docs: fix internal links to pages with custom URLs

docs/content/features/face-recognition.md

@@ -408,5 +408,5 @@ The people list is kept in the browser because the server has no list call. Afte
   detection; `/v1/detection` works with the insightface backend too.
 - [Embeddings](/features/embeddings/) - raw vector extraction; face
   embeddings live in the same endpoint under the hood.
-- [Stores](/features/stores/) - the generic vector store powering the
+- [Stores](/stores/) - the generic vector store powering the
   1:N recognition pipeline.

docs/content/features/voice-recognition.md

@@ -579,7 +579,7 @@ The clip you test with is sent to the model on the server and is not kept. Analy
 - [Audio to Text](/features/audio-to-text/) - transcription (Whisper,
   Voxtral, faster-whisper). Runs in addition to, not instead of,
   voice recognition.
-- [Stores](/features/stores/) - the generic vector store powering
+- [Stores](/stores/) - the generic vector store powering
   both the face and voice 1:N recognition pipelines.
 - [Embeddings](/features/embeddings/) - text-only OpenAI-compatible
   embedding endpoint; for audio embeddings use `/v1/voice/embed`.

docs/content/getting-started/containers.md

@@ -272,7 +272,7 @@ After installation:
 1. Access the WebUI at `http://localhost:8080`
 2. Check available models: `curl http://localhost:8080/v1/models`
 3. [Install additional models](/getting-started/models/)
-4. [Try out examples](/getting-started/try-it-out/)
+4. [Try out examples](/basics/try/)
 
 ## Troubleshooting
 
@@ -421,4 +421,4 @@ These images are compatible with Nvidia ARM64 devices with CUDA 13, such as the
 - [Full image reference](#full-image-reference) - Complete Quay and Docker Hub image matrix
 - [Install Models](/getting-started/models/) - Install and configure models
 - [GPU Acceleration](/features/gpu-acceleration/) - GPU setup and optimization
-- [Kubernetes Installation](/installation/kubernetes/) - Deploy on Kubernetes
+- [Kubernetes Installation](/basics/kubernetes/) - Deploy on Kubernetes

docs/content/getting-started/troubleshooting.md

@@ -113,7 +113,7 @@ uname -m
 
 **Common causes and fixes:**
 
-- **Wrong backend:** Ensure the backend in your model YAML matches the model format. GGUF models use `llama-cpp`, diffusion models use `diffusers`, etc. See the [compatibility table](/docs/reference/compatibility-table/) for details.
+- **Wrong backend:** Ensure the backend in your model YAML matches the model format. GGUF models use `llama-cpp`, diffusion models use `diffusers`, etc. See the [compatibility table](/model-compatibility/) for details.
 - **Backend not installed:** Check installed backends:
   ```bash
   local-ai backends list
@@ -475,7 +475,7 @@ LOCALAI_P2P_DISABLE_DHT=true local-ai run
 - Workers must be detected before inference starts - you cannot add workers mid-inference
 - Workers mode supports llama-cpp compatible models only
 
-See the [Distributed Inferencing guide](/features/distributed-inferencing/) for full setup instructions.
+See the [Distributed Inferencing guide](/features/distribute/) for full setup instructions.
 
 ## Still Having Issues?
 

docs/content/reference/p2p-api.md

@@ -7,7 +7,7 @@ url = "/features/p2p/"
 
 LocalAI supports peer-to-peer (P2P) networking for distributed inference. The P2P API endpoints allow you to monitor connected worker and federated nodes, retrieve the P2P network token, and get cluster statistics.
 
-For an overview of distributed inference setup, see [Distributed Inference](/features/distributed_inferencing/).
+For an overview of distributed inference setup, see [Distributed Inference](/features/distribute/).
 
 ## Endpoints