fix(AbstractGraph): model selection bug

scrapegraphai/graphs/abstract_graph.py

@@ -131,15 +131,15 @@ class AbstractGraph(ABC):
                         "ollama", "oneapi", "nvidia", "groq", "anthropic" "bedrock", "mistralai",
                         "hugging_face", "deepseek", "ernie", "fireworks"}
 
-        split_model_provider = llm_params["model"].split("/")
+        split_model_provider = llm_params["model"].split("/", 1)
         llm_params["model_provider"] = split_model_provider[0]
-        llm_params["model"] = split_model_provider[1:]
+        llm_params["model"] = split_model_provider[1]
 
         if llm_params["model_provider"] not in known_providers:
             raise ValueError(f"Provider {llm_params['model_provider']} is not supported. If possible, try to use a model instance instead.")
 
         try:
-            self.model_token = models_tokens[llm_params["model_provider"]].get(llm_params["model"][0])
+            self.model_token = models_tokens[llm_params["model_provider"]][llm_params["model"]]
         except KeyError:
             print("Model not found, using default token size (8192)")
             self.model_token = 8192
@@ -150,18 +150,21 @@ class AbstractGraph(ABC):
                     warnings.simplefilter("ignore")
                     return init_chat_model(**llm_params)
             else:
-                if "deepseek" in llm_params["model"]:
+                if llm_params["model_provider"] == "deepseek":
                     return DeepSeek(**llm_params)
 
-                if "ernie" in llm_params["model"]:
+                if llm_params["model_provider"] == "ernie":
                     from langchain_community.chat_models import ErnieBotChat
                     return ErnieBotChat(**llm_params)
 
-                if "oneapi" in llm_params["model"]:
+                if llm_params["model_provider"] == "oneapi":
                     return OneApi(**llm_params)
 
-                if "nvidia" in llm_params["model"]:
-                    from langchain_nvidia_ai_endpoints import ChatNVIDIA
+                if llm_params["model_provider"] == "nvidia":
+                    try:
+                        from langchain_nvidia_ai_endpoints import ChatNVIDIA
+                    except ImportError:
+                        raise ImportError("The langchain_nvidia_ai_endpoints module is not installed. Please install it using `pip install langchain_nvidia_ai_endpoints`.")
                     return ChatNVIDIA(**llm_params)
 
         except Exception as e:

tests/graphs/abstract_graph_test.py

@@ -3,29 +3,80 @@ Tests for the AbstractGraph.
 """
 import pytest
 from unittest.mock import patch
-from scrapegraphai.graphs import AbstractGraph
+from scrapegraphai.graphs import AbstractGraph, BaseGraph
+from scrapegraphai.nodes import (
+    FetchNode,
+    ParseNode
+)
+from scrapegraphai.models import OneApi, DeepSeek
+from langchain_openai import ChatOpenAI, AzureChatOpenAI
+from langchain_community.chat_models import ChatOllama
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+
+
+class TestGraph(AbstractGraph):
+    def __init__(self, prompt: str, config: dict):
+        super().__init__(prompt, config)
+
+    def _create_graph(self) -> BaseGraph:
+        fetch_node = FetchNode(
+            input="url| local_dir",
+            output=["doc", "link_urls", "img_urls"],
+            node_config={
+                "llm_model": self.llm_model,
+                "force": self.config.get("force", False),
+                "cut": self.config.get("cut", True),
+                "loader_kwargs": self.config.get("loader_kwargs", {}),
+                "browser_base": self.config.get("browser_base")
+            }
+        )
+        parse_node = ParseNode(
+            input="doc",
+            output=["parsed_doc"],
+            node_config={
+                "chunk_size": self.model_token
+            }
+        )
+        return BaseGraph(
+            nodes=[
+                fetch_node,
+                parse_node
+            ],
+            edges=[
+                (fetch_node, parse_node),
+            ],
+            entry_point=fetch_node,
+            graph_name=self.__class__.__name__
+        )
+
+    def run(self) -> str:
+        inputs = {"user_prompt": self.prompt, self.input_key: self.source}
+        self.final_state, self.execution_info = self.graph.execute(inputs)
+
+        return self.final_state.get("answer", "No answer found.")
+
 
 class TestAbstractGraph:
     @pytest.mark.parametrize("llm_config, expected_model", [
-        ({"model": "openai/gpt-3.5-turbo"}, "ChatOpenAI"),
-        ({"model": "azure_openai/gpt-3.5-turbo"}, "AzureChatOpenAI"),
-        ({"model": "google_genai/gemini-pro"}, "ChatGoogleGenerativeAI"),
-        ({"model": "google_vertexai/chat-bison"}, "ChatVertexAI"),
-        ({"model": "ollama/llama2"}, "Ollama"),
-        ({"model": "oneapi/text-davinci-003"}, "OneApi"),
-        ({"model": "nvidia/clara-instant-1-base"}, "ChatNVIDIA"),
-        ({"model": "deepseek/deepseek-coder-6.7b-instruct"}, "DeepSeek"),
-        ({"model": "ernie/ernie-bot"}, "ErnieBotChat"),
+        ({"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-randomtest001"}, ChatOpenAI),
+        ({
+            "model": "azure_openai/gpt-3.5-turbo",
+            "api_key": "random-api-key",
+            "api_version": "no version",
+            "azure_endpoint": "https://www.example.com/"},
+            AzureChatOpenAI),
+        ({"model": "google_genai/gemini-pro", "google_api_key": "google-key-test"}, ChatGoogleGenerativeAI),
+        ({"model": "ollama/llama2"}, ChatOllama),
+        ({"model": "oneapi/qwen-turbo"}, OneApi),
+        ({"model": "deepseek/deepseek-coder"}, DeepSeek),
     ])
+
     def test_create_llm(self, llm_config, expected_model):
-        graph = AbstractGraph("Test prompt", {"llm": llm_config})
+        graph = TestGraph("Test prompt", {"llm": llm_config})
         assert isinstance(graph.llm_model, expected_model)
 
     def test_create_llm_unknown_provider(self):
         with pytest.raises(ValueError):
-            AbstractGraph("Test prompt", {"llm": {"model": "unknown_provider/model"}})
+            TestGraph("Test prompt", {"llm": {"model": "unknown_provider/model"}})
 
-    def test_create_llm_error(self):
-        with patch("your_module.init_chat_model", side_effect=Exception("Test error")):
-            with pytest.raises(Exception):
-                AbstractGraph("Test prompt", {"llm": {"model": "openai/gpt-3.5-turbo"}})