fix: shallow copy config of create_embedder

Author: liaoliaojun

scrapegraphai/graphs/abstract_graph.py

@@ -333,40 +333,41 @@ def _create_embedder(self, embedder_config: dict) -> object:
         Raises:
             KeyError: If the model is not supported.
         """
+        embedder_params = {**embedder_config}
         if "model_instance" in embedder_config:
-            return embedder_config["model_instance"]
+            return embedder_params["model_instance"]
         # Instantiate the embedding model based on the model name
-        if "openai" in embedder_config["model"]:
-            return OpenAIEmbeddings(api_key=embedder_config["api_key"])
-        elif "azure" in embedder_config["model"]:
+        if "openai" in embedder_params["model"]:
+            return OpenAIEmbeddings(api_key=embedder_params["api_key"])
+        elif "azure" in embedder_params["model"]:
             return AzureOpenAIEmbeddings()
-        elif "ollama" in embedder_config["model"]:
-            embedder_config["model"] = embedder_config["model"].split("ollama/")[-1]
+        elif "ollama" in embedder_params["model"]:
+            embedder_params["model"] = embedder_params["model"].split("ollama/")[-1]
             try:
-                models_tokens["ollama"][embedder_config["model"]]
+                models_tokens["ollama"][embedder_params["model"]]
             except KeyError as exc:
                 raise KeyError("Model not supported") from exc
-            return OllamaEmbeddings(**embedder_config)
-        elif "hugging_face" in embedder_config["model"]:
+            return OllamaEmbeddings(**embedder_params)
+        elif "hugging_face" in embedder_params["model"]:
             try:
-                models_tokens["hugging_face"][embedder_config["model"]]
+                models_tokens["hugging_face"][embedder_params["model"]]
             except KeyError as exc:
                 raise KeyError("Model not supported") from exc
-            return HuggingFaceHubEmbeddings(model=embedder_config["model"])
-        elif "gemini" in embedder_config["model"]:
+            return HuggingFaceHubEmbeddings(model=embedder_params["model"])
+        elif "gemini" in embedder_params["model"]:
             try:
-                models_tokens["gemini"][embedder_config["model"]]
+                models_tokens["gemini"][embedder_params["model"]]
             except KeyError as exc:
                 raise KeyError("Model not supported") from exc
-            return GoogleGenerativeAIEmbeddings(model=embedder_config["model"])
-        elif "bedrock" in embedder_config["model"]:
-            embedder_config["model"] = embedder_config["model"].split("/")[-1]
-            client = embedder_config.get("client", None)
+            return GoogleGenerativeAIEmbeddings(model=embedder_params["model"])
+        elif "bedrock" in embedder_params["model"]:
+            embedder_params["model"] = embedder_params["model"].split("/")[-1]
+            client = embedder_params.get("client", None)
             try:
-                models_tokens["bedrock"][embedder_config["model"]]
+                models_tokens["bedrock"][embedder_params["model"]]
             except KeyError as exc:
                 raise KeyError("Model not supported") from exc
-            return BedrockEmbeddings(client=client, model_id=embedder_config["model"])
+            return BedrockEmbeddings(client=client, model_id=embedder_params["model"])
         else:
             raise ValueError("Model provided by the configuration not supported")