fix: bugs

pyproject.toml

@@ -60,6 +60,7 @@ keywords = [
     "web scraping tool",
     "webscraping",
     "graph",
+    "llm"
 ]
 classifiers = [
     "Intended Audience :: Developers",

scrapegraphai/nodes/generate_answer_csv_node.py

@@ -60,7 +60,7 @@ class GenerateAnswerCSVNode(BaseNode):
 
         self.additional_info = node_config.get("additional_info")
 
-    def execute(self, state):
+    async def execute(self, state):
         """
         Generates an answer by constructing a prompt from the user's input and the scraped
         content, querying the language model, and parsing its response.
@@ -157,7 +157,7 @@ class GenerateAnswerCSVNode(BaseNode):
             )
 
         merge_chain = merge_prompt | self.llm_model | output_parser
-        answer = merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
+        answer = await merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
 
         state.update({self.output[0]: answer})
         return state

scrapegraphai/nodes/generate_answer_node.py

@@ -1,3 +1,6 @@
+"""
+GenerateAnswerNode Module
+"""
 from typing import List, Optional
 from langchain.prompts import PromptTemplate
 from langchain_core.output_parsers import JsonOutputParser
@@ -15,6 +18,26 @@ from ..prompts import (
 )
 
 class GenerateAnswerNode(BaseNode):
+    """
+    Initializes the GenerateAnswerNode class.
+
+    Args:
+        input (str): The input data type for the node.
+        output (List[str]): The output data type(s) for the node.
+        node_config (Optional[dict]): Configuration dictionary for the node,
+        which includes the LLM model, verbosity, schema, and other settings.
+        Defaults to None.
+        node_name (str): The name of the node. Defaults to "GenerateAnswer".
+
+    Attributes:
+        llm_model: The language model specified in the node configuration.
+        verbose (bool): Whether verbose mode is enabled.
+        force (bool): Whether to force certain behaviors, overriding defaults.
+        script_creator (bool): Whether the node is in script creation mode.
+        is_md_scraper (bool): Whether the node is scraping markdown data.
+        additional_info (Optional[str]): Any additional information to be
+        included in the prompt templates.
+    """
     def __init__(
         self,
         input: str,
@@ -34,7 +57,17 @@ class GenerateAnswerNode(BaseNode):
         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:
+    async def execute(self, state: dict) -> dict:
+        """
+        Executes the GenerateAnswerNode.
+
+        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.
+        """
         self.logger.info(f"--- Executing {self.node_name} Node ---")
 
         input_keys = self.get_input_keys(state)
@@ -90,7 +123,7 @@ class GenerateAnswerNode(BaseNode):
             chain = prompt | self.llm_model
             if output_parser:
                 chain = chain | output_parser
-            answer = chain.ainvoke({"question": user_prompt})
+            answer = await chain.ainvoke({"question": user_prompt})
 
             state.update({self.output[0]: answer})
             return state
@@ -110,7 +143,7 @@ class GenerateAnswerNode(BaseNode):
                 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 = await async_runner.ainvoke({"question": user_prompt})
 
         merge_prompt = PromptTemplate(
             template=template_merge_prompt,
@@ -121,7 +154,7 @@ class GenerateAnswerNode(BaseNode):
         merge_chain = merge_prompt | self.llm_model
         if output_parser:
             merge_chain = merge_chain | output_parser
-        answer = merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
+        answer = await merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
 
         state.update({self.output[0]: answer})
         return state