docs: update documentation to reflect OpenAI and Anthropic provider implementations

Update all documentation files to reflect the successful implementation of OpenAI and Anthropic providers with advanced template matching system:

- Update README.md with OpenAI (65+ models) and Anthropic (8 models) support status
- Add template-based vs direct conversion implementation patterns
- Update CLAUDE.md Next Steps section marking OpenAI/Anthropic as completed
- Enhanced .claude/provider_implementer.md with template matching system documentation
- Updated .claude/data_converter.md with template-based provider examples
- Corrected .claude/format_validator.md aggregated JSON format and validation commands
- Added comprehensive API key configuration documentation
- Updated example commands to include anthropic provider

Key documentation improvements:
- Template matching system with multi-pattern support via 'match' arrays
- Auto-configuration system for unmatched models with intelligent capability detection
- Clear distinction between template-based providers (OpenAI, Anthropic) and direct conversion providers (PPInfra, OpenRouter)
- Updated validation tools and format specifications
- Comprehensive implementation guides for both provider patterns

Author: zerob13

.claude/data_converter.md

@@ -46,18 +46,70 @@ Different providers return model information in varying formats and structures.
 }
 ```
 
-### OpenAI API  
+### OpenAI API (with Template Matching)
 ```json
+// API Response (minimal metadata)
 {
   "id": "gpt-4o",
   "object": "model",
   "created": 1687882411,
   "owned_by": "openai"
 }
+
+// Template Definition (rich metadata)
+{
+  "id": "gpt-4o",
+  "name": "GPT-4o",
+  "contextLength": 128000,
+  "maxTokens": 8192,
+  "vision": true,
+  "functionCall": true,
+  "reasoning": true,
+  "type": "chat",
+  "description": "Omnimodal model with native audio, vision, and text capabilities",
+  "match": ["gpt-4o", "gpt-4o-2024-05-13", "gpt-4o-2024-08-06"]
+}
+```
+
+### Anthropic API (with Template Matching)
+```json
+// API Response
+{
+  "id": "claude-3-5-sonnet-20241022",
+  "type": "model"
+}
+
+// Template Definition
+{
+  "id": "claude-3-5-sonnet-20241022",
+  "name": "Claude 3.5 Sonnet",
+  "contextLength": 204800,
+  "maxTokens": 8192,
+  "vision": true,
+  "functionCall": true,
+  "reasoning": false,
+  "type": "chat",
+  "description": "Balanced model with strong performance across most tasks"
+}
 ```
 
 ## Data Mapping Strategies
 
+### Template-Based vs Direct Conversion
+
+**Template-Based Providers** (OpenAI, Anthropic):
+- API provides minimal metadata (ID, object type only)
+- Rich metadata stored in template files (`templates/{provider}.json`)
+- Multi-pattern matching via `match` arrays handles versioned model IDs
+- Auto-configuration for unmatched models using intelligent detection
+- Provides consistent, comprehensive model information
+
+**Direct Conversion Providers** (PPInfra, OpenRouter, etc.):
+- API provides rich metadata directly
+- Map API fields to ModelInfo structure
+- Extract capabilities from response features/tags
+- Handle provider-specific data formats
+
 ### Model Capabilities Detection
 
 #### Vision Support
@@ -138,12 +190,37 @@ context_length: 4096, // conservative default
 max_tokens: context_length / 4,
 ```
 
-### Capability Detection
+### Capability Detection (Auto-Configuration)
 ```rust
-// Infer from model ID patterns
-vision: id.contains("vision") || id.contains("image"),
-function_call: id.contains("gpt-4") || id.contains("claude-3"),
-reasoning: id.contains("o1") || id.contains("reasoning"),
+// For unmatched models in template-based providers
+fn create_default_model(&self, model_id: &str) -> ModelInfo {
+    // OpenAI pattern detection
+    let is_reasoning = model_id.contains("o1") || model_id.contains("o3") || model_id.contains("o4");
+    let has_vision = model_id.contains("4o") || model_id.contains("gpt-4") || model_id.contains("vision");
+    let has_function_call = !model_id.contains("instruct") && !model_id.contains("embedding");
+    
+    // Anthropic pattern detection  
+    let is_claude = model_id.starts_with("claude-");
+    let has_vision = is_claude && !model_id.contains("text-");
+    
+    // Set appropriate defaults
+    ModelInfo::new(
+        model_id.to_string(),
+        format!("Auto: {}", model_id),
+        default_context_length,
+        default_max_tokens, 
+        has_vision,
+        has_function_call,
+        is_reasoning,
+        ModelType::Chat,
+        Some(format!("Auto-configured model: {}", model_id)),
+    )
+}
+
+// For direct conversion providers
+vision: id.contains("vision") || id.contains("image") || features.contains("vision"),
+function_call: id.contains("gpt-4") || features.contains("function-calling"),
+reasoning: id.contains("o1") || features.contains("reasoning"),
 ```
 
 ## Quality Assurance

.claude/format_validator.md

@@ -43,17 +43,26 @@ This project generates standardized JSON files describing AI models from various
 {
   "version": "string",       // Schema version
   "generatedAt": "ISO8601",  // Generation timestamp
+  "totalModels": number,     // Total across all providers
   "providers": {
     "providerId": {
-      "providerName": "string",
-      "modelCount": number,
-      "lastUpdated": "ISO8601"
+      "providerId": "string",    // Provider ID (matches key)
+      "providerName": "string",  // Human-readable provider name
+      "models": [
+        {
+          "id": "string",          // Model identifier
+          "name": "string",        // Display name
+          "contextLength": number, // Max context tokens
+          "maxTokens": number,     // Max output tokens
+          "vision": boolean,       // Image input support
+          "functionCall": boolean, // Tool/function calling support
+          "reasoning": boolean,    // Reasoning capabilities
+          "type": "string",        // Model type
+          "description": "string?" // Optional description
+        }
+      ]
     }
-  },
-  "totalModels": number,     // Total across all providers
-  "allModels": [
-    // Array of all models with additional providerId/providerName fields
-  ]
+  }
 }
 ```
 
@@ -123,14 +132,23 @@ jq '.' file.json
 
 ### Data Validation
 ```bash
-# Check required fields
+# Check required fields in single provider files
 jq '.models[] | select(.id == null or .name == null)' file.json
 
+# Check required fields in aggregated files
+jq '.providers[].models[] | select(.id == null or .name == null)' all.json
+
 # Verify token counts are positive
 jq '.models[] | select(.contextLength <= 0 or .maxTokens <= 0)' file.json
 
-# Check for duplicates
+# Check for duplicates within provider
 jq '.models | group_by(.id) | map(select(length > 1))' file.json
+
+# Validate template matching (for OpenAI/Anthropic)
+jq '.models[] | select(.description and (.description | contains("Auto:")))' openai.json
+
+# Check model counts in aggregated file
+jq '.totalModels == ([.providers[].models[]] | length)' all.json
 ```
 
 ## Automated Validation Script

.claude/provider_implementer.md

@@ -22,6 +22,7 @@ pub struct {ProviderName}Provider {
     api_url: String,
     api_key: Option<String>,
     client: reqwest::Client,
+    templates: HashMap<String, TemplateModel>, // For template-based providers
 }
 ```
 
@@ -30,11 +31,17 @@ pub struct {ProviderName}Provider {
 - `provider_id() -> &str`
 - `provider_name() -> &str`
 
-### 3. Data Conversion
-Map provider-specific fields to ModelInfo:
-- Extract vision/function_call/reasoning capabilities from features/tags
+### 3. Template-Based vs Direct Conversion
+**Template-Based Providers** (OpenAI, Anthropic):
+- Load model templates from `templates/{provider}.json`
+- Use template matching with `match` arrays for multi-pattern support
+- Auto-configure unmatched models with intelligent capability detection
+- Provides consistent metadata and handles API versioning
+
+**Direct Conversion Providers** (PPInfra, OpenRouter):
+- Map provider-specific fields directly to ModelInfo
+- Extract capabilities from API response features/tags
 - Convert context_length/max_tokens to u32
-- Map model_type to ModelType enum
 - Handle optional description field
 
 ## Output Format Requirements
@@ -74,8 +81,29 @@ When implementing a new provider:
 
 ## Example API Response Patterns
 
-### PPInfra Format
+### Template-Based Pattern (OpenAI, Anthropic)
 ```json
+// API Response
+{
+  "data": [{
+    "id": "gpt-5-chat-latest",
+    "object": "model"
+  }]
+}
+
+// Template File (templates/openai.json)
+[{
+  "id": "gpt-5-chat",
+  "name": "GPT-5 Chat",
+  "contextLength": 272000,
+  "maxTokens": 16384,
+  "match": ["gpt-5-chat", "gpt-5-chat-latest"]
+}]
+```
+
+### Direct Conversion Pattern (PPInfra, OpenRouter)
+```json
+// PPInfra Format
 {
   "data": [{
     "id": "model-id",
@@ -86,10 +114,8 @@ When implementing a new provider:
     "model_type": "chat"
   }]
 }
-```
 
-### OpenRouter Format  
-```json
+// OpenRouter Format  
 {
   "id": "model-id",
   "name": "Model Name",
@@ -121,4 +147,72 @@ Follow provider-specific limits:
 - PPInfra: 10 requests/second
 - Add delays between requests as needed
 
-When implementing a provider, always verify the API documentation for current rate limits and authentication requirements.
+## Template Matching System
+
+For providers with complex model naming or versioning (OpenAI, Anthropic), use the template matching system:
+
+### Template Structure
+```json
+{
+  "id": "base-model-id",
+  "name": "Display Name", 
+  "contextLength": 128000,
+  "maxTokens": 8192,
+  "vision": true,
+  "functionCall": true,
+  "reasoning": false,
+  "type": "chat",
+  "description": "Model description",
+  "match": ["exact-api-id", "versioned-api-id", "alias"]
+}
+```
+
+### Implementation Pattern
+```rust
+// Load templates in constructor
+let templates = Self::load_templates()?;
+let template_map: HashMap<String, TemplateModel> = templates
+    .into_iter()
+    .flat_map(|template| {
+        template.match_patterns
+            .iter()
+            .map(|pattern| (pattern.clone(), template.clone()))
+            .collect::<Vec<_>>()
+    })
+    .collect();
+
+// Match models in fetch_models()
+if let Some(template) = self.templates.get(&api_model.id) {
+    // Use template configuration
+    models.push(template.to_model_info());
+    matched_models.insert(api_model.id.clone());
+} else {
+    // Auto-configure unmatched model
+    models.push(self.create_default_model(&api_model.id));
+}
+```
+
+### Auto-Configuration for Unmatched Models
+```rust
+fn create_default_model(&self, model_id: &str) -> ModelInfo {
+    // Intelligent detection based on model ID patterns
+    let is_reasoning = model_id.contains("o1") || model_id.contains("o3");
+    let has_vision = model_id.contains("4o") || model_id.contains("vision");
+    let has_function_call = !model_id.contains("instruct");
+    
+    // Set appropriate defaults based on analysis
+    ModelInfo::new(
+        model_id.to_string(),
+        format!("Auto: {}", model_id),
+        default_context_length,
+        default_max_tokens,
+        has_vision,
+        has_function_call, 
+        is_reasoning,
+        ModelType::Chat,
+        Some(format!("Auto-configured model: {}", model_id)),
+    )
+}
+```
+
+When implementing a provider, choose template-based approach for providers with complex versioning, direct conversion for providers with rich API metadata.

CLAUDE.md

@@ -15,7 +15,7 @@ cargo build
 cargo run -- fetch-all
 
 # Run with specific providers  
-cargo run -- fetch-providers -p ppinfra,openai
+cargo run -- fetch-providers -p ppinfra,openai,anthropic
 
 # Run tests
 cargo test
@@ -53,6 +53,10 @@ du -h dist/*.json
 
 - `src/main.rs` - CLI entry point
 - `src/providers/ppinfra.rs` - PPInfra API implementation
+- `src/providers/openai.rs` - OpenAI API implementation with template matching
+- `src/providers/anthropic.rs` - Anthropic API implementation
+- `templates/openai.json` - OpenAI model template definitions
+- `templates/anthropic.json` - Anthropic model template definitions
 - `docs/architecture-overview.md` - Complete architecture documentation
 - `.github/workflows/fetch-models.yml` - Automated fetching workflow
 
@@ -197,8 +201,10 @@ The workflow automatically:
 ## Environment Variables
 
 Optional API keys can be set as GitHub secrets:
-- `OPENAI_API_KEY`
-- `OPENROUTER_API_KEY`
+- `OPENAI_API_KEY` - Required for OpenAI provider
+- `ANTHROPIC_API_KEY` - Required for Anthropic provider
+- `GROQ_API_KEY` - Required for Groq provider
+- `GEMINI_API_KEY` - Optional for Gemini provider (enhances model list)
 - Add others as needed
 
 ## Common Issues
@@ -210,9 +216,12 @@ Optional API keys can be set as GitHub secrets:
 
 ## Next Steps
 
+- [x] Add OpenAI provider implementation (65+ models with template matching)
+- [x] Add Anthropic provider implementation (8 Claude models with API key support)
+- [x] Implement configuration file loading
 - [ ] Add OpenRouter provider implementation
-- [ ] Add OpenAI provider implementation  
 - [ ] Add Google Gemini provider implementation
-- [ ] Implement configuration file loading
 - [ ] Add rate limiting and retry logic
-- [ ] Add comprehensive error handling
+- [ ] Add comprehensive error handling
+- [ ] Implement template validation system
+- [ ] Add provider health check endpoints