fix: async invocation

scrapegraphai/builders/graph_builder.py

@@ -119,7 +119,7 @@ class GraphBuilder:
         Returns:
             dict: A JSON representation of the graph configuration.
         """
-        return self.chain.ainvoke(self.prompt)
+        return self.chain.invoke(self.prompt)
 
     @staticmethod
     def convert_json_to_graphviz(json_data, format: str = 'pdf'):

scrapegraphai/nodes/generate_answer_csv_node.py

@@ -126,7 +126,7 @@ class GenerateAnswerCSVNode(BaseNode):
             )
 
             chain =  prompt | self.llm_model | output_parser
-            answer = chain.ainvoke({"question": user_prompt})
+            answer = chain.invoke({"question": user_prompt})
             state.update({self.output[0]: answer})
             return state
 

scrapegraphai/nodes/generate_answer_node_k_level.py

@@ -143,7 +143,7 @@ class GenerateAnswerNodeKLevel(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 = merge_chain.invoke({"context": batch_results, "question": user_prompt})
 
         state["answer"] = answer
 

scrapegraphai/nodes/generate_answer_omni_node.py

@@ -117,7 +117,7 @@ class GenerateAnswerOmniNode(BaseNode):
             )
 
             chain =  prompt | self.llm_model | output_parser
-            answer = chain.ainvoke({"question": user_prompt})
+            answer = chain.invoke({"question": user_prompt})
 
             state.update({self.output[0]: answer})
             return state
@@ -149,7 +149,7 @@ class GenerateAnswerOmniNode(BaseNode):
             )
 
         merge_chain = merge_prompt | self.llm_model | output_parser
-        answer = merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
+        answer = merge_chain.invoke({"context": batch_results, "question": user_prompt})
 
         state.update({self.output[0]: answer})
         return state

scrapegraphai/nodes/generate_code_node.py

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

scrapegraphai/nodes/generate_scraper_node.py

@@ -130,7 +130,7 @@ class GenerateScraperNode(BaseNode):
         )
         map_chain = prompt | self.llm_model | StrOutputParser()
 
-        answer = map_chain.ainvoke({"question": user_prompt})
+        answer = map_chain.invoke({"question": user_prompt})
 
         state.update({self.output[0]: answer})
         return state

scrapegraphai/nodes/html_analyzer_node.py

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

scrapegraphai/nodes/merge_answers_node.py

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

scrapegraphai/nodes/merge_generated_scripts_node.py

@@ -74,7 +74,7 @@ class MergeGeneratedScriptsNode(BaseNode):
         )
 
         merge_chain = prompt_template | self.llm_model | StrOutputParser()
-        answer = merge_chain.ainvoke({"user_prompt": user_prompt})
+        answer = merge_chain.invoke({"user_prompt": user_prompt})
 
         state.update({self.output[0]: answer})
         return state

scrapegraphai/nodes/prompt_refiner_node.py

@@ -96,7 +96,7 @@ class PromptRefinerNode(BaseNode):
         output_parser = StrOutputParser()
 
         chain =  prompt | self.llm_model | output_parser
-        refined_prompt = chain.ainvoke({})
+        refined_prompt = chain.invoke({})
 
         state.update({self.output[0]: refined_prompt})
         return state

scrapegraphai/nodes/reasoning_node.py

@@ -91,7 +91,7 @@ class ReasoningNode(BaseNode):
         output_parser = StrOutputParser()
 
         chain =  prompt | self.llm_model | output_parser
-        refined_prompt = chain.ainvoke({})
+        refined_prompt = chain.invoke({})
 
         state.update({self.output[0]: refined_prompt})
         return state

scrapegraphai/nodes/robots_node.py

@@ -108,7 +108,7 @@ class RobotsNode(BaseNode):
             )
 
             chain = prompt | self.llm_model | output_parser
-            is_scrapable = chain.ainvoke({"path": source})[0]
+            is_scrapable = chain.invoke({"path": source})[0]
 
             if "no" in is_scrapable:
                 self.logger.warning(

scrapegraphai/nodes/search_link_node.py

@@ -142,7 +142,7 @@ class SearchLinkNode(BaseNode):
                     input_variables=["content", "user_prompt"],
                 )
                 merge_chain = merge_prompt | self.llm_model | output_parser
-                answer = merge_chain.ainvoke(
+                answer = merge_chain.invoke(
                     {"content": chunk.page_content}
                 )
                 relevant_links += answer

scrapegraphai/utils/code_error_analysis.py

@@ -33,7 +33,7 @@ def syntax_focused_analysis(state: dict, llm_model) -> str:
     prompt = PromptTemplate(template=TEMPLATE_SYNTAX_ANALYSIS,
                             input_variables=["generated_code", "errors"])
     chain = prompt | llm_model | StrOutputParser()
-    return chain.ainvoke({
+    return chain.invoke({
         "generated_code": state["generated_code"],
         "errors": state["errors"]["syntax"]
     })
@@ -53,7 +53,7 @@ def execution_focused_analysis(state: dict, llm_model) -> str:
                             input_variables=["generated_code", "errors",
                                               "html_code", "html_analysis"])
     chain = prompt | llm_model | StrOutputParser()
-    return chain.ainvoke({
+    return chain.invoke({
         "generated_code": state["generated_code"],
         "errors": state["errors"]["execution"],
         "html_code": state["html_code"],
@@ -76,7 +76,7 @@ def validation_focused_analysis(state: dict, llm_model) -> str:
                             input_variables=["generated_code", "errors", 
                                              "json_schema", "execution_result"])
     chain = prompt | llm_model | StrOutputParser()
-    return chain.ainvoke({
+    return chain.invoke({
         "generated_code": state["generated_code"],
         "errors": state["errors"]["validation"],
         "json_schema": state["json_schema"],
@@ -100,7 +100,7 @@ def semantic_focused_analysis(state: dict, comparison_result: Dict[str, Any], ll
                             input_variables=["generated_code", 
                                              "differences", "explanation"])
     chain = prompt | llm_model | StrOutputParser()
-    return chain.ainvoke({
+    return chain.invoke({
         "generated_code": state["generated_code"],
         "differences": json.dumps(comparison_result["differences"], indent=2),
         "explanation": comparison_result["explanation"]

scrapegraphai/utils/code_error_correction.py

@@ -32,7 +32,7 @@ def syntax_focused_code_generation(state: dict, analysis: str, llm_model) -> str
     prompt = PromptTemplate(template=TEMPLATE_SYNTAX_CODE_GENERATION,
                             input_variables=["analysis", "generated_code"])
     chain = prompt | llm_model | StrOutputParser()
-    return chain.ainvoke({
+    return chain.invoke({
         "analysis": analysis,
         "generated_code": state["generated_code"]
     })
@@ -52,7 +52,7 @@ def execution_focused_code_generation(state: dict, analysis: str, llm_model) ->
     prompt = PromptTemplate(template=TEMPLATE_EXECUTION_CODE_GENERATION,
                             input_variables=["analysis", "generated_code"])
     chain = prompt | llm_model | StrOutputParser()
-    return chain.ainvoke({
+    return chain.invoke({
         "analysis": analysis,
         "generated_code": state["generated_code"]
     })
@@ -72,7 +72,7 @@ def validation_focused_code_generation(state: dict, analysis: str, llm_model) ->
     prompt = PromptTemplate(template=TEMPLATE_VALIDATION_CODE_GENERATION,
                             input_variables=["analysis", "generated_code", "json_schema"])
     chain = prompt | llm_model | StrOutputParser()
-    return chain.ainvoke({
+    return chain.invoke({
         "analysis": analysis,
         "generated_code": state["generated_code"],
         "json_schema": state["json_schema"]
@@ -93,7 +93,7 @@ def semantic_focused_code_generation(state: dict, analysis: str, llm_model) -> s
     prompt = PromptTemplate(template=TEMPLATE_SEMANTIC_CODE_GENERATION,
                             input_variables=["analysis", "generated_code", "generated_result", "reference_result"])
     chain = prompt | llm_model | StrOutputParser()
-    return chain.ainvoke({
+    return chain.invoke({
         "analysis": analysis,
         "generated_code": state["generated_code"],
         "generated_result": json.dumps(state["execution_result"], indent=2),