Update alternative.py

alternative.py

@@ -34,8 +34,8 @@ from dotenv import load_dotenv
 from scrapegraphai.graphs import SmartScraperGraph
 from scrapegraphai.utils import prettify_exec_info
 
-from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
-from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
+from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
+from langchain_huggingface.llms.huggingface_endpoint import HuggingFaceEndpoint
 from langchain_community.document_loaders import (AsyncHtmlLoader,
                                                NewsURLLoader, PubMedLoader,
                                                PlaywrightURLLoader,
@@ -53,19 +53,26 @@ from langchain_community.document_loaders import (AsyncHtmlLoader,
 load_dotenv()
 HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
 
-# Foudational Model HF repo ID
-REPO_ID = "mistralai/Mistral-7B-Instruct-v0.3"
+# Foudational Model and Embeeding Model HF repo ID
+FM_REPO_ID = "mistralai/Mistral-7B-Instruct-v0.3"
+EM_REPO_ID="sentence-transformers/all-MiniLM-l6-v2"
+
 
 # Initialize the model instances
 llm_model_instance = HuggingFaceEndpoint(
-    repo_id=repo_id, 
+    repo_id=FM_REPO_ID, 
+    max_new_tokens=8192,
+    top_k=10,
+    top_p=0.95,
+    typical_p=0.95,
+    temperature=0.1,
+    repetition_penalty=1.03,
     huggingfacehub_api_token = HUGGINGFACEHUB_API_TOKEN,
-    task = "conversational",
-    **model_kwargs={"temperature": 0.5, "max_length": 8192, "timeout": 6000},
 )
 
 embedder_model_instance = HuggingFaceInferenceAPIEmbeddings(
-    api_key=HUGGINGFACEHUB_API_TOKEN, model_name="sentence-transformers/all-MiniLM-l6-v2"
+    api_key=HUGGINGFACEHUB_API_TOKEN, 
+    model_name=EM_REPO_ID
 )
 
 graph_config = {