chore: update documentation site configuration

- Update MkDocs site URL and repository links - Modify README diagram formatting for improved readability
chore: refine configuration and setup scripts for improved usability
2025-02-05 02:23:36 +01:00 · 2025-02-05 02:20:08 +01:00 · 2025-02-05 00:48:45 +01:00 · 2025-02-04 22:51:11 +01:00 · 2025-02-04 22:51:06 +01:00 · 2025-02-04 21:42:50 +01:00
14 changed files with 706 additions and 249 deletions
--- a/11
+++ b/11
@@ -17,13 +17,15 @@ RUN apt-get update && apt-get install -y --no-install-recommends \

 # Set build-time environment variables
 ENV NODE_ENV=production \
-    NODE_OPTIONS="--max-old-space-size=2048"
+    NODE_OPTIONS="--max-old-space-size=2048" \
+    BUN_INSTALL_CACHE=0

 # Copy only package files first
 COPY package.json ./

-# Install dependencies without lockfile
-RUN bun install --no-cache
+# Install dependencies with a clean slate
+RUN rm -rf node_modules .bun bun.lockb && \
+    bun install --no-save

 # Copy source files and build
 COPY src ./src
@@ -51,6 +53,9 @@ COPY --from=builder --chown=bunjs:nodejs /app/dist ./dist
 COPY --from=builder --chown=bunjs:nodejs /app/node_modules ./node_modules
 COPY --chown=bunjs:nodejs package.json ./

+# Create logs directory with proper permissions
+RUN mkdir -p /app/logs && chown -R bunjs:nodejs /app/logs
+
 # Switch to non-root user
 USER bunjs

--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@

 Welcome to the **Model Context Protocol (MCP) Server for Home Assistant**! This robust platform bridges Home Assistant with cutting-edge Language Learning Models (LLMs), enabling natural language interactions and real-time automation of your smart devices. Imagine entering your home, saying:  

-> “Hey MCP, dim the lights and start my evening playlist,”  
+> "Hey MCP, dim the lights and start my evening playlist,"  

 and watching your home transform instantly—that's the magic that MCP Server delivers!

@@ -58,17 +58,17 @@ Our architecture is engineered for performance, scalability, and security. The f
 ```mermaid
 graph TD
    subgraph Client
-       A[Client Application<br/>(Web / Mobile / Voice)]
+       A[Client Application\n(Web / Mobile / Voice)]
    end
    subgraph CDN
       B[CDN / Cache]
    end
    subgraph Server
       C[Bun Native Server]
-       E[NLP Engine<br/>& Language Processing Module]
+       E[NLP Engine &\nLanguage Processing Module]
    end
    subgraph Integration
-       D[Home Assistant<br/>(Devices, Lights, Thermostats)]
+       D[Home Assistant\n(Devices, Lights, Thermostats)]
    end

    A -->|HTTP Request| B
@@ -294,3 +294,79 @@ This project is licensed under the MIT License. See [LICENSE](LICENSE) for full
 ---

 🔋 Batteries included.
+
+## MCP Client Integration
+
+This MCP server can be integrated with various clients that support the Model Context Protocol. Below are instructions for different client integrations:
+
+### Cursor Integration
+
+The server can be integrated with Cursor by adding the configuration to `.cursor/config/config.json`:
+
+```json
+{
+  "mcpServers": {
+    "homeassistant-mcp": {
+      "command": "bun",
+      "args": ["run", "start"],
+      "cwd": "${workspaceRoot}",
+      "env": {
+        "NODE_ENV": "development"
+      }
+    }
+  }
+}
+```
+
+### Claude Desktop Integration
+
+For Claude Desktop, add the following to your Claude configuration file:
+
+```json
+{
+  "mcpServers": {
+    "homeassistant-mcp": {
+      "command": "bun",
+      "args": ["run", "start", "--port", "8080"],
+      "env": {
+        "NODE_ENV": "production"
+      }
+    }
+  }
+}
+```
+
+### Cline Integration
+
+For Cline-based clients, add the following configuration:
+
+```json
+{
+  "mcpServers": {
+    "homeassistant-mcp": {
+      "command": "bun",
+      "args": [
+        "run",
+        "start",
+        "--enable-cline",
+        "--config",
+        "${configDir}/.env"
+      ],
+      "env": {
+        "NODE_ENV": "production",
+        "CLINE_MODE": "true"
+      }
+    }
+  }
+}
+```
+
+### Command Line Usage
+
+#### Windows
+A CMD script is provided in the `scripts` directory. To use it:
+
+1. Navigate to the `scripts` directory
+2. Run `start_mcp.cmd`
+
+The script will start the MCP server with default configuration.
--- a/bun.lock
+++ b/bun.lock
@@ -1,8 +1,7 @@
 {
-  "lockfileVersion": 1,
+  "lockfileVersion": 0,
  "workspaces": {
    "": {
-      "name": "homeassistant-mcp",
      "dependencies": {
        "@elysiajs/cors": "^1.2.0",
        "@elysiajs/swagger": "^1.2.0",
@@ -17,6 +16,8 @@
        "node-fetch": "^3.3.2",
        "sanitize-html": "^2.11.0",
        "typescript": "^5.3.3",
+        "winston": "^3.11.0",
+        "winston-daily-rotate-file": "^5.0.0",
        "ws": "^8.16.0",
        "zod": "^3.22.4",
      },
@@ -36,6 +37,10 @@
    },
  },
  "packages": {
+    "@colors/colors": ["@colors/colors@1.6.0", "", {}, "sha512-Ir+AOibqzrIsL6ajt3Rz3LskB7OiMVHqltZmspbW/TJuTVuyOMirVqAkjfY6JISiLHgyNqicAC8AyHHGzNd/dA=="],
+
+    "@dabh/diagnostics": ["@dabh/diagnostics@2.0.3", "", { "dependencies": { "colorspace": "1.1.x", "enabled": "2.0.x", "kuler": "^2.0.0" } }, "sha512-hrlQOIi7hAfzsMqlGSFyVucrx38O+j6wiGOf//H2ecvIEqYN4ADBSS2iLMh5UFyDunCNniUIPk/q3riFv45xRA=="],
+
    "@elysiajs/cors": ["@elysiajs/cors@1.2.0", "", { "peerDependencies": { "elysia": ">= 1.2.0" } }, "sha512-qsJwDAg6WfdQRMfj6uSMcDPSpXvm/zQFeAX1uuJXhIgazH8itSfcDxcH9pMuXVRX1yQNi2pPwNQLJmAcw5mzvw=="],

    "@elysiajs/swagger": ["@elysiajs/swagger@1.2.0", "", { "dependencies": { "@scalar/themes": "^0.9.52", "@scalar/types": "^0.0.12", "openapi-types": "^12.1.3", "pathe": "^1.1.2" }, "peerDependencies": { "elysia": ">= 1.2.0" } }, "sha512-OPx93DP6rM2VHjA3D44Xiz5MYm9AYlO2NGWPsnSsdyvaOCiL9wJj529583h7arX4iIEYE5LiLB0/A45unqbopw=="],
@@ -70,17 +75,19 @@

    "@sinclair/typebox": ["@sinclair/typebox@0.34.15", "", {}, "sha512-xeIzl3h1Znn9w/LTITqpiwag0gXjA+ldi2ZkXIBxGEppGCW211Tza+eL6D4pKqs10bj5z2umBWk5WL6spQ2OCQ=="],

-    "@types/jsonwebtoken": ["@types/jsonwebtoken@9.0.8", "", { "dependencies": { "@types/ms": "*", "@types/node": "*" } }, ""],
+    "@types/jsonwebtoken": ["@types/jsonwebtoken@9.0.8", "", { "dependencies": { "@types/ms": "*", "@types/node": "*" } }, "sha512-7fx54m60nLFUVYlxAB1xpe9CBWX2vSrk50Y6ogRJ1v5xxtba7qXTg5BgYDN5dq+yuQQ9HaVlHJyAAt1/mxryFg=="],

-    "@types/ms": ["@types/ms@2.1.0", "", {}, ""],
+    "@types/ms": ["@types/ms@2.1.0", "", {}, "sha512-GsCCIZDE/p3i96vtEqx+7dBUGXrc7zeSK3wwPHIaRThS+9OhWIXRqzs4d6k1SVU8g91DrNRWxWUGhp5KXQb2VA=="],

-    "@types/node": ["@types/node@20.17.16", "", { "dependencies": { "undici-types": "~6.19.2" } }, ""],
+    "@types/node": ["@types/node@20.17.17", "", { "dependencies": { "undici-types": "~6.19.2" } }, "sha512-/WndGO4kIfMicEQLTi/mDANUu/iVUhT7KboZPdEqqHQ4aTS+3qT3U5gIqWDFV+XouorjfgGqvKILJeHhuQgFYg=="],

    "@types/sanitize-html": ["@types/sanitize-html@2.13.0", "", { "dependencies": { "htmlparser2": "^8.0.0" } }, "sha512-X31WxbvW9TjIhZZNyNBZ/p5ax4ti7qsNDBDEnH4zAgmEh35YnFD1UiS6z9Cd34kKm0LslFW0KPmTQzu/oGtsqQ=="],

+    "@types/triple-beam": ["@types/triple-beam@1.3.5", "", {}, "sha512-6WaYesThRMCl19iryMYP7/x2OVgCtbIVflDGFpWnb9irXI3UjYE4AzmYuiUKY1AJstGijoY+MgUszMgRxIYTYw=="],
+
    "@types/uuid": ["@types/uuid@10.0.0", "", {}, "sha512-7gqG38EyHgyP1S+7+xomFtL+ZNHcKv6DwNaCZmJmo1vgMugyF3TCnXVg4t1uk89mLNwnLtnY3TpOpCOyp1/xHQ=="],

-    "@types/ws": ["@types/ws@8.5.14", "", { "dependencies": { "@types/node": "*" } }, ""],
+    "@types/ws": ["@types/ws@8.5.14", "", { "dependencies": { "@types/node": "*" } }, "sha512-bd/YFLW+URhBzMXurx7lWByOu+xzU9+kb3RboOteXYDfW+tr+JZa99OyNmPINEGB/ahzKrEuc8rcv4gnpJmxTw=="],

    "@typescript-eslint/eslint-plugin": ["@typescript-eslint/eslint-plugin@7.18.0", "", { "dependencies": { "@eslint-community/regexpp": "^4.10.0", "@typescript-eslint/scope-manager": "7.18.0", "@typescript-eslint/type-utils": "7.18.0", "@typescript-eslint/utils": "7.18.0", "@typescript-eslint/visitor-keys": "7.18.0", "graphemer": "^1.4.0", "ignore": "^5.3.1", "natural-compare": "^1.4.0", "ts-api-utils": "^1.3.0" }, "peerDependencies": { "@typescript-eslint/parser": "^7.0.0", "eslint": "^8.56.0" } }, "sha512-94EQTWZ40mzBc42ATNIBimBEDltSJ9RQHCC8vc/PDbxi4k8dVwUAv4o98dk50M1zB+JGFxp43FP7f8+FP8R6Sw=="],

@@ -102,57 +109,65 @@

    "@unhead/schema": ["@unhead/schema@1.11.18", "", { "dependencies": { "hookable": "^5.5.3", "zhead": "^2.2.4" } }, "sha512-a3TA/OJCRdfbFhcA3Hq24k1ZU1o9szicESrw8DZcGyQFacHnh84mVgnyqSkMnwgCmfN4kvjSiTBlLEHS6+wATw=="],

-    "acorn": ["acorn@8.14.0", "", { "bin": "bin/acorn" }, "sha512-cl669nCJTZBsL97OF4kUQm5g5hC2uihk0NxY3WENAC0TYdILVkAyHymAntgxGkl7K+t0cXIrH5siy5S4XkFycA=="],
+    "acorn": ["acorn@8.14.0", "", { "bin": { "acorn": "bin/acorn" } }, "sha512-cl669nCJTZBsL97OF4kUQm5g5hC2uihk0NxY3WENAC0TYdILVkAyHymAntgxGkl7K+t0cXIrH5siy5S4XkFycA=="],

    "acorn-jsx": ["acorn-jsx@5.3.2", "", { "peerDependencies": { "acorn": "^6.0.0 || ^7.0.0 || ^8.0.0" } }, "sha512-rq9s+JNhf0IChjtDXxllJ7g41oZk5SlXtp0LHwyA5cejwn7vKmKp4pPri6YEePv2PU65sAsegbXtIinmDFDXgQ=="],

    "ajv": ["ajv@6.12.6", "", { "dependencies": { "fast-deep-equal": "^3.1.1", "fast-json-stable-stringify": "^2.0.0", "json-schema-traverse": "^0.4.1", "uri-js": "^4.2.2" } }, "sha512-j3fVLgvTo527anyYyJOGTYJbG+vnnQYvE0m5mmkc1TK+nxAppkCLMIL0aZ4dblVCNoGShhm+kzE4ZUykBoMg4g=="],

-    "ansi-regex": ["ansi-regex@5.0.1", "", {}, ""],
+    "ansi-regex": ["ansi-regex@5.0.1", "", {}, "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ=="],

    "ansi-styles": ["ansi-styles@4.3.0", "", { "dependencies": { "color-convert": "^2.0.1" } }, "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg=="],

-    "argparse": ["argparse@2.0.1", "", {}, ""],
+    "argparse": ["argparse@2.0.1", "", {}, "sha512-8+9WqebbFzpX9OR+Wa6O29asIogeRMzcGtAINdpMHHyAg10f05aSFVBbcEqGf/PXw1EjAZ+q2/bEBg3DvurK3Q=="],

    "array-union": ["array-union@2.1.0", "", {}, "sha512-HGyxoOTYUyCM6stUe6EJgnd4EoewAI7zMdfqO+kGjnlZmBDz/cR5pf8r/cR4Wq60sL/p0IkcjUEEPwS3GFrIyw=="],

-    "asap": ["asap@2.0.6", "", {}, ""],
+    "asap": ["asap@2.0.6", "", {}, "sha512-BSHWgDSAiKs50o2Re8ppvp3seVHXSRM44cdSsT9FfNEUUZLOGWVCsiWaRPWM1Znn+mqZ1OfVZ3z3DWEzSp7hRA=="],

-    "asynckit": ["asynckit@0.4.0", "", {}, ""],
+    "async": ["async@3.2.6", "", {}, "sha512-htCUDlxyyCLMgaM3xXg0C0LW2xqfuQ6p05pCEIsXuyQ+a1koYKTuBMzRNwmybfLgvJDMd0r1LTn4+E0Ti6C2AA=="],

-    "balanced-match": ["balanced-match@1.0.2", "", {}, ""],
+    "asynckit": ["asynckit@0.4.0", "", {}, "sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q=="],
+
+    "balanced-match": ["balanced-match@1.0.2", "", {}, "sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw=="],

    "brace-expansion": ["brace-expansion@1.1.11", "", { "dependencies": { "balanced-match": "^1.0.0", "concat-map": "0.0.1" } }, "sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA=="],

-    "braces": ["braces@3.0.3", "", { "dependencies": { "fill-range": "^7.1.1" } }, ""],
+    "braces": ["braces@3.0.3", "", { "dependencies": { "fill-range": "^7.1.1" } }, "sha512-yQbXgO/OSZVD2IsiLlro+7Hf6Q18EJrKSEsdoMzKePKXct3gvD8oLcOQdIzGupr5Fj+EDe8gO/lxc1BzfMpxvA=="],

-    "buffer-equal-constant-time": ["buffer-equal-constant-time@1.0.1", "", {}, ""],
+    "buffer-equal-constant-time": ["buffer-equal-constant-time@1.0.1", "", {}, "sha512-zRpUiDwd/xk6ADqPMATG8vc9VPrkck7T07OIx0gnjmJAnHnTVXNQG3vfvWNuiZIkwu9KrKdA1iJKfsfTVxE6NA=="],

    "bun-types": ["bun-types@1.2.2", "", { "dependencies": { "@types/node": "*", "@types/ws": "~8.5.10" } }, "sha512-RCbMH5elr9gjgDGDhkTTugA21XtJAy/9jkKe/G3WR2q17VPGhcquf9Sir6uay9iW+7P/BV0CAHA1XlHXMAVKHg=="],

-    "call-bind-apply-helpers": ["call-bind-apply-helpers@1.0.1", "", { "dependencies": { "es-errors": "^1.3.0", "function-bind": "^1.1.2" } }, ""],
+    "call-bind-apply-helpers": ["call-bind-apply-helpers@1.0.1", "", { "dependencies": { "es-errors": "^1.3.0", "function-bind": "^1.1.2" } }, "sha512-BhYE+WDaywFg2TBWYNXAE+8B1ATnThNBqXHP5nQu0jWJdVvY2hvkpyB3qOmtmDePiS5/BDQ8wASEWGMWRG148g=="],

-    "call-bound": ["call-bound@1.0.3", "", { "dependencies": { "call-bind-apply-helpers": "^1.0.1", "get-intrinsic": "^1.2.6" } }, ""],
+    "call-bound": ["call-bound@1.0.3", "", { "dependencies": { "call-bind-apply-helpers": "^1.0.1", "get-intrinsic": "^1.2.6" } }, "sha512-YTd+6wGlNlPxSuri7Y6X8tY2dmm12UMH66RpKMhiX6rsk5wXXnYgbUcOt8kiS31/AjfoTOvCsE+w8nZQLQnzHA=="],

-    "callsites": ["callsites@3.1.0", "", {}, ""],
+    "callsites": ["callsites@3.1.0", "", {}, "sha512-P8BjAsXvZS+VIDUI11hHCQEv74YT67YUi5JJFNWIqL235sBmjX4+qx9Muvls5ivyNENctx46xQLQ3aTuE7ssaQ=="],

    "chalk": ["chalk@4.1.2", "", { "dependencies": { "ansi-styles": "^4.1.0", "supports-color": "^7.1.0" } }, "sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA=="],

-    "color-convert": ["color-convert@2.0.1", "", { "dependencies": { "color-name": "~1.1.4" } }, ""],
+    "color": ["color@3.2.1", "", { "dependencies": { "color-convert": "^1.9.3", "color-string": "^1.6.0" } }, "sha512-aBl7dZI9ENN6fUGC7mWpMTPNHmWUSNan9tuWN6ahh5ZLNk9baLJOnSMlrQkHcrfFgz2/RigjUVAjdx36VcemKA=="],

-    "color-name": ["color-name@1.1.4", "", {}, ""],
+    "color-convert": ["color-convert@2.0.1", "", { "dependencies": { "color-name": "~1.1.4" } }, "sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ=="],

-    "combined-stream": ["combined-stream@1.0.8", "", { "dependencies": { "delayed-stream": "~1.0.0" } }, ""],
+    "color-name": ["color-name@1.1.4", "", {}, "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA=="],

-    "component-emitter": ["component-emitter@1.3.1", "", {}, ""],
+    "color-string": ["color-string@1.9.1", "", { "dependencies": { "color-name": "^1.0.0", "simple-swizzle": "^0.2.2" } }, "sha512-shrVawQFojnZv6xM40anx4CkoDP+fZsw/ZerEMsW/pyzsRbElpsL/DBVW7q3ExxwusdNXI3lXpuhEZkzs8p5Eg=="],

-    "concat-map": ["concat-map@0.0.1", "", {}, ""],
+    "colorspace": ["colorspace@1.1.4", "", { "dependencies": { "color": "^3.1.3", "text-hex": "1.0.x" } }, "sha512-BgvKJiuVu1igBUF2kEjRCZXol6wiiGbY5ipL/oVPwm0BL9sIpMIzM8IK7vwuxIIzOXMV3Ey5w+vxhm0rR/TN8w=="],
+
+    "combined-stream": ["combined-stream@1.0.8", "", { "dependencies": { "delayed-stream": "~1.0.0" } }, "sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg=="],
+
+    "component-emitter": ["component-emitter@1.3.1", "", {}, "sha512-T0+barUSQRTUQASh8bx02dl+DhF54GtIDY13Y3m9oWTklKbb3Wv974meRpeZ3lp1JpLVECWWNHC4vaG2XHXouQ=="],
+
+    "concat-map": ["concat-map@0.0.1", "", {}, "sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg=="],

    "cookie": ["cookie@1.0.2", "", {}, "sha512-9Kr/j4O16ISv8zBBhJoi4bXOYNTkFLOqSL3UDB0njXxCXNezjeyVrJyGOWtgfs/q2km1gwBcfH8q1yEGoMYunA=="],

-    "cookiejar": ["cookiejar@2.1.4", "", {}, ""],
+    "cookiejar": ["cookiejar@2.1.4", "", {}, "sha512-LDx6oHrK+PhzLKJU9j5S7/Y3jM/mUHvD/DeI1WQmJn652iPC5Y4TBzC9l+5OMOXlyTTA+SmVUPm0HQUwpD5Jqw=="],

-    "cross-spawn": ["cross-spawn@7.0.6", "", { "dependencies": { "path-key": "^3.1.0", "shebang-command": "^2.0.0", "which": "^2.0.1" } }, ""],
+    "cross-spawn": ["cross-spawn@7.0.6", "", { "dependencies": { "path-key": "^3.1.0", "shebang-command": "^2.0.0", "which": "^2.0.1" } }, "sha512-uV2QOWP2nWzsy2aMp8aRibhi9dlzF5Hgh5SHaB9OiTGEyDTiJJyx0uy51QXdyWbtAHNua4XJzUKca3OzKUd3vA=="],

    "data-uri-to-buffer": ["data-uri-to-buffer@4.0.1", "", {}, "sha512-0R9ikRb668HB7QDxT1vkpuUBtqc53YyAwMwGeUFKRojY/NWKvdZ+9UYtRfGmhqNbRkTSVpMbmyhXipFFv2cb/A=="],

@@ -160,11 +175,11 @@

    "deep-is": ["deep-is@0.1.4", "", {}, "sha512-oIPzksmTg4/MriiaYGO+okXDT7ztn/w3Eptv/+gSIdMdKsJo0u4CfYNFJPy+4SKMuCqGw2wxnA+URMg3t8a/bQ=="],

-    "deepmerge": ["deepmerge@4.3.1", "", {}, ""],
+    "deepmerge": ["deepmerge@4.3.1", "", {}, "sha512-3sUqbMEc77XqpdNO7FRyRog+eW3ph+GYCbj+rK+uYyRMuwsVy0rMiVtPn+QJlKFvWP/1PYpapqYn0Me2knFn+A=="],

-    "delayed-stream": ["delayed-stream@1.0.0", "", {}, ""],
+    "delayed-stream": ["delayed-stream@1.0.0", "", {}, "sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ=="],

-    "dezalgo": ["dezalgo@1.0.4", "", { "dependencies": { "asap": "^2.0.0", "wrappy": "1" } }, ""],
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+    "dunder-proto": ["dunder-proto@1.0.1", "", { "dependencies": { "call-bind-apply-helpers": "^1.0.1", "es-errors": "^1.3.0", "gopd": "^1.2.0" } }, "sha512-KIN/nDJBQRcXw0MLVhZE9iQHmG68qAVIBg9CqmUYjmQIhgij9U5MFvrqkUL5FbtyyzZuOeOt0zdeRe4UY7ct+A=="],

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+    "eslint-plugin-prettier": ["eslint-plugin-prettier@5.2.3", "", { "dependencies": { "prettier-linter-helpers": "^1.0.0", "synckit": "^0.9.1" }, "peerDependencies": { "@types/eslint": ">=8.0.0", "eslint": ">=8.0.0", "eslint-config-prettier": "*", "prettier": ">=3.0.0" }, "optionalPeers": ["@types/eslint", "eslint-config-prettier"] }, "sha512-qJ+y0FfCp/mQYQ/vWQ3s7eUlFEL4PyKfAJxsnYTJ4YT73nsJBWqmEpFryxV9OeUiqmsTsYJ5Y+KDNaeP31wrRw=="],

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@@ -216,25 +233,29 @@

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-    "fast-deep-equal": ["fast-deep-equal@3.1.3", "", {}, ""],
+    "fast-deep-equal": ["fast-deep-equal@3.1.3", "", {}, "sha512-f3qQ9oQy9j2AhBe/H9VC91wLmKBCCU/gDOnKNAYG5hswO7BLKj09Hc5HYNz9cGI++xlpDCIgDaitVs03ATR84Q=="],

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-    "fast-json-stable-stringify": ["fast-json-stable-stringify@2.1.0", "", {}, ""],
+    "fast-json-stable-stringify": ["fast-json-stable-stringify@2.1.0", "", {}, "sha512-lhd/wF+Lk98HZoTCtlVraHtfh5XYijIjalXck7saUtuanSDyLMxnHhSXEDJqHxD7msR8D0uCmqlkwjCV8xvwHw=="],

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-    "fast-safe-stringify": ["fast-safe-stringify@2.1.1", "", {}, ""],
+    "fast-safe-stringify": ["fast-safe-stringify@2.1.1", "", {}, "sha512-W+KJc2dmILlPplD/H4K9l9LcAHAfPtP6BY84uVLXQ6Evcz9Lcg33Y2z1IVblT6xdY54PXYVHEv+0Wpq8Io6zkA=="],

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+    "fecha": ["fecha@4.2.3", "", {}, "sha512-OP2IUU6HeYKJi3i0z4A19kHMQoLVs4Hc+DPqqxI2h/DPZHTm/vjsfC6P0b4jCMy14XizLBqvndQ+UilD7707Jw=="],
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-    "fill-range": ["fill-range@7.1.1", "", { "dependencies": { "to-regex-range": "^5.0.1" } }, ""],
+    "file-stream-rotator": ["file-stream-rotator@0.6.1", "", { "dependencies": { "moment": "^2.29.1" } }, "sha512-u+dBid4PvZw17PmDeRcNOtCP9CCK/9lRN2w+r1xIS7yOL9JFrIBKTvrYsxT4P0pGtThYTn++QS5ChHaUov3+zQ=="],
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@@ -242,19 +263,21 @@

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-    "form-data": ["form-data@4.0.1", "", { "dependencies": { "asynckit": "^0.4.0", "combined-stream": "^1.0.8", "mime-types": "^2.1.12" } }, ""],
+    "fn.name": ["fn.name@1.1.0", "", {}, "sha512-GRnmB5gPyJpAhTQdSZTSp9uaPSvl09KoYcMQtsB9rQoOmzs9dH6ffeccH+Z+cv6P68Hu5bC6JjRh4Ah/mHSNRw=="],
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+    "form-data": ["form-data@4.0.1", "", { "dependencies": { "asynckit": "^0.4.0", "combined-stream": "^1.0.8", "mime-types": "^2.1.12" } }, "sha512-tzN8e4TX8+kkxGPK8D5u0FNmjPUjw3lwC9lSLxxoB/+GtsJG91CO8bSWy73APlgAZzZbXEYZJuxjkHH2w+Ezhw=="],

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+    "formidable": ["formidable@2.1.2", "", { "dependencies": { "dezalgo": "^1.0.4", "hexoid": "^1.0.0", "once": "^1.4.0", "qs": "^6.11.0" } }, "sha512-CM3GuJ57US06mlpQ47YcunuUZ9jpm8Vx+P2CGt2j7HpgkKZO/DJYQ0Bobim8G6PFQmK5lOqOOdUXboU+h73A4g=="],

-    "fs.realpath": ["fs.realpath@1.0.0", "", {}, ""],
+    "fs.realpath": ["fs.realpath@1.0.0", "", {}, "sha512-OO0pH2lK6a0hZnAdau5ItzHPI6pUlvI7jMVnxUQRtw4owF2wk8lOSabtGDCTP4Ggrg2MbGnWO9X8K1t4+fGMDw=="],

-    "function-bind": ["function-bind@1.1.2", "", {}, ""],
+    "function-bind": ["function-bind@1.1.2", "", {}, "sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA=="],

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+    "get-intrinsic": ["get-intrinsic@1.2.7", "", { "dependencies": { "call-bind-apply-helpers": "^1.0.1", "es-define-property": "^1.0.1", "es-errors": "^1.3.0", "es-object-atoms": "^1.0.0", "function-bind": "^1.1.2", "get-proto": "^1.0.0", "gopd": "^1.2.0", "has-symbols": "^1.1.0", "hasown": "^2.0.2", "math-intrinsics": "^1.1.0" } }, "sha512-VW6Pxhsrk0KAOqs3WEd0klDiF/+V7gQOpAvY1jVU/LHmaD/kQO4523aiJuikX/QAKYiW6x8Jh+RJej1almdtCA=="],

-    "get-proto": ["get-proto@1.0.1", "", { "dependencies": { "dunder-proto": "^1.0.1", "es-object-atoms": "^1.0.0" } }, ""],
+    "get-proto": ["get-proto@1.0.1", "", { "dependencies": { "dunder-proto": "^1.0.1", "es-object-atoms": "^1.0.0" } }, "sha512-sTSfBjoXBp89JvIKIefqw7U2CCebsc74kiY6awiGogKtoSGbgjYE/G/+l9sF3MWFPNc9IcoOC4ODfKHfxFmp0g=="],

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@@ -264,49 +287,53 @@

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-    "gopd": ["gopd@1.2.0", "", {}, ""],
+    "gopd": ["gopd@1.2.0", "", {}, "sha512-ZUKRh6/kUFoAiTAtTYPZJ3hw9wNxx+BIBOijnlG9PnrJsCcSjs1wyyD6vJpaYtgnzDrKYRSqf3OO6Rfa93xsRg=="],

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-    "has-flag": ["has-flag@4.0.0", "", {}, ""],
+    "has-flag": ["has-flag@4.0.0", "", {}, "sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ=="],

-    "has-symbols": ["has-symbols@1.1.0", "", {}, ""],
+    "has-symbols": ["has-symbols@1.1.0", "", {}, "sha512-1cDNdwJ2Jaohmb3sg4OmKaMBwuC48sYni5HUw2DvsC8LjGTLK9h+eb1X6RyuOHe4hT0ULCW68iomhjUoKUqlPQ=="],

-    "hasown": ["hasown@2.0.2", "", { "dependencies": { "function-bind": "^1.1.2" } }, ""],
+    "hasown": ["hasown@2.0.2", "", { "dependencies": { "function-bind": "^1.1.2" } }, "sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ=="],

-    "helmet": ["helmet@7.2.0", "", {}, ""],
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-    "is-number": ["is-number@7.0.0", "", {}, ""],
+    "is-number": ["is-number@7.0.0", "", {}, "sha512-41Cifkg6e8TylSpdtTpeLVMqvSBEVzTttHvERD741+pnZ8ANv0004MRL43QKPDlK9cGvNp6NZWZUBlbGXYxxng=="],

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-    "isexe": ["isexe@2.0.0", "", {}, ""],
+    "is-stream": ["is-stream@2.0.1", "", {}, "sha512-hFoiJiTl63nn+kstHGBtewWSKnQLpyb155KHheA1l39uvtO9nWIop1p3udqPcUd/xbF1VLMO4n7OI6p7RbngDg=="],

-    "js-yaml": ["js-yaml@4.1.0", "", { "dependencies": { "argparse": "^2.0.1" }, "bin": "bin/js-yaml.js" }, ""],
+    "isexe": ["isexe@2.0.0", "", {}, "sha512-RHxMLp9lnKHGHRng9QFhRCMbYAcVpn69smSGcq3f36xjgVVWThj4qqLbTLlq7Ssj8B+fIQ1EuCEGI2lKsyQeIw=="],
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-    "lodash.includes": ["lodash.includes@4.3.0", "", {}, ""],
+    "lodash.includes": ["lodash.includes@4.3.0", "", {}, "sha512-W3Bx6mdkRTGtlJISOvVD/lbqjTlPPUDTMnlXZFnVwi9NKJ6tiAk6LVdlhZMm17VZisqhKcgzpO5Wz91PCt5b0w=="],

-    "lodash.isboolean": ["lodash.isboolean@3.0.3", "", {}, ""],
+    "lodash.isboolean": ["lodash.isboolean@3.0.3", "", {}, "sha512-Bz5mupy2SVbPHURB98VAcw+aHh4vRV5IPNhILUCsOzRmsTmSQ17jIuqopAentWoehktxGd9e/hbIXq980/1QJg=="],

-    "lodash.isinteger": ["lodash.isinteger@4.0.4", "", {}, ""],
+    "lodash.isinteger": ["lodash.isinteger@4.0.4", "", {}, "sha512-DBwtEWN2caHQ9/imiNeEA5ys1JoRtRfY3d7V9wkqtbycnAmTvRRmbHKDV4a0EYc678/dia0jrte4tjYwVBaZUA=="],

-    "lodash.isnumber": ["lodash.isnumber@3.0.3", "", {}, ""],
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-    "lodash.isplainobject": ["lodash.isplainobject@4.0.6", "", {}, ""],
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-    "lodash.isstring": ["lodash.isstring@4.0.1", "", {}, ""],
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-    "lodash.once": ["lodash.once@4.1.1", "", {}, ""],
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-    "math-intrinsics": ["math-intrinsics@1.1.0", "", {}, ""],
+    "logform": ["logform@2.7.0", "", { "dependencies": { "@colors/colors": "1.6.0", "@types/triple-beam": "^1.3.2", "fecha": "^4.2.0", "ms": "^2.1.1", "safe-stable-stringify": "^2.3.1", "triple-beam": "^1.3.0" } }, "sha512-TFYA4jnP7PVbmlBIfhlSe+WKxs9dklXMTEGcBCIvLhE/Tn3H6Gk1norupVW7m5Cnd4bLcr08AytbyV/xj7f/kQ=="],
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-    "mime": ["mime@2.6.0", "", { "bin": "cli.js" }, ""],
+    "mime": ["mime@2.6.0", "", { "bin": { "mime": "cli.js" } }, "sha512-USPkMeET31rOMiarsBNIHZKLGgvKc/LrjofAnBlOttf5ajRvqiRA8QsenbcooctK6d6Ts6aqZXBA+XbkKthiQg=="],

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-    "ms": ["ms@2.1.3", "", {}, ""],
+    "moment": ["moment@2.30.1", "", {}, "sha512-uEmtNhbDOrWPFS+hdjFCBfy9f2YoyzRpwcl+DqpC6taX21FzsTLQVbMV/W7PzNSX6x/bhC1zA3c2UQ5NzH6how=="],

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-    "natural-compare": ["natural-compare@1.4.0", "", {}, ""],
+    "nanoid": ["nanoid@3.3.8", "", { "bin": { "nanoid": "bin/nanoid.cjs" } }, "sha512-WNLf5Sd8oZxOm+TzppcYk8gVOgP+l58xNy58D0nbUnOxOWRWvlcCV4kUF7ltmI6PsrLl/BgKEyS4mqsGChFN0w=="],
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    "node-fetch": ["node-fetch@3.3.2", "", { "dependencies": { "data-uri-to-buffer": "^4.0.0", "fetch-blob": "^3.1.4", "formdata-polyfill": "^4.0.10" } }, "sha512-dRB78srN/l6gqWulah9SrxeYnxeddIG30+GOqK/9OlLVyLg3HPnr6SqOWTWOXKRwC2eGYCkZ59NNuSgvSrpgOA=="],

-    "object-inspect": ["object-inspect@1.13.3", "", {}, ""],
+    "object-hash": ["object-hash@3.0.0", "", {}, "sha512-RSn9F68PjH9HqtltsSnqYC1XXoWe9Bju5+213R98cNGttag9q9yAOTzdbsqvIa7aNm5WffBZFpWYr2aWrklWAw=="],

-    "once": ["once@1.4.0", "", { "dependencies": { "wrappy": "1" } }, ""],
+    "object-inspect": ["object-inspect@1.13.3", "", {}, "sha512-kDCGIbxkDSXE3euJZZXzc6to7fCrKHNI/hSRQnRuQ+BWjFNzZwiFF8fj/6o2t2G9/jTj8PSIYTfCLelLZEeRpA=="],
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-    "p-limit": ["p-limit@3.1.0", "", { "dependencies": { "yocto-queue": "^0.1.0" } }, ""],
+    "p-limit": ["p-limit@3.1.0", "", { "dependencies": { "yocto-queue": "^0.1.0" } }, "sha512-TYOanM3wGwNGsZN2cVTYPArw454xnXj5qmWF1bEoAc4+cU/ol7GVh7odevjp1FNHduHc3KZMcFduxU5Xc6uJRQ=="],

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@@ -386,79 +423,93 @@

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-    "path-exists": ["path-exists@4.0.0", "", {}, ""],
+    "path-exists": ["path-exists@4.0.0", "", {}, "sha512-ak9Qy5Q7jYb2Wwcey5Fpvg2KoAc/ZIhLSLOSBmRmygPsGwkVVt0fZa0qrtMz+m6tJTAHfZQ8FnmB4MG4LWy7/w=="],

-    "path-is-absolute": ["path-is-absolute@1.0.1", "", {}, ""],
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-    "path-key": ["path-key@3.1.1", "", {}, ""],
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@@ -468,60 +519,52 @@

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-    "undici-types": ["undici-types@6.19.8", "", {}, ""],
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+    "yocto-queue": ["yocto-queue@0.1.0", "", {}, "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q=="],

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-
-    "@humanwhocodes/config-array/minimatch": ["minimatch@3.1.2", "", { "dependencies": { "brace-expansion": "^1.1.7" } }, "sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw=="],
+    "zod": ["zod@3.24.1", "", {}, "sha512-muH7gBL9sI1nciMZV67X5fTKKBLtwpZ5VBp1vsOQzj1MhrBZ4wlVCm3gedKZWLp0Oyel8sIGfeiz54Su+OVT+A=="],

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+    "color/color-convert": ["color-convert@1.9.3", "", { "dependencies": { "color-name": "1.1.3" } }, "sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg=="],
+
+    "color-string/color-name": ["color-name@1.1.3", "", {}, "sha512-72fSenhMw2HZMTVHeCA9KCmpEIbzWiQsjN+BHcBbS9vr1mtt+vJjPdksIBNUmKAW8TFUDPJK5SUU3QhE9NEXDw=="],
+
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-    "glob/minimatch": ["minimatch@3.1.2", "", { "dependencies": { "brace-expansion": "^1.1.7" } }, "sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw=="],
-
-    "sanitize-html/escape-string-regexp": ["escape-string-regexp@4.0.0", "", {}, "sha512-TtpcNJ3XAzx3Gq8sWRzJaVajRs0uVxA2YAkdb1jm2YkPz4G6egUFAyA3n5vtEIZefPk5Wa4UXbKuS5fKkJWdgA=="],
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-
-    "@eslint/eslintrc/globals/type-fest": ["type-fest@0.20.2", "", {}, "sha512-Ne+eE4r0/iWnpAxD852z3A+N0Bt5RN//NjJwRd2VFHEmrywxf5vsZlh4R6lixl6B+wz/8d+maTSAkN1FIkI3LQ=="],
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-
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    "@typescript-eslint/typescript-estree/minimatch/brace-expansion": ["brace-expansion@2.0.1", "", { "dependencies": { "balanced-match": "^1.0.0" } }, "sha512-XnAIvQ8eM+kC6aULx6wuQiwVsnzsi9d3WxzV3FpWTGA19F621kwdbsAcFKXgKUHZWsy+mY6iL1sHTxWEFCytDA=="],

-    "glob/minimatch/brace-expansion": ["brace-expansion@1.1.11", "", { "dependencies": { "balanced-match": "^1.0.0", "concat-map": "0.0.1" } }, "sha512-iCuPHDFgrHX7H2vEI/5xpz07zSHB00TpugqhmYtVmMO6518mCuRMoOYFldEBl0g187ufozdaHgWKcYFb61qGiA=="],
+    "color/color-convert/color-name": ["color-name@1.1.3", "", {}, "sha512-72fSenhMw2HZMTVHeCA9KCmpEIbzWiQsjN+BHcBbS9vr1mtt+vJjPdksIBNUmKAW8TFUDPJK5SUU3QhE9NEXDw=="],
  }
 }
--- a/docker/speech/Dockerfile
+++ b/docker/speech/Dockerfile
@@ -13,8 +13,10 @@ RUN python -m venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"

 # Install Python dependencies with specific versions and CPU-only variants
-RUN pip install --no-cache-dir torch==2.1.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cpu
-RUN pip install --no-cache-dir faster-whisper==0.10.0 openwakeword==0.4.0 pyaudio==0.2.14 sounddevice==0.4.6
+RUN pip install --no-cache-dir "numpy>=1.24.3,<2.0.0" && \
+    pip install --no-cache-dir torch==2.1.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cpu && \
+    pip install --no-cache-dir faster-whisper==0.10.0 openwakeword==0.4.0 pyaudio==0.2.14 sounddevice==0.4.6 requests==2.31.0 && \
+    pip freeze > /opt/venv/requirements.txt

 # Create final image
 FROM python:3.10-slim
@@ -23,10 +25,14 @@ FROM python:3.10-slim
 COPY --from=builder /opt/venv /opt/venv
 ENV PATH="/opt/venv/bin:$PATH"

-# Install only runtime dependencies
+# Install audio dependencies
 RUN apt-get update && apt-get install -y \
    portaudio19-dev \
    python3-pyaudio \
+    alsa-utils \
+    libasound2 \
+    libasound2-plugins \
+    pulseaudio \
    && rm -rf /var/lib/apt/lists/*

 # Create necessary directories
@@ -54,5 +60,9 @@ ENV PYTHONMALLOC=malloc \
 HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD ps aux | grep '[p]ython' || exit 1

-# Run the wake word detection service with resource constraints
-CMD ["python", "-X", "faulthandler", "wake_word_detector.py"] 
+# Copy audio setup script
+COPY setup-audio.sh /setup-audio.sh
+RUN chmod +x /setup-audio.sh
+
+# Start command
+CMD ["/bin/bash", "-c", "/setup-audio.sh && python -u wake_word_detector.py"] 
--- a/docker/speech/setup-audio.sh
+++ b/docker/speech/setup-audio.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+# Wait for PulseAudio to be ready
+sleep 2
+
+# Mute the monitor to prevent feedback
+pactl set-source-mute alsa_output.pci-0000_00_1b.0.analog-stereo.monitor 1
+
+# Set microphone sensitivity to 65%
+pactl set-source-volume alsa_input.pci-0000_00_1b.0.analog-stereo 65%
+
+# Set speaker volume to 40%
+pactl set-sink-volume alsa_output.pci-0000_00_1b.0.analog-stereo 40%
+
+# Make the script executable
+chmod +x /setup-audio.sh 
--- a/docker/speech/wake_word_detector.py
+++ b/docker/speech/wake_word_detector.py
@@ -8,46 +8,274 @@ from openwakeword import Model
 from datetime import datetime
 import wave
 from faster_whisper import WhisperModel
+import requests
+import logging
+import time
+
+# Set up logging
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)

 # Configuration
 SAMPLE_RATE = 16000
 CHANNELS = 1
 CHUNK_SIZE = 1024
-BUFFER_DURATION = 30  # seconds to keep in buffer
+BUFFER_DURATION = 10  # seconds to keep in buffer
 DETECTION_THRESHOLD = 0.5
+CONTINUOUS_TRANSCRIPTION_INTERVAL = 3  # seconds between transcriptions
+MAX_MODEL_LOAD_RETRIES = 3
+MODEL_LOAD_RETRY_DELAY = 5  # seconds
+MODEL_DOWNLOAD_TIMEOUT = 600  # 10 minutes timeout for model download

-# Wake word models to use
-WAKE_WORDS = ["hey_jarvis", "ok_google", "alexa"]
+# Audio processing parameters
+NOISE_THRESHOLD = 0.08  # Increased threshold for better noise filtering
+MIN_SPEECH_DURATION = 2.0  # Longer minimum duration to avoid fragments
+SILENCE_DURATION = 1.0  # Longer silence duration
+MAX_REPETITIONS = 1  # More aggressive repetition filtering
+ECHO_THRESHOLD = 0.75  # More sensitive echo detection
+MIN_SEGMENT_DURATION = 1.0  # Longer minimum segment duration
+FEEDBACK_WINDOW = 5  # Window size for feedback detection in seconds

-# Initialize the ASR model
-asr_model = WhisperModel(
-    model_size_or_path=os.environ.get('ASR_MODEL', 'base.en'),
+# Feature flags from environment
+WAKE_WORD_ENABLED = os.environ.get('ENABLE_WAKE_WORD', 'false').lower() == 'true'
+SPEECH_ENABLED = os.environ.get('ENABLE_SPEECH_FEATURES', 'true').lower() == 'true'
+
+# Wake word models to use (only if wake word is enabled)
+WAKE_WORDS = ["alexa"]  # Using 'alexa' as temporary replacement for 'gaja'
+WAKE_WORD_ALIAS = "gaja"  # What we print when wake word is detected
+
+# Home Assistant Configuration
+HASS_HOST = os.environ.get('HASS_HOST', 'http://homeassistant.local:8123')
+HASS_TOKEN = os.environ.get('HASS_TOKEN')
+
+def initialize_asr_model():
+    """Initialize the ASR model with retries and timeout"""
+    model_path = os.environ.get('ASR_MODEL_PATH', '/models')
+    model_name = os.environ.get('ASR_MODEL', 'large-v3')
+    
+    start_time = time.time()
+    for attempt in range(MAX_MODEL_LOAD_RETRIES):
+        try:
+            if time.time() - start_time > MODEL_DOWNLOAD_TIMEOUT:
+                logger.error("Model download timeout exceeded")
+                raise TimeoutError("Model download took too long")
+                
+            logger.info(f"Loading ASR model (attempt {attempt + 1}/{MAX_MODEL_LOAD_RETRIES})")
+            model = WhisperModel(
+                model_size_or_path=model_name,
                device="cpu",
                compute_type="int8",
-    download_root=os.environ.get('ASR_MODEL_PATH', '/models')
+                download_root=model_path,
+                num_workers=1  # Reduce concurrent downloads
            )
+            logger.info("ASR model loaded successfully")
+            return model
+        except Exception as e:
+            logger.error(f"Failed to load ASR model (attempt {attempt + 1}): {e}")
+            if attempt < MAX_MODEL_LOAD_RETRIES - 1:
+                logger.info(f"Retrying in {MODEL_LOAD_RETRY_DELAY} seconds...")
+                time.sleep(MODEL_LOAD_RETRY_DELAY)
+            else:
+                logger.error("Failed to load ASR model after all retries")
+                raise
+
+# Initialize the ASR model with retries
+try:
+    asr_model = initialize_asr_model()
+except Exception as e:
+    logger.error(f"Critical error initializing ASR model: {e}")
+    raise
+
+def send_command_to_hass(domain, service, entity_id):
+    """Send command to Home Assistant"""
+    if not HASS_TOKEN:
+        logger.error("Error: HASS_TOKEN not set")
+        return False
+
+    headers = {
+        "Authorization": f"Bearer {HASS_TOKEN}",
+        "Content-Type": "application/json",
+    }
+
+    url = f"{HASS_HOST}/api/services/{domain}/{service}"
+    data = {"entity_id": entity_id}
+
+    try:
+        response = requests.post(url, headers=headers, json=data)
+        response.raise_for_status()
+        logger.info(f"Command sent: {domain}.{service} for {entity_id}")
+        return True
+    except Exception as e:
+        logger.error(f"Error sending command to Home Assistant: {e}")
+        return False
+
+def is_speech(audio_data, threshold=NOISE_THRESHOLD):
+    """Detect if audio segment contains speech based on amplitude and frequency content"""
+    # Calculate RMS amplitude
+    rms = np.sqrt(np.mean(np.square(audio_data)))
+    
+    # Calculate signal energy in speech frequency range (100-4000 Hz)
+    fft = np.fft.fft(audio_data)
+    freqs = np.fft.fftfreq(len(audio_data), 1/SAMPLE_RATE)
+    speech_mask = (np.abs(freqs) >= 100) & (np.abs(freqs) <= 4000)
+    speech_energy = np.sum(np.abs(fft[speech_mask])) / len(audio_data)
+    
+    # Enhanced echo detection
+    # 1. Check for periodic patterns in the signal
+    autocorr = np.correlate(audio_data, audio_data, mode='full')
+    autocorr = autocorr[len(autocorr)//2:]  # Use only positive lags
+    peaks = np.where(autocorr > ECHO_THRESHOLD * np.max(autocorr))[0]
+    peak_spacing = np.diff(peaks)
+    has_periodic_echo = len(peak_spacing) > 2 and np.std(peak_spacing) < 0.1 * np.mean(peak_spacing)
+    
+    # 2. Check for sudden amplitude changes
+    amplitude_envelope = np.abs(audio_data)
+    amplitude_changes = np.diff(amplitude_envelope)
+    has_feedback_spikes = np.any(np.abs(amplitude_changes) > threshold * 2)
+    
+    # 3. Check frequency distribution
+    freq_magnitudes = np.abs(fft)[:len(fft)//2]
+    peak_freqs = freqs[:len(fft)//2][np.argsort(freq_magnitudes)[-3:]]
+    has_feedback_freqs = np.any((peak_freqs > 2000) & (peak_freqs < 4000))
+    
+    # Combine all criteria
+    is_valid_speech = (
+        rms > threshold and
+        speech_energy > threshold and
+        not has_periodic_echo and
+        not has_feedback_spikes and
+        not has_feedback_freqs
+    )
+    
+    return is_valid_speech
+
+def process_command(text):
+    """Process the transcribed command and execute appropriate action"""
+    text = text.lower().strip()
+    
+    # Skip if text is too short or contains numbers (likely noise)
+    if len(text) < 5 or any(char.isdigit() for char in text):
+        logger.debug("Text too short or contains numbers, skipping")
+        return
+    
+    # Enhanced noise pattern detection
+    noise_patterns = ["lei", "los", "und", "aber", "nicht mehr", "das das", "und und"]
+    for pattern in noise_patterns:
+        if text.count(pattern) > 1:  # More aggressive pattern filtering
+            logger.debug(f"Detected noise pattern '{pattern}', skipping")
+            return
+    
+    # More aggressive repetition detection
+    words = text.split()
+    if len(words) >= 2:
+        # Check for immediate word repetitions
+        for i in range(len(words)-1):
+            if words[i] == words[i+1]:
+                logger.debug(f"Detected immediate word repetition: '{words[i]}', skipping")
+                return
+        
+        # Check for phrase repetitions
+        phrases = [' '.join(words[i:i+2]) for i in range(len(words)-1)]
+        phrase_counts = {}
+        for phrase in phrases:
+            phrase_counts[phrase] = phrase_counts.get(phrase, 0) + 1
+            if phrase_counts[phrase] > MAX_REPETITIONS:
+                logger.debug(f"Skipping due to excessive repetition: '{phrase}'")
+                return
+    
+    # German command mappings
+    commands = {
+        "ausschalten": "turn_off",
+        "einschalten": "turn_on",
+        "an": "turn_on",
+        "aus": "turn_off"
+    }
+    
+    rooms = {
+        "wohnzimmer": "living_room",
+        "küche": "kitchen",
+        "schlafzimmer": "bedroom",
+        "bad": "bathroom"
+    }
+    
+    # Detect room
+    detected_room = None
+    for german_room, english_room in rooms.items():
+        if german_room in text:
+            detected_room = english_room
+            break
+    
+    # Detect command
+    detected_command = None
+    for german_cmd, english_cmd in commands.items():
+        if german_cmd in text:
+            detected_command = english_cmd
+            break
+    
+    if detected_room and detected_command:
+        # Construct entity ID (assuming light)
+        entity_id = f"light.{detected_room}"
+        
+        # Send command to Home Assistant
+        if send_command_to_hass("light", detected_command, entity_id):
+            logger.info(f"Executed: {detected_command} for {entity_id}")
+        else:
+            logger.error("Failed to execute command")
+    else:
+        logger.debug(f"No command found in text: '{text}'")

 class AudioProcessor:
    def __init__(self):
-        # Initialize wake word detection model
-        self.wake_word_model = Model(
-            custom_model_paths=None,  # Use default models
-            inference_framework="onnx"  # Use ONNX for better performance
-        )
-
-        # Pre-load the wake word models
-        for wake_word in WAKE_WORDS:
-            self.wake_word_model.add_model(wake_word)
-
+        logger.info("Initializing AudioProcessor...")
        self.audio_buffer = queue.Queue()
        self.recording = False
        self.buffer = np.zeros(SAMPLE_RATE * BUFFER_DURATION)
        self.buffer_lock = threading.Lock()
+        self.last_transcription_time = 0
+        self.stream = None
+        self.speech_detected = False
+        self.silence_frames = 0
+        self.speech_frames = 0
+
+        # Initialize wake word detection only if enabled
+        if WAKE_WORD_ENABLED:
+            try:
+                logger.info("Initializing wake word model...")
+                self.wake_word_model = Model(vad_threshold=0.5)
+                self.last_prediction = None
+                logger.info("Wake word model initialized successfully")
+            except Exception as e:
+                logger.error(f"Failed to initialize wake word model: {e}")
+                raise
+        else:
+            self.wake_word_model = None
+            self.last_prediction = None
+            logger.info("Wake word detection disabled")
+
+    def should_transcribe(self):
+        """Determine if we should transcribe based on mode and timing"""
+        current_time = datetime.now().timestamp()
+        if not WAKE_WORD_ENABLED:
+            # Check if enough time has passed since last transcription
+            time_since_last = current_time - self.last_transcription_time
+            if time_since_last >= CONTINUOUS_TRANSCRIPTION_INTERVAL:
+                # Only transcribe if we detect speech
+                frames_per_chunk = CHUNK_SIZE
+                min_speech_frames = int(MIN_SPEECH_DURATION * SAMPLE_RATE / frames_per_chunk)
+                
+                if self.speech_frames >= min_speech_frames:
+                    self.last_transcription_time = current_time
+                    self.speech_frames = 0  # Reset counter
+                    return True
+        return False

    def audio_callback(self, indata, frames, time, status):
        """Callback for audio input"""
        if status:
-            print(f"Audio callback status: {status}")
+            logger.warning(f"Audio callback status: {status}")
        
        # Convert to mono if necessary
        if CHANNELS > 1:
@@ -55,25 +283,45 @@ class AudioProcessor:
        else:
            audio_data = indata.flatten()

+        # Check for speech
+        if is_speech(audio_data):
+            self.speech_frames += 1
+            self.silence_frames = 0
+        else:
+            self.silence_frames += 1
+            frames_per_chunk = CHUNK_SIZE
+            silence_frames_threshold = int(SILENCE_DURATION * SAMPLE_RATE / frames_per_chunk)
+            
+            if self.silence_frames >= silence_frames_threshold:
+                self.speech_frames = 0
+
        # Update circular buffer
        with self.buffer_lock:
            self.buffer = np.roll(self.buffer, -len(audio_data))
            self.buffer[-len(audio_data):] = audio_data

+        if WAKE_WORD_ENABLED:
            # Process for wake word detection
-        prediction = self.wake_word_model.predict(audio_data)
+            self.last_prediction = self.wake_word_model.predict(audio_data)
            
            # Check if wake word detected
            for wake_word in WAKE_WORDS:
-            if prediction[wake_word] > DETECTION_THRESHOLD:
-                print(f"Wake word detected: {wake_word} (confidence: {prediction[wake_word]:.2f})")
-                self.save_audio_segment(wake_word)
+                confidence = self.last_prediction[wake_word]
+                if confidence > DETECTION_THRESHOLD:
+                    logger.info(
+                        f"Wake word: {WAKE_WORD_ALIAS} (confidence: {confidence:.2f})"
+                    )
+                    self.process_audio()
                    break
+        else:
+            # Continuous transcription mode
+            if self.should_transcribe():
+                self.process_audio()

-    def save_audio_segment(self, wake_word):
-        """Save the audio buffer when wake word is detected"""
+    def process_audio(self):
+        """Process the current audio buffer (save and transcribe)"""
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        filename = f"/audio/wake_word_{wake_word}_{timestamp}.wav"
+        filename = f"/audio/audio_segment_{timestamp}.wav"
        
        # Save the audio buffer to a WAV file
        with wave.open(filename, 'wb') as wf:
@@ -85,89 +333,83 @@ class AudioProcessor:
            audio_data = (self.buffer * 32767).astype(np.int16)
            wf.writeframes(audio_data.tobytes())
        
-        print(f"Saved audio segment to {filename}")
+        logger.info(f"Saved audio segment to {filename}")

-        # Transcribe the audio
+        # Transcribe the audio with German language preference
        try:
            segments, info = asr_model.transcribe(
                filename,
-                language="en",
+                language="de",  # Set German as preferred language
                beam_size=5,
                temperature=0
            )
            
-            # Format the transcription result
-            result = {
-                "text": " ".join(segment.text for segment in segments),
-                "segments": [
-                    {
-                        "text": segment.text,
-                        "start": segment.start,
-                        "end": segment.end,
-                        "confidence": segment.confidence
-                    }
-                    for segment in segments
-                ]
-            }
+            # Get the full transcribed text
+            transcribed_text = " ".join(segment.text for segment in segments)
+            logger.info(f"Transcribed text: {transcribed_text}")
            
-            # Save metadata and transcription
-            metadata = {
-                "timestamp": timestamp,
-                "wake_word": wake_word,
-                "wake_word_confidence": float(prediction[wake_word]),
-                "sample_rate": SAMPLE_RATE,
-                "channels": CHANNELS,
-                "duration": BUFFER_DURATION,
-                "transcription": result
-            }
-            
-            with open(f"{filename}.json", 'w') as f:
-                json.dump(metadata, f, indent=2)
-                
-            print("\nTranscription result:")
-            print(f"Text: {result['text']}")
-            print("\nSegments:")
-            for segment in result["segments"]:
-                print(f"[{segment['start']:.2f}s - {segment['end']:.2f}s] ({segment['confidence']:.2%})")
-                print(f'"{segment["text"]}"')
+            # Process the command
+            process_command(transcribed_text)
                
        except Exception as e:
-            print(f"Error during transcription: {e}")
-            metadata = {
-                "timestamp": timestamp,
-                "wake_word": wake_word,
-                "wake_word_confidence": float(prediction[wake_word]),
-                "sample_rate": SAMPLE_RATE,
-                "channels": CHANNELS,
-                "duration": BUFFER_DURATION,
-                "error": str(e)
-            }
-            with open(f"{filename}.json", 'w') as f:
-                json.dump(metadata, f, indent=2)
+            logger.error(f"Error during transcription or processing: {e}")

    def start(self):
        """Start audio processing"""
        try:
-            print("Initializing wake word detection...")
-            print(f"Loaded wake words: {', '.join(WAKE_WORDS)}")
+            logger.info("Starting audio processor...")
            
+            # Log configuration
+            logger.debug(f"Sample Rate: {SAMPLE_RATE}")
+            logger.debug(f"Channels: {CHANNELS}")
+            logger.debug(f"Chunk Size: {CHUNK_SIZE}")
+            logger.debug(f"Buffer Duration: {BUFFER_DURATION}")
+            logger.debug(f"Wake Word Enabled: {WAKE_WORD_ENABLED}")
+            logger.debug(f"Speech Enabled: {SPEECH_ENABLED}")
+            logger.debug(f"ASR Model: {os.environ.get('ASR_MODEL')}")
+            
+            if WAKE_WORD_ENABLED:
+                logger.info("Initializing wake word detection...")
+                logger.info(f"Loaded wake words: {', '.join(WAKE_WORDS)}")
+            else:
+                logger.info("Starting continuous transcription mode...")
+                interval = CONTINUOUS_TRANSCRIPTION_INTERVAL
+                logger.info(f"Will transcribe every {interval} seconds")
+            
+            try:
+                logger.debug("Setting up audio input stream...")
                with sd.InputStream(
                    channels=CHANNELS,
                    samplerate=SAMPLE_RATE,
                    blocksize=CHUNK_SIZE,
                    callback=self.audio_callback
                ):
-                print("\nWake word detection started. Listening...")
-                print("Press Ctrl+C to stop")
+                    logger.info("Audio input stream started successfully")
+                    logger.info("Listening for audio input...")
+                    logger.info("Press Ctrl+C to stop")
                    
                    while True:
                        sd.sleep(1000)  # Sleep for 1 second
                        
-        except KeyboardInterrupt:
-            print("\nStopping wake word detection...")
+            except sd.PortAudioError as e:
+                logger.error(f"Error setting up audio stream: {e}")
+                logger.error("Check if microphone is connected and accessible")
+                raise
            except Exception as e:
-            print(f"Error in audio processing: {e}")
+                logger.error(f"Unexpected error in audio stream: {e}")
+                raise
+                    
+        except KeyboardInterrupt:
+            logger.info("\nStopping audio processing...")
+        except Exception as e:
+            logger.error("Critical error in audio processing", exc_info=True)
+            raise

 if __name__ == "__main__":
+    try:
+        logger.info("Initializing AudioProcessor...")
        processor = AudioProcessor()
        processor.start()
+    except Exception as e:
+        logger.error("Failed to start AudioProcessor", exc_info=True)
+        raise 
--- a/docs/claude_desktop_config.json
+++ b/docs/claude_desktop_config.json
@@ -0,0 +1,16 @@
+{
+    "mcpServers": {
+        "homeassistant-mcp": {
+            "command": "bun",
+            "args": [
+                "run",
+                "start",
+                "--port",
+                "8080"
+            ],
+            "env": {
+                "NODE_ENV": "production"
+            }
+        }
+    }
+}
--- a/docs/cline_config.json
+++ b/docs/cline_config.json
@@ -0,0 +1,18 @@
+{
+    "mcpServers": {
+        "homeassistant-mcp": {
+            "command": "bun",
+            "args": [
+                "run",
+                "start",
+                "--enable-cline",
+                "--config",
+                "${configDir}/.env"
+            ],
+            "env": {
+                "NODE_ENV": "production",
+                "CLINE_MODE": "true"
+            }
+        }
+    }
+}
--- a/extra/claude-desktop-macos-setup.sh
+++ b/extra/claude-desktop-macos-setup.sh
@@ -327,3 +327,7 @@ if [[ $REPLY =~ ^[Yy]$ ]]; then
        echo -e "${GREEN}Home Assistant MCP test successful!${NC}"
    fi
 fi
+
+# macOS environment configuration
+HASS_SOCKET_URL="${HASS_HOST/http/ws}/api/websocket" # WebSocket URL conversion
+chmod 600 "$CLAUDE_CONFIG_DIR/claude_desktop_config.json" # Security hardening 
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,6 +1,6 @@
 site_name: Home Assistant Model Context Protocol (MCP)
-site_url: https://yourusername.github.io/your-repo-name/
-repo_url: https://github.com/yourusername/your-repo-name
+site_url: https://jango-blockchained.github.io/advanced-homeassistant-mcp/
+repo_url: https://github.com/jango-blockchained/advanced-homeassistant-mcp

 theme:
  name: material
--- a/package.json
+++ b/package.json
@@ -21,7 +21,6 @@
    "profile": "bun --inspect src/index.ts",
    "clean": "rm -rf dist .bun coverage",
    "typecheck": "bun x tsc --noEmit",
-    "preinstall": "bun install --frozen-lockfile",
    "example:speech": "bun run examples/speech-to-text-example.ts"
  },
  "dependencies": {
--- a/scripts/start_mcp.cmd
+++ b/scripts/start_mcp.cmd
@@ -0,0 +1,21 @@
+@echo off
+setlocal
+
+:: Set environment variables
+set NODE_ENV=production
+
+:: Change to the script's directory
+cd /d "%~dp0"
+cd ..
+
+:: Start the MCP server
+echo Starting Home Assistant MCP Server...
+bun run start --port 8080
+
+if errorlevel 1 (
+    echo Error starting MCP server
+    pause
+    exit /b 1
+)
+
+pause 
--- a/smithery.yaml
+++ b/smithery.yaml
@@ -18,4 +18,11 @@ startCommand:
  commandFunction:
    # A function that produces the CLI command to start the MCP on stdio.
    |-
-    config => ({command: 'bun', args: ['--smol', 'run', 'start'], env: { HASS_TOKEN: config.hassToken, PORT: config.port.toString() }})
+    config => ({
+      command: 'bun', 
+      args: ['--smol', 'run', 'start'], 
+      env: { 
+        HASS_TOKEN: config.hassToken,
+        PORT: config.port.toString()
+      }
+    })
--- a/src/config/app.config.ts
+++ b/src/config/app.config.ts
@@ -24,7 +24,7 @@ config({ path: resolve(process.cwd(), envFile) });
 */
 export const AppConfigSchema = z.object({
  /** Server Configuration */
-  PORT: z.number().default(4000),
+  PORT: z.coerce.number().default(4000),
  NODE_ENV: z
    .enum(["development", "production", "test"])
    .default("development"),
Author	SHA1	Message	Date
jango-blockchained	01991c0060	chore: update documentation site configuration - Update MkDocs site URL and repository links - Modify README diagram formatting for improved readability	2025-02-05 02:23:36 +01:00
jango-blockchained	3f8d67b145	chore: refine configuration and setup scripts for improved usability - Update README with minor text formatting - Improve Smithery configuration command formatting - Enhance macOS setup script with WebSocket URL conversion and security hardening	2025-02-05 02:20:08 +01:00
jango-blockchained	ab8b597843	docs: add MCP client integration documentation and scripts - Update README with integration instructions for Cursor, Claude Desktop, and Cline - Add configuration examples for different MCP client integrations - Create Windows CMD script for starting MCP server - Include configuration files for Claude Desktop and Cline clients	2025-02-05 00:48:45 +01:00
jango-blockchained	ddf9070a64	Merge commit 'f5c01ad83a43dd6495b7906bee63a0652c9d1100'	2025-02-04 22:51:11 +01:00
jango-blockchained	b9727981cc	feat(speech): enhance speech processing with advanced audio setup and detection - Add audio setup script for PulseAudio configuration - Improve wake word detection with advanced noise filtering - Implement continuous transcription and command processing - Update speech Dockerfile with additional audio dependencies - Enhance logging and error handling in wake word detector	2025-02-04 22:51:06 +01:00
jango-blockchained	e1db799b1d	chore(dependencies): update Bun lockfile and package configuration - Update bun.lock with latest package versions - Modify Dockerfile to improve dependency installation - Remove preinstall script from package.json - Add winston logging dependencies - Adjust Docker build process for cleaner dependency management	2025-02-04 21:42:50 +01:00