Merge branch '731-fetchnode-object-has-no-attribute-update_state' into pre/beta

Author: VinciGit00

examples/openai/md_scraper_openai.py

@@ -37,7 +37,7 @@
 # ************************************************
 
 md_scraper_graph = DocumentScraperGraph(
-    prompt="List me all the authors, title and genres of the books",
+    prompt="List me all the projects",
     source=text,  # Pass the content of the file, not the file object
     config=graph_config
 )

scrapegraphai/builders/graph_builder.py

@@ -119,7 +119,7 @@ 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

@@ -60,7 +60,7 @@ def __init__(
 
         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.
@@ -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 = await merge_chain.ainvoke({"context": batch_results, "question": user_prompt})
 
         state.update({self.output[0]: answer})
         return state

scrapegraphai/nodes/generate_answer_node.py

@@ -1,5 +1,5 @@
 """
-generate_answer_node module
+GenerateAnswerNode Module
 """
 from typing import List, Optional
 from langchain.prompts import PromptTemplate
@@ -19,24 +19,24 @@
 
 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.
+    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,
@@ -57,7 +57,17 @@ def __init__(
         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)
@@ -113,7 +123,7 @@ 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 = await chain.ainvoke({"question": user_prompt})
 
             state.update({self.output[0]: answer})
             return state
@@ -133,7 +143,7 @@ def execute(self, state: dict) -> dict:
                 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,
@@ -144,7 +154,7 @@ 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 = await 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

@@ -117,7 +117,7 @@ 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
@@ -149,7 +149,7 @@ 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_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

@@ -130,7 +130,7 @@ 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})