Enhance DFSDeepCrawlStrategy documentation for clarity and detail

crawl4ai/deep_crawling/dfs_strategy.py

@@ -4,14 +4,26 @@ from typing import AsyncGenerator, Optional, Set, Dict, List, Tuple
 from ..models import CrawlResult
 from .bfs_strategy import BFSDeepCrawlStrategy  # noqa
 from ..types import AsyncWebCrawler, CrawlerRunConfig
+from ..utils import normalize_url_for_deep_crawl
 
 class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
     """
-    Depth-First Search (DFS) deep crawling strategy.
+    Depth-first deep crawling with familiar BFS rules.
 
-    Inherits URL validation and link discovery from BFSDeepCrawlStrategy.
-    Overrides _arun_batch and _arun_stream to use a stack (LIFO) for DFS traversal.
+    We reuse the same filters, scoring, and page limits from :class:`BFSDeepCrawlStrategy`,
+    but walk the graph with a stack so we fully explore one branch before hopping to the
+    next. DFS also keeps its own ``_dfs_seen`` set so we can drop duplicate links at
+    discovery time without accidentally marking them as “already crawled”.
     """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._dfs_seen: Set[str] = set()
+
+    def _reset_seen(self, start_url: str) -> None:
+        """Start each crawl with a clean dedupe set seeded with the root URL."""
+        self._dfs_seen = {start_url}
+
     async def _arun_batch(
         self,
         start_url: str,
@@ -19,14 +31,19 @@ class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
         config: CrawlerRunConfig,
     ) -> List[CrawlResult]:
         """
-        Batch (non-streaming) DFS mode.
-        Uses a stack to traverse URLs in DFS order, aggregating CrawlResults into a list.
+        Crawl level-by-level but emit results at the end.
+
+        We keep a stack of ``(url, parent, depth)`` tuples, pop one at a time, and
+        hand it to ``crawler.arun_many`` with deep crawling disabled so we remain
+        in control of traversal. Every successful page bumps ``_pages_crawled`` and
+        seeds new stack items discovered via :meth:`link_discovery`.
         """
         visited: Set[str] = set()
         # Stack items: (url, parent_url, depth)
         stack: List[Tuple[str, Optional[str], int]] = [(start_url, None, 0)]
         depths: Dict[str, int] = {start_url: 0}
         results: List[CrawlResult] = []
+        self._reset_seen(start_url)
 
         while stack and not self._cancel_event.is_set():
             url, parent, depth = stack.pop()
@@ -71,12 +88,16 @@ class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
         config: CrawlerRunConfig,
     ) -> AsyncGenerator[CrawlResult, None]:
         """
-        Streaming DFS mode.
-        Uses a stack to traverse URLs in DFS order and yields CrawlResults as they become available.
+        Same traversal as :meth:`_arun_batch`, but yield pages immediately.
+
+        Each popped URL is crawled, its metadata annotated, then the result gets
+        yielded before we even look at the next stack entry. Successful crawls
+        still feed :meth:`link_discovery`, keeping DFS order intact.
         """
         visited: Set[str] = set()
         stack: List[Tuple[str, Optional[str], int]] = [(start_url, None, 0)]
         depths: Dict[str, int] = {start_url: 0}
+        self._reset_seen(start_url)
 
         while stack and not self._cancel_event.is_set():
             url, parent, depth = stack.pop()
@@ -108,3 +129,92 @@ class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
                     for new_url, new_parent in reversed(new_links):
                         new_depth = depths.get(new_url, depth + 1)
                         stack.append((new_url, new_parent, new_depth))
+
+    async def link_discovery(
+        self,
+        result: CrawlResult,
+        source_url: str,
+        current_depth: int,
+        _visited: Set[str],
+        next_level: List[Tuple[str, Optional[str]]],
+        depths: Dict[str, int],
+    ) -> None:
+        """
+        Find the next URLs we should push onto the DFS stack.
+
+        Parameters
+        ----------
+        result : CrawlResult
+            Output of the page we just crawled; its ``links`` block is our raw material.
+        source_url : str
+            URL of the parent page; stored so callers can track ancestry.
+        current_depth : int
+            Depth of the parent; children naturally sit at ``current_depth + 1``.
+        _visited : Set[str]
+            Present to match the BFS signature, but we rely on ``_dfs_seen`` instead.
+        next_level : list of tuples
+            The stack buffer supplied by the caller; we append new ``(url, parent)`` items here.
+        depths : dict
+            Shared depth map so future metadata tagging knows how deep each URL lives.
+
+        Notes
+        -----
+        - ``_dfs_seen`` keeps us from pushing duplicates without touching the traversal guard.
+        - Validation, scoring, and capacity trimming mirror the BFS version so behaviour stays consistent.
+        """
+        next_depth = current_depth + 1
+        if next_depth > self.max_depth:
+            return
+
+        remaining_capacity = self.max_pages - self._pages_crawled
+        if remaining_capacity <= 0:
+            self.logger.info(
+                f"Max pages limit ({self.max_pages}) reached, stopping link discovery"
+            )
+            return
+
+        links = result.links.get("internal", [])
+        if self.include_external:
+            links += result.links.get("external", [])
+
+        seen = self._dfs_seen
+        valid_links: List[Tuple[str, float]] = []
+
+        for link in links:
+            raw_url = link.get("href")
+            if not raw_url:
+                continue
+
+            normalized_url = normalize_url_for_deep_crawl(raw_url, source_url)
+            if not normalized_url or normalized_url in seen:
+                continue
+
+            if not await self.can_process_url(raw_url, next_depth):
+                self.stats.urls_skipped += 1
+                continue
+
+            score = self.url_scorer.score(normalized_url) if self.url_scorer else 0
+            if score < self.score_threshold:
+                self.logger.debug(
+                    f"URL {normalized_url} skipped: score {score} below threshold {self.score_threshold}"
+                )
+                self.stats.urls_skipped += 1
+                continue
+
+            seen.add(normalized_url)
+            valid_links.append((normalized_url, score))
+
+        if len(valid_links) > remaining_capacity:
+            if self.url_scorer:
+                valid_links.sort(key=lambda x: x[1], reverse=True)
+            valid_links = valid_links[:remaining_capacity]
+            self.logger.info(
+                f"Limiting to {remaining_capacity} URLs due to max_pages limit"
+            )
+
+        for url, score in valid_links:
+            if score:
+                result.metadata = result.metadata or {}
+                result.metadata["score"] = score
+            next_level.append((url, source_url))
+            depths[url] = next_depth

docs/examples/dfs_crawl_demo.py

@@ -0,0 +1,39 @@
+"""
+Simple demonstration of the DFS deep crawler visiting multiple pages.
+
+Run with:  python docs/examples/dfs_crawl_demo.py
+"""
+import asyncio
+
+from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig
+from crawl4ai.async_webcrawler import AsyncWebCrawler
+from crawl4ai.cache_context import CacheMode
+from crawl4ai.deep_crawling.dfs_strategy import DFSDeepCrawlStrategy
+from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
+
+
+async def main() -> None:
+    dfs_strategy = DFSDeepCrawlStrategy(
+        max_depth=3,
+        max_pages=50,
+        include_external=False,
+    )
+
+    config = CrawlerRunConfig(
+        deep_crawl_strategy=dfs_strategy,
+        cache_mode=CacheMode.BYPASS,
+        markdown_generator=DefaultMarkdownGenerator(),
+        stream=True,
+    )
+
+    seed_url = "https://docs.python.org/3/"  # Plenty of internal links
+
+    async with AsyncWebCrawler(config=BrowserConfig(headless=True)) as crawler:
+        async for result in await crawler.arun(url=seed_url, config=config):
+            depth = result.metadata.get("depth")
+            status = "SUCCESS" if result.success else "FAILED"
+            print(f"[{status}] depth={depth} url={result.url}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

docs/md_v2/extraction/llm-strategies.md

@@ -20,10 +20,10 @@ In some cases, you need to extract **complex or unstructured** information from
 
 ## 2. Provider-Agnostic via LiteLLM
 
-You can use LLMConfig, to quickly configure multiple variations of LLMs and experiment with them to find the optimal one for your use case. You can read more about LLMConfig [here](/api/parameters).
+You can use LlmConfig, to quickly configure multiple variations of LLMs and experiment with them to find the optimal one for your use case. You can read more about LlmConfig [here](/api/parameters).
 
 ```python
-llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
+llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
 ```
 
 Crawl4AI uses a “provider string” (e.g., `"openai/gpt-4o"`, `"ollama/llama2.0"`, `"aws/titan"`) to identify your LLM. **Any** model that LiteLLM supports is fair game. You just provide:
@@ -58,7 +58,7 @@ For structured data, `"schema"` is recommended. You provide `schema=YourPydantic
 
 Below is an overview of important LLM extraction parameters. All are typically set inside `LLMExtractionStrategy(...)`. You then put that strategy in your `CrawlerRunConfig(..., extraction_strategy=...)`.
 
-1. **`llm_config`** (LLMConfig): e.g., `"openai/gpt-4"`, `"ollama/llama2"`.
+1. **`llmConfig`** (LlmConfig): e.g., `"openai/gpt-4"`, `"ollama/llama2"`.    
 2. **`schema`** (dict): A JSON schema describing the fields you want. Usually generated by `YourModel.model_json_schema()`.  
 3. **`extraction_type`** (str): `"schema"` or `"block"`.  
 4. **`instruction`** (str): Prompt text telling the LLM what you want extracted. E.g., “Extract these fields as a JSON array.”  
@@ -112,7 +112,7 @@ async def main():
     # 1. Define the LLM extraction strategy
     llm_strategy = LLMExtractionStrategy(
         llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv('OPENAI_API_KEY')),
-        schema=Product.model_json_schema(), # Or use model_json_schema()
+        schema=Product.schema_json(), # Or use model_json_schema()
         extraction_type="schema",
         instruction="Extract all product objects with 'name' and 'price' from the content.",
         chunk_token_threshold=1000,
@@ -238,7 +238,7 @@ class KnowledgeGraph(BaseModel):
 async def main():
     # LLM extraction strategy
     llm_strat = LLMExtractionStrategy(
-        llm_config = LLMConfig(provider="openai/gpt-4", api_token=os.getenv('OPENAI_API_KEY')),
+        llmConfig = LLMConfig(provider="openai/gpt-4", api_token=os.getenv('OPENAI_API_KEY')),
         schema=KnowledgeGraph.model_json_schema(),
         extraction_type="schema",
         instruction="Extract entities and relationships from the content. Return valid JSON.",