Bug fixes

scrapegraphai/graphs/abstract_graph.py

@@ -188,7 +188,6 @@ class AbstractGraph(ABC):
         Raises:
             ValueError: If the model is not supported.
         """
-
         if isinstance(self.llm_model, OpenAI):
             return OpenAIEmbeddings(api_key=self.llm_model.openai_api_key)
         elif isinstance(self.llm_model, AzureOpenAIEmbeddings):
@@ -223,6 +222,9 @@ class AbstractGraph(ABC):
         Raises:
             KeyError: If the model is not supported.
         """
+
+        if 'model_instance' in embedder_config:
+            return embedder_config['model_instance']
         
         # Instantiate the embedding model based on the model name
         if "openai" in embedder_config["model"]:

scrapegraphai/nodes/rag_node.py

@@ -8,9 +8,6 @@ from langchain.retrievers import ContextualCompressionRetriever
 from langchain.retrievers.document_compressors import EmbeddingsFilter, DocumentCompressorPipeline
 from langchain_community.document_transformers import EmbeddingsRedundantFilter
 from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import OllamaEmbeddings
-from langchain_openai import OpenAIEmbeddings, AzureOpenAIEmbeddings
-from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
 
 from .base_node import BaseNode
 
@@ -86,33 +83,7 @@ class RAGNode(BaseNode):
             print("--- (updated chunks metadata) ---")
 
         # check if embedder_model is provided, if not use llm_model
-        embedding_model = self.embedder_model if self.embedder_model else self.llm_model
-
-        if isinstance(embedding_model, OpenAI):
-            embeddings = OpenAIEmbeddings(
-                api_key=embedding_model.openai_api_key)
-        elif isinstance(embedding_model, AzureOpenAIEmbeddings):
-            embeddings = embedding_model
-        elif isinstance(embedding_model, HuggingFaceInferenceAPIEmbeddings):
-            embeddings = embedding_model
-
-        elif isinstance(embedding_model, AzureOpenAI):
-            embeddings = AzureOpenAIEmbeddings()
-        elif isinstance(embedding_model, Ollama):
-            # unwrap the kwargs from the model whihc is a dict
-            params = embedding_model._lc_kwargs
-            # remove streaming and temperature
-            params.pop("streaming", None)
-            params.pop("temperature", None)
-
-            embeddings = OllamaEmbeddings(**params)
-        elif isinstance(embedding_model, HuggingFace):
-            embeddings = HuggingFaceHubEmbeddings(model=embedding_model.model)
-        elif isinstance(embedding_model, Bedrock):
-            embeddings = BedrockEmbeddings(
-                client=None, model_id=embedding_model.model_id)
-        else:
-            raise ValueError("Embedding Model missing or not supported")
+        self.embedder_model = self.embedder_model if self.embedder_model else self.llm_model
         embeddings = self.embedder_model
 
         retriever = FAISS.from_documents(