feat: async invocation

Author: VinciGit00

scrapegraphai/builders/graph_builder.py

@@ -120,7 +120,7 @@ def build_graph(self):
         Returns:
             dict: A JSON representation of the graph configuration.
         """
-        return self.chain.invoke(self.prompt)
+        return self.chain.ainvoke(self.prompt)
 
     @staticmethod
     def convert_json_to_graphviz(json_data, format: str = 'pdf'):

scrapegraphai/nodes/generate_answer_csv_node.py

@@ -126,7 +126,7 @@ def execute(self, state):
             )
 
             chain =  prompt | self.llm_model | output_parser
-            answer = chain.invoke({"question": user_prompt})
+            answer = chain.ainvoke({"question": user_prompt})
             state.update({self.output[0]: answer})
             return state
 
@@ -157,7 +157,7 @@ def execute(self, state):
             )
 
         merge_chain = merge_prompt | self.llm_model | output_parser
-        answer = merge_chain.invoke({"context": batch_results, "question": user_prompt})
+        answer = merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
 
         state.update({self.output[0]: answer})
         return state

scrapegraphai/nodes/generate_answer_node.py

@@ -1,6 +1,3 @@
-"""
-generate_answer_node module
-"""
 from typing import List, Optional
 from langchain.prompts import PromptTemplate
 from langchain_core.output_parsers import JsonOutputParser
@@ -18,26 +15,6 @@
 )
 
 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,
@@ -113,7 +90,7 @@ def execute(self, state: dict) -> dict:
             chain = prompt | self.llm_model
             if output_parser:
                 chain = chain | output_parser
-            answer = chain.invoke({"question": user_prompt})
+            answer = chain.ainvoke({"question": user_prompt})
 
             state.update({self.output[0]: answer})
             return state
@@ -144,7 +121,7 @@ def execute(self, state: dict) -> dict:
         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})
+        answer = merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
 
         state.update({self.output[0]: answer})
         return state

scrapegraphai/nodes/generate_answer_node_k_level.py

@@ -143,7 +143,7 @@ def execute(self, state: dict) -> dict:
         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})
+        answer = merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
 
         state["answer"] = answer
 

scrapegraphai/nodes/generate_answer_omni_node.py

@@ -121,7 +121,7 @@ def execute(self, state: dict) -> dict:
             )
 
             chain =  prompt | self.llm_model | output_parser
-            answer = chain.invoke({"question": user_prompt})
+            answer = chain.ainvoke({"question": user_prompt})
 
             state.update({self.output[0]: answer})
             return state
@@ -154,7 +154,7 @@ def execute(self, state: dict) -> dict:
             )
 
         merge_chain = merge_prompt | self.llm_model | output_parser
-        answer = merge_chain.invoke({"context": batch_results, "question": user_prompt})
+        answer = merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
 
         state.update({self.output[0]: answer})
         return state

scrapegraphai/nodes/generate_answer_pdf_node.py

@@ -128,7 +128,7 @@ def execute(self, state):
                 },
             )
             chain =  prompt | self.llm_model | output_parser
-            answer = chain.invoke({"question": user_prompt})
+            answer = chain.ainvoke({"question": user_prompt})
 
 
             state.update({self.output[0]: answer})
@@ -162,7 +162,7 @@ def execute(self, state):
             )
 
         merge_chain = merge_prompt | self.llm_model | output_parser
-        answer = merge_chain.invoke({"context": batch_results, "question": user_prompt})
+        answer = merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
 
         state.update({self.output[0]: answer})
         return state

scrapegraphai/nodes/generate_code_node.py

@@ -325,7 +325,7 @@ def generate_initial_code(self, state: dict) -> str:
         output_parser = StrOutputParser()
 
         chain =  prompt | self.llm_model | output_parser
-        generated_code = chain.invoke({})
+        generated_code = chain.ainvoke({})
         return generated_code
 
     def semantic_comparison(self, generated_result: Any, reference_result: Any) -> Dict[str, Any]:
@@ -368,7 +368,7 @@ def semantic_comparison(self, generated_result: Any, reference_result: Any) -> D
         )
 
         chain = prompt | self.llm_model | output_parser
-        return chain.invoke({
+        return chain.ainvoke({
             "generated_result": json.dumps(generated_result, indent=2),
             "reference_result": json.dumps(reference_result_dict, indent=2)
         })

scrapegraphai/nodes/generate_scraper_node.py

@@ -131,7 +131,7 @@ def execute(self, state: dict) -> dict:
         )
         map_chain = prompt | self.llm_model | StrOutputParser()
 
-        answer = map_chain.invoke({"question": user_prompt})
+        answer = map_chain.ainvoke({"question": user_prompt})
 
         state.update({self.output[0]: answer})
         return state

scrapegraphai/nodes/html_analyzer_node.py

@@ -93,7 +93,7 @@ def execute(self, state: dict) -> dict:
         output_parser = StrOutputParser()
 
         chain =  prompt | self.llm_model | output_parser
-        html_analysis = chain.invoke({})
+        html_analysis = chain.ainvoke({})
 
         state.update({self.output[0]: html_analysis, self.output[1]: reduced_html})
         return state

scrapegraphai/nodes/merge_answers_node.py

@@ -95,7 +95,7 @@ def execute(self, state: dict) -> dict:
         )
 
         merge_chain = prompt_template | self.llm_model | output_parser
-        answer = merge_chain.invoke({"user_prompt": user_prompt})
+        answer = merge_chain.ainvoke({"user_prompt": user_prompt})
         answer["sources"] = state.get("urls", [])
 
         state.update({self.output[0]: answer})