create search_link_graph

Author: VinciGit00

examples/local_models/search_link_graph_ollama.py

@@ -0,0 +1,43 @@
+""" 
+Basic example of scraping pipeline using SmartScraper
+"""
+from scrapegraphai.graphs import SearchLinkGraph
+from scrapegraphai.utils import prettify_exec_info
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "model": "ollama/llama3",
+        "temperature": 0,
+        "format": "json",  # Ollama needs the format to be specified explicitly
+        # "base_url": "http://localhost:11434", # set ollama URL arbitrarily
+    },
+    "embeddings": {
+        "model": "ollama/nomic-embed-text",
+        "temperature": 0,
+        # "base_url": "http://localhost:11434",  # set ollama URL arbitrarily
+    },
+    "verbose": True,
+    "headless": False
+}
+
+# ************************************************
+# Create the SearchLinkGraph instance and run it
+# ************************************************
+
+smart_scraper_graph = SearchLinkGraph(
+    source="https://sport.sky.it/nba?gr=www",
+    config=graph_config
+)
+
+result = smart_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = smart_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))

scrapegraphai/graphs/__init__.py

@@ -23,3 +23,4 @@
 from .script_creator_multi_graph import ScriptCreatorMultiGraph
 from .markdown_scraper_graph import MDScraperGraph
 from .markdown_scraper_multi_graph import MDScraperMultiGraph
+from .search_link_graph import SearchLinkGraph

scrapegraphai/graphs/search_link_graph.py

@@ -0,0 +1,104 @@
+""" SearchLinkGraph Module """
+from typing import Optional
+import logging
+from pydantic import BaseModel
+from .base_graph import BaseGraph
+from .abstract_graph import AbstractGraph
+
+
+from ..nodes import ( FetchNode, ParseNode, SearchLinkNode )
+
+class SearchLinkGraph(AbstractGraph): 
+    """ 
+    SearchLinkGraph is a scraping pipeline that automates the process of extracting information from web pages using a natural language model to interpret and answer prompts.
+
+    Attributes:
+        prompt (str): The prompt for the graph.
+        source (str): The source of the graph.
+        config (dict): Configuration parameters for the graph.
+        schema (BaseModel): The schema for the graph output.
+        llm_model: An instance of a language model client, configured for generating answers.
+        embedder_model: An instance of an embedding model client, 
+        configured for generating embeddings.
+        verbose (bool): A flag indicating whether to show print statements during execution.
+        headless (bool): A flag indicating whether to run the graph in headless mode.
+
+    Args:
+        source (str): The source of the graph.
+        config (dict): Configuration parameters for the graph.
+        schema (BaseModel, optional): The schema for the graph output. Defaults to None.
+
+    Example:
+        >>> smart_scraper = SearchLinkGraph(
+        ...     "List me all the attractions in Chioggia.",
+        ...     "https://en.wikipedia.org/wiki/Chioggia",
+        ...     {"llm": {"model": "gpt-3.5-turbo"}}
+        ... )
+        >>> result = smart_scraper.run()
+    """
+
+    def __init__(self, source: str, config: dict, schema: Optional[BaseModel] = None):
+        super().__init__("", config, source, schema)
+
+        self.input_key = "url" if source.startswith("http") else "local_dir"
+
+    def _create_graph(self) -> BaseGraph:
+        """
+        Creates the graph of nodes representing the workflow for web scraping.
+
+        Returns:
+            BaseGraph: A graph instance representing the web scraping workflow.
+        """
+
+        fetch_node = FetchNode(
+            input="url| local_dir",
+            output=["doc", "link_urls", "img_urls"],
+            node_config={
+                "llm_model": self.llm_model,
+                "force": self.config.get("force", False),
+                "cut": self.config.get("cut", True),
+                "loader_kwargs": self.config.get("loader_kwargs", {}),
+            }
+        )
+        parse_node = ParseNode(
+            input="doc",
+            output=["parsed_doc"],
+            node_config={
+                "chunk_size": self.model_token
+            }
+        )
+        search_link_node = SearchLinkNode(
+            input="doc",
+            output=["parsed_doc"],
+            node_config={
+                "llm_model": self.llm_model,
+                "chunk_size": self.model_token
+            }
+        )
+
+        return BaseGraph(
+            nodes=[
+                fetch_node,
+                parse_node,
+                search_link_node
+            ],
+            edges=[
+                (fetch_node, parse_node),
+                (parse_node, search_link_node)
+            ],
+            entry_point=fetch_node,
+            graph_name=self.__class__.__name__
+        )
+
+    def run(self) -> str:
+        """
+        Executes the scraping process and returns the answer to the prompt.
+
+        Returns:
+            str: The answer to the prompt.
+        """
+
+        inputs = {"user_prompt": self.prompt, self.input_key: self.source}
+        self.final_state, self.execution_info = self.graph.execute(inputs)
+
+        return self.final_state.get("parsed_doc", "No answer found.")

scrapegraphai/nodes/search_link_node.py

@@ -68,11 +68,8 @@ def execute(self, state: dict) -> dict:
 
         self.logger.info(f"--- Executing {self.node_name} Node ---")
 
-        # Interpret input keys based on the provided input expression
-        input_keys = self.get_input_keys(state)
 
-        user_prompt = state[input_keys[0]]
-        parsed_content_chunks = state[input_keys[1]]
+        parsed_content_chunks = state.get("doc")
         output_parser = JsonOutputParser()
 
         relevant_links = []
@@ -86,7 +83,8 @@ def execute(self, state: dict) -> dict:
         ):
             try:
                 # Primary approach: Regular expression to extract links
-                links = re.findall(r'(https?://\S+)', chunk.page_content)
+                links = re.findall(r'https?://[^\s"<>\]]+', str(chunk.page_content))
+
                 relevant_links += links
             except Exception as e:
                 # Fallback approach: Using the LLM to extract links
@@ -95,9 +93,6 @@ def execute(self, state: dict) -> dict:
                     You are a website scraper and you have just scraped the following content from a website.
                     Content: {content}
                     
-                    You are now tasked with identifying all hyper links within the content that are potentially
-                    relevant to the user task: {user_prompt}
-                    
                     Assume relevance broadly, including any links that might be related or potentially useful 
                     in relation to the task.
 
@@ -124,9 +119,9 @@ def execute(self, state: dict) -> dict:
                 )
                 merge_chain = merge_prompt | self.llm_model | output_parser
                 answer = merge_chain.invoke(
-                    {"content": chunk.page_content, "user_prompt": user_prompt}
+                    {"content": chunk.page_content}
                 )
                 relevant_links += answer
 
         state.update({self.output[0]: relevant_links})
-        return state
+        return state