponentContextType = {
+  enabledComponent: string;
+  setEnabledComponent: (component: string) => void;
+};
+
+const ComponentContext = createContext<ComponentContextType>({
+  enabledComponent: '',
+  setEnabledComponent: () => {},
+});
+
+export const useComponent = () => useContext(ComponentContext);
+
+export const ComponentProvider: React.FC<{ children: React.ReactNode }> = ({ children }) => {
+  const [enabledComponent, setEnabledComponent] = useState<string>('');
+
+  return (
+    <ComponentContext.Provider value={{ enabledComponent, setEnabledComponent }}>
+      {children}
+    </ComponentContext.Provider>
+  );
+};
+
+export default ComponentContext;

packages/client/src/components/install/Install.tsx

@@ -0,0 +1,7 @@
+import React, { useEffect, useState } from 'react';
+
+function InstallButton() {
+  return <button onClick={handleInstall}>Install App</button>;
+}
+
+export default InstallButton;

packages/client/src/components/install/InstallButton.tsx

@@ -0,0 +1,61 @@
+import { IconButton } from '@chakra-ui/react';
+import { HardDriveDownload } from 'lucide-react';
+import React, { useEffect, useState } from 'react';
+
+import { toolbarButtonZIndex } from '../toolbar/Toolbar.tsx';
+
+function InstallButton() {
+  const [deferredPrompt, setDeferredPrompt] = useState(null);
+  const [isInstalled, setIsInstalled] = useState(false);
+
+  useEffect(() => {
+    const handleBeforeInstallPrompt = e => {
+      // Prevent the default prompt
+      e.preventDefault();
+      setDeferredPrompt(e);
+    };
+
+    window.addEventListener('beforeinstallprompt', handleBeforeInstallPrompt);
+
+    return () => {
+      window.removeEventListener('beforeinstallprompt', handleBeforeInstallPrompt);
+    };
+  }, []);
+
+  const handleInstall = () => {
+    if (deferredPrompt) {
+      deferredPrompt.prompt();
+      deferredPrompt.userChoice.then(choiceResult => {
+        if (choiceResult.outcome === 'accepted') {
+          console.log('User accepted the installation prompt');
+        } else {
+          console.log('User dismissed the installation prompt');
+        }
+      });
+      setDeferredPrompt(null);
+    }
+  };
+
+  return (
+    <IconButton
+      aria-label="Install App"
+      title="Install App"
+      icon={<HardDriveDownload />}
+      size="md"
+      bg="transparent"
+      stroke="text.accent"
+      color="text.accent"
+      onClick={handleInstall}
+      _hover={{
+        bg: 'transparent',
+        svg: {
+          stroke: 'accent.secondary',
+          transition: 'stroke 0.3s ease-in-out',
+        },
+      }}
+      zIndex={toolbarButtonZIndex}
+    />
+  );
+}
+
+export default InstallButton;

packages/client/src/components/landing-component/LandingComponent.tsx

@@ -0,0 +1,73 @@
+import { Box } from '@chakra-ui/react';
+import React, { useEffect, useState } from 'react';
+
+import { useComponent } from '../contexts/ComponentContext.tsx';
+
+// import { BevyScene } from './BevyScene.tsx';
+import Tweakbox from './Tweakbox.tsx';
+
+export const LandingComponent: React.FC = () => {
+  const [intensity, setIntensity] = useState(0.99);
+  const [mapActive, setMapActive] = useState(false);
+  const [aiActive, setAiActive] = useState(true);
+
+  const component = useComponent();
+  const { setEnabledComponent } = component;
+
+  useEffect(() => {
+    if (mapActive) {
+      setEnabledComponent('gpsmap');
+    }
+    if (aiActive) {
+      setEnabledComponent('ai');
+    }
+  }, []);
+
+  return (
+    <Box as="section" bg="background.primary" overflow="hidden">
+      <Box position="fixed" right={0} maxWidth="300px" minWidth="200px" zIndex={1000}>
+        <Tweakbox
+          sliders={{
+            intensity: {
+              value: intensity,
+              onChange: setIntensity,
+              label: 'Brightness',
+              min: 0.01,
+              max: 0.99,
+              step: 0.01,
+              ariaLabel: 'effect-intensity',
+            },
+          }}
+          switches={{
+            // GpsMap: {
+            //   value: mapActive,
+            //   onChange(enabled) {
+            //     if (enabled) {
+            //       setEnabledComponent('gpsmap');
+            //       setAiActive(false);
+            //     } else {
+            //       setEnabledComponent('');
+            //     }
+            //     setMapActive(enabled);
+            //   },
+            //   label: 'GPS',
+            // },
+            AI: {
+              value: aiActive,
+              onChange(enabled) {
+                if (enabled) {
+                  setEnabledComponent('ai');
+                  setMapActive(false);
+                } else {
+                  setEnabledComponent('');
+                }
+                setAiActive(enabled);
+              },
+              label: 'AI',
+            },
+          }}
+        />
+      </Box>
+    </Box>
+  );
+};

packages/client/src/components/landing-component/Map.tsx

@@ -0,0 +1,63 @@
+import ReactMap from 'react-map-gl/mapbox'; // ↔ v5+ uses this import path
+import 'mapbox-gl/dist/mapbox-gl.css';
+import { Box, HStack, Button, Input, Center } from '@chakra-ui/react';
+import { useState, useEffect, useCallback } from 'react';
+
+import MapNext from './MapNext.tsx';
+
+// Types for bevy_flurx_ipc communication
+interface GpsPosition {
+  latitude: number;
+  longitude: number;
+  zoom: number;
+}
+
+interface VesselStatus {
+  latitude: number;
+  longitude: number;
+  heading: number;
+  speed: number;
+}
+
+interface MapViewParams {
+  latitude: number;
+  longitude: number;
+  zoom: number;
+}
+
+interface AuthParams {
+  authenticated: boolean;
+  token: string | null;
+}
+
+// public key
+const key =
+  'cGsuZXlKMUlqb2laMlZ2Wm1aelpXVWlMQ0poSWpvaVkycDFOalo0YkdWNk1EUTRjRE41YjJnNFp6VjNNelp6YXlKOS56LUtzS1l0X3VGUGdCSDYwQUFBNFNn';
+
+function Map(props: { visible: boolean }) {
+  return (
+    /* Full-screen wrapper — fills the viewport and becomes the positioning context */
+    <Box position={'absolute'} top={0} w="100vw" h={'100vh'} overflow="hidden">
+      {/* Button bar — absolutely positioned inside the wrapper */}
+
+      <MapNext mapboxPublicKey={atob(key)} />
+      {/*<Map*/}
+      {/*    mapboxAccessToken={atob(key)}*/}
+      {/*    initialViewState={mapView}*/}
+      {/*    onMove={handleMapViewChange}*/}
+      {/*    mapStyle="mapbox://styles/mapbox/dark-v11"*/}
+      {/*    reuseMaps*/}
+      {/*    attributionControl={false}*/}
+      {/*    style={{width: '100%', height: '100%'}}  // let the wrapper dictate size*/}
+      {/*>*/}
+      {/*    /!*{vesselPosition && (*!/*/}
+      {/*    /!*    <Source id="vessel-data" type="geojson" data={vesselGeojson}>*!/*/}
+      {/*    /!*        <Layer {...vesselLayerStyle} />*!/*/}
+      {/*    /!*    </Source>*!/*/}
+      {/*    /!*)}*!/*/}
+      {/*</Map>*/}
+    </Box>
+  );
+}
+
+export default Map;

packages/client/src/components/landing-component/MapNext.tsx

@@ -0,0 +1,172 @@
+import { Box } from '@chakra-ui/react';
+import { useCallback, useEffect, useMemo, useState } from 'react';
+import Map, {
+  FullscreenControl,
+  GeolocateControl,
+  Marker,
+  NavigationControl,
+  Popup,
+  ScaleControl,
+} from 'react-map-gl/mapbox';
+
+import PORTS from './nautical-base-data.json';
+import Pin from './pin';
+
+export default function MapNext(props: any = { mapboxPublicKey: '' } as any) {
+  const [popupInfo, setPopupInfo] = useState(null);
+  const [isSearchOpen, setIsSearchOpen] = useState(false);
+  const [isTokenLoading, setIsTokenLoading] = useState(false);
+  const [authenticated, setAuthenticated] = useState(false);
+
+  useEffect(() => {
+    setAuthenticated(true);
+    setIsTokenLoading(false);
+  }, []);
+
+  const [mapView, setMapView] = useState({
+    longitude: -122.4,
+    latitude: 37.8,
+    zoom: 14,
+  });
+
+  const handleNavigationClick = useCallback(async () => {
+    console.log('handling navigation in map');
+  }, []);
+
+  const handleSearchClick = useCallback(async () => {
+    console.log('handling click search in map');
+  }, []);
+
+  const handleMapViewChange = useCallback(async (evt: any) => {
+    const { longitude, latitude, zoom } = evt.viewState;
+    setMapView({ longitude, latitude, zoom });
+  }, []);
+
+  const pins = useMemo(
+    () =>
+      PORTS.map((city, index) => (
+        <Marker
+          key={`marker-${index}`}
+          longitude={city.longitude}
+          latitude={city.latitude}
+          anchor="bottom"
+          onClick={e => {
+            // If we let the click event propagates to the map, it will immediately close the popup
+            // with `closeOnClick: true`
+            e.originalEvent.stopPropagation();
+            /*
+            src/MapNext.tsx:34:38 - error TS2345: Argument of type '{ city: string; population: string; image: string; state: string; latitude: number; longitude: number; }' is not assignable to parameter of type 'SetStateAction<null>'.
+Type '{ city: string; population: string; image: string; state: string; latitude: number; longitude: number; }' provides no match for the signature '(prevState: null): null'.
+             */
+            // @ts-ignore
+            setPopupInfo(city);
+          }}
+        >
+          <Pin />
+        </Marker>
+      )),
+    [],
+  );
+
+  return (
+    <Box justifySelf={'right'} w={'100%'}>
+      {/*<HStack position="absolute" top={4} right={4} zIndex={1}>*/}
+      {/*  <Box display="flex" alignItems="center">*/}
+      {/*    <Button colorScheme="teal" size="sm" variant="solid" onClick={handleSearchClick} mr={2}>*/}
+      {/*      Search*/}
+      {/*    </Button>*/}
+      {/*    {isSearchOpen && (*/}
+      {/*      <Box*/}
+      {/*        w="200px"*/}
+      {/*        transition="all 0.3s"*/}
+      {/*        transform={`translateX(${isSearchOpen ? '0' : '100%'})`}*/}
+      {/*        opacity={isSearchOpen ? 1 : 0}*/}
+      {/*        color="white"*/}
+      {/*      >*/}
+      {/*        <Input*/}
+      {/*          placeholder="Search..."*/}
+      {/*          size="sm"*/}
+      {/*          _placeholder={{*/}
+      {/*            color: '#d1cfcf',*/}
+      {/*          }}*/}
+      {/*        />*/}
+      {/*      </Box>*/}
+      {/*    )}*/}
+      {/*  </Box>*/}
+      {/*  <Button colorScheme="blue" size="sm" variant="solid" onClick={handleNavigationClick}>*/}
+      {/*    Layer*/}
+      {/*  </Button>*/}
+      {/*</HStack>*/}
+      <Map
+        initialViewState={{
+          latitude: 40,
+          longitude: -100,
+          zoom: 3.5,
+          bearing: 0,
+          pitch: 0,
+        }}
+        mapStyle="mapbox://styles/geoffsee/cmd1qz39x01ga01qv5acea02y"
+        attributionControl={false}
+        mapboxAccessToken={props.mapboxPublicKey}
+        style={{
+          position: 'absolute',
+          width: '100%',
+          // height: '50%',
+          bottom: 0,
+          top: 0,
+          left: 0,
+          right: 0,
+        }}
+      >
+        <GeolocateControl position="top-left" style={{ marginTop: '6rem' }} />
+        <FullscreenControl position="top-left" />
+        <NavigationControl position="top-left" />
+        <ScaleControl position="top-left" />
+        {pins}
+
+        {popupInfo && (
+          <Popup
+            anchor="top"
+            /*
+            src/MapNext.tsx:66:53 - error TS2339: Property 'longitude' does not exist on type 'never'.
+
+66                         longitude={Number(popupInfo.longitude)}
+             */
+            // @ts-ignore
+            longitude={Number(popupInfo.longitude)}
+            /*
+            src/MapNext.tsx:67:52 - error TS2339: Property 'latitude' does not exist on type 'never'.
+
+67                         latitude={Number(popupInfo.latitude)}
+                                          ~~~~~~~~
+             */
+            // @ts-ignore
+            latitude={Number(popupInfo.latitude)}
+            onClose={() => setPopupInfo(null)}
+          >
+            <div style={{ color: 'black' }}>
+              {/*src/MapNext.tsx:71:40 - error TS2339: Property 'city' does not exist on type 'never'.
+
+71                             {popupInfo.city}, {popupInfo.state} |{' '}
+                                          ~~~~*/}
+              {/*@ts-ignore*/}
+              {/*@ts-ignore*/}
+              {popupInfo.city},{popupInfo.state}
+              {/*@ts-ignore*/}
+            </div>
+            {/*@ts-ignore*/}
+            <img width="100%" src={popupInfo.image} />
+            <br />
+            <a
+              style={{ color: 'blue' }}
+              target="_new"
+              href={`http://en.wikipedia.org/w/index.php?title=Special:Search&search=${(popupInfo as any).city}, ${(popupInfo as any).state}`}
+            >
+              Wikipedia
+            </a>
+          </Popup>
+        )}
+      </Map>
+    </Box>
+  );
+}

packages/client/src/components/landing-component/MatrixRain.tsx

@@ -0,0 +1,124 @@
+import { useBreakpointValue, useTheme } from '@chakra-ui/react';
+import React, { useEffect, useRef, useMemo } from 'react';
+
+const MATRIX_CHARS =
+  'アイウエオカキクケコサシスセソタチツテトナニヌネノハヒフヘホ0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ';
+
+interface MatrixRainProps {
+  speed?: number;
+  glow?: boolean;
+  intensity?: number;
+  visible?: boolean;
+}
+
+export const MatrixRain: React.FC<MatrixRainProps> = ({
+  speed = 1,
+  glow = false,
+  intensity = 1,
+  visible,
+}) => {
+  const fontSize = useBreakpointValue({ base: 14, md: 18, lg: 22 }) ?? 14;
+  const theme = useTheme();
+  const canvasRef = useRef<HTMLCanvasElement | null>(null);
+  const animationRef = useRef<number | null>(null);
+  const dropsRef = useRef<number[]>([]);
+  const columnsRef = useRef<number>(0);
+
+  const colors = useMemo(
+    () => ({
+      background: theme.colors.background.primary,
+      textAccent: theme.colors.text.accent,
+    }),
+    [theme.colors.background.primary, theme.colors.text.accent],
+  );
+
+  const colorsRef = useRef(colors);
+  colorsRef.current = colors;
+
+  useEffect(() => {
+    const canvas = canvasRef.current;
+    if (!canvas) return;
+    const ctx = canvas.getContext('2d');
+    if (!ctx) return;
+
+    const resize = () => {
+      canvas.width = window.innerWidth;
+      canvas.height = window.innerHeight;
+
+      const newColumns = Math.floor(canvas.width / fontSize);
+      if (newColumns !== columnsRef.current) {
+        columnsRef.current = newColumns;
+        const newDrops: number[] = [];
+
+        for (let i = 0; i < newColumns; i++) {
+          if (i < dropsRef.current.length) {
+            newDrops[i] = dropsRef.current[i];
+          } else {
+            newDrops[i] = Math.random() * (canvas.height / fontSize);
+          }
+        }
+        dropsRef.current = newDrops;
+      }
+    };
+
+    resize();
+    window.addEventListener('resize', resize);
+
+    if (dropsRef.current.length === 0) {
+      const columns = Math.floor(canvas.width / fontSize);
+      columnsRef.current = columns;
+
+      for (let i = 0; i < columns; i++) {
+        dropsRef.current[i] = Math.random() * (canvas.height / fontSize);
+      }
+    }
+
+    const draw = () => {
+      if (!ctx || !canvas) return;
+
+      const currentColors = colorsRef.current;
+
+      ctx.fillStyle = currentColors.background;
+      ctx.fillRect(0, 0, canvas.width, canvas.height);
+
+      ctx.font = `${fontSize}px monospace`;
+
+      for (let i = 0; i < dropsRef.current.length; i++) {
+        const text = MATRIX_CHARS[Math.floor(Math.random() * MATRIX_CHARS.length)];
+        const x = i * fontSize;
+        const y = dropsRef.current[i] * fontSize;
+
+        ctx.fillStyle = currentColors.textAccent;
+        if (glow) {
+          ctx.shadowBlur = 10;
+          ctx.shadowColor = currentColors.textAccent;
+        }
+        ctx.fillText(text, x, y);
+
+        if (y > canvas.height) {
+          dropsRef.current[i] = -Math.random() * 5;
+        } else {
+          dropsRef.current[i] += (0.1 + Math.random() * 0.5) * speed * intensity;
+        }
+      }
+
+      animationRef.current = requestAnimationFrame(draw);
+    };
+
+    animationRef.current = requestAnimationFrame(draw);
+
+    return () => {
+      window.removeEventListener('resize', resize);
+      if (animationRef.current) {
+        cancelAnimationFrame(animationRef.current);
+      }
+    };
+  }, [fontSize, speed, glow, intensity, visible]);
+
+  return (
+    <canvas
+      ref={canvasRef}
+      style={{ display: visible ? 'block' : 'none', pointerEvents: 'none' }}
+    />
+  );
+};

packages/client/src/components/landing-component/Particles.tsx

@@ -0,0 +1,162 @@
+import { Box, useTheme } from '@chakra-ui/react';
+import React, { useEffect, useRef } from 'react';
+
+interface ParticlesProps {
+  speed: number;
+  intensity: number;
+  particles: boolean;
+  glow: boolean;
+  visible?: boolean;
+}
+
+interface Particle {
+  x: number;
+  y: number;
+  vx: number;
+  vy: number;
+  size: number;
+}
+
+const Particles: React.FC<ParticlesProps> = ({ speed, intensity, glow, visible }) => {
+  const canvasRef = useRef<HTMLCanvasElement>(null);
+  const particlesRef = useRef<Particle[]>([]);
+  const animationFrameRef = useRef<number | undefined>(undefined);
+  const theme = useTheme();
+
+  // Helper function to create a single particle with proper canvas dimensions
+  const createParticle = (canvas: HTMLCanvasElement): Particle => ({
+    x: Math.random() * canvas.parentElement!.getBoundingClientRect().width,
+    y: Math.random() * canvas.parentElement!.getBoundingClientRect().height,
+    vx: (Math.random() - 0.5) * speed,
+    vy: (Math.random() - 0.5) * speed,
+    size: Math.random() * 3 + 1,
+  });
+
+  // Main animation effect
+  useEffect(() => {
+    if (!visible) {
+      if (animationFrameRef.current) {
+        cancelAnimationFrame(animationFrameRef.current);
+        animationFrameRef.current = undefined;
+      }
+      particlesRef.current = []; // Clear particles when disabled
+      return;
+    }
+
+    const canvas = canvasRef.current;
+    if (!canvas) return;
+
+    const ctx = canvas.getContext('2d');
+    if (!ctx) return;
+
+    const resizeCanvas = () => {
+      canvas.width = window.innerWidth;
+      canvas.height = window.innerHeight;
+
+      // Reposition existing particles that are outside new bounds
+      particlesRef.current.forEach(particle => {
+        if (particle.x > canvas.width) particle.x = Math.random() * canvas.width;
+        if (particle.y > canvas.height) particle.y = Math.random() * canvas.height;
+      });
+    };
+
+    const ensureParticleCount = () => {
+      const targetCount = Math.floor(intensity * 100);
+      const currentCount = particlesRef.current.length;
+
+      if (currentCount < targetCount) {
+        // Add new particles
+        const newParticles = Array.from({ length: targetCount - currentCount }, () =>
+          createParticle(canvas),
+        );
+        particlesRef.current = [...particlesRef.current, ...newParticles];
+      } else if (currentCount > targetCount) {
+        // Remove excess particles
+        particlesRef.current = particlesRef.current.slice(0, targetCount);
+      }
+    };
+
+    const updateParticles = () => {
+      particlesRef.current.forEach(particle => {
+        particle.x += particle.vx;
+        particle.y += particle.vy;
+
+        if (particle.x < 0) particle.x = canvas.width;
+        if (particle.x > canvas.width) particle.x = 0;
+        if (particle.y < 0) particle.y = canvas.height;
+        if (particle.y > canvas.height) particle.y = 0;
+      });
+    };
+
+    const drawParticles = () => {
+      ctx.clearRect(0, 0, canvas.width, canvas.height);
+      ctx.fillStyle = theme.colors.text.accent;
+      ctx.globalCompositeOperation = 'lighter';
+
+      if (glow) {
+        ctx.shadowBlur = 10;
+        ctx.shadowColor = 'white';
+      } else {
+        ctx.shadowBlur = 0;
+      }
+
+      particlesRef.current.forEach(particle => {
+        ctx.beginPath();
+        ctx.arc(particle.x, particle.y, particle.size, 0, Math.PI * 2);
+        ctx.fill();
+      });
+    };
+
+    const animate = () => {
+      updateParticles();
+      drawParticles();
+      animationFrameRef.current = requestAnimationFrame(animate);
+    };
+
+    const handleResize = () => {
+      resizeCanvas();
+    };
+
+    window.addEventListener('resize', handleResize);
+    resizeCanvas(); // Set canvas size first
+    ensureParticleCount(); // Then create particles with proper dimensions
+    animate();
+
+    return () => {
+      window.removeEventListener('resize', handleResize);
+      if (animationFrameRef.current) {
+        cancelAnimationFrame(animationFrameRef.current);
+        animationFrameRef.current = undefined;
+      }
+    };
+  }, [visible, intensity, speed, glow, theme.colors.text.accent]);
+
+  // Separate effect for speed changes - update existing particle velocities
+  useEffect(() => {
+    if (!visible) return;
+
+    particlesRef.current.forEach(particle => {
+      const currentSpeed = Math.sqrt(particle.vx * particle.vx + particle.vy * particle.vy);
+      if (currentSpeed > 0) {
+        const normalizedVx = particle.vx / currentSpeed;
+        const normalizedVy = particle.vy / currentSpeed;
+        particle.vx = normalizedVx * speed;
+        particle.vy = normalizedVy * speed;
+      } else {
+        particle.vx = (Math.random() - 0.5) * speed;
+        particle.vy = (Math.random() - 0.5) * speed;
+      }
+    });
+  }, [speed, visible]);
+
+  return (
+    <Box zIndex={0} pointerEvents={'none'}>
+      <canvas
+        ref={canvasRef}
+        style={{ display: visible ? 'block' : 'none', pointerEvents: 'none' }}
+      />
+    </Box>
+  );
+};
+
+export default Particles;

packages/client/src/components/landing-component/Tweakbox.tsx

@@ -0,0 +1,111 @@
+import {
+  Box,
+  Grid,
+  GridItem,
+  Heading,
+  Slider,
+  SliderTrack,
+  SliderFilledTrack,
+  SliderThumb,
+  Text,
+  Switch,
+  Collapse,
+  IconButton,
+} from '@chakra-ui/react';
+import { ChevronDownIcon, ChevronUpIcon } from 'lucide-react';
+import { observer } from 'mobx-react-lite';
+import React, { useState } from 'react';
+
+interface SliderControl {
+  value: number;
+  onChange: (value: number) => void;
+  label: string;
+  min: number;
+  max: number;
+  step: number;
+  ariaLabel: string;
+}
+
+interface SwitchControl {
+  value: boolean;
+  onChange: (enabled: boolean) => void;
+  label: string;
+  exclusive?: boolean;
+}
+
+interface TweakboxProps {
+  sliders: {
+    speed: SliderControl;
+    intensity: SliderControl;
+  };
+  switches: {
+    particles: SwitchControl;
+    glow: SwitchControl;
+  } & Record<string, SwitchControl>;
+}
+
+const Tweakbox = observer(({ sliders, switches }: TweakboxProps) => {
+  const [isCollapsed, setIsCollapsed] = useState(false);
+
+  return (
+    <Box display="flex" alignItems="flex-start">
+      <IconButton
+        aria-label="Toggle controls"
+        borderRadius="lg"
+        bg="whiteAlpha.300"
+        backdropFilter="blur(10px)"
+        boxShadow="xl"
+        icon={isCollapsed ? <ChevronUpIcon /> : <ChevronDownIcon />}
+        onClick={() => setIsCollapsed(!isCollapsed)}
+        size="sm"
+        marginRight={2}
+      />
+      <Collapse in={!isCollapsed} style={{ width: '100%' }}>
+        <Box p={4} borderRadius="lg" bg="whiteAlpha.100" backdropFilter="blur(10px)" boxShadow="xl">
+          <Grid templateColumns="1fr" gap={4}>
+            <GridItem>
+              <Heading hidden={true} size="sm" mb={4} color="text.accent">
+                Controls
+              </Heading>
+            </GridItem>
+            {Object.keys(switches).map(key => {
+              return (
+                <GridItem key={key}>
+                  <Text mb={2} color="text.accent">
+                    {switches[key].label}
+                  </Text>
+                  <Switch
+                    isChecked={switches[key].value}
+                    onChange={e => switches[key].onChange(e.target.checked)}
+                  />
+                </GridItem>
+              );
+            })}
+            {Object.entries(sliders).map(([key, slider]) => (
+              <GridItem key={key}>
+                <Text mb={2} color="text.accent">
+                  {slider.label}
+                </Text>
+                <Slider
+                  aria-label={slider.ariaLabel}
+                  value={slider.value}
+                  min={slider.min}
+                  step={slider.step}
+                  max={slider.max}
+                  onChange={slider.onChange}
+                >
+                  <SliderTrack>
+                    <SliderFilledTrack />
+                  </SliderTrack>
+                  <SliderThumb />
+                </Slider>
+              </GridItem>
+            ))}
+          </Grid>
+        </Box>
+      </Collapse>
+    </Box>
+  );
+});
+
+export default Tweakbox;

packages/client/src/components/landing-component/control-panel.tsx

@@ -0,0 +1,24 @@
+import * as React from 'react';
+
+function ControlPanel() {
+  return (
+    <div className="control-panel">
+      <p>
+        Data source:{' '}
+        <a href="https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population">
+          Wikipedia
+        </a>
+      </p>
+      <div className="source-link">
+        <a
+          href="https://github.com/visgl/react-map-gl/tree/8.0-release/examples/mapbox/controls"
+          target="_new"
+        >
+          View Code ↗
+        </a>
+      </div>
+    </div>
+  );
+}
+
+export default React.memo(ControlPanel);

packages/client/src/components/landing-component/nautical-base-data.json

@@ -0,0 +1,22 @@
+[
+  {"city":"New York","population":"8,335,897","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/b/b9/Above_Gotham.jpg/240px-Above_Gotham.jpg","state":"New York","latitude":40.7128,"longitude":-74.0060},
+  {"city":"Los Angeles","population":"3,822,238","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/5/57/LA_Skyline_Mountains2.jpg/240px-LA_Skyline_Mountains2.jpg","state":"California","latitude":34.0522,"longitude":-118.2437},
+  {"city":"Long Beach","population":"456,062","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/d/d9/Long_Beach_skyline_from_Shoreline_Village.jpg/240px-Long_Beach_skyline_from_Shoreline_Village.jpg","state":"California","latitude":33.7701,"longitude":-118.1937},
+  {"city":"Seattle","population":"749,256","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/3/36/SeattleI5Skyline.jpg/240px-SeattleI5Skyline.jpg","state":"Washington","latitude":47.6062,"longitude":-122.3321},
+  {"city":"San Francisco","population":"808,437","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/6/6a/San_Francisco_skyline_from_Coit_Tower.jpg/240px-San_Francisco_skyline_from_Coit_Tower.jpg","state":"California","latitude":37.7749,"longitude":-122.4194},
+  {"city":"San Diego","population":"1,386,932","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/5/53/US_Navy_110604-N-NS602-574_Navy_and_Marine_Corps_personnel%2C_along_with_community_leaders_from_the_greater_San_Diego_area_come_together_to_commemora.jpg/240px-US_Navy_110604-N-NS602-574_Navy_and_Marine_Corps_personnel%2C_along_with_community_leaders_from_the_greater_San_Diego_area_come_together_to_commemora.jpg","state":"California","latitude":32.7157,"longitude":-117.1611},
+  {"city":"Norfolk","population":"235,089","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/Norfolk_Skyline_from_Portsmouth.jpg/240px-Norfolk_Skyline_from_Portsmouth.jpg","state":"Virginia","latitude":36.8508,"longitude":-76.2859},
+  {"city":"Miami","population":"449,514","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/4/4b/Miami_skyline_201807_cat.jpg/240px-Miami_skyline_201807_cat.jpg","state":"Florida","latitude":25.7617,"longitude":-80.1918},
+  {"city":"Boston","population":"675,647","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/1/1b/Boston_skyline_and_Boston_Harbor.jpg/240px-Boston_skyline_and_Boston_Harbor.jpg","state":"Massachusetts","latitude":42.3601,"longitude":-71.0589},
+  {"city":"Baltimore","population":"585,708","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/3/3b/Baltimore_Skyline.jpg/240px-Baltimore_Skyline.jpg","state":"Maryland","latitude":39.2904,"longitude":-76.6122},
+  {"city":"Charleston","population":"151,612","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/a/a7/Charleston_SC_Skyline.jpg/240px-Charleston_SC_Skyline.jpg","state":"South Carolina","latitude":32.7765,"longitude":-79.9311},
+  {"city":"Savannah","population":"147,780","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Savannah_GA%2C_River_Street.jpg/240px-Savannah_GA%2C_River_Street.jpg","state":"Georgia","latitude":32.0809,"longitude":-81.0912},
+  {"city":"Tampa","population":"403,364","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/4/4d/Tampa_skyline_from_South%2C_2022.jpg/240px-Tampa_skyline_from_South%2C_2022.jpg","state":"Florida","latitude":27.9506,"longitude":-82.4572},
+  {"city":"Mobile","population":"187,041","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/7/70/Mobile_skyline_from_Mobile_River.jpg/240px-Mobile_skyline_from_Mobile_River.jpg","state":"Alabama","latitude":30.6954,"longitude":-88.0399},
+  {"city":"Anchorage","population":"288,121","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/5/55/Anchorage_skyline_and_susitna.jpg/240px-Anchorage_skyline_and_susitna.jpg","state":"Alaska","latitude":61.2181,"longitude":-149.9003},
+  {"city":"Portland","population":"68,408","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/4/48/Portland_Maine_skyline.jpg/240px-Portland_Maine_skyline.jpg","state":"Maine","latitude":43.6591,"longitude":-70.2568},
+  {"city":"Honolulu","population":"349,547","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/1/10/Honolulu_and_Diamond_Head.jpg/240px-Honolulu_and_Diamond_Head.jpg","state":"Hawaii","latitude":21.3069,"longitude":-157.8583},
+  {"city":"New Orleans","population":"376,971","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/f/fb/New_Orleans_skyline_sunset_Dec_28_2021_PANO_DSC07177-07179.jpg/240px-New_Orleans_skyline_sunset_Dec_28_2021_PANO_DSC07177-07179.jpg","state":"Louisiana","latitude":29.9511,"longitude":-90.0715},
+  {"city":"Jacksonville","population":"971,319","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/f/f3/Skyline_of_Jacksonville_FL%2C_South_view_20160706_1.jpg/240px-Skyline_of_Jacksonville_FL%2C_South_view_20160706_1.jpg","state":"Florida","latitude":30.3322,"longitude":-81.6557},
+  {"city":"Houston","population":"2,302,878","image":"https://upload.wikimedia.org/wikipedia/commons/thumb/6/60/Aerial_views_of_the_Houston%2C_Texas%2C_28005u.jpg/240px-Aerial_views_of_the_Houston%2C_Texas%2C_28005u.jpg","state":"Texas","latitude":29.7604,"longitude":-95.3698}
+]

packages/client/src/components/landing-component/pin.tsx

@@ -0,0 +1,21 @@
+import * as React from 'react';
+
+const ICON = `M20.2,15.7L20.2,15.7c1.1-1.6,1.8-3.6,1.8-5.7c0-5.6-4.5-10-10-10S2,4.5,2,10c0,2,0.6,3.9,1.6,5.4c0,0.1,0.1,0.2,0.2,0.3
+  c0,0,0.1,0.1,0.1,0.2c0.2,0.3,0.4,0.6,0.7,0.9c2.6,3.1,7.4,7.6,7.4,7.6s4.8-4.5,7.4-7.5c0.2-0.3,0.5-0.6,0.7-0.9
+  C20.1,15.8,20.2,15.8,20.2,15.7z`;
+
+const pinStyle = {
+    cursor: 'pointer',
+    fill: '#d00',
+    stroke: 'none'
+};
+
+function Pin({size = 20}) {
+    return (
+        <svg height={size} viewBox="0 0 24 24" style={pinStyle}>
+            <path d={ICON} />
+        </svg>
+    );
+}
+
+export default React.memo(Pin);

packages/client/src/components/toolbar/Toolbar.tsx

@@ -2,6 +2,7 @@ import { Flex } from '@chakra-ui/react';
 import React from 'react';
 
 import BuiltWithButton from '../BuiltWithButton';
+import InstallButton from '../install/InstallButton.tsx';
 
 import GithubButton from './GithubButton';
 import SupportThisSiteButton from './SupportThisSiteButton';
@@ -17,6 +18,7 @@ function ToolBar({ isMobile }) {
       alignItems={isMobile ? 'flex-start' : 'flex-end'}
       pb={4}
     >
+      <InstallButton />
       <SupportThisSiteButton />
       <GithubButton />
       <BuiltWithButton />

packages/client/src/layout/Content.tsx

@@ -6,7 +6,7 @@ import { useIsMobile } from '../components/contexts/MobileContext';
 function Content({ children }) {
   const isMobile = useIsMobile();
   return (
-    <Flex flexDirection="column" w="100%" h="100vh" p={!isMobile ? 4 : 1}>
+    <Flex flexDirection="column" w="100%" h="100vh">
       {children}
     </Flex>
   );

packages/client/src/layout/Hero.tsx

@@ -10,16 +10,16 @@ export default function Hero() {
   const isMobile = useIsMobile();
 
   return (
-    <Box p={2}>
+    <Box p={2} mt={2}>
       <Box>
         <Heading
           textAlign={isMobile ? 'left' : 'right'}
           minWidth="90px"
           maxWidth={'220px'}
           color="text.accent"
-          as="h3"
+          // as="h3"
           letterSpacing={'tight'}
-          size="lg"
+          size="xl"
         >
           {Routes[normalizePath(pageContext.urlPathname)]?.heroLabel}
         </Heading>

packages/client/src/layout/Layout.tsx

@@ -2,6 +2,7 @@ import { observer } from 'mobx-react-lite';
 import React, { useEffect, useState } from 'react';
 
 import { Chakra } from '../components/contexts/ChakraContext';
+import ComponentContext, { ComponentProvider } from '../components/contexts/ComponentContext.tsx';
 import { MobileProvider } from '../components/contexts/MobileContext';
 import { PageContextProvider } from '../renderer/usePageContext';
 import userOptionsStore from '../stores/UserOptionsStore';
@@ -13,6 +14,7 @@ export { Layout };
 
 const Layout = observer(({ pageContext, children }) => {
   const [activeTheme, setActiveTheme] = useState<string>('darknight');
+  const [enabledComponent, setEnabledComponent] = useState('gpsmap');
 
   useEffect(() => {
     if (userOptionsStore.theme !== activeTheme) {
@@ -47,7 +49,9 @@ const Layout = observer(({ pageContext, children }) => {
       <PageContextProvider pageContext={pageContext}>
         <MobileProvider>
           <Chakra theme={getTheme(activeTheme)}>
+            <ComponentProvider>
               <LayoutComponent>{children}</LayoutComponent>
+            </ComponentProvider>
           </Chakra>
         </MobileProvider>
       </PageContextProvider>

packages/client/src/layout/NavItem.tsx

@@ -5,7 +5,7 @@ function NavItem({ path, children, color, onClick, as, cursor }) {
   return (
     <Box
       as={as ?? 'a'}
-      href={path}
+      href={path && path.length > 1 ? path : '/'}
       mb={2}
       cursor={cursor}
       // ml={5}

packages/client/src/layout/Navigation.tsx

@@ -1,4 +1,4 @@
-import { Box, Collapse, Grid, GridItem, useBreakpointValue } from '@chakra-ui/react';
+import { Box, Collapse, Grid, GridItem, useBreakpointValue, useTheme } from '@chakra-ui/react';
 import { MenuIcon } from 'lucide-react';
 import { observer } from 'mobx-react-lite';
 import React, { useEffect } from 'react';
@@ -18,6 +18,8 @@ const Navigation = observer(({ children, routeRegistry }) => {
 
   const currentPath = pageContext.urlPathname || '/';
 
+  const theme = useTheme();
+
   const getTopValue = () => {
     if (!isMobile) return undefined;
     if (currentPath === '/') return 12;
@@ -40,6 +42,7 @@ const Navigation = observer(({ children, routeRegistry }) => {
 
   return (
     <Grid templateColumns="1fr" templateRows="auto 1fr">
+      {/*this is the menu button*/}
       <GridItem
         p={4}
         position="fixed"
@@ -53,9 +56,10 @@ const Navigation = observer(({ children, routeRegistry }) => {
           <GridItem>
             <MenuIcon
               cursor="pointer"
+              color="text.accent"
               w={6}
               h={6}
-              stroke={getTheme(userOptionsStore.theme).colors.text.accent}
+              stroke={theme.colors.text.accent}
               onClick={() => {
                 switch (menuState.isOpen) {
                   case true:

packages/client/src/layout/theme/color-themes/OneDark.ts

@@ -15,8 +15,8 @@ export default {
   },
 
   background: {
-    primary: 'linear-gradient(360deg, #15171C 100%, #353A47 100%)',
+    // primary: 'linear-gradient(360deg, #15171C 100%, #353A47 100%)',
-
+    primary: '#15171C',
     secondary: '#1B1F26',
     tertiary: '#1E1E2E',
   },

packages/client/src/pages/+client.ts

@@ -1,4 +1,27 @@
 // runs before anything else
+import { registerSW } from 'virtual:pwa-register';
+
 import UserOptionsStore from '../stores/UserOptionsStore';
 
 UserOptionsStore.initialize();
+
+try {
+  const isLocal = window.location.hostname.includes('localhost');
+  if (!isLocal) {
+    if ('serviceWorker' in navigator) {
+      // && !/localhost/.test(window.location)) {
+      registerSW();
+    }
+    // navigator.serviceWorker.register('/service-worker.js');
+  } else {
+    (async () => {
+      await navigator.serviceWorker.getRegistrations().then(registrations => {
+        registrations.map(r => {
+          r.unregister();
+        });
+      });
+    })();
+  }
+} catch (e) {
+  // fail silent
+}

packages/client/src/pages/index/+Page.tsx

@@ -1,7 +1,10 @@
-import { Stack } from '@chakra-ui/react';
+import { Box } from '@chakra-ui/react';
 import React, { useEffect } from 'react';
 
-import Chat from '../../components/chat/Chat';
+import Chat from '../../components/chat/Chat.tsx';
+import { useComponent } from '../../components/contexts/ComponentContext.tsx';
+import { LandingComponent } from '../../components/landing-component/LandingComponent.tsx';
+import ReactMap from '../../components/landing-component/Map.tsx';
 import clientChatStore from '../../stores/ClientChatStore';
 
 // renders "/"
@@ -16,9 +19,29 @@ export default function IndexPage() {
     }
   }, []);
 
+  const component = useComponent();
+
   return (
-    <Stack direction="column" height="100%" width="100%" spacing={0}>
+    <Box height="100%" width="100%">
-      <Chat height="100%" width="100%" />
+      <LandingComponent />
-    </Stack>
+
+      <Box
+        display={component.enabledComponent === 'ai' ? undefined : 'none'}
+        width="100%"
+        height="100%"
+        overflowY="scroll"
+        padding={'unset'}
+      >
+        <Chat />
+      </Box>
+      <Box
+        display={component.enabledComponent === 'gpsmap' ? undefined : 'none'}
+        width="100%"
+        height="100%"
+        padding={'unset'}
+      >
+        <ReactMap visible={component.enabledComponent === 'gpsmap'} />
+      </Box>
+    </Box>
   );
 }

packages/client/src/renderer/+onRenderHtml.tsx

@@ -28,10 +28,9 @@ const onRenderHtml: OnRenderHtmlAsync = async (pageContext): ReturnType<OnRender
 <html data-theme="dark" lang="en">
 <head>
 <title>open-gsio</title>
-<link rel="apple-touch-icon" sizes="180x180" href="/apple-touch-icon.png">
+<link rel="icon" href="/favicon.ico" sizes="48x48">
-<link rel="icon" type="image/png" sizes="32x32" href="/favicon-32x32.png">
+<link rel="icon" href="/favicon.svg" sizes="any" type="image/svg+xml">
-<link rel="icon" type="image/png" sizes="16x16" href="/favicon-16x16.png">
+<link rel="apple-touch-icon" href="/apple-touch-icon-180x180.png">
-<link rel="manifest" href="/site.webmanifest">
 <meta name="viewport" content="width=device-width, initial-scale=1">
 <meta charset="UTF-8">
 <meta name="description" content="Maker Site">

packages/client/src/renderer/routes.ts

@@ -1,5 +1,5 @@
 export default {
-  '/': { sidebarLabel: 'Home', heroLabel: 'gsio' },
+  '/': { sidebarLabel: 'Home', heroLabel: 'va-chat' },
   '/connect': { sidebarLabel: 'Connect', heroLabel: 'connect' },
   '/privacy-policy': {
     sidebarLabel: '',

packages/client/src/static-data/welcome_home_text.ts

@@ -1,7 +1,7 @@
 export const welcome_home_text = `
-# welcome!
+# open-gsio
 ---
-Please enjoy [responsibly](https://centerforresponsible.ai/the-center)
+
 <br/>
 <br/>
 `;

packages/client/vite.config.ts

@@ -7,10 +7,15 @@ import { VitePWA } from 'vite-plugin-pwa';
 // eslint-disable-next-line import/no-unresolved
 import { configDefaults } from 'vitest/config';
 
+import { getColorThemes } from './src/layout/theme/color-themes';
+
 const prebuildPlugin = () => ({
   name: 'prebuild',
   config(config, { command }) {
     if (command === 'build') {
+      console.log('Generate PWA Assets -> public/');
+      child_process.execSync('bun generate:pwa:assets');
+      console.log('Generated Sitemap -> public/sitemap.xml');
       child_process.execSync('bun generate:sitemap');
       console.log('Generated Sitemap -> public/sitemap.xml');
       child_process.execSync('bun run generate:robotstxt');
@@ -21,6 +26,13 @@ const prebuildPlugin = () => ({
   },
 });
 
+// eslint-disable-next-line @typescript-eslint/no-require-imports
+// const PROJECT_SOURCES_HASH = sha512Dir('./src');
+//
+// console.log({ PROJECT_SOURCES_HASH });
+
+const buildId = crypto.randomUUID();
+
 export default defineConfig(({ command }) => {
   return {
     mode: 'production',
@@ -31,6 +43,62 @@ export default defineConfig(({ command }) => {
         prerender: true,
         disableAutoFullBuild: false,
       }),
+      VitePWA({
+        registerType: 'autoUpdate',
+        injectRegister: null,
+        minify: true,
+        disable: false,
+        filename: 'service-worker.js',
+        devOptions: {
+          enabled: false,
+          navigateFallback: 'index.html',
+          suppressWarnings: true,
+          type: 'module',
+        },
+        manifest: {
+          name: `open-gsio`,
+          short_name: 'open-gsio',
+          display: 'standalone',
+          description: `open-gsio client`,
+          theme_color: getColorThemes().at(0)?.colors.text.accent,
+          background_color: getColorThemes().at(0)?.colors.background.primary,
+          scope: '/',
+          start_url: '/',
+          icons: [
+            {
+              src: 'pwa-64x64.png',
+              sizes: '64x64',
+              type: 'image/png',
+            },
+            {
+              src: 'pwa-192x192.png',
+              sizes: '192x192',
+              type: 'image/png',
+            },
+            {
+              src: 'pwa-512x512.png',
+              sizes: '512x512',
+              type: 'image/png',
+              purpose: 'any',
+            },
+            {
+              src: 'maskable-icon-512x512.png',
+              sizes: '512x512',
+              type: 'image/png',
+              purpose: 'maskable',
+            },
+          ],
+        },
+
+        workbox: {
+          globPatterns: ['**/*.{js,css,html,ico,png,svg,wasm}'],
+          navigateFallbackDenylist: [/^\/api\//],
+          maximumFileSizeToCacheInBytes: 25000000,
+          cacheId: buildId,
+          cleanupOutdatedCaches: true,
+          clientsClaim: true,
+        },
+      }),
       // PWA plugin saves money on data transfer by caching assets on the client
       /*
                 For safari, use this script in the console to unregister the service worker.
@@ -41,22 +109,15 @@ export default defineConfig(({ command }) => {
                     })
                 })
              */
-      // VitePWA({
-      //     registerType: 'autoUpdate',
-      //     devOptions: {
-      //         enabled: false,
-      //     },
-      //     manifest: {
-      //         name: "open-gsio",
-      //         short_name: "open-gsio",
-      //         description: "Assistant"
-      //     },
-      //     workbox: {
-      //         globPatterns: ['**/*.{js,css,html,ico,png,svg}'],
-      //         navigateFallbackDenylist: [/^\/api\//],
-      //     }
-      // })
     ],
+    workbox: {
+      globPatterns: ['**/*.{js,css,html,ico,png,svg,wasm}'],
+      navigateFallbackDenylist: [/^\/api\//],
+      maximumFileSizeToCacheInBytes: 25000000,
+      cacheId: buildId,
+      cleanupOutdatedCaches: true,
+      clientsClaim: true,
+    },
     server: {
       port: 3000,
       proxy: {

packages/cloudflare-workers/analytics/package.json

@@ -0,0 +1,3 @@
+{
+  "name": "@open-gsio/analytics-worker"
+}

packages/cloudflare-workers/open-gsio/main.ts

@@ -1,6 +1,7 @@
 import { ServerCoordinator } from '@open-gsio/coordinators';
 import Router from '@open-gsio/router';
+import { error } from 'itty-router';
 
 export { ServerCoordinator };
 
-export default Router.Router();
+export default Router.Router().catch(error);

packages/cloudflare-workers/open-gsio/wrangler.jsonc

@@ -1,5 +1,5 @@
 {
-  "$schema": "https://workers.cloudflare.com/sites/config-schema.json",
+  "$schema": "../../../node_modules/wrangler/config-schema.json",
   "name": "open-gsio",
   "assets": {
     "binding": "ASSETS",
@@ -20,9 +20,9 @@
     {
       "binding": "KV_STORAGE",
       // $ npx wrangler kv namespace create open-gsio
-      "id": "placeholderId",
+      "id": "",
       // $ npx wrangler kv namespace create open-gsio --preview
-      "preview_id": "placeholderIdPreview"
+      "preview_id": ""
     }
   ],
   "migrations": [

packages/router/src/router.ts

@@ -52,13 +52,15 @@ export function createRouter() {
       // })
 
       .get('/api/metrics*', async (r, e, c) => {
-        const { metricsService } = createRequestContext(e, c);
+        return new Response('ok');
-        return metricsService.handleMetricsRequest(r);
+        // const { metricsService } = createRequestContext(e, c);
+        // return metricsService.handleMetricsRequest(r);
       })
 
       .post('/api/metrics*', async (r, e, c) => {
-        const { metricsService } = createRequestContext(e, c);
+        return new Response('ok');
-        return metricsService.handleMetricsRequest(r);
+        // const { metricsService } = createRequestContext(e, c);
+        // return metricsService.handleMetricsRequest(r);
       })
 
       // renders the app

packages/scripts/cleanup.sh

@@ -15,7 +15,12 @@ find . -name ".wrangler" -type d -prune -exec rm -rf {} \;
 
 # Remove build directories
 find . -name "dist" -type d -prune -exec rm -rf {} \;
-find . -name "build" -type d -prune -exec rm -rf {} \;
+
+
+#-----
+# crates/yachtpit uses a directory called build for staging assets so it can't be removed
+#find . -name "build" -type d -prune -exec rm -rf {} \;
+#-----
 
 find . -name "fonts" -type d -prune -exec rm -rf {} \;
 

packages/server/package.json

@@ -2,8 +2,8 @@
   "name": "@open-gsio/server",
   "type": "module",
   "scripts": {
-    "dev": "bun src/server/server.ts",
+    "dev": "bun --watch src/server/server.ts",
-    "build": "bun run src/server/build.ts"
+    "build": "bun ./src/server/build.ts"
   },
   "devDependencies": {
     "@open-gsio/env": "workspace:*",
@@ -24,8 +24,11 @@
     "mobx-state-tree": "^6.0.1",
     "moo": "^0.5.2",
     "typescript": "^5.7.2",
-    "vike": "0.4.193",
+    "vike": "0.4.235",
+    "vite": "^7.0.0",
     "zod": "^3.23.8",
-    "dotenv": "^16.5.0"
+    "dotenv": "^17.0.0",
+    "bun": "^1.2.17",
+    "@types/bun": "^1.2.17"
   }
 }

packages/server/src/server/asset-handler.ts

@@ -0,0 +1,53 @@
+import { readdir } from 'node:fs/promises';
+
+export const assetHandler = {
+  ASSETS: {
+    /**
+     * Fetches the requested static asset from local dist
+     *
+     * @param {Request} request - The incoming Fetch API Request object.
+     * @returns {Promise<Response>} A Promise that resolves with the Response for the requested asset,
+     *                              or a 404 Response if the asset is not found or an error occurs.
+     */
+    async fetch(request: Request): Promise<Response> {
+      // Serialize incoming request URL
+      const originalUrl = new URL(request.url);
+      const url = new URL(request.url);
+
+      // Fixed path: go up to packages level, then to client/public
+      const PUBLIC_DIR = new URL('../../../client/public/', import.meta.url).pathname;
+
+      let publicFiles: string[] = [];
+      try {
+        publicFiles = await readdir(PUBLIC_DIR, { recursive: true });
+      } catch (error) {
+        console.warn(`Could not read public directory ${PUBLIC_DIR}:`, error);
+        // Continue without public files list
+      }
+
+      // Get the filename from pathname and remove any path traversal attempts
+      const filename = url.pathname.split('/').pop()?.replace(/\.\./g, '') || '';
+
+      const isStatic = publicFiles.some(file => file === filename);
+
+      if (url.pathname === '/') {
+        url.pathname = '/index.html';
+      } else if (isStatic && !url.pathname.startsWith('/static')) {
+        // leave it alone
+      } else if (isStatic) {
+        url.pathname = `/static${url.pathname}`;
+      }
+
+      // Fixed path: go up to packages level, then to client/dist/client
+      const dist = new URL('../../../client/dist/client', import.meta.url).pathname;
+
+      try {
+        return new Response(Bun.file(`${dist}${url.pathname}`));
+      } catch (error) {
+        // Log the error with the original requested path
+        console.error(`Error reading asset from path ${originalUrl.pathname}:`, error);
+        return new Response(null, { status: 404 });
+      }
+    },
+  },
+};

packages/server/src/server/build.ts

@@ -1,9 +1,10 @@
-// handles builds the server into js
 await Bun.build({
-  entrypoints: ['./server.ts'],
+  entrypoints: [import.meta.dir + '/server.ts'],
-  outdir: '../dist',
+  outdir: './dist', // Changed from '../dist' to './dist'
   minify: true,
   target: 'node',
   splitting: true,
+  format: 'esm', // Explicitly set ESM format
   throw: true,
+  external: ['@open-gsio/client'], // Mark client as external to avoid bundling issues
 });

packages/server/src/server/create-server.ts

@@ -0,0 +1,61 @@
+import ServerCoordinator from '@open-gsio/coordinators/src/ServerCoordinatorBun.ts';
+import Router from '@open-gsio/router';
+import { config } from 'dotenv';
+import type { RequestLike } from 'itty-router';
+import { error } from 'itty-router';
+
+import { BunSqliteKVNamespace } from '../storage/BunSqliteKVNamespace.ts';
+
+import { assetHandler } from './asset-handler.ts';
+
+export function createServer() {
+  const router = Router.Router();
+  config({
+    path: '.env',
+    debug: true,
+    // defaults: {
+    //     EVENTSOURCE_HOST: "https://eventsource.seemueller.io",
+    // }
+  });
+
+  // bootstrap the root path of the existing router to the asset handler defined here
+  router.get('/', async (request: RequestLike, env: any) => {
+    return await assetHandler.ASSETS.fetch(request as Request);
+  });
+
+  const server = {
+    port: 3003,
+    fetch: async (request: RequestLike, env: { [key: string]: any }, ctx: any) => {
+      // console.log("[trace] request: ", request.method, request.url, "headers: ", request.headers.get("referer"), "body: ", request.body, "env: ", env, "ctx: ", ctx, "")
+
+      env['SERVER_COORDINATOR'] = ServerCoordinator;
+      env['ASSETS'] = assetHandler.ASSETS;
+      env['EVENTSOURCE_HOST'] = process.env.EVENTSOURCE_HOST;
+      env['GROQ_API_KEY'] = process.env.GROQ_API_KEY;
+      env['ANTHROPIC_API_KEY'] = process.env.ANTHROPIC_API_KEY;
+      env['FIREWORKS_API_KEY'] = process.env.FIREWORKS_API_KEY;
+      env['XAI_API_KEY'] = process.env.XAI_API_KEY;
+      env['CEREBRAS_API_KEY'] = process.env.CEREBRAS_API_KEY;
+      env['CLOUDFLARE_API_KEY'] = process.env.CLOUDFLARE_API_KEY;
+      env['CLOUDFLARE_ACCOUNT_ID'] = process.env.CLOUDFLARE_ACCOUNT_ID;
+      env['MLX_API_KEY'] = process.env.MLX_API_KEY;
+      env['OLLAMA_API_KEY'] = process.env.OLLAMA_API_KEY;
+      env['KV_STORAGE'] = new BunSqliteKVNamespace({ namespace: 'open-gsio' });
+
+      try {
+        const controller = new AbortController();
+        const timeout = new Promise((_, reject) =>
+          setTimeout(() => {
+            controller.abort();
+            reject(new Error('Request timeout after 5s'));
+          }, 5000),
+        );
+        return await Promise.race([router.fetch(request, env, ctx).catch(error), timeout]);
+      } catch (e) {
+        console.error('Error handling request:', e);
+        return new Response('Server Error', { status: 500 });
+      }
+    },
+  };
+  return { server, router, assetHandler };
+}

packages/server/src/server/server.ts

@@ -1,111 +1,6 @@
-import { readdir } from 'node:fs/promises';
+import { createServer } from './create-server.ts';
 
-import ServerCoordinator from '@open-gsio/coordinators/src/ServerCoordinatorBun.ts';
+// creates a bun server with the itty router
-import Router from '@open-gsio/router';
+const { server } = createServer();
-import { config } from 'dotenv';
-import type { RequestLike } from 'itty-router';
 
-import { BunSqliteKVNamespace } from '../storage/BunSqliteKVNamespace.ts';
+export default server;
-
-const router = Router.Router();
-
-config({
-  path: '.env',
-  debug: true,
-  // defaults: {
-  //     EVENTSOURCE_HOST: "https://eventsource.seemueller.io",
-  // }
-});
-
-// bootstrap the root path of the existing router to the asset handler defined here
-router.get('/', async (request: RequestLike, env: any) => {
-  return await assetHandler.ASSETS.fetch(request as Request);
-});
-
-export default {
-  port: 3003,
-  fetch: async (request: RequestLike, env: { [key: string]: any }, ctx: any) => {
-    // console.log("[trace] request: ", request.method, request.url, "headers: ", request.headers.get("referer"), "body: ", request.body, "env: ", env, "ctx: ", ctx, "")
-
-    env['SERVER_COORDINATOR'] = ServerCoordinator;
-    env['ASSETS'] = assetHandler.ASSETS;
-    env['EVENTSOURCE_HOST'] = process.env.EVENTSOURCE_HOST;
-    env['GROQ_API_KEY'] = process.env.GROQ_API_KEY;
-    env['ANTHROPIC_API_KEY'] = process.env.ANTHROPIC_API_KEY;
-    env['FIREWORKS_API_KEY'] = process.env.FIREWORKS_API_KEY;
-    env['XAI_API_KEY'] = process.env.XAI_API_KEY;
-    env['CEREBRAS_API_KEY'] = process.env.CEREBRAS_API_KEY;
-    env['CLOUDFLARE_API_KEY'] = process.env.CLOUDFLARE_API_KEY;
-    env['CLOUDFLARE_ACCOUNT_ID'] = process.env.CLOUDFLARE_ACCOUNT_ID;
-    env['MLX_API_KEY'] = process.env.MLX_API_KEY;
-    env['OLLAMA_API_KEY'] = process.env.OLLAMA_API_KEY;
-    env['KV_STORAGE'] = new BunSqliteKVNamespace({ namespace: 'open-gsio' });
-
-    try {
-      const controller = new AbortController();
-      const timeout = new Promise((_, reject) =>
-        setTimeout(() => {
-          controller.abort();
-          reject(new Error('Request timeout after 5s'));
-        }, 5000),
-      );
-
-      return await Promise.race([router.fetch(request, env, ctx), timeout]);
-    } catch (e) {
-      console.error('Error handling request:', e);
-      return new Response('Server Error', { status: 500 });
-    }
-  },
-};
-
-export const assetHandler = {
-  ASSETS: {
-    /**
-     * Fetches the requested static asset from local dist
-     *
-     * @param {Request} request - The incoming Fetch API Request object.
-     * @returns {Promise<Response>} A Promise that resolves with the Response for the requested asset,
-     *                              or a 404 Response if the asset is not found or an error occurs.
-     */
-    async fetch(request: Request): Promise<Response> {
-      // Serialize incoming request URL
-      const originalUrl = new URL(request.url);
-      const url = new URL(request.url);
-
-      // Fixed path: go up to packages level, then to client/public
-      const PUBLIC_DIR = new URL('../../../client/public/', import.meta.url).pathname;
-
-      let publicFiles: string[] = [];
-      try {
-        publicFiles = await readdir(PUBLIC_DIR, { recursive: true });
-      } catch (error) {
-        console.warn(`Could not read public directory ${PUBLIC_DIR}:`, error);
-        // Continue without public files list
-      }
-
-      // Get the filename from pathname and remove any path traversal attempts
-      const filename = url.pathname.split('/').pop()?.replace(/\.\./g, '') || '';
-
-      const isStatic = publicFiles.some(file => file === filename);
-
-      if (url.pathname === '/') {
-        url.pathname = '/index.html';
-      } else if (isStatic && !url.pathname.startsWith('/static')) {
-        // leave it alone
-      } else if (isStatic) {
-        url.pathname = `/static${url.pathname}`;
-      }
-
-      // Fixed path: go up to packages level, then to client/dist/client
-      const dist = new URL('../../../client/dist/client', import.meta.url).pathname;
-
-      try {
-        return new Response(Bun.file(`${dist}${url.pathname}`));
-      } catch (error) {
-        // Log the error with the original requested path
-        console.error(`Error reading asset from path ${originalUrl.pathname}:`, error);
-        return new Response(null, { status: 404 });
-      }
-    },
-  },
-};

packages/server/vite.config.ts

@@ -4,9 +4,12 @@ import { configDefaults } from 'vitest/config';
 
 export default defineConfig(({ command }) => {
   return {
+    build: {
+      cssMinify: 'esbuild',
+    },
     test: {
       globals: true,
-      environment: 'jsdom',
+      environment: 'node',
       registerNodeLoader: false,
       // setupFiles: ['./src/test/setup.ts'],
       exclude: [...configDefaults.exclude, 'dist/**', '.open-gsio/**'],

packages/services/package.json

@@ -39,6 +39,6 @@
     "vitest": "^3.1.4",
     "wrangler": "^4.18.0",
     "zod": "^3.23.8",
-    "dotenv": "^16.5.0"
+    "dotenv": "^17.0.0"
   }
 }

packages/services/src/__tests__/ChatService.test.ts

@@ -37,6 +37,18 @@ vi.mock('../../lib/handleStreamData', () => ({
   default: vi.fn().mockReturnValue(() => {}),
 }));
 
+// Mock ProviderRepository
+vi.mock('@open-gsio/ai/providers/_ProviderRepository.ts', () => {
+  return {
+    ProviderRepository: class {
+      constructor() {}
+      getProviders() {
+        return [{ name: 'openai', key: 'test-key', endpoint: 'https://api.openai.com/v1' }];
+      }
+    },
+  };
+});
+
 describe('ChatService', () => {
   let chatService: any;
   let mockEnv: any;
@@ -221,6 +233,105 @@ describe('ChatService', () => {
       Response.json = originalResponseJson;
       localService.getSupportedModels = originalGetSupportedModels;
     });
+
+    it('should test the cache refresh mechanism when providers change', async () => {
+      // This test verifies that the cache is refreshed when providers change
+      // and that the cache is used when providers haven't changed.
+
+      // Mock data for the first scenario (cache hit)
+      const cachedModels = [
+        { id: 'model-1', provider: 'openai' },
+        { id: 'model-2', provider: 'openai' },
+      ];
+      const providersSignature = JSON.stringify(['openai']);
+
+      // Mock KV_STORAGE for the first scenario (cache hit)
+      const mockKVStorage = {
+        get: vi.fn().mockImplementation(key => {
+          if (key === 'supportedModels') return Promise.resolve(JSON.stringify(cachedModels));
+          if (key === 'providersSignature') return Promise.resolve(providersSignature);
+          return Promise.resolve(null);
+        }),
+        put: vi.fn().mockResolvedValue(undefined),
+      };
+
+      // The ProviderRepository is already mocked at the top of the file
+
+      // Create a service instance with the mocked environment
+      const service = ChatService.create({
+        maxTokens: 2000,
+        systemPrompt: 'You are a helpful assistant.',
+      });
+
+      // Set up the environment with the mocked KV_STORAGE
+      service.setEnv({
+        ...mockEnv,
+        KV_STORAGE: mockKVStorage,
+      });
+
+      // Scenario 1: Cache hit - providers haven't changed
+      const response1 = await service.getSupportedModels();
+      const data1 = await response1.json();
+
+      // Verify the cache was used
+      expect(mockKVStorage.get).toHaveBeenCalledWith('supportedModels');
+      expect(mockKVStorage.get).toHaveBeenCalledWith('providersSignature');
+      expect(data1).toEqual(cachedModels);
+      expect(mockKVStorage.put).not.toHaveBeenCalled();
+
+      // Reset the mock calls for the next scenario
+      vi.clearAllMocks();
+
+      // Scenario 2: Cache miss - providers have changed
+      // Update the mock to return a different providers signature
+      mockKVStorage.get.mockImplementation(key => {
+        if (key === 'supportedModels') {
+          return Promise.resolve(JSON.stringify(cachedModels));
+        }
+        if (key === 'providersSignature') {
+          // Different signature
+          return Promise.resolve(JSON.stringify(['openai', 'anthropic']));
+        }
+        return Promise.resolve(null);
+      });
+
+      // Mock the provider models fetching to avoid actual API calls
+      const mockModels = [
+        { id: 'new-model-1', provider: 'openai' },
+        { id: 'new-model-2', provider: 'openai' },
+      ];
+
+      // Mock OpenAI instance for the second scenario
+      const mockOpenAIInstance = {
+        models: {
+          list: vi.fn().mockResolvedValue({
+            data: mockModels,
+          }),
+          retrieve: vi.fn().mockImplementation(id => {
+            return Promise.resolve({ id, provider: 'openai' });
+          }),
+        },
+      };
+
+      // Update the OpenAI mock
+      vi.mocked(OpenAI).mockImplementation(() => mockOpenAIInstance as any);
+
+      // Call getSupportedModels again
+      const response2 = await service.getSupportedModels();
+
+      // Verify the cache was refreshed
+      expect(mockKVStorage.get).toHaveBeenCalledWith('supportedModels');
+      expect(mockKVStorage.get).toHaveBeenCalledWith('providersSignature');
+      expect(mockKVStorage.put).toHaveBeenCalledTimes(2); // Called twice: once for models, once for signature
+      expect(mockKVStorage.put).toHaveBeenCalledWith('supportedModels', expect.any(String), {
+        expirationTtl: 60 * 60 * 24,
+      });
+      expect(mockKVStorage.put).toHaveBeenCalledWith('providersSignature', expect.any(String), {
+        expirationTtl: 60 * 60 * 24,
+      });
+
+      // No need to restore mocks as we're using vi.mock at the module level
+    });
   });
 
   // TODO: Fix this test suite

packages/services/src/chat-service/ChatService.ts

@@ -118,11 +118,19 @@ const ChatService = types
 
         const useCache = true;
 
+        // Create a signature of the current providers
+        const providerRepo = new ProviderRepository(self.env);
+        const providers = providerRepo.getProviders();
+        const currentProvidersSignature = JSON.stringify(providers.map(p => p.name).sort());
+
         if (useCache) {
           // ----- 1. Try cached value ---------------------------------------------
           try {
             const cached = yield self.env.KV_STORAGE.get('supportedModels');
-            if (cached) {
+            const cachedSignature = yield self.env.KV_STORAGE.get('providersSignature');
+
+            // Check if cache exists and providers haven't changed
+            if (cached && cachedSignature && cachedSignature === currentProvidersSignature) {
               const parsed = JSON.parse(cached as string);
               if (Array.isArray(parsed) && parsed.length > 0) {
                 logger.info('Cache hit – returning supportedModels from KV');
@@ -130,6 +138,11 @@ const ChatService = types
               }
               logger.warn('Cache entry malformed – refreshing');
               throw new Error('Malformed cache entry');
+            } else if (
+              cached &&
+              (!cachedSignature || cachedSignature !== currentProvidersSignature)
+            ) {
+              logger.info('Providers changed – refreshing cache');
             }
           } catch (err) {
             logger.warn('Error reading/parsing supportedModels cache', err);
@@ -137,8 +150,6 @@ const ChatService = types
         }
 
         // ----- 2. Build fresh list ---------------------------------------------
-        const providerRepo = new ProviderRepository(self.env);
-        const providers = providerRepo.getProviders();
 
         const providerModels = new Map<string, any[]>();
         const modelMeta = new Map<string, any>();
@@ -150,19 +161,29 @@ const ChatService = types
 
           const openai = new OpenAI({ apiKey: provider.key, baseURL: provider.endpoint });
 
-          // 2‑a. List models
+          const basicFilters = (model: any) => {
+            return (
+              !model.id.includes('whisper') &&
+              !model.id.includes('flux') &&
+              !model.id.includes('ocr') &&
+              !model.id.includes('tts') &&
+              !model.id.includes('guard')
+            );
+          }; // 2‑a. List models
           try {
             const listResp: any = yield openai.models.list(); // <‑‑ async
             const models = 'data' in listResp ? listResp.data : listResp;
 
             providerModels.set(
               provider.name,
-              models.filter(
+              models.filter((mdl: any) => {
-                (mdl: any) =>
+                if ('supports_chat' in mdl && mdl.supports_chat) {
-                  !mdl.id.includes('whisper') &&
+                  return basicFilters(mdl);
-                  !mdl.id.includes('tts') &&
+                } else if ('supports_chat' in mdl && !mdl.supports_chat) {
-                  !mdl.id.includes('guard'),
+                  return false;
-              ),
+                }
+                return basicFilters(mdl);
+              }),
             );
 
             // 2‑b. Retrieve metadata
@@ -195,11 +216,20 @@ const ChatService = types
 
         // ----- 4. Cache fresh list ---------------------------------------------
         try {
+          // Store the models
           yield self.env.KV_STORAGE.put(
             'supportedModels',
             JSON.stringify(resultArr),
-            { expirationTtl: 60 * 60 * 24 }, // 24
+            { expirationTtl: 60 * 60 * 24 }, // 24 hours
           );
+
+          // Store the providers signature
+          yield self.env.KV_STORAGE.put(
+            'providersSignature',
+            currentProvidersSignature,
+            { expirationTtl: 60 * 60 * 24 }, // 24 hours
+          );
+
           logger.info('supportedModels cache refreshed');
         } catch (err) {
           logger.error('KV put failed for supportedModels', err);
@@ -298,7 +328,8 @@ const ChatService = types
               );
             }
             if (message.includes('404')) {
-              throw new ClientError(`Something went wrong, try again.`, 413, {});
+              console.log(message);
+              throw new ClientError(`Something went wrong, try again.`, 404, {});
             }
             throw error;
           }