feat: make LLM backoff configurable end-to-end

- extend LLMConfig with backoff delay/attempt/factor fields and thread them
  through LLMExtractionStrategy, LLMContentFilter, table extraction, and
  Docker API handlers
- expose the backoff parameter knobs on perform_completion_with_backoff/aperform_completion_with_backoff
  and document them in the md_v2 guides

docs/md_v2/api/parameters.md

@@ -439,10 +439,19 @@ LLMConfig is useful to pass LLM provider config to strategies and functions that
 | **`provider`**    | `"ollama/llama3","groq/llama3-70b-8192","groq/llama3-8b-8192", "openai/gpt-4o-mini" ,"openai/gpt-4o","openai/o1-mini","openai/o1-preview","openai/o3-mini","openai/o3-mini-high","anthropic/claude-3-haiku-20240307","anthropic/claude-3-opus-20240229","anthropic/claude-3-sonnet-20240229","anthropic/claude-3-5-sonnet-20240620","gemini/gemini-pro","gemini/gemini-1.5-pro","gemini/gemini-2.0-flash","gemini/gemini-2.0-flash-exp","gemini/gemini-2.0-flash-lite-preview-02-05","deepseek/deepseek-chat"`<br/>*(default: `"openai/gpt-4o-mini"`)* | Which LLM provider to use. 
 | **`api_token`**         |1.Optional. When not provided explicitly, api_token will be read from environment variables based on provider. For example: If a gemini model is passed as provider then,`"GEMINI_API_KEY"` will be read from environment variables  <br/> 2. API token of LLM provider <br/> eg: `api_token = "gsk_1ClHGGJ7Lpn4WGybR7vNWGdyb3FY7zXEw3SCiy0BAVM9lL8CQv"` <br/> 3. Environment variable - use with prefix "env:" <br/> eg:`api_token = "env: GROQ_API_KEY"`              | API token to use for the given provider 
 | **`base_url`**         |Optional. Custom API endpoint | If your provider has a custom endpoint
+| **`backoff_base_delay`** |Optional. `int` *(default: `2`)* | Seconds to wait before the first retry when the provider throttles a request.
+| **`backoff_max_attempts`** |Optional. `int` *(default: `3`)* | Total tries (initial call + retries) before surfacing an error.
+| **`backoff_exponential_factor`** |Optional. `int` *(default: `2`)* | Multiplier that increases the wait time for each retry (`delay = base_delay * factor^attempt`).
 
 ## 3.2 Example Usage
 ```python
-llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
+llm_config = LLMConfig(
+    provider="openai/gpt-4o-mini",
+    api_token=os.getenv("OPENAI_API_KEY"),
+    backoff_base_delay=1, # optional
+    backoff_max_attempts=5, # optional
+    backoff_exponential_factor=3, # optional
+)
 ```
 
 ## 4. Putting It All Together

docs/md_v2/complete-sdk-reference.md

@@ -1593,8 +1593,20 @@ The `clone()` method:
     - Environment variable - use with prefix "env:" <br/> eg:`api_token = "env: GROQ_API_KEY"`            
 3. **`base_url`**:  
    - If your provider has a custom endpoint
+
+4. **Backoff controls** *(optional)*:  
+   - `backoff_base_delay` *(default `2` seconds)* – how long to pause before the first retry if the provider rate-limits you.  
+   - `backoff_max_attempts` *(default `3`)* – total tries for the same prompt (initial call + retries).  
+   - `backoff_exponential_factor` *(default `2`)* – how quickly the pause grows between retries. A factor of 2 yields waits like 2s → 4s → 8s.  
+   - Because these plug into Crawl4AI’s retry helper, every LLM strategy automatically follows the pacing you define here.
 ```python
-llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
+llm_config = LLMConfig(
+    provider="openai/gpt-4o-mini",
+    api_token=os.getenv("OPENAI_API_KEY"),
+    backoff_base_delay=1, # optional
+    backoff_max_attempts=5, # optional
+    backoff_exponential_factor=3, # optional
+)
 ```
 ## 4. Putting It All Together
 In a typical scenario, you define **one** `BrowserConfig` for your crawler session, then create **one or more** `CrawlerRunConfig` & `LLMConfig` depending on each call's needs:

docs/md_v2/core/browser-crawler-config.md

@@ -308,8 +308,20 @@ The `clone()` method:
 3.⠀**`base_url`**:  
    - If your provider has a custom endpoint
 
+4.⠀**Retry/backoff controls** *(optional)*:  
+   - `backoff_base_delay` *(default `2` seconds)* – base delay inserted before the first retry when the provider returns a rate-limit response.  
+   - `backoff_max_attempts` *(default `3`)* – total number of attempts (initial call plus retries) before the request is surfaced as an error.  
+   - `backoff_exponential_factor` *(default `2`)* – growth rate for the retry delay (`delay = base_delay * factor^attempt`).  
+   - These values are forwarded to the shared `perform_completion_with_backoff` helper, ensuring every strategy that consumes your `LLMConfig` honors the same throttling policy.
+
 ```python
-llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
+llm_config = LLMConfig(
+    provider="openai/gpt-4o-mini",
+    api_token=os.getenv("OPENAI_API_KEY"),
+    backoff_base_delay=1, # optional
+    backoff_max_attempts=5, # optional
+    backoff_exponential_factor=3, #optional
+)
 ```
 
 ## 4. Putting It All Together