Upload rag_with_mircosoftphi2_and_hf_embeddings.py

rag_with_mircosoftphi2_and_hf_embeddings.py

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+# -*- 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()
+
+"""New sectiond"""
+
+from llama_index.core.prompts.prompts import SimpleInputPrompt
+from llama_index.llms.llama_cpp import LlamaCPP
+
+system_prompt = "You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided."
+
+# This will wrap the default prompts that are internal to llama-index
+query_wrapper_prompt = SimpleInputPrompt("<|USER|>{query_str}<|ASSISTANT|>")
+
+# model_url = "https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/resolve/main/llama-2-13b-chat.ggmlv3.q4_0.bin"
+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,
+    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,
+    # 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,
+)
+
+"""HuggingFace Embeddings"""
+
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+# loads BAAI/bge-small-en-v1.5
+embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+
+service_context = ServiceContext.from_defaults(
+    chunk_size=256,
+    llm=llm,
+    embed_model=embed_model
+)
+
+"""predict"""
+
+index = VectorStoreIndex.from_documents(documents, service_context=service_context)
+
+query_engine = index.as_query_engine()
+
+def predict(input, history):
+  response = query_engine.query(input)
+  return str(response)
+
+"""Gradio"""
+
+import gradio as gr
+
+gr.ChatInterface(predict).launch(share=True)