fix: update langchain imports for v1.0+ compatibility

pyproject.toml

@@ -11,6 +11,7 @@ authors = [
 
 dependencies = [
     "langchain>=1.2.0",
+    "langchain-classic>=1.0.0",
     "langchain-openai>=1.1.6",
     "langchain-mistralai>=1.1.1",
     "langchain_community>=0.3.31",
@@ -26,7 +27,7 @@ dependencies = [
     "playwright>=1.57.0",
     "undetected-playwright>=0.3.0",
     "semchunk>=3.2.5",
-    "async-timeout>=5.0.1",
+    "async-timeout>=4.0.0",
     "simpleeval>=1.0.3",
     "jsonschema>=4.25.1",
     "duckduckgo-search>=8.1.1",

scrapegraphai/builders/graph_builder.py

@@ -2,7 +2,7 @@
 GraphBuilder Module
 """
 
-from langchain.chains import create_extraction_chain
+from langchain_classic.chains import create_extraction_chain
 from langchain_community.chat_models import ErnieBotChat
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
@@ -113,7 +113,9 @@ def _create_extraction_chain(self):
         {nodes_description}
 
         Based on the user's input: "{input}", identify the essential nodes required for the task and suggest a graph configuration that outlines the flow between the chosen nodes.
-        """.format(nodes_description=self.nodes_description, input="{input}")
+        """.format(
+            nodes_description=self.nodes_description, input="{input}"
+        )
         extraction_prompt = ChatPromptTemplate.from_template(
             create_graph_prompt_template
         )

scrapegraphai/nodes/generate_answer_node_k_level.py

@@ -4,10 +4,10 @@
 
 from typing import List, Optional
 
-from langchain_core.prompts import PromptTemplate
 from langchain_aws import ChatBedrock
 from langchain_community.chat_models import ChatOllama
 from langchain_core.output_parsers import JsonOutputParser
+from langchain_core.prompts import PromptTemplate
 from langchain_core.runnables import RunnableParallel
 from langchain_mistralai import ChatMistralAI
 from langchain_openai import ChatOpenAI
@@ -151,7 +151,7 @@ def execute(self, state: dict) -> dict:
                 template=template_chunks_prompt,
                 input_variables=["format_instructions"],
                 partial_variables={
-                    "context": chunk.get("document"),
+                    "content": chunk.get("document"),
                     "chunk_id": i + 1,
                 },
             )
@@ -163,14 +163,14 @@ def execute(self, state: dict) -> dict:
 
         merge_prompt = PromptTemplate(
             template=template_merge_prompt,
-            input_variables=["context", "question"],
+            input_variables=["content", "question"],
             partial_variables={"format_instructions": format_instructions},
         )
 
         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.invoke({"content": batch_results, "question": user_prompt})
 
         state["answer"] = answer
 

scrapegraphai/nodes/generate_code_node.py

@@ -12,10 +12,10 @@
 from bs4 import BeautifulSoup
 from jsonschema import ValidationError as JSONSchemaValidationError
 from jsonschema import validate
-from langchain_core.output_parsers import ResponseSchema, StructuredOutputParser
-from langchain_core.prompts import PromptTemplate
+from langchain_classic.output_parsers import ResponseSchema, StructuredOutputParser
 from langchain_community.chat_models import ChatOllama
 from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
 
 from ..prompts import TEMPLATE_INIT_CODE_GENERATION, TEMPLATE_SEMANTIC_COMPARISON
 from ..utils import (