free up space before container build

- Introduced a new inline confirmation button to clear tags from the form.
- Enhanced the component by importing and integrating the ConfirmActionInline component.
- Added logic to reset the tags array when the clear action is confirmed.

.github/workflows/ci.yml

@@ -19,6 +19,25 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
+      - name: Remove unnecessary files to release disk space
+        run: |
+          sudo rm -rf \
+            "$AGENT_TOOLSDIRECTORY" \
+            /opt/ghc \
+            /opt/google/chrome \
+            /opt/microsoft/msedge \
+            /opt/microsoft/powershell \
+            /opt/pipx \
+            /usr/lib/mono \
+            /usr/local/julia* \
+            /usr/local/lib/android \
+            /usr/local/lib/node_modules \
+            /usr/local/share/chromium \
+            /usr/local/share/powershell \
+            /usr/local/share/powershell \
+            /usr/share/dotnet \
+            /usr/share/swift
+
       - name: Log in to GHCR
         uses: docker/login-action@v3
         with:

.github/workflows/test-container-build.yml

@@ -14,6 +14,25 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
+      - name: Remove unnecessary files to release disk space
+        run: |
+          sudo rm -rf \
+            "$AGENT_TOOLSDIRECTORY" \
+            /opt/ghc \
+            /opt/google/chrome \
+            /opt/microsoft/msedge \
+            /opt/microsoft/powershell \
+            /opt/pipx \
+            /usr/lib/mono \
+            /usr/local/julia* \
+            /usr/local/lib/android \
+            /usr/local/lib/node_modules \
+            /usr/local/share/chromium \
+            /usr/local/share/powershell \
+            /usr/local/share/powershell \
+            /usr/share/dotnet \
+            /usr/share/swift
+
       - name: Log in to GHCR
         uses: docker/login-action@v3
         with:

.gitignore

@@ -31,4 +31,5 @@ scenes/
 !scenes/infinity-quest/infinity-quest.json
 tts_voice_samples/*.wav
 third-party-docs/
-legacy-state-reinforcements.yaml
+legacy-state-reinforcements.yaml
+CLAUDE.md

Dockerfile

@@ -45,6 +45,9 @@ WORKDIR /app
 
 RUN apt-get update && apt-get install -y \
     bash \
+    wget \
+    tar \
+    xz-utils \
     && rm -rf /var/lib/apt/lists/*
 
 # Install uv in the final stage
@@ -53,6 +56,21 @@ RUN pip install uv
 # Copy virtual environment from backend-build stage
 COPY --from=backend-build /app/.venv /app/.venv
 
+# Download and install FFmpeg 8.0 with shared libraries into .venv (matching Windows installer approach)
+# Using BtbN FFmpeg builds which provide shared libraries - verified to work
+# Note: We tried using jrottenberg/ffmpeg:8.0-ubuntu image but copying libraries from it didn't work properly,
+#       so we use the direct download approach which is more reliable and matches the Windows installer
+RUN cd /tmp && \
+    wget -q https://github.com/BtbN/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-linux64-gpl-shared.tar.xz -O ffmpeg.tar.xz && \
+    tar -xf ffmpeg.tar.xz && \
+    cp -a ffmpeg-master-latest-linux64-gpl-shared/bin/* /app/.venv/bin/ && \
+    cp -a ffmpeg-master-latest-linux64-gpl-shared/lib/* /app/.venv/lib/ && \
+    rm -rf ffmpeg-master-latest-linux64-gpl-shared ffmpeg.tar.xz && \
+    LD_LIBRARY_PATH=/app/.venv/lib /app/.venv/bin/ffmpeg -version | head -n 1
+
+# Set LD_LIBRARY_PATH so torchcodec can find ffmpeg libraries at runtime
+ENV LD_LIBRARY_PATH=/app/.venv/lib:${LD_LIBRARY_PATH}
+
 # Copy Python source code
 COPY --from=backend-build /app/src /app/src
 

docs/dev/client/example/runpod_vllm/__init__.py

@@ -1,134 +0,0 @@
-"""
-An attempt to write a client against the runpod serverless vllm worker.
-
-This is close to functional, but since runpod serverless gpu availability is currently terrible, i have
-been unable to properly test it.
-
-Putting it here for now since i think it makes a decent example of how to write a client against a new service.
-"""
-
-import pydantic
-import structlog
-import runpod
-import asyncio
-import aiohttp
-from talemate.client.base import ClientBase, ExtraField
-from talemate.client.registry import register
-from talemate.emit import emit
-from talemate.config import Client as BaseClientConfig
-
-log = structlog.get_logger("talemate.client.runpod_vllm")
-
-
-class Defaults(pydantic.BaseModel):
-    max_token_length: int = 4096
-    model: str = ""
-    runpod_id: str = ""
-
-
-class ClientConfig(BaseClientConfig):
-    runpod_id: str = ""
-
-
-@register()
-class RunPodVLLMClient(ClientBase):
-    client_type = "runpod_vllm"
-    conversation_retries = 5
-    config_cls = ClientConfig
-
-    class Meta(ClientBase.Meta):
-        title: str = "Runpod VLLM"
-        name_prefix: str = "Runpod VLLM"
-        enable_api_auth: bool = True
-        manual_model: bool = True
-        defaults: Defaults = Defaults()
-        extra_fields: dict[str, ExtraField] = {
-            "runpod_id": ExtraField(
-                name="runpod_id",
-                type="text",
-                label="Runpod ID",
-                required=True,
-                description="The Runpod ID to connect to.",
-            )
-        }
-
-    def __init__(self, model=None, runpod_id=None, **kwargs):
-        self.model_name = model
-        self.runpod_id = runpod_id
-        super().__init__(**kwargs)
-
-    @property
-    def experimental(self):
-        return False
-
-    def set_client(self, **kwargs):
-        log.debug("set_client", kwargs=kwargs, runpod_id=self.runpod_id)
-        self.runpod_id = kwargs.get("runpod_id", self.runpod_id)
-
-    def tune_prompt_parameters(self, parameters: dict, kind: str):
-        super().tune_prompt_parameters(parameters, kind)
-
-        keys = list(parameters.keys())
-
-        valid_keys = ["temperature", "top_p", "max_tokens"]
-
-        for key in keys:
-            if key not in valid_keys:
-                del parameters[key]
-
-    async def get_model_name(self):
-        return self.model_name
-
-    async def generate(self, prompt: str, parameters: dict, kind: str):
-        """
-        Generates text from the given prompt and parameters.
-        """
-        prompt = prompt.strip()
-
-        self.log.debug("generate", prompt=prompt[:128] + " ...", parameters=parameters)
-
-        try:
-            async with aiohttp.ClientSession() as session:
-                endpoint = runpod.AsyncioEndpoint(self.runpod_id, session)
-
-                run_request = await endpoint.run(
-                    {
-                        "input": {
-                            "prompt": prompt,
-                        }
-                        # "parameters": parameters
-                    }
-                )
-
-                while (await run_request.status()) not in [
-                    "COMPLETED",
-                    "FAILED",
-                    "CANCELLED",
-                ]:
-                    status = await run_request.status()
-                    log.debug("generate", status=status)
-                    await asyncio.sleep(0.1)
-
-                status = await run_request.status()
-
-                log.debug("generate", status=status)
-
-                response = await run_request.output()
-
-                log.debug("generate", response=response)
-
-                return response["choices"][0]["tokens"][0]
-
-        except Exception as e:
-            self.log.error("generate error", e=e)
-            emit(
-                "status", message="Error during generation (check logs)", status="error"
-            )
-            return ""
-
-    def reconfigure(self, **kwargs):
-        if kwargs.get("model"):
-            self.model_name = kwargs["model"]
-        if "runpod_id" in kwargs:
-            self.api_auth = kwargs["runpod_id"]
-        self.set_client(**kwargs)

docs/getting-started/installation/windows.md

@@ -12,14 +12,6 @@
 !!! note "First start can take a while"
     The initial download and dependency installation may take several minutes, especially on slow internet connections. The console will keep you updated – just wait until the Talemate logo shows up.
 
-### Optional: CUDA support
-
-If you have an NVIDIA GPU and want CUDA acceleration for larger embedding models:
-
-1. Close Talemate (if it is running).
-2. Double-click **`install-cuda.bat`**. This script swaps the CPU-only Torch build for the CUDA 12.8 build.
-3. Start Talemate again via **`start.bat`**.
-
 ## Maintenance & advanced usage
 
 | Script | Purpose |