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tinyllama_1_1b_llm_rag_research_chatbot (1).py

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+# -*- coding: utf-8 -*-
+"""TinyLlama 1.1B LLM RAG Research Chatbot.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1gKNj3wQw1pUbUXLJ4TcQCW16ezvL8pPo
+"""
+
+!pip install pypdf
+!pip install python-dotenv
+!pip install -q transformers
+!CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install  llama-cpp-python --no-cache-dir
+!pip install -q llama-index
+!pip install -q transformers einops accelerate langchain bitsandbytes
+!pip install sentence_transformers
+!pip install llama-index-llms-huggingface
+!pip install -q gradio
+!pip install einops
+!pip install accelerate
+
+import logging
+import sys
+
+logging.basicConfig(stream=sys.stdout, level=logging.INFO)
+logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
+from llama_index.llms.huggingface import HuggingFaceLLM
+from llama_index.core import Settings
+
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
+
+documents = SimpleDirectoryReader("/content/Data/").load_data()
+
+len(documents)
+
+documents[10]
+
+from llama_index.core import PromptTemplate
+
+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 = PromptTemplate("<|USER|>{query_str}<|ASSISTANT|>")
+
+from llama_index.llms.huggingface import HuggingFaceLLM
+
+import torch
+llm = HuggingFaceLLM(
+    context_window=2048,
+    max_new_tokens=256,
+    generate_kwargs={"temperature": 0.0, "do_sample": False},
+    system_prompt=system_prompt,
+    query_wrapper_prompt=query_wrapper_prompt,
+    tokenizer_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    device_map="cuda",
+    # uncomment this if using CUDA to reduce memory usage
+    model_kwargs={"torch_dtype": torch.bfloat16},
+)
+
+from langchain.embeddings import HuggingFaceEmbeddings
+from llama_index.embeddings.langchain import LangchainEmbedding
+
+lc_embed_model = HuggingFaceEmbeddings(
+    model_name="BAAI/bge-small-en-v1.5"
+)
+embed_model = LangchainEmbedding(lc_embed_model)
+
+service_context = ServiceContext.from_defaults(
+    chunk_size=1024,
+    llm=llm,
+    embed_model=embed_model
+)
+
+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)
+
+import gradio as gr
+gr.ChatInterface(predict).launch(share=True)