Update main.py

main.py

@@ -37,24 +37,17 @@ if not TOGETHER_AI_API:
 try:
     embeddings = HuggingFaceEmbeddings(
         model_name="nomic-ai/nomic-embed-text-v1",
-        model_kwargs={"trust_remote_code": True},
+        model_kwargs={"trust_remote_code": True,"revision":"289f532e14dbbbd5a04753fa58739e9ba766f3c7"},
     )
 except Exception as e:
     logger.error(f"Error loading embeddings: {e}")
     raise RuntimeError("Failed to initialize embeddings.")
 
 # Load FAISS vectorstore
-try:
-    db = FAISS.load_local("ipc_vector_db", embeddings, allow_dangerous_deserialization=True)
-    db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 5, "score_threshold": 0.8})
-except Exception as e:
-    logger.error(f"Error loading FAISS vectorstore: {e}")
-    loader = DirectoryLoader('./data')
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-    documents = text_splitter.split_documents(loader.load())
-    db = FAISS.from_documents(documents, embeddings)
-    db.save_local("ipc_vector_db")
-    db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 5, "score_threshold": 0.8})
+
+db = FAISS.load_local("ipc_vector_db", embeddings, allow_dangerous_deserialization=True)
+db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2, "max-length":512})
+
 
 # Define prompt template
 prompt_template = """
@@ -71,8 +64,8 @@ prompt = PromptTemplate(template=prompt_template, input_variables=["context", "q
 try:
     llm = Together(
         model="mistralai/Mistral-7B-Instruct-v0.2",
-        temperature=0.3,
-        max_tokens=512,
+        temperature=0.5,
+        max_tokens=1024,
         together_api_key=TOGETHER_AI_API,
     )
 except Exception as e: