llama_rag/llama_rag_pipeline.ipynb (7,687 lines of code) (raw):
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ๐ค LLM Text Generation with Retrieval-Augmented Generation (RAG)\n",
"\n",
"Welcome to this beginner-friendly guide! ๐ In this project, you'll learn how to:\n",
"- Use **LLaMA**, a powerful large language model (LLM), for **text generation**.\n",
"- Retrieve relevant text snippets using **Sentence Transformers**, a popular tool for embedding text.\n",
"- Combine these techniques to answer questions based on context provided from retrieved snippets."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run this command to install everything you need:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TNCFBlv589Hg",
"outputId": "5e9c93ff-dad7-455e-ba35-4801a01057ff"
},
"outputs": [],
"source": [
"!pip install -Uq sentence-transformers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ๐ ๏ธ Setup: Installing the Necessary Libraries\n",
"\n",
"First, we need to install some Python libraries that will help us:\n",
"- **`transformers`**: Provides access to LLaMA and other pre-trained models for text generation. ๐\n",
"- **`sentence-transformers`**: Helps generate embeddings, which are essential for comparing text snippets. ๐ง "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "u6Y1I-Rz89Hi"
},
"outputs": [],
"source": [
"from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
"from sentence_transformers import SentenceTransformer\n",
"import torch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ๐ Step 1: Model Setup and Tokenizer"
]
},
{
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"base_uri": "https://localhost:8080/",
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"source": [
"chechpoint = \"meta-llama/Llama-3.2-3B-Instruct\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(chechpoint)\n",
"llama_model = AutoModelForCausalLM.from_pretrained(chechpoint, torch_dtype=torch.bfloat16)\n",
"generator = pipeline(\"text-generation\", model=llama_model, tokenizer=tokenizer, device=\"cuda\") # Create a simple text generation pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ๐Step 2: Text Snippet Retrieval Setup"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "lGXAEeN689Hj"
},
"outputs": [],
"source": [
"text_snippets = [\n",
" \"Fiona thanked Ethan for his unwavering support and promised to cherish their friendship.\",\n",
" \"As they ventured deeper into the forest, they encountered a wide array of obstacles.\",\n",
" \"Ethan and Fiona crossed treacherous ravines using rickety bridges, relying on each other's strength.\",\n",
" \"Overwhelmed with joy, Fiona thanked Ethan and disappeared into the embrace of her family.\",\n",
" \"Ethan returned to his cottage, heart full of memories and a smile brighter than ever before.\",\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ๐ Step 3: Convert text snippets to embeddings for later comparison."
]
},
{
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"base_uri": "https://localhost:8080/",
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"outputId": "fca91b14-74c3-433c-db6c-ec0041acec5c"
},
"outputs": [],
"source": [
"model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
"embeddings_text_snippets = model.encode(text_snippets) # Generate embeddings for the text snippets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ๐งฎ Step 4: Create a function to retrieve the closest matching snippet using cosine similarity."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "iHXqhoIh89Hk"
},
"outputs": [],
"source": [
"def retrieve_snippet(query):\n",
" query_embedded = model.encode([query]) # Encode the query to obtain its embedding\n",
" similarities = model.similarity(embeddings_text_snippets, query_embedded) # Calculate cosine similarities between the query embedding and the snippet embeddings\n",
" retrieved_texts = text_snippets[similarities.argmax().item()] # Retrieve the text snippet with the highest similarity\n",
" return retrieved_texts\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ๐ฌ Step 5: Create a function to generate the answer based on the retrieved snippet and query.\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "8JwW2Ftv89Hk"
},
"outputs": [],
"source": [
"# In this step, we utilize the retrieved context snippets to generate a relevant answer using LLaMA, exemplifying the power of RAG in enhancing the quality of responses.\n",
"\n",
"def ask_query(query):\n",
" retrieved_texts = retrieve_snippet(query)\n",
"\n",
" # Prepare the messages for the text generation pipeline\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": \"You are a helpful AI assistant.\"\n",
" \"Provide one Answer ONLY the following query based on the context provided below. \"\n",
" \"Do not generate or answer any other questions. \"\n",
" \"Do not make up or infer any information that is not directly stated in the context. \"\n",
" \"Provide a concise answer.\"\n",
" f\"{retrieved_texts}\"},\n",
" {\"role\": \"user\", \"content\": query}\n",
" ]\n",
"\n",
" # Generate a response using the text generation pipeline\n",
" response = generator(messages, max_new_tokens=128)[-1][\"generated_text\"][-1][\"content\"]\n",
" print(f\"Query: \\n\\t{query}\")\n",
" print(f\"Context: \\n\\t{retrieved_texts}\")\n",
" print(f\"Answer: \\n\\t{response}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ๐ฃ๏ธ Step 6: Ask a Question "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EIcFOoGl89Hl",
"outputId": "d2e55663-5f19-4277-ff78-2e2f048730be"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Query: \n",
"\tWhy did Fiona thank Ethan?\n",
"Context: \n",
"\tFiona thanked Ethan for his unwavering support and promised to cherish their friendship.\n",
"Answer: \n",
"\tFiona thanked Ethan for his unwavering support.\n"
]
}
],
"source": [
"query = \"Why did Fiona thank Ethan?\"\n",
"ask_query(query)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "llama_rag_env",
"language": "python",
"name": "llama_rag_env"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
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