feat(node): multiple url search in SearchGraph + fixes

Implemented GraphIteratorNode and MergeAnswersNode to create multiple istances of a graph and merge the scraped content from multiple pages

examples/openai/search_graph_multi.py

@@ -45,6 +45,7 @@ search_internet_node = SearchInternetNode(
     output=["urls"],
     node_config={
         "llm_model": llm_model,
+        "max_results": 5,  # num of search results to fetch
         "verbose": True,
     }
 )

examples/openai/search_graph_openai.py

@@ -19,6 +19,8 @@ graph_config = {
         "api_key": openai_key,
         "model": "gpt-3.5-turbo",
     },
+    "max_results": 5,
+    "verbose": True,
 }
 
 # ************************************************
@@ -26,7 +28,7 @@ graph_config = {
 # ************************************************
 
 search_graph = SearchGraph(
-    prompt="List me top 5 eyeliner products for a gift.",
+    prompt="List me the best escursions near Trento",
     config=graph_config
 )
 

scrapegraphai/graphs/search_graph.py

@@ -5,12 +5,11 @@ SearchGraph Module
 from .base_graph import BaseGraph
 from ..nodes import (
     SearchInternetNode,
-    FetchNode,
+    GraphIteratorNode,
-    ParseNode,
+    MergeAnswersNode
-    RAGNode,
-    GenerateAnswerNode
 )
 from .abstract_graph import AbstractGraph
+from .smart_scraper_graph import SmartScraperGraph
 
 
 class SearchGraph(AbstractGraph):
@@ -38,6 +37,11 @@ class SearchGraph(AbstractGraph):
         >>> result = search_graph.run()
     """
 
+    def __init__(self, prompt: str, config: dict):
+
+        self.max_results = config.get("max_results", 3)
+        super().__init__(prompt, config)
+
     def _create_graph(self) -> BaseGraph:
         """
         Creates the graph of nodes representing the workflow for web scraping and searching.
@@ -46,53 +50,53 @@ class SearchGraph(AbstractGraph):
             BaseGraph: A graph instance representing the web scraping and searching workflow.
         """
 
+        # ************************************************
+        # Create a SmartScraperGraph instance
+        # ************************************************
+
+        smart_scraper_instance = SmartScraperGraph(
+            prompt="",
+            source="",
+            config=self.config
+        )
+
+        # ************************************************
+        # Define the graph nodes
+        # ************************************************
+
         search_internet_node = SearchInternetNode(
             input="user_prompt",
-            output=["url"],
+            output=["urls"],
-            node_config={
-                "llm_model": self.llm_model
-            }
-        )
-        fetch_node = FetchNode(
-            input="url | local_dir",
-            output=["doc"]
-        )
-        parse_node = ParseNode(
-            input="doc",
-            output=["parsed_doc"],
-            node_config={
-                "chunk_size": self.model_token
-            }
-        )
-        rag_node = RAGNode(
-            input="user_prompt & (parsed_doc | doc)",
-            output=["relevant_chunks"],
             node_config={
                 "llm_model": self.llm_model,
-                "embedder_model": self.embedder_model
+                "max_results": self.max_results
             }
         )
-        generate_answer_node = GenerateAnswerNode(
+        graph_iterator_node = GraphIteratorNode(
-            input="user_prompt & (relevant_chunks | parsed_doc | doc)",
+            input="user_prompt & urls",
+            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
+                "llm_model": self.llm_model,
             }
         )
 
         return BaseGraph(
             nodes=[
                 search_internet_node,
-                fetch_node,
+                graph_iterator_node,
-                parse_node,
+                merge_answers_node
-                rag_node,
-                generate_answer_node,
             ],
             edges=[
-                (search_internet_node, fetch_node),
+                (search_internet_node, graph_iterator_node),
-                (fetch_node, parse_node),
+                (graph_iterator_node, merge_answers_node)
-                (parse_node, rag_node),
-                (rag_node, generate_answer_node)
             ],
             entry_point=search_internet_node
         )

scrapegraphai/nodes/graph_iterator_node.py

@@ -10,11 +10,8 @@ from .base_node import BaseNode
 
 class GraphIteratorNode(BaseNode):
     """
-    A node responsible for parsing HTML content from a document. 
+    A node responsible for instantiating and running multiple graph instances in parallel.
-    The parsed content is split into chunks for further processing.
+    It creates as many graph instances as the number of elements in the input list.
-
-    This node enhances the scraping workflow by allowing for targeted extraction of 
-    content, thereby optimizing the processing of large HTML documents.
 
     Attributes:
         verbose (bool): A flag indicating whether to show print statements during execution.
@@ -33,18 +30,18 @@ class GraphIteratorNode(BaseNode):
 
     def execute(self,  state: dict) -> dict:
         """
-        Executes the node's logic to parse the HTML document content and split it into chunks.
+        Executes the node's logic to instantiate and run multiple graph instances in parallel.
 
         Args:
-            state (dict): The current state of the graph. The input keys will be used to fetch the
+            state (dict): The current state of the graph. The input keys will be used to fetch
-                            correct data from the state.
+                            the correct data from the state.
 
         Returns:
-            dict: The updated state with the output key containing the parsed content chunks.
+            dict: The updated state with the output key containing the results of the graph instances.
 
         Raises:
             KeyError: If the input keys are not found in the state, indicating that the
-                        necessary information for parsing the content is missing.
+                        necessary information for running the graph instances is missing.
         """
 
         if self.verbose:
@@ -79,5 +76,4 @@ class GraphIteratorNode(BaseNode):
             graphs_answers.append(result)
 
         state.update({self.output[0]: graphs_answers})
-
         return state

scrapegraphai/nodes/merge_answers_node.py

@@ -9,7 +9,6 @@ from tqdm import tqdm
 # Imports from Langchain
 from langchain.prompts import PromptTemplate
 from langchain_core.output_parsers import JsonOutputParser
-from langchain_core.runnables import RunnableParallel
 
 # Imports from the library
 from .base_node import BaseNode
@@ -17,10 +16,7 @@ from .base_node import BaseNode
 
 class MergeAnswersNode(BaseNode):
     """
-    A node that generates an answer using a large language model (LLM) based on the user's input
+    A node responsible for merging the answers from multiple graph instances into a single answer.
-    and the content extracted from a webpage. It constructs a prompt from the user's input
-    and the scraped content, feeds it to the LLM, and parses the LLM's response to produce
-    an answer.
 
     Attributes:
         llm_model: An instance of a language model client, configured for generating answers.
@@ -42,8 +38,7 @@ class MergeAnswersNode(BaseNode):
 
     def execute(self, state: dict) -> dict:
         """
-        Generates an answer by constructing a prompt from the user's input and the scraped
+        Executes the node's logic to merge the answers from multiple graph instances into a single answer.
-        content, querying the language model, and parsing its response.
 
         Args:
             state (dict): The current state of the graph. The input keys will be used