feat(docker): add flexible LLM provider configuration

- Support LLM_PROVIDER env var to override default provider (openai/gpt-4o-mini)
- Add optional 'provider' parameter to API endpoints for per-request overrides
- Implement provider validation to ensure API keys exist
- Update documentation and examples with new configuration options

Closes the need to hardcode providers in config.yml

deploy/docker/.llm.env.example

@@ -5,4 +5,9 @@ ANTHROPIC_API_KEY=your_anthropic_key_here
 GROQ_API_KEY=your_groq_key_here
 TOGETHER_API_KEY=your_together_key_here
 MISTRAL_API_KEY=your_mistral_key_here
-GEMINI_API_TOKEN=your_gemini_key_here
+GEMINI_API_TOKEN=your_gemini_key_here
+
+# Optional: Override the default LLM provider
+# Examples: "openai/gpt-4", "anthropic/claude-3-opus", "deepseek/chat", etc.
+# If not set, uses the provider specified in config.yml (default: openai/gpt-4o-mini)
+# LLM_PROVIDER=anthropic/claude-3-opus

deploy/docker/README.md

@@ -154,6 +154,29 @@ cp deploy/docker/.llm.env.example .llm.env
 # Now edit .llm.env and add your API keys
 ```
 
+**Flexible LLM Provider Configuration:**
+
+The Docker setup now supports flexible LLM provider configuration through three methods:
+
+1. **Environment Variable** (Highest Priority): Set `LLM_PROVIDER` to override the default
+   ```bash
+   export LLM_PROVIDER="anthropic/claude-3-opus"
+   # Or in your .llm.env file:
+   # LLM_PROVIDER=anthropic/claude-3-opus
+   ```
+
+2. **API Request Parameter**: Specify provider per request
+   ```json
+   {
+     "url": "https://example.com",
+     "provider": "groq/mixtral-8x7b"
+   }
+   ```
+
+3. **Config File Default**: Falls back to `config.yml` (default: `openai/gpt-4o-mini`)
+
+The system automatically selects the appropriate API key based on the provider.
+
 #### 3. Build and Run with Compose
 
 The `docker-compose.yml` file in the project root provides a simplified approach that automatically handles architecture detection using buildx.
@@ -668,7 +691,7 @@ app:
 
 # Default LLM Configuration
 llm:
-  provider: "openai/gpt-4o-mini"
+  provider: "openai/gpt-4o-mini"  # Can be overridden by LLM_PROVIDER env var
   api_key_env: "OPENAI_API_KEY"
   # api_key: sk-...  # If you pass the API key directly then api_key_env will be ignored
 

deploy/docker/api.py

@@ -40,7 +40,9 @@ from utils import (
     get_base_url,
     is_task_id,
     should_cleanup_task,
-    decode_redis_hash
+    decode_redis_hash,
+    get_llm_api_key,
+    validate_llm_provider
 )
 
 import psutil, time
@@ -89,10 +91,12 @@ async def handle_llm_qa(
 
     Answer:"""
 
+        # api_token=os.environ.get(config["llm"].get("api_key_env", ""))
+
         response = perform_completion_with_backoff(
             provider=config["llm"]["provider"],
             prompt_with_variables=prompt,
-            api_token=os.environ.get(config["llm"].get("api_key_env", ""))
+            api_token=get_llm_api_key(config)
         )
 
         return response.choices[0].message.content
@@ -110,19 +114,23 @@ async def process_llm_extraction(
     url: str,
     instruction: str,
     schema: Optional[str] = None,
-    cache: str = "0"
+    cache: str = "0",
+    provider: Optional[str] = None
 ) -> None:
     """Process LLM extraction in background."""
     try:
-        # If config['llm'] has api_key then ignore the api_key_env
-        api_key = ""
-        if "api_key" in config["llm"]:
-            api_key = config["llm"]["api_key"]
-        else:
-            api_key = os.environ.get(config["llm"].get("api_key_env", None), "")
+        # Validate provider
+        is_valid, error_msg = validate_llm_provider(config, provider)
+        if not is_valid:
+            await redis.hset(f"task:{task_id}", mapping={
+                "status": TaskStatus.FAILED,
+                "error": error_msg
+            })
+            return
+        api_key = get_llm_api_key(config, provider)
         llm_strategy = LLMExtractionStrategy(
             llm_config=LLMConfig(
-                provider=config["llm"]["provider"],
+                provider=provider or config["llm"]["provider"],
                 api_token=api_key
             ),
             instruction=instruction,
@@ -169,10 +177,19 @@ async def handle_markdown_request(
     filter_type: FilterType,
     query: Optional[str] = None,
     cache: str = "0",
-    config: Optional[dict] = None
+    config: Optional[dict] = None,
+    provider: Optional[str] = None
 ) -> str:
     """Handle markdown generation requests."""
     try:
+        # Validate provider if using LLM filter
+        if filter_type == FilterType.LLM:
+            is_valid, error_msg = validate_llm_provider(config, provider)
+            if not is_valid:
+                raise HTTPException(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    detail=error_msg
+                )
         decoded_url = unquote(url)
         if not decoded_url.startswith(('http://', 'https://')):
             decoded_url = 'https://' + decoded_url
@@ -185,8 +202,8 @@ async def handle_markdown_request(
                 FilterType.BM25: BM25ContentFilter(user_query=query or ""),
                 FilterType.LLM: LLMContentFilter(
                     llm_config=LLMConfig(
-                        provider=config["llm"]["provider"],
-                        api_token=os.environ.get(config["llm"].get("api_key_env", None), ""),
+                        provider=provider or config["llm"]["provider"],
+                        api_token=get_llm_api_key(config, provider),
                     ),
                     instruction=query or "Extract main content"
                 )
@@ -230,7 +247,8 @@ async def handle_llm_request(
     query: Optional[str] = None,
     schema: Optional[str] = None,
     cache: str = "0",
-    config: Optional[dict] = None
+    config: Optional[dict] = None,
+    provider: Optional[str] = None
 ) -> JSONResponse:
     """Handle LLM extraction requests."""
     base_url = get_base_url(request)
@@ -260,7 +278,8 @@ async def handle_llm_request(
             schema,
             cache,
             base_url,
-            config
+            config,
+            provider
         )
 
     except Exception as e:
@@ -304,7 +323,8 @@ async def create_new_task(
     schema: Optional[str],
     cache: str,
     base_url: str,
-    config: dict
+    config: dict,
+    provider: Optional[str] = None
 ) -> JSONResponse:
     """Create and initialize a new task."""
     decoded_url = unquote(input_path)
@@ -328,7 +348,8 @@ async def create_new_task(
         decoded_url,
         query,
         schema,
-        cache
+        cache,
+        provider
     )
 
     return JSONResponse({

deploy/docker/job.py

@@ -36,6 +36,7 @@ class LlmJobPayload(BaseModel):
     q:      str
     schema: Optional[str] = None
     cache:  bool = False
+    provider: Optional[str] = None
 
 
 class CrawlJobPayload(BaseModel):
@@ -61,6 +62,7 @@ async def llm_job_enqueue(
         schema=payload.schema,
         cache=payload.cache,
         config=_config,
+        provider=payload.provider,
     )
 
 

deploy/docker/schemas.py

@@ -15,6 +15,7 @@ class MarkdownRequest(BaseModel):
     f:   FilterType             = Field(FilterType.FIT, description="Content‑filter strategy: fit, raw, bm25, or llm")
     q:   Optional[str] = Field(None,  description="Query string used by BM25/LLM filters")
     c:   Optional[str] = Field("0",   description="Cache‑bust / revision counter")
+    provider: Optional[str] = Field(None, description="LLM provider override (e.g., 'anthropic/claude-3-opus')")
 
 
 class RawCode(BaseModel):

deploy/docker/server.py

@@ -241,7 +241,7 @@ async def get_markdown(
         raise HTTPException(
             400, "URL must be absolute and start with http/https")
     markdown = await handle_markdown_request(
-        body.url, body.f, body.q, body.c, config
+        body.url, body.f, body.q, body.c, config, body.provider
     )
     return JSONResponse({
         "url": body.url,

deploy/docker/utils.py

@@ -1,6 +1,7 @@
 import dns.resolver
 import logging
 import yaml
+import os
 from datetime import datetime
 from enum import Enum
 from pathlib import Path
@@ -19,10 +20,24 @@ class FilterType(str, Enum):
     LLM = "llm"
 
 def load_config() -> Dict:
-    """Load and return application configuration."""
+    """Load and return application configuration with environment variable overrides."""
     config_path = Path(__file__).parent / "config.yml"
     with open(config_path, "r") as config_file:
-        return yaml.safe_load(config_file)
+        config = yaml.safe_load(config_file)
+    
+    # Override LLM provider from environment if set
+    llm_provider = os.environ.get("LLM_PROVIDER")
+    if llm_provider:
+        config["llm"]["provider"] = llm_provider
+        logging.info(f"LLM provider overridden from environment: {llm_provider}")
+    
+    # Also support direct API key from environment if the provider-specific key isn't set
+    llm_api_key = os.environ.get("LLM_API_KEY")
+    if llm_api_key and "api_key" not in config["llm"]:
+        config["llm"]["api_key"] = llm_api_key
+        logging.info("LLM API key loaded from LLM_API_KEY environment variable")
+    
+    return config
 
 def setup_logging(config: Dict) -> None:
     """Configure application logging."""
@@ -56,6 +71,52 @@ def decode_redis_hash(hash_data: Dict[bytes, bytes]) -> Dict[str, str]:
 
 
 
+def get_llm_api_key(config: Dict, provider: Optional[str] = None) -> str:
+    """Get the appropriate API key based on the LLM provider.
+    
+    Args:
+        config: The application configuration dictionary
+        provider: Optional provider override (e.g., "openai/gpt-4")
+    
+    Returns:
+        The API key for the provider, or empty string if not found
+    """
+        
+    # Use provided provider or fall back to config
+    if not provider:
+        provider = config["llm"]["provider"]
+    
+    # Check if direct API key is configured
+    if "api_key" in config["llm"]:
+        return config["llm"]["api_key"]
+    
+    # Fall back to the configured api_key_env if no match
+    return os.environ.get(config["llm"].get("api_key_env", ""), "")
+
+
+def validate_llm_provider(config: Dict, provider: Optional[str] = None) -> tuple[bool, str]:
+    """Validate that the LLM provider has an associated API key.
+    
+    Args:
+        config: The application configuration dictionary
+        provider: Optional provider override (e.g., "openai/gpt-4")
+    
+    Returns:
+        Tuple of (is_valid, error_message)
+    """
+    # Use provided provider or fall back to config
+    if not provider:
+        provider = config["llm"]["provider"]
+    
+    # Get the API key for this provider
+    api_key = get_llm_api_key(config, provider)
+    
+    if not api_key:
+        return False, f"No API key found for provider '{provider}'. Please set the appropriate environment variable."
+    
+    return True, ""
+
+
 def verify_email_domain(email: str) -> bool:
     try:
         domain = email.split('@')[1]