Update app.py

app.py

@@ -9,14 +9,21 @@ from gitingest import ingest
 from llama_index.core import Settings
 from llama_index.llms.sambanovasystems import SambaNovaCloud
 from llama_index.core import PromptTemplate
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.embeddings.mixedbreadai import MixedbreadAIEmbedding
 from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
 from llama_index.core.node_parser import MarkdownNodeParser
 
 import streamlit as st
 
-# Set up SambaNova API key
-os.environ["SAMBANOVA_API_KEY"] = "your_sambanova_api_key"  # Replace with your actual SambaNova API key
+# Fetch API keys securely from Hugging Face secrets
+SAMBANOVA_API_KEY = os.getenv("SAMBANOVA_API_KEY")
+MXBAI_API_KEY = os.getenv("MXBAI_API_KEY")
+
+# Ensure both API keys are available
+if not SAMBANOVA_API_KEY:
+    raise ValueError("SAMBANOVA_API_KEY is not set in the Hugging Face secrets.")
+if not MXBAI_API_KEY:
+    raise ValueError("MXBAI_API_KEY is not set in the Hugging Face secrets.")
 
 if "id" not in st.session_state:
     st.session_state.id = uuid.uuid4()
@@ -85,7 +92,13 @@ with st.sidebar:
 
                     # setup llm & embedding model
                     llm = load_llm()
-                    embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-large-en-v1.5", trust_remote_code=True)
+
+                    # Mixedbread AI embedding setup
+                    embed_model = MixedbreadAIEmbedding(
+                        api_key=MXBAI_API_KEY,  # Use the API key from Hugging Face secret
+                        model_name="mixedbread-ai/mxbai-embed-large-v1",  # Specify the model name
+                    )
+
                     # Creating an index over loaded data
                     Settings.embed_model = embed_model
                     node_parser = MarkdownNodeParser()