Update rag_with_mircosoftphi2_and_hf_embeddings.py

rag_with_mircosoftphi2_and_hf_embeddings.py

@@ -1,28 +1,7 @@
-# -*- coding: utf-8 -*-
-"""RAG_with_MircosoftPhi2_and_HF_Embeddings.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/github/sumant1122/RAG-Phi2-LlamaIndex/blob/main/RAG_with_MircosoftPhi2_and_HF_Embeddings.ipynb
-"""
-
-!pip install -q pypdf
-!pip install -q python-dotenv
-!pip install -q llama-index
-!pip install -q llama-index-llms-huggingface
-!pip install -q llama-index-embeddings-huggingface
-!pip install -q gradio
-!pip install einops
-!pip install accelerate
-!pip install -q llama-cpp-python
-
-!pip install llama-index-llms-llama-cpp llama-index-embeddings-huggingface
-
 from llama_index.core import VectorStoreIndex,SimpleDirectoryReader,ServiceContext
 import torch
 
-documents = SimpleDirectoryReader("/content/rag").load_data()
+documents = SimpleDirectoryReader("data").load_data()
 
 """New sectiond"""
 
@@ -38,20 +17,18 @@ query_wrapper_prompt = SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")
 model_url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/resolve/main/llama-2-13b-chat.Q4_0.gguf"
 
 llm = LlamaCPP(
-    # You can pass in the URL to a GGML model to download it automatically
-    model_url=model_url,
     # optionally, you can set the path to a pre-downloaded model instead of model_url
-    model_path=None,
+    model_path="model.gguf",
     temperature=0.1,
     max_new_tokens=256,
     # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
-    context_window=3900,
+    context_window=4096,
     # kwargs to pass to __call__()
     generate_kwargs={},
     # kwargs to pass to __init__()
     # set to at least 1 to use GPU
-    model_kwargs={"n_gpu_layers": 1},
-    verbose=True,
+    model_kwargs={"n_gpu_layers": 0},
+    verbose=True
 )
 
 """HuggingFace Embeddings"""