add example

examples/local_models/json_scraper_multi_ollama.py

@@ -0,0 +1,47 @@
+"""
+Module for showing how PDFScraper multi works
+"""
+import os 
+from scrapegraphai.graphs import PdfScraperMultiGraph
+
+graph_config = {
+    "llm": {
+        "model": "ollama/llama3",
+        "temperature": 0,
+        "format": "json",  # Ollama needs the format to be specified explicitly
+        "model_tokens": 4000,
+    },
+    "embeddings": {
+        "model": "ollama/nomic-embed-text",
+        "temperature": 0,
+    },
+    "verbose": True,
+    "headless": False,
+}
+FILE_NAME = "inputs/example.json"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+    
+json_scraper_graph = JSONScraperGraph(
+    prompt="List me all the authors, title and genres of the books",
+    source=text,  # Pass the content of the file, not the file object
+    config=graph_config
+)
+
+
+
+results = []
+for source in sources:
+    pdf_scraper_graph = PdfScraperMultiGraph(
+        prompt=prompt,
+        source=source,
+        config=graph_config
+    )
+    result = pdf_scraper_graph.run()
+    results.append(result)
+
+print(results)

examples/local_models/pdf_scraper_multi_ollama.py

@@ -1,6 +1,7 @@
 """
 Module for showing how PDFScraper multi works
 """
+import json
 from scrapegraphai.graphs import PdfScraperMultiGraph
 
 graph_config = {
@@ -56,14 +57,16 @@ Independent Variable (IV): Exposure to social media.
 Dependent Variable (DV): Mental health outcomes.
 Exogenous Shock: staggered introduction of Facebook across U.S. colleges.
 """
-results = []
-for source in sources:
-    pdf_scraper_graph = PdfScraperMultiGraph(
-        prompt=prompt,
-        source=source,
-        config=graph_config
-    )
-    result = pdf_scraper_graph.run()
-    results.append(result)
+# *******************************************************
+# Create the SmartScraperMultiGraph instance and run it
+# *******************************************************
 
-print(results)
+multiple_search_graph = PdfScraperMultiGraph(
+    prompt=prompt,
+    source= sources,
+    schema=None,
+    config=graph_config
+)
+
+result = multiple_search_graph.run()
+print(json.dumps(result, indent=4))

examples/openai/smart_scraper_multi_openai.py

@@ -2,7 +2,8 @@
 Basic example of scraping pipeline using SmartScraper
 """
 
-import os, json
+import os
+import json
 from dotenv import load_dotenv
 from scrapegraphai.graphs import SmartScraperMultiGraph
 

scrapegraphai/graphs/__init__.py

@@ -17,3 +17,4 @@ from .omni_scraper_graph import OmniScraperGraph
 from .omni_search_graph import OmniSearchGraph
 from .smart_scraper_multi_graph import SmartScraperMultiGraph
 from .pdf_scraper_multi import PdfScraperMultiGraph
+from .json_scraper_multi import JsonScraperMultiGraph

scrapegraphai/graphs/json_scraper_multi.py

@@ -0,0 +1,116 @@
+""" 
+JsonScraperMultiGraph Module
+"""
+
+from copy import copy, deepcopy
+from typing import List, Optional
+
+from .base_graph import BaseGraph
+from .abstract_graph import AbstractGraph
+from .json_scraper_graph import JSONScraperGraph
+
+from ..nodes import (
+    GraphIteratorNode,
+    MergeAnswersNode
+)
+
+
+class JsonScraperMultiGraph(AbstractGraph):
+    """ 
+    JsonScraperMultiGraph is a scraping pipeline that scrapes a list of URLs and generates answers to a given prompt.
+    It only requires a user prompt and a list of URLs.
+
+    Attributes:
+        prompt (str): The user prompt to search the internet.
+        llm_model (dict): The configuration for the language model.
+        embedder_model (dict): The configuration for the embedder model.
+        headless (bool): A flag to run the browser in headless mode.
+        verbose (bool): A flag to display the execution information.
+        model_token (int): The token limit for the language model.
+
+    Args:
+        prompt (str): The user prompt to search the internet.
+        source (List[str]): The source of the graph.
+        config (dict): Configuration parameters for the graph.
+        schema (Optional[str]): The schema for the graph output.
+
+    Example:
+        >>> search_graph = MultipleSearchGraph(
+        ...     "What is Chioggia famous for?",
+        ...     {"llm": {"model": "gpt-3.5-turbo"}}
+        ... )
+        >>> result = search_graph.run()
+    """
+
+    def __init__(self, prompt: str, source: List[str], config: dict, schema: Optional[str] = None):
+
+        self.max_results = config.get("max_results", 3)
+
+        if all(isinstance(value, str) for value in config.values()):
+            self.copy_config = copy(config)
+        else:
+            self.copy_config = deepcopy(config)
+
+        super().__init__(prompt, config, source, schema)
+
+    def _create_graph(self) -> BaseGraph:
+        """
+        Creates the graph of nodes representing the workflow for web scraping and searching.
+
+        Returns:
+            BaseGraph: A graph instance representing the web scraping and searching workflow.
+        """
+
+        # ************************************************
+        # Create a SmartScraperGraph instance
+        # ************************************************
+
+        smart_scraper_instance = JSONScraperGraph(
+            prompt="",
+            source="",
+            config=self.copy_config,
+        )
+
+        # ************************************************
+        # Define the graph nodes
+        # ************************************************
+
+        graph_iterator_node = GraphIteratorNode(
+            input="user_prompt & jsons",
+            output=["results"],
+            node_config={
+                "graph_instance": smart_scraper_instance,
+            }
+        )
+
+        merge_answers_node = MergeAnswersNode(
+            input="user_prompt & results",
+            output=["answer"],
+            node_config={
+                "llm_model": self.llm_model,
+                "schema": self.schema
+            }
+        )
+
+        return BaseGraph(
+            nodes=[
+                graph_iterator_node,
+                merge_answers_node,
+            ],
+            edges=[
+                (graph_iterator_node, merge_answers_node),
+            ],
+            entry_point=graph_iterator_node
+        )
+
+    def run(self) -> str:
+        """
+        Executes the web scraping and searching process.
+
+        Returns:
+            str: The answer to the prompt.
+        """
+        inputs = {"user_prompt": self.prompt, "jsons": self.source}
+        self.final_state, self.execution_info = self.graph.execute(inputs)
+
+        return self.final_state.get("answer", "No answer found.")