Merge pull request #689 from ScrapeGraphAI/687-smartscrapermulticoncatgraph-error-with-bedrock

687 smartscrapermulticoncatgraph error with bedrock

Author: VinciGit00

scrapegraphai/graphs/smart_scraper_multi_concat_graph.py

@@ -60,19 +60,14 @@ def _create_graph(self) -> BaseGraph:
             BaseGraph: A graph instance representing the web scraping and searching workflow.
         """
 
-        smart_scraper_instance = SmartScraperGraph(
-            prompt="",
-            source="",
-            config=self.copy_config,
-            schema=self.copy_schema
-        )
-
         graph_iterator_node = GraphIteratorNode(
             input="user_prompt & urls",
             output=["results"],
             node_config={
-                "graph_instance": smart_scraper_instance,
-            }
+                "graph_instance": SmartScraperGraph,
+                "scraper_config": self.copy_config,
+            },
+            schema=self.copy_schema,
         )
 
         concat_answers_node = ConcatAnswersNode(

scrapegraphai/nodes/generate_answer_node.py

@@ -1,6 +1,3 @@
-"""
-GenerateAnswerNode Module
-"""
 from typing import List, Optional
 from langchain.prompts import PromptTemplate
 from langchain_core.output_parsers import JsonOutputParser
@@ -12,29 +9,12 @@
 from tqdm import tqdm
 from .base_node import BaseNode
 from ..utils.output_parser import get_structured_output_parser, get_pydantic_output_parser
-from ..prompts import (TEMPLATE_CHUNKS,
-                       TEMPLATE_NO_CHUNKS, TEMPLATE_MERGE,
-                       TEMPLATE_CHUNKS_MD, TEMPLATE_NO_CHUNKS_MD,
-                       TEMPLATE_MERGE_MD)
+from ..prompts import (
+    TEMPLATE_CHUNKS, TEMPLATE_NO_CHUNKS, TEMPLATE_MERGE,
+    TEMPLATE_CHUNKS_MD, TEMPLATE_NO_CHUNKS_MD, TEMPLATE_MERGE_MD
+)
 
 class GenerateAnswerNode(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,
@@ -43,121 +23,102 @@ def __init__(
         node_name: str = "GenerateAnswer",
     ):
         super().__init__(node_name, "node", input, output, 2, node_config)
-
         self.llm_model = node_config["llm_model"]
 
         if isinstance(node_config["llm_model"], ChatOllama):
-            self.llm_model.format="json"
-
-        self.verbose = (
-            True if node_config is None else node_config.get("verbose", False)
-        )
-        self.force = (
-            False if node_config is None else node_config.get("force", False)
-        )
-        self.script_creator = (
-            False if node_config is None else node_config.get("script_creator", False)
-        )
-        self.is_md_scraper = (
-            False if node_config is None else node_config.get("is_md_scraper", False)
-        )
+            self.llm_model.format = "json"
 
+        self.verbose = node_config.get("verbose", False)
+        self.force = node_config.get("force", False)
+        self.script_creator = node_config.get("script_creator", False)
+        self.is_md_scraper = node_config.get("is_md_scraper", False)
         self.additional_info = node_config.get("additional_info")
 
     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.
-        """
-
         self.logger.info(f"--- Executing {self.node_name} Node ---")
 
-        input_keys = self.get_input_keys(state)        
+        input_keys = self.get_input_keys(state)
         input_data = [state[key] for key in input_keys]
         user_prompt = input_data[0]
         doc = input_data[1]
 
         if self.node_config.get("schema", None) is not None:
-
             if isinstance(self.llm_model, (ChatOpenAI, ChatMistralAI)):
                 self.llm_model = self.llm_model.with_structured_output(
-                    schema = self.node_config["schema"])
+                    schema=self.node_config["schema"]
+                )
                 output_parser = get_structured_output_parser(self.node_config["schema"])
                 format_instructions = "NA"
             else:
                 if not isinstance(self.llm_model, ChatBedrock):
                     output_parser = get_pydantic_output_parser(self.node_config["schema"])
                     format_instructions = output_parser.get_format_instructions()
-
+                else:
+                    output_parser = None
+                    format_instructions = ""
         else:
             if not isinstance(self.llm_model, ChatBedrock):
                 output_parser = JsonOutputParser()
                 format_instructions = output_parser.get_format_instructions()
+            else:
+                output_parser = None
+                format_instructions = ""
 
         if isinstance(self.llm_model, (ChatOpenAI, AzureChatOpenAI)) \
             and not self.script_creator \
             or self.force \
             and not self.script_creator or self.is_md_scraper:
-
-            template_no_chunks_prompt  = TEMPLATE_NO_CHUNKS_MD
-            template_chunks_prompt  = TEMPLATE_CHUNKS_MD
-            template_merge_prompt  = TEMPLATE_MERGE_MD
+            template_no_chunks_prompt = TEMPLATE_NO_CHUNKS_MD
+            template_chunks_prompt = TEMPLATE_CHUNKS_MD
+            template_merge_prompt = TEMPLATE_MERGE_MD
         else:
-            template_no_chunks_prompt  = TEMPLATE_NO_CHUNKS
-            template_chunks_prompt  = TEMPLATE_CHUNKS
-            template_merge_prompt  = TEMPLATE_MERGE
+            template_no_chunks_prompt = TEMPLATE_NO_CHUNKS
+            template_chunks_prompt = TEMPLATE_CHUNKS
+            template_merge_prompt = TEMPLATE_MERGE
 
         if self.additional_info is not None:
-            template_no_chunks_prompt  = self.additional_info + template_no_chunks_prompt
-            template_chunks_prompt  = self.additional_info + template_chunks_prompt
-            template_merge_prompt  = self.additional_info + template_merge_prompt
+            template_no_chunks_prompt = self.additional_info + template_no_chunks_prompt
+            template_chunks_prompt = self.additional_info + template_chunks_prompt
+            template_merge_prompt = self.additional_info + template_merge_prompt
 
         if len(doc) == 1:
             prompt = PromptTemplate(
-                template=template_no_chunks_prompt ,
+                template=template_no_chunks_prompt,
                 input_variables=["question"],
-                partial_variables={"context": doc,
-                                    "format_instructions": format_instructions})
-            chain =  prompt | self.llm_model | output_parser
+                partial_variables={"context": doc, "format_instructions": format_instructions}
+            )
+            chain = prompt | self.llm_model
+            if output_parser:
+                chain = chain | output_parser
             answer = chain.invoke({"question": user_prompt})
 
             state.update({self.output[0]: answer})
             return state
 
         chains_dict = {}
         for i, chunk in enumerate(tqdm(doc, desc="Processing chunks", disable=not self.verbose)):
-
             prompt = PromptTemplate(
-                template=TEMPLATE_CHUNKS,
+                template=template_chunks_prompt,
                 input_variables=["question"],
-                partial_variables={"context": chunk,
-                                "chunk_id": i + 1,
-                                "format_instructions": format_instructions})
+                partial_variables={"context": chunk, "chunk_id": i + 1, "format_instructions": format_instructions}
+            )
             chain_name = f"chunk{i+1}"
-            chains_dict[chain_name] = prompt | self.llm_model | output_parser
+            chains_dict[chain_name] = prompt | self.llm_model
+            if output_parser:
+                chains_dict[chain_name] = chains_dict[chain_name] | output_parser
 
         async_runner = RunnableParallel(**chains_dict)
-
-        batch_results =  async_runner.invoke({"question": user_prompt})
+        batch_results = async_runner.invoke({"question": user_prompt})
 
         merge_prompt = PromptTemplate(
-                template = template_merge_prompt ,
-                input_variables=["context", "question"],
-                partial_variables={"format_instructions": format_instructions},
-            )
+            template=template_merge_prompt,
+            input_variables=["context", "question"],
+            partial_variables={"format_instructions": format_instructions}
+        )
 
-        merge_chain = merge_prompt | self.llm_model | output_parser
+        merge_chain = merge_prompt | self.llm_model
+        if output_parser:
+            merge_chain = merge_chain | output_parser
         answer = merge_chain.invoke({"context": batch_results, "question": user_prompt})
 
         state.update({self.output[0]: answer})

scrapegraphai/nodes/graph_iterator_node.py

@@ -130,7 +130,7 @@ async def _async_run(graph):
             if url.startswith("http"):
                 graph.input_key = "url"
             participants.append(graph)
-        
+
         futures = [_async_run(graph) for graph in participants]
 
         answers = await tqdm.gather(

scrapegraphai/utils/research_web.py

@@ -60,7 +60,9 @@ def search_on_web(query: str, search_engine: str = "Google",
 
     elif search_engine.lower() == "searxng":
         url = f"http://localhost:{port}"
-        params = {"q": query, "format": "json"}
+        params = {"q": query,
+                  "format": "json",
+                  "engines": "google,duckduckgo,brave,qwant,bing"}
 
         response = requests.get(url, params=params)