docs: remove troubleshooting sections from integration docs, update t…

…itles and add redirect maps

- Remove troubleshooting sections from all integration docs
- Remove 'Structured outputs with' prefix from navigation titles
- Add redirect maps for moved documentation pages
- Clean up documentation structure

docs/integrations/anthropic.md

@@ -246,14 +246,6 @@ Anthropic offers several Claude models:
 - Complex Reasoning Tasks
 - Multi-step Processing
 
-## Troubleshooting
-
-Common issues and solutions:
-1. API Authentication
-2. Rate Limiting
-3. Context Length
-4. Response Validation
-
 ## Related Resources
 
 - [Anthropic API Documentation](https://docs.anthropic.com/)

docs/integrations/anyscale.md

@@ -279,34 +279,6 @@ Anyscale provides access to various open-source models:
 - API Response Formatting
 - Configuration Generation
 
-## Troubleshooting
-
-Common issues and solutions:
-
-### 1. API Key Issues
-- **Missing API Key**: Ensure `ANYSCALE_API_KEY` environment variable is set
-- **Invalid API Key**: Verify the key is valid and has not expired
-- **Permission Issues**: Check if your API key has access to the required models
-- **Rate Limiting**: Monitor your API usage and implement proper rate limiting
-
-### 2. Streaming Issues
-- **Connection Timeouts**: Implement proper timeout handling
-- **Partial Response Errors**: Handle incomplete responses gracefully
-- **Memory Issues**: Monitor memory usage with large streaming responses
-- **Rate Limits**: Implement backoff strategies for streaming requests
-
-### 3. Model-Specific Issues
-- **Model Access**: Ensure your account has access to required models
-- **Context Length**: Monitor and handle context length limits
-- **Token Usage**: Track token usage to avoid quota issues
-- **Response Format**: Handle model-specific response formats
-
-### 4. Integration Issues
-- **Version Compatibility**: Keep OpenAI and Instructor versions in sync
-- **Type Validation**: Handle validation errors with proper retry logic
-- **Schema Complexity**: Simplify complex schemas if needed
-- **Async/Sync Usage**: Use appropriate client for your use case
-
 ## Related Resources
 
 - [Anyscale Endpoints Documentation](https://docs.endpoints.anyscale.com/)

docs/integrations/cerebras.md

@@ -201,7 +201,6 @@ Cerebras offers several model options:
    - Monitor model responses
    - Use appropriate timeout settings
 
-
 ## Common Use Cases
 
 - High-Performance Computing
@@ -210,14 +209,6 @@ Cerebras offers several model options:
 - Research Applications
 - Batch Processing
 
-## Troubleshooting
-
-Common issues and solutions:
-1. Hardware Configuration
-2. Resource Management
-3. Response Validation
-4. Performance Optimization
-
 ## Related Resources
 
 - [Cerebras Documentation](https://docs.cerebras.ai/)

docs/integrations/cohere.md

@@ -209,14 +209,6 @@ Cohere offers several model options:
 - Semantic Search Integration
 - Classification Tasks
 
-## Troubleshooting
-
-Common issues and solutions:
-1. API Authentication
-2. Rate Limiting
-3. Response Validation
-4. Model Selection
-
 ## Related Resources
 
 - [Cohere API Documentation](https://docs.cohere.com/)

docs/integrations/fireworks.md

@@ -259,32 +259,6 @@ Fireworks offers several model options:
 - Research Applications
 - Production Deployments
 
-## Troubleshooting
-
-Common issues and solutions:
-1. API Authentication
-2. Model Selection
-3. Response Validation
-4. Performance Optimization
-5. Streaming Issues
-
-### Streaming-Specific Troubleshooting
-
-1. **Connection Issues**
-   - Implement proper retry logic
-   - Use appropriate timeouts
-   - Monitor connection stability
-
-2. **Model Compatibility**
-   - Verify model streaming support
-   - Test with smaller payloads first
-   - Monitor response patterns
-
-3. **Performance Issues**
-   - Implement proper error handling
-   - Use appropriate batch sizes
-   - Monitor system resources
-
 ## Related Resources
 
 - [Fireworks Documentation](https://docs.fireworks.ai/)

docs/integrations/google.md

@@ -234,14 +234,6 @@ Google offers several Gemini models:
 - Multimodal Processing
 - Complex Reasoning Tasks
 
-## Troubleshooting
-
-Common issues and solutions:
-1. API Authentication
-2. Quota Management
-3. Response Validation
-4. Model Availability
-
 ## Related Resources
 
 - [Google AI Documentation](https://ai.google.dev/)

docs/integrations/litellm.md

@@ -273,15 +273,6 @@ LiteLLM supports multiple providers:
 - Cross-Provider Testing
 - Unified API Integration
 
-## Troubleshooting
-
-Common issues and solutions:
-1. Provider Authentication
-2. Model Availability
-3. Provider-Specific Errors
-4. Rate Limiting
-5. Streaming Compatibility
-
 ## Related Resources
 
 - [LiteLLM Documentation](https://docs.litellm.ai/)

docs/integrations/llama-cpp-python.md

@@ -232,28 +232,6 @@ client = patch(
 - Offline Processing
 - Resource-Constrained Environments
 
-## Troubleshooting
-
-Common issues and solutions:
-
-1. **Slow Inference**
-   - Reduce context window size
-   - Use smaller model variants
-   - Implement appropriate timeouts
-   - Consider alternative clients for production use
-
-2. **Memory Issues**
-   - Reduce batch size
-   - Use quantized models
-   - Monitor and limit concurrent requests
-   - Implement proper cleanup
-
-3. **Extraction Failures**
-   - Verify prompt format
-   - Check context window limits
-   - Implement retry logic
-   - Use simpler model responses
-
 ## Related Resources
 
 - [llama-cpp-python Documentation](https://llama-cpp-python.readthedocs.io/)

docs/integrations/mistral.md

@@ -222,14 +222,6 @@ Mistral AI provides several powerful models:
 - Document Analysis
 - Configuration Generation
 
-## Troubleshooting
-
-Common issues and solutions:
-1. Model Loading Issues
-2. Memory Management
-3. Response Validation
-4. API Rate Limits
-
 ## Related Resources
 
 - [Mistral AI Documentation](https://docs.mistral.ai/)

docs/integrations/ollama.md

@@ -269,78 +269,6 @@ Ollama supports various models:
 - Rapid Prototyping
 - Edge Computing
 
-## Troubleshooting
-
-Common issues and solutions:
-
-### 1. Connection Issues
-- **Server Not Running**: Ensure Ollama server is running (`ollama serve`)
-- **Wrong Endpoint**: Verify base URL is correct (`http://localhost:11434/v1`)
-- **Port Conflicts**: Check if port 11434 is available
-- **Network Issues**: Verify local network connectivity
-
-### 2. Function Calling Errors
-- **Error**: "llama2 does not support tools"
-- **Solution**: Use JSON mode instead of tools mode
-```python
-# Correct way to initialize client
-client = instructor.patch(client, mode=instructor.Mode.JSON)
-```
-
-### 3. Streaming Issues
-- **Error**: "create_partial not available"
-- **Solution**: Use batch processing approach
-```python
-# Instead of streaming, break down into smaller requests
-initial_response = client.chat.completions.create(
-    model="llama2",
-    messages=[{"role": "user", "content": "First part of request"}],
-    response_model=YourModel
-)
-```
-
-### 4. Model Loading Issues
-- **Model Not Found**: Run `ollama pull model_name`
-- **Memory Issues**:
-  - Error: "model requires more system memory than available"
-  - Solutions:
-    1. Use a quantized model (recommended for < 8GB RAM):
-    ```bash
-    # Pull a smaller, quantized model
-    ollama pull mistral-7b-instruct-v0.2-q4
-    ```
-    2. Free up system memory:
-       - Close unnecessary applications
-       - Monitor memory usage with `free -h`
-       - Consider increasing swap space
-- **GPU Issues**: Verify CUDA configuration
-```bash
-# Check available models
-ollama list
-# Pull specific model
-ollama pull mistral-7b-instruct-v0.2-q4  # Smaller, quantized model
-```
-
-### 5. Response Validation
-- **Invalid JSON**: Ensure proper prompt formatting
-- **Schema Mismatch**: Verify model output matches expected schema
-- **Retry Logic**: Implement proper error handling
-```python
-try:
-    response = client.chat.completions.create(
-        model="llama2",
-        messages=[{"role": "user", "content": "Your prompt"}],
-        response_model=YourModel
-    )
-except Exception as e:
-    if "connection refused" in str(e).lower():
-        print("Error: Ollama server not running")
-    elif "model not found" in str(e).lower():
-        print("Error: Model not available. Run 'ollama pull model_name'")
-    else:
-        print(f"Unexpected error: {str(e)}")
-```
-
 ## Related Resources
 
 - [Ollama Documentation](https://ollama.ai/docs)
@@ -350,4 +278,6 @@ except Exception as e:
 
 ## Updates and Compatibility
 
-Instructor maintains compatibility with Ollama's OpenAI-compatible endpoints. Check the [changelog](../../CHANGELOG.md) for updates. Note that some Instructor features may not be available due to Ollama's API limitations.
+Instructor maintains compatibility with Ollama's latest releases. Check the [changelog](../../CHANGELOG.md) for updates.
+
+Note: Always verify model-specific features and limitations before implementation.

docs/integrations/openai.md

@@ -272,34 +272,6 @@ client = instructor.patch(
 - Document Analysis
 - Configuration Generation
 
-## Troubleshooting
-
-Common issues and solutions:
-
-### 1. API Key Issues
-- **Missing API Key**: Ensure `OPENAI_API_KEY` environment variable is set
-- **Invalid API Key**: Verify the key is valid and has not expired
-- **Permission Issues**: Check if your API key has access to the required models
-- **Rate Limiting**: Monitor your API usage and implement proper rate limiting
-
-### 2. Streaming Issues
-- **Connection Timeouts**: Implement proper timeout handling
-- **Partial Response Errors**: Handle incomplete responses gracefully
-- **Memory Issues**: Monitor memory usage with large streaming responses
-- **Rate Limits**: Implement backoff strategies for streaming requests
-
-### 3. Model-Specific Issues
-- **Model Access**: Ensure your account has access to required models
-- **Context Length**: Monitor and handle context length limits
-- **Token Usage**: Track token usage to avoid quota issues
-- **Response Format**: Handle model-specific response formats
-
-### 4. Integration Issues
-- **Version Compatibility**: Keep OpenAI and Instructor versions in sync
-- **Type Validation**: Handle validation errors with proper retry logic
-- **Schema Complexity**: Simplify complex schemas if needed
-- **Async/Sync Usage**: Use appropriate client for your use case
-
 ## Related Resources
 
 - [OpenAI Documentation](https://platform.openai.com/docs)

docs/integrations/vertex.md

@@ -212,14 +212,6 @@ Vertex AI offers several model options:
 - Compliance-Aware Processing
 - Large-Scale Deployments
 
-## Troubleshooting
-
-Common issues and solutions:
-1. Authentication Setup
-2. Project Configuration
-3. Quota Management
-4. Response Validation
-
 ## Related Resources
 
 - [Vertex AI Documentation](https://cloud.google.com/vertex-ai/docs)

mkdocs.yml

@@ -202,20 +202,20 @@ nav:
     - Extracting Relevant Clips from YouTube Videos: "hub/youtube_clips.md"
     - Building Knowledge Graphs with Structured Outputs: 'tutorials/5-knowledge-graphs.ipynb'
   - Integrations:
-    - Structured outputs with Anyscale: 'integrations/anyscale.md'
-    - Structured outputs with Anthropic: 'integrations/anthropic.md'
-    - Structured outputs with Cerebras: 'integrations/cerebras.md'
-    - Structured outputs with Cohere: 'integrations/cohere.md'
-    - Structured outputs with Fireworks: 'integrations/fireworks.md'
-    - Structured outputs with Google: 'integrations/google.md'
-    - Structured outputs with Groq: 'integrations/groq.md'
-    - Structured outputs with LiteLLM: 'integrations/litellm.md'
-    - Structured outputs with llama-cpp-python: 'integrations/llama-cpp-python.md'
-    - Structured outputs with Mistral: 'integrations/mistral.md'
-    - Structured outputs with Ollama: 'integrations/ollama.md'
-    - Structured outputs with OpenAI: 'integrations/openai.md'
-    - Structured outputs with Together: 'integrations/together.md'
-    - Structured outputs with Vertex AI: 'integrations/vertexai.md'
+    - Anyscale: 'integrations/anyscale.md'
+    - Anthropic: 'integrations/anthropic.md'
+    - Cerebras: 'integrations/cerebras.md'
+    - Cohere: 'integrations/cohere.md'
+    - Fireworks: 'integrations/fireworks.md'
+    - Google: 'integrations/google.md'
+    - Groq: 'integrations/groq.md'
+    - LiteLLM: 'integrations/litellm.md'
+    - llama-cpp-python: 'integrations/llama-cpp-python.md'
+    - Mistral: 'integrations/mistral.md'
+    - Ollama: 'integrations/ollama.md'
+    - OpenAI: 'integrations/openai.md'
+    - Together: 'integrations/together.md'
+    - Vertex AI: 'integrations/vertexai.md'
   - CLI Reference:
       - "CLI Reference": "cli/index.md"
       - "Finetuning GPT-3.5": "cli/finetune.md"
@@ -293,12 +293,26 @@ plugins:
   - redirects:
       redirect_maps:
          jobs.md: https://jobs.applied-llms.org/
+         'hub/clients/vertexai.md': 'integrations/vertexai.md'
+         'hub/clients/ollama.md': 'integrations/ollama.md'
+         'hub/clients/openai.md': 'integrations/openai.md'
+         'hub/clients/anthropic.md': 'integrations/anthropic.md'
+         'hub/clients/anyscale.md': 'integrations/anyscale.md'
+         'hub/clients/cohere.md': 'integrations/cohere.md'
+         'hub/clients/fireworks.md': 'integrations/fireworks.md'
+         'hub/clients/google.md': 'integrations/google.md'
+         'hub/clients/litellm.md': 'integrations/litellm.md'
+         'hub/clients/llama-cpp-python.md': 'integrations/llama-cpp-python.md'
+         'hub/clients/mistral.md': 'integrations/mistral.md'
+         'hub/clients/cerebras.md': 'integrations/cerebras.md'
+         'hub/clients/groq.md': 'integrations/groq.md'
+         'hub/clients/together.md': 'integrations/together.md'
   - mkdocs-jupyter:
       ignore_h1_titles: true
       execute: false
   - social
   - search:
-      separator: '[\s\u200b\-_,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;|(?!\b)(?=[A-Z][a-z])'
+      separator: '[\s\u200b\-_,:!=\[\]()"`/]+|\.(?!\b)(?=[A-Z][a-z])'
   - minify:
       minify_html: true
   - mkdocstrings: