feat: multiple graph instances

examples/openai/search_graph_multi.py

@@ -6,8 +6,8 @@ import os
 from dotenv import load_dotenv
 from langchain_openai import OpenAIEmbeddings
 from scrapegraphai.models import OpenAI
-from scrapegraphai.graphs import BaseGraph
+from scrapegraphai.graphs import BaseGraph, SmartScraperGraph
-from scrapegraphai.nodes import FetchNode, ParseNode, RAGNode, GenerateAnswerNode, SearchInternetNode
+from scrapegraphai.nodes import SearchInternetNode, GraphIteratorNode, MergeAnswersNode
 load_dotenv()
 
 # ************************************************
@@ -23,6 +23,16 @@ graph_config = {
     },
 }
 
+# ************************************************
+# Create a SmartScraperGraph instance
+# ************************************************
+
+smart_scraper_graph = SmartScraperGraph(
+    prompt="",
+    source="",
+    config=graph_config
+)
+
 # ************************************************
 # Define the graph nodes
 # ************************************************
@@ -32,38 +42,24 @@ embedder = OpenAIEmbeddings(api_key=llm_model.openai_api_key)
 
 search_internet_node = SearchInternetNode(
     input="user_prompt",
-    output=["url"],
+    output=["urls"],
-    node_config={
-        "llm_model": llm_model
-    }
-)
-fetch_node = FetchNode(
-    input="url | local_dir",
-    output=["doc"],
-    node_config={
-        "verbose": True,
-        "headless": True,
-    }
-)
-parse_node = ParseNode(
-    input="doc",
-    output=["parsed_doc"],
-    node_config={
-        "chunk_size": 4096,
-        "verbose": True,
-    }
-)
-rag_node = RAGNode(
-    input="user_prompt & (parsed_doc | doc)",
-    output=["relevant_chunks"],
     node_config={
         "llm_model": llm_model,
-        "embedder_model": embedder,
         "verbose": True,
     }
 )
-generate_answer_node = GenerateAnswerNode(
+
-    input="user_prompt & (relevant_chunks | parsed_doc | doc)",
+graph_iterator_node = GraphIteratorNode(
+    input="user_prompt & urls",
+    output=["results"],
+    node_config={
+        "graph_instance": smart_scraper_graph,
+        "verbose": True,
+    }
+)
+
+merge_answers_node = MergeAnswersNode(
+    input="user_prompt & results",
     output=["answer"],
     node_config={
         "llm_model": llm_model,
@@ -78,16 +74,12 @@ generate_answer_node = GenerateAnswerNode(
 graph = 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/__init__.py

@@ -17,3 +17,5 @@ from .search_link_node import SearchLinkNode
 from .robots_node import RobotsNode
 from .generate_answer_csv_node import GenerateAnswerCSVNode
 from .generate_answer_pdf_node import GenerateAnswerPDFNode
+from .graph_iterator_node import GraphIteratorNode
+from .merge_answers_node import MergeAnswersNode

scrapegraphai/nodes/generate_answer_csv_node.py

@@ -2,7 +2,7 @@
 Module for generating the answer node
 """
 # Imports from standard library
-from typing import List
+from typing import List, Optional
 from tqdm import tqdm
 
 # Imports from Langchain
@@ -39,7 +39,7 @@ class GenerateAnswerCSVNode(BaseNode):
                         updating the state with the generated answer under the 'answer' key.
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict] = None,
                  node_name: str = "GenerateAnswer"):
         """
         Initializes the GenerateAnswerNodeCsv with a language model client and a node name.

scrapegraphai/nodes/generate_answer_node.py

@@ -3,7 +3,7 @@ GenerateAnswerNode Module
 """
 
 # Imports from standard library
-from typing import List
+from typing import List, Optional
 from tqdm import tqdm
 
 # Imports from Langchain
@@ -33,7 +33,7 @@ class GenerateAnswerNode(BaseNode):
         node_name (str): The unique identifier name for the node, defaulting to "GenerateAnswer".
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None,
                  node_name: str = "GenerateAnswer"):
         super().__init__(node_name, "node", input, output, 2, node_config)
         

scrapegraphai/nodes/generate_answer_node_csv.py

@@ -2,7 +2,7 @@
 Module for generating the answer node
 """
 # Imports from standard library
-from typing import List
+from typing import List, Optional
 from tqdm import tqdm
 
 # Imports from Langchain
@@ -39,7 +39,7 @@ class GenerateAnswerCSVNode(BaseNode):
                         updating the state with the generated answer under the 'answer' key.
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict] = None,
                  node_name: str = "GenerateAnswer"):
         """
         Initializes the GenerateAnswerNodeCsv with a language model client and a node name.

scrapegraphai/nodes/generate_answer_pdf_node.py

@@ -2,7 +2,7 @@
 Module for generating the answer node
 """
 # Imports from standard library
-from typing import List
+from typing import List, Optional
 from tqdm import tqdm
 
 # Imports from Langchain
@@ -39,7 +39,7 @@ class GenerateAnswerPDFNode(BaseNode):
                         updating the state with the generated answer under the 'answer' key.
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict] = None,
                  node_name: str = "GenerateAnswer"):
         """
         Initializes the GenerateAnswerNodePDF with a language model client and a node name.

scrapegraphai/nodes/generate_scraper_node.py

@@ -3,7 +3,7 @@ GenerateScraperNode Module
 """
 
 # Imports from standard library
-from typing import List
+from typing import List, Optional
 from tqdm import tqdm
 
 # Imports from Langchain
@@ -36,8 +36,8 @@ class GenerateScraperNode(BaseNode):
 
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], library: str, website: str,
-                 library: str, website: str, node_name: str = "GenerateAnswer"):
+                 node_config: Optional[dict]=None, node_name: str = "GenerateAnswer"):
         super().__init__(node_name, "node", input, output, 2, node_config)
 
         self.llm_model = node_config["llm_model"]

scrapegraphai/nodes/get_probable_tags_node.py

@@ -2,7 +2,7 @@
 GetProbableTagsNode Module
 """
 
-from typing import List
+from typing import List, Optional
 from langchain.output_parsers import CommaSeparatedListOutputParser
 from langchain.prompts import PromptTemplate
 from .base_node import BaseNode

scrapegraphai/nodes/graph_iterator_node.py

@@ -0,0 +1,83 @@
+"""
+GraphIterator Module
+"""
+
+from typing import List, Optional
+import copy
+from tqdm import tqdm
+from .base_node import BaseNode
+
+
+class GraphIteratorNode(BaseNode):
+    """
+    A node responsible for parsing HTML content from a document. 
+    The parsed content is split into chunks for further processing.
+
+    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.
+
+    Args:
+        input (str): Boolean expression defining the input keys needed from the state.
+        output (List[str]): List of output keys to be updated in the state.
+        node_config (dict): Additional configuration for the node.
+        node_name (str): The unique identifier name for the node, defaulting to "Parse".
+    """
+
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None, node_name: str = "GraphIterator"):
+        super().__init__(node_name, "node", input, output, 2, node_config)
+
+        self.verbose = False if node_config is None else node_config.get("verbose", False)
+
+    def execute(self,  state: dict) -> dict:
+        """
+        Executes the node's logic to parse the HTML document content and split it into chunks.
+
+        Args:
+            state (dict): The current state of the graph. The input keys will be used to fetch the
+                            correct data from the state.
+
+        Returns:
+            dict: The updated state with the output key containing the parsed content chunks.
+
+        Raises:
+            KeyError: If the input keys are not found in the state, indicating that the
+                        necessary information for parsing the content is missing.
+        """
+
+        if self.verbose:
+            print(f"--- Executing {self.node_name} Node ---")
+
+        # Interpret input keys based on the provided input expression
+        input_keys = self.get_input_keys(state)
+
+        # Fetching data from the state based on the input keys
+        input_data = [state[key] for key in input_keys]
+
+        user_prompt = input_data[0]
+        urls = input_data[1]
+
+        graph_instance = self.node_config.get("graph_instance", None)
+        if graph_instance is None:
+            raise ValueError("Graph instance is required for graph iteration.")
+        
+        # set the prompt and source for each url
+        graph_instance.prompt = user_prompt
+        graphs_instances = []
+        for url in urls:
+            # make a copy of the graph instance
+            copy_graph_instance = copy.copy(graph_instance)
+            copy_graph_instance.source = url
+            graphs_instances.append(copy_graph_instance)
+
+        # run the graph for each url and use tqdm for progress bar
+        graphs_answers = []
+        for graph in tqdm(graphs_instances, desc="Processing Graph Instances", disable=not self.verbose):
+            result = graph.run()
+            graphs_answers.append(result)
+
+        state.update({self.output[0]: graphs_answers})
+
+        return state

scrapegraphai/nodes/image_to_text_node.py

@@ -2,7 +2,7 @@
 ImageToTextNode Module
 """
 
-from typing import List
+from typing import List, Optional
 from .base_node import BaseNode
 
 
@@ -21,7 +21,7 @@ class ImageToTextNode(BaseNode):
         node_name (str): The unique identifier name for the node, defaulting to "ImageToText".
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None,
                  node_name: str = "ImageToText"):
         super().__init__(node_name, "node", input, output, 1, node_config)
 

scrapegraphai/nodes/merge_answers_node.py

@@ -0,0 +1,104 @@
+"""
+MergeAnswersNode Module
+"""
+
+# Imports from standard library
+from typing import List, Optional
+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
+
+
+class MergeAnswersNode(BaseNode):
+    """
+    A node that generates an answer using a large language model (LLM) based on the user's input
+    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.
+        verbose (bool): A flag indicating whether to show print statements during execution.
+
+    Args:
+        input (str): Boolean expression defining the input keys needed from the state.
+        output (List[str]): List of output keys to be updated in the state.
+        node_config (dict): Additional configuration for the node.
+        node_name (str): The unique identifier name for the node, defaulting to "GenerateAnswer".
+    """
+
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None,
+                 node_name: str = "MergeAnswers"):
+        super().__init__(node_name, "node", input, output, 2, node_config)
+        
+        self.llm_model = node_config["llm_model"]
+        self.verbose = True if node_config is None else node_config.get("verbose", False)
+
+    def execute(self, state: dict) -> dict:
+        """
+        Generates an answer by constructing a prompt from the user's input and the scraped
+        content, querying the language model, and parsing its response.
+
+        Args:
+            state (dict): The current state of the graph. The input keys will be used
+                            to fetch the correct data from the state.
+
+        Returns:
+            dict: The updated state with the output key containing the generated answer.
+
+        Raises:
+            KeyError: If the input keys are not found in the state, indicating
+                      that the necessary information for generating an answer is missing.
+        """
+
+        if self.verbose:
+            print(f"--- Executing {self.node_name} Node ---")
+
+        # Interpret input keys based on the provided input expression
+        input_keys = self.get_input_keys(state)
+
+        # Fetching data from the state based on the input keys
+        input_data = [state[key] for key in input_keys]
+
+        user_prompt = input_data[0]
+        answers = input_data[1]
+        
+        # merge the answers in one string
+        answers_str = ""
+        for i, answer in enumerate(answers):
+            answers_str += f"CONTENT WEBSITE {i+1}: {answer}\n"
+    
+        output_parser = JsonOutputParser()
+        format_instructions = output_parser.get_format_instructions()
+
+        template_merge = """
+        You are a website scraper and you have just scraped some content from multiple websites.\n
+        You are now asked to provide an answer to a USER PROMPT based on the content you have scraped.\n
+        You need to merge the content from the different websites into a single answer without repetitions (if there are any). \n
+        The scraped contents are in a JSON format and you need to merge them based on the context and providing a correct JSON structure.\n
+        OUTPUT INSTRUCTIONS: {format_instructions}\n
+        USER PROMPT: {user_prompt}\n
+        {website_content}
+        """
+
+        prompt_template = PromptTemplate(
+            template=template_merge,
+            input_variables=["user_prompt"],
+            partial_variables={
+                "format_instructions": format_instructions,
+                "website_content": answers_str,
+            },
+        )
+
+        merge_chain = prompt_template | self.llm_model | output_parser
+        answer = merge_chain.invoke({"user_prompt": user_prompt})
+
+        # Update the state with the generated answer
+        state.update({self.output[0]: answer})
+        return state

scrapegraphai/nodes/parse_node.py

@@ -2,7 +2,7 @@
 ParseNode Module
 """
 
-from typing import List
+from typing import List, Optional
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.document_transformers import Html2TextTransformer
 from .base_node import BaseNode
@@ -26,7 +26,7 @@ class ParseNode(BaseNode):
         node_name (str): The unique identifier name for the node, defaulting to "Parse".
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict, node_name: str = "Parse"):
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None, node_name: str = "Parse"):
         super().__init__(node_name, "node", input, output, 1, node_config)
 
         self.verbose = True if node_config is None else node_config.get("verbose", False)

scrapegraphai/nodes/rag_node.py

@@ -2,7 +2,7 @@
 RAGNode Module
 """
 
-from typing import List
+from typing import List, Optional
 from langchain.docstore.document import Document
 from langchain.retrievers import ContextualCompressionRetriever
 from langchain.retrievers.document_compressors import EmbeddingsFilter, DocumentCompressorPipeline
@@ -31,7 +31,7 @@ class RAGNode(BaseNode):
         node_name (str): The unique identifier name for the node, defaulting to "Parse".
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict, node_name: str = "RAG"):
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None, node_name: str = "RAG"):
         super().__init__(node_name, "node", input, output, 2, node_config)
 
         self.llm_model = node_config["llm_model"]

scrapegraphai/nodes/search_internet_node.py

@@ -2,7 +2,7 @@
 SearchInternetNode Module
 """
 
-from typing import List
+from typing import List, Optional
 from langchain.output_parsers import CommaSeparatedListOutputParser
 from langchain.prompts import PromptTemplate
 from ..utils.research_web import search_on_web
@@ -27,12 +27,13 @@ class SearchInternetNode(BaseNode):
         node_name (str): The unique identifier name for the node, defaulting to "SearchInternet".
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None,
                  node_name: str = "SearchInternet"):
         super().__init__(node_name, "node", input, output, 1, node_config)
 
         self.llm_model = node_config["llm_model"]
         self.verbose = True if node_config is None else node_config.get("verbose", False)
+        self.max_results = node_config.get("max_results", 3)
 
     def execute(self, state: dict) -> dict:
         """
@@ -85,8 +86,11 @@ class SearchInternetNode(BaseNode):
         if self.verbose:
             print(f"Search Query: {search_query}")
             
-        # TODO: handle multiple URLs
+        answer = search_on_web(query=search_query, max_results=self.max_results)
-        answer = search_on_web(query=search_query, max_results=1)[0]
+
+        if len(answer) == 0:
+            # raise an exception if no answer is found
+            raise ValueError("Zero results found for the search query.")
 
         # Update the state with the generated answer
         state.update({self.output[0]: answer})

scrapegraphai/nodes/search_link_node.py

@@ -3,7 +3,7 @@ SearchLinkNode Module
 """
 
 # Imports from standard library
-from typing import List
+from typing import List, Optional
 from tqdm import tqdm
 from bs4 import BeautifulSoup
 
@@ -33,7 +33,7 @@ class SearchLinkNode(BaseNode):
         node_name (str): The unique identifier name for the node, defaulting to "GenerateAnswer".
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None,
                  node_name: str = "GenerateLinks"):
         super().__init__(node_name, "node", input, output, 1, node_config)
 

scrapegraphai/nodes/text_to_speech_node.py

@@ -2,7 +2,7 @@
 TextToSpeechNode Module
 """
 
-from typing import List
+from typing import List, Optional
 from .base_node import BaseNode
 
 
@@ -22,7 +22,7 @@ class TextToSpeechNode(BaseNode):
     """
 
     def __init__(self, input: str, output: List[str],
-                 node_config: dict, node_name: str = "TextToSpeech"):
+                 node_config: Optional[dict]=None, node_name: str = "TextToSpeech"):
         super().__init__(node_name, "node", input, output, 1, node_config)
 
         self.tts_model = node_config["tts_model"]