fix: examples and graphs

Author: VinciGit00

CONTRIBUTING.md

@@ -51,6 +51,7 @@ Please make sure to format your code accordingly before submitting a pull reques
 - [Style Guide for Python Code](https://www.python.org/dev/peps/pep-0008/)
 - [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html)
 - [The Hitchhiker's Guide to Python](https://docs.python-guide.org/writing/style/)
+- [Pylint style of code for the documentation](https://pylint.pycqa.org/en/1.6.0/tutorial.html)
 
 ## Submitting a Pull Request
 

examples/single_node/fetch_node.py

@@ -12,6 +12,9 @@
 robots_node = FetchNode(
     input="url | local_dir",
     output=["doc"],
+    node_config={
+        "headless": False
+    }
 )
 
 # ************************************************

examples/single_node/robot_node.py

@@ -26,7 +26,9 @@
 robots_node = RobotsNode(
     input="url",
     output=["is_scrapable"],
-    node_config={"llm": llm_model}
+    node_config={"llm": llm_model,
+                 "headless": False
+                 }
 )
 
 # ************************************************

scrapegraphai/graphs/json_scraper_graph.py

@@ -21,7 +21,8 @@ class JSONScraperGraph(AbstractGraph):
         source (str): The source of the graph.
         config (dict): Configuration parameters for the graph.
         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.
+        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.
 
@@ -47,7 +48,7 @@ def __init__(self, prompt: str, source: str, config: dict):
     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.
         """

scrapegraphai/graphs/script_creator_graph.py

@@ -21,7 +21,8 @@ class ScriptCreatorGraph(AbstractGraph):
         source (str): The source of the graph.
         config (dict): Configuration parameters for the graph.
         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.
+        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.
         model_token (int): The token limit for the language model.
@@ -44,7 +45,7 @@ class ScriptCreatorGraph(AbstractGraph):
     def __init__(self, prompt: str, source: str, config: dict):
 
         self.library = config['library']
-        
+
         super().__init__(prompt, config, source)
 
         self.input_key = "url" if source.startswith("http") else "local_dir"
@@ -61,25 +62,29 @@ def _create_graph(self) -> BaseGraph:
             input="url | local_dir",
             output=["doc"],
             node_config={
-                "headless": True if self.config is None else self.config.get("headless", True)}
+                "headless": True if self.config is None else self.config.get("headless", True),
+                "verbose": self.verbose}
         )
         parse_node = ParseNode(
             input="doc",
             output=["parsed_doc"],
-            node_config={"chunk_size": self.model_token}
+            node_config={"chunk_size": self.model_token,
+                         "verbose": self.verbose}
         )
         rag_node = RAGNode(
             input="user_prompt & (parsed_doc | doc)",
             output=["relevant_chunks"],
             node_config={
                 "llm": self.llm_model,
-                "embedder_model": self.embedder_model
+                "embedder_model": self.embedder_model,
+                "verbose": self.verbose
             }
         )
         generate_scraper_node = GenerateScraperNode(
             input="user_prompt & (relevant_chunks | parsed_doc | doc)",
             output=["answer"],
-            node_config={"llm": self.llm_model},
+            node_config={"llm": self.llm_model,
+                         "verbose": self.verbose},
             library=self.library,
             website=self.source
         )
@@ -106,7 +111,7 @@ def run(self) -> str:
         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)
 

scrapegraphai/graphs/smart_scraper_graph.py

@@ -14,15 +14,17 @@
 
 class SmartScraperGraph(AbstractGraph):
     """
-    SmartScraper is a scraping pipeline that automates the process of extracting information from web pages
+    SmartScraper 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.
         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.
+        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.
 
@@ -45,7 +47,7 @@ def __init__(self, prompt: str, source: str, config: dict):
         super().__init__(prompt, config, source)
 
         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.

scrapegraphai/graphs/xml_scraper_graph.py

@@ -22,7 +22,8 @@ class XMLScraperGraph(AbstractGraph):
         source (str): The source of the graph.
         config (dict): Configuration parameters for the graph.
         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.
+        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.
         model_token (int): The token limit for the language model.
@@ -49,7 +50,7 @@ def __init__(self, prompt: str, source: str, config: dict):
     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.
         """
@@ -110,7 +111,7 @@ def run(self) -> str:
         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)
 

tests/graphs/scrape_json_ollama.py

@@ -0,0 +1,56 @@
+""" 
+Module for scraping json documents
+"""
+import os
+import pytest
+from scrapegraphai.graphs import JSONScraperGraph
+
+
+@pytest.fixture
+def sample_json():
+    """
+    Example of text
+    """
+    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()
+
+    return text
+
+
+@pytest.fixture
+def graph_config():
+    """
+    Configuration of the graph
+    """
+    return {
+        "llm": {
+            "model": "ollama/mistral",
+            "temperature": 0,
+            "format": "json",
+            "base_url": "http://localhost:11434",
+        },
+        "embeddings": {
+            "model": "ollama/nomic-embed-text",
+            "temperature": 0,
+            "base_url": "http://localhost:11434",
+        }
+    }
+
+
+def test_scraping_pipeline(sample_json: str, graph_config: dict):
+    """
+    Start of the scraping pipeline
+    """
+    smart_scraper_graph = JSONScraperGraph(
+        prompt="List me all the titles",
+        source=sample_json,
+        config=graph_config
+    )
+
+    result = smart_scraper_graph.run()
+
+    assert result is not None

tests/graphs/scrape_xml_ollama_test.py

@@ -3,7 +3,7 @@
 """
 import os
 import pytest
-from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.graphs import XMLScraperGraph
 
 
 @pytest.fixture
@@ -45,7 +45,7 @@ def test_scraping_pipeline(sample_xml: str, graph_config: dict):
     """
     Start of the scraping pipeline
     """
-    smart_scraper_graph = SmartScraperGraph(
+    smart_scraper_graph = XMLScraperGraph(
         prompt="List me all the authors, title and genres of the books",
         source=sample_xml,
         config=graph_config

tests/graphs/script_generator_test.py

@@ -46,6 +46,4 @@ def test_script_creator_graph(graph_config: dict):
 
     assert graph_exec_info is not None
 
-    assert isinstance(graph_exec_info, dict)
-
     print(prettify_exec_info(graph_exec_info))

tests/nodes/fetch_node_test.py

@@ -17,6 +17,9 @@ def setup():
     robots_node = FetchNode(
         input="url | local_dir",
         output=["doc"],
+        node_config={
+            "headless": False
+        }
     )
 
     return robots_node

tests/nodes/robot_node_test.py

@@ -32,7 +32,9 @@ def setup():
     robots_node = RobotsNode(
         input="url",
         output=["is_scrapable"],
-        node_config={"llm": llm_model}
+        node_config={"llm": llm_model,
+                     "headless": False
+                     }
     )
 
     return robots_node