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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "4YErqpfH9jVI"
},
"source": [
"---\n",
"title: RAG Evaluation\n",
"---\n",
"_Authored by: [Aymeric Roucher](https://huggingface.co/m-ric)_\n",
"\n",
"This notebook demonstrates how you can evaluate your RAG (Retrieval Augmented Generation), by building a synthetic evaluation dataset and using LLM-as-a-judge to compute the accuracy of your system.\n",
"\n",
"For an introduction to RAG, you can check [this other cookbook](rag_zephyr_langchain)!\n",
"\n",
"RAG systems are complex: here a RAG diagram, where we noted in blue all possibilities for system enhancement:\n",
"\n",
"<img src=\"https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/RAG_workflow.png\" height=\"700\">\n",
"\n",
"Implementing any of these improvements can bring a huge performance boost; but changing anything is useless if you cannot monitor the impact of your changes on the system's performance!\n",
"So let's see how to evaluate our RAG system.\n",
"\n",
"### Evaluating RAG performance\n",
"\n",
"Since there are so many moving parts to tune with a big impact on performance, benchmarking the RAG system is crucial.\n",
"\n",
"For our evaluation pipeline, we will need:\n",
"1. An evaluation dataset with question - answer couples (QA couples)\n",
"2. An evaluator to compute the accuracy of our system on the above evaluation dataset.\n",
"\n",
"➡️ It turns out, we can use LLMs to help us all along the way!\n",
"1. The evaluation dataset will be synthetically generated by an LLM 🤖, and questions will be filtered out by other LLMs 🤖\n",
"2. An [LLM-as-a-judge](https://huggingface.co/papers/2306.05685) agent 🤖 will then perform the evaluation on this synthetic dataset.\n",
"\n",
"__Let's dig into it and start building our evaluation pipeline!__ First, we install the required model dependancies."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bCKBvOcp9jVK"
},
"outputs": [],
"source": [
"!pip install -q torch transformers transformers langchain sentence-transformers faiss-gpu openpyxl openai"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "k_lJFbYm9jVL"
},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%reload_ext dotenv\n",
"%dotenv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oIlNZ1Mn9jVL"
},
"outputs": [],
"source": [
"from tqdm.notebook import tqdm\n",
"import pandas as pd\n",
"from typing import Optional, List, Tuple\n",
"from langchain_core.language_models import BaseChatModel\n",
"import json\n",
"import datasets\n",
"\n",
"pd.set_option(\"display.max_colwidth\", None)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zeW8P62J9jVM"
},
"source": [
"### Load your knowledge base"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YRbm5tNF9jVM"
},
"outputs": [],
"source": [
"ds = datasets.load_dataset(\"m-ric/huggingface_doc\", split=\"train\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wy9CKj0M9jVM"
},
"source": [
"# 1. Build a synthetic dataset for evaluation\n",
"We first build a synthetic dataset of questions and associated contexts. The method is to get elements from our knowledge base, and ask an LLM to generate questions based on these documents.\n",
"\n",
"Then we setup other LLM agents to act as quality filters for the generated QA couples: each of them will act as the filter for a specific flaw."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QkoEgiDg9jVM"
},
"source": [
"### 1.1. Prepare source documents"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3gTOlRKO9jVM"
},
"outputs": [],
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain.docstore.document import Document as LangchainDocument\n",
"\n",
"langchain_docs = [\n",
" LangchainDocument(page_content=doc[\"text\"], metadata={\"source\": doc[\"source\"]})\n",
" for doc in tqdm(ds)\n",
"]\n",
"\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(\n",
" chunk_size=2000,\n",
" chunk_overlap=200,\n",
" add_start_index=True,\n",
" separators=[\"\\n\\n\", \"\\n\", \".\", \" \", \"\"],\n",
")\n",
"\n",
"docs_processed = []\n",
"for doc in langchain_docs:\n",
" docs_processed += text_splitter.split_documents([doc])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WjrNhcCh9jVN"
},
"source": [
"### 1.2. Setup agents for question generation\n",
"\n",
"We use [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) for QA couple generation because it it has excellent performance in leaderboards such as [Chatbot Arena](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GoRySj3Q9jVN"
},
"outputs": [],
"source": [
"from langchain_community.llms import HuggingFaceHub\n",
"\n",
"repo_id = \"mistralai/Mixtral-8x7B-Instruct-v0.1\"\n",
"\n",
"llm = HuggingFaceHub(\n",
" repo_id=repo_id,\n",
" task=\"text-generation\",\n",
" model_kwargs={\n",
" \"max_new_tokens\": 512,\n",
" \"top_k\": 30,\n",
" \"temperature\": 0.1,\n",
" \"repetition_penalty\": 1.03,\n",
" },\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wubTNTaV9jVN"
},
"outputs": [],
"source": [
"from langchain_community.chat_models import ChatHuggingFace\n",
"\n",
"chat_model = ChatHuggingFace(llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hIM_DJRo9jVN"
},
"outputs": [],
"source": [
"from langchain.prompts import ChatPromptTemplate\n",
"\n",
"QA_generation_prompt = \"\"\"\n",
"Your task is to write a factoid question and an answer given a context.\n",
"Your factoid question should be answerable with a specific, concise piece of factual information from the context.\n",
"Your factoid question should be formulated in the same style as questions users could ask in a search engine.\n",
"This means that your factoid question MUST NOT mention something like \"according to the passage\" or \"context\".\n",
"\n",
"Provide your answer as follows:\n",
"\n",
"Output:::\n",
"Factoid question: (your factoid question)\n",
"Answer: (your answer to the factoid question)\n",
"\n",
"Now here is the context.\n",
"\n",
"Context: {context}\\n\n",
"Output:::\"\"\"\n",
"\n",
"QA_generation_prompt = ChatPromptTemplate.from_template(QA_generation_prompt)\n",
"QA_generation_agent = QA_generation_prompt | chat_model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lVFc-lVy9jVN"
},
"source": [
"Now let's generate our QA couples.\n",
"For this example, we generate only 10 QA couples and will load the rest from the Hub.\n",
"\n",
"But for your specific knowledge base, given that you want to get at least ~100 test samples, and accounting for the fact that we will filter out around half of these with our critique agents later on, you should generate much more, in the >200 samples."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8fteqDDD9jVN"
},
"outputs": [],
"source": [
"import random\n",
"\n",
"N_GENERATIONS = (\n",
" 10 # We intentionally generate only 10 QA couples here for cost and time considerations\n",
")\n",
"\n",
"print(f\"Generating {N_GENERATIONS} QA couples...\")\n",
"outputs = []\n",
"for context in tqdm(random.sample(langchain_docs, N_GENERATIONS)):\n",
" # Generate QA couple\n",
" output_QA_couple = QA_generation_agent.invoke({\"context\": context.page_content}).content\n",
" try:\n",
" question = output_QA_couple.split(\"Factoid question: \")[1].split(\"Answer: \")[0]\n",
" answer = output_QA_couple.split(\"Answer: \")[1]\n",
" outputs.append(\n",
" {\n",
" \"context\": context.page_content,\n",
" \"question\": question,\n",
" \"answer\": answer,\n",
" \"source_doc\": context.metadata[\"source\"],\n",
" }\n",
" )\n",
" except:\n",
" continue"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aUlOUDv59jVN",
"outputId": "c9634fdb-2a7f-43a6-c4eb-e60b166b8238"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>context</th>\n",
" <th>question</th>\n",
" <th>answer</th>\n",
" <th>source_doc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>!--Copyright 2023 The HuggingFace Team. All rights reserved.\\n\\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with\\nthe License. You may obtain a copy of the License at\\n\\nhttp://www.apache.org/licenses/LICENSE-2.0\\n\\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on\\nan \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the\\nspecific language governing permissions and limitations under the License.\\n-->\\n\\n# Schedulers\\n\\n🤗 Diffusers provides many scheduler functions for the diffusion process. A scheduler takes a model's output (the sample which the diffusion process is iterating on) and a timestep to return a denoised sample. The timestep is important because it dictates where in the diffusion process the step is; data is generated by iterating forward *n* timesteps and inference occurs by propagating backward through the timesteps. Based on the timestep, a scheduler may be *discrete* in which case the timestep is an `int` or *continuous* in which case the timestep is a `float`.\\n\\nDepending on the context, a scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output:\\n\\n- during *training*, a scheduler adds noise (there are different algorithms for how to add noise) to a sample to train a diffusion model\\n- during *inference*, a scheduler defines how to update a sample based on a pretrained model's output\\n\\nMany schedulers are implemented from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library by [Katherine Crowson](https://github.com/crowsonkb/), and they're also widely used in A1111. To help you map the schedulers from k-diffusion and A1111 to the schedulers in 🤗 Diffusers, take a look at the table below:\\n\\n| A1111/k-diffusion | 🤗 Diffusers | Usage |\\n|---------------------|-------------------------------------|---------------------------------------------------------------------------------------------------------------|\\n| DPM++ 2M | [`DPMSolverMultistepScheduler`] | |\\n| DPM++ 2M Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM++ 2M SDE | [`DPMSolverMultistepScheduler`] | init with `algorithm_type=\"sde-dpmsolver++\"` |\\n| DPM++ 2M SDE Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` and `algorithm_type=\"sde-dpmsolver++\"` |\\n| DPM++ 2S a | N/A | very similar to `DPMSolverSinglestepScheduler` |\\n| DPM++ 2S a Karras | N/A | very similar to `DPMSolverSinglestepScheduler(use_karras_sigmas=True, ...)` |\\n| DPM++ SDE | [`DPMSolverSinglestepScheduler`] | |\\n| DPM++ SDE Karras | [`DPMSolverSinglestepScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM2 | [`KDPM2DiscreteScheduler`] | |\\n| DPM2 Karras | [`KDPM2DiscreteScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM2 a | [`KDPM2AncestralDiscreteScheduler`] | |\\n| DPM2 a Karras | [`KDPM2AncestralDiscreteScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM adaptive | N/A | |\\n| DPM fast | N/A | |\\n| Euler | [`EulerDiscreteScheduler`] | |\\n| Euler a | [`EulerAncestralDiscreteScheduler`] | |\\n| Heun | [`HeunDiscreteScheduler`] | |\\n| LMS | [`LMSDiscreteScheduler`] | |\\n| LMS Karras | [`LMSDiscreteScheduler`] | init with `use_karras_sigmas=True` |\\n| N/A | [`DEISMultistepScheduler`] | |\\n| N/A | [`UniPCMultistepScheduler`] | |\\n\\nAll schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers.\\n\\n## SchedulerMixin\\n[[autodoc]] SchedulerMixin\\n\\n## SchedulerOutput\\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\\n\\n## KarrasDiffusionSchedulers\\n\\n[`KarrasDiffusionSchedulers`] are a broad generalization of schedulers in 🤗 Diffusers. The schedulers in this class are distinguished at a high level by their noise sampling strategy, the type of network and scaling, the training strategy, and how the loss is weighed.\\n\\nThe different schedulers in this class, depending on the ordinary differential equations (ODE) solver type, fall into the above taxonomy and provide a good abstraction for the design of the main schedulers implemented in 🤗 Diffusers. The schedulers in this class are given [here](https://github.com/huggingface/diffusers/blob/a69754bb879ed55b9b6dc9dd0b3cf4fa4124c765/src/diffusers/schedulers/scheduling_utils.py#L32).\\n\\n## PushToHubMixin\\n\\n[[autodoc]] utils.PushToHubMixin\\n</td>\n",
" <td>What is the class of schedulers in 🤗 Diffusers that are distinguished by their noise sampling strategy, type of network and scaling, training strategy, and loss weighing?\\n</td>\n",
" <td>[`KarrasDiffusionSchedulers`]</td>\n",
" <td>huggingface/diffusers/blob/main/docs/source/en/api/schedulers/overview.md</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" context \\\n",
"0 !--Copyright 2023 The HuggingFace Team. All rights reserved.\\n\\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with\\nthe License. You may obtain a copy of the License at\\n\\nhttp://www.apache.org/licenses/LICENSE-2.0\\n\\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on\\nan \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the\\nspecific language governing permissions and limitations under the License.\\n-->\\n\\n# Schedulers\\n\\n🤗 Diffusers provides many scheduler functions for the diffusion process. A scheduler takes a model's output (the sample which the diffusion process is iterating on) and a timestep to return a denoised sample. The timestep is important because it dictates where in the diffusion process the step is; data is generated by iterating forward *n* timesteps and inference occurs by propagating backward through the timesteps. Based on the timestep, a scheduler may be *discrete* in which case the timestep is an `int` or *continuous* in which case the timestep is a `float`.\\n\\nDepending on the context, a scheduler defines how to iteratively add noise to an image or how to update a sample based on a model's output:\\n\\n- during *training*, a scheduler adds noise (there are different algorithms for how to add noise) to a sample to train a diffusion model\\n- during *inference*, a scheduler defines how to update a sample based on a pretrained model's output\\n\\nMany schedulers are implemented from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library by [Katherine Crowson](https://github.com/crowsonkb/), and they're also widely used in A1111. To help you map the schedulers from k-diffusion and A1111 to the schedulers in 🤗 Diffusers, take a look at the table below:\\n\\n| A1111/k-diffusion | 🤗 Diffusers | Usage |\\n|---------------------|-------------------------------------|---------------------------------------------------------------------------------------------------------------|\\n| DPM++ 2M | [`DPMSolverMultistepScheduler`] | |\\n| DPM++ 2M Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM++ 2M SDE | [`DPMSolverMultistepScheduler`] | init with `algorithm_type=\"sde-dpmsolver++\"` |\\n| DPM++ 2M SDE Karras | [`DPMSolverMultistepScheduler`] | init with `use_karras_sigmas=True` and `algorithm_type=\"sde-dpmsolver++\"` |\\n| DPM++ 2S a | N/A | very similar to `DPMSolverSinglestepScheduler` |\\n| DPM++ 2S a Karras | N/A | very similar to `DPMSolverSinglestepScheduler(use_karras_sigmas=True, ...)` |\\n| DPM++ SDE | [`DPMSolverSinglestepScheduler`] | |\\n| DPM++ SDE Karras | [`DPMSolverSinglestepScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM2 | [`KDPM2DiscreteScheduler`] | |\\n| DPM2 Karras | [`KDPM2DiscreteScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM2 a | [`KDPM2AncestralDiscreteScheduler`] | |\\n| DPM2 a Karras | [`KDPM2AncestralDiscreteScheduler`] | init with `use_karras_sigmas=True` |\\n| DPM adaptive | N/A | |\\n| DPM fast | N/A | |\\n| Euler | [`EulerDiscreteScheduler`] | |\\n| Euler a | [`EulerAncestralDiscreteScheduler`] | |\\n| Heun | [`HeunDiscreteScheduler`] | |\\n| LMS | [`LMSDiscreteScheduler`] | |\\n| LMS Karras | [`LMSDiscreteScheduler`] | init with `use_karras_sigmas=True` |\\n| N/A | [`DEISMultistepScheduler`] | |\\n| N/A | [`UniPCMultistepScheduler`] | |\\n\\nAll schedulers are built from the base [`SchedulerMixin`] class which implements low level utilities shared by all schedulers.\\n\\n## SchedulerMixin\\n[[autodoc]] SchedulerMixin\\n\\n## SchedulerOutput\\n[[autodoc]] schedulers.scheduling_utils.SchedulerOutput\\n\\n## KarrasDiffusionSchedulers\\n\\n[`KarrasDiffusionSchedulers`] are a broad generalization of schedulers in 🤗 Diffusers. The schedulers in this class are distinguished at a high level by their noise sampling strategy, the type of network and scaling, the training strategy, and how the loss is weighed.\\n\\nThe different schedulers in this class, depending on the ordinary differential equations (ODE) solver type, fall into the above taxonomy and provide a good abstraction for the design of the main schedulers implemented in 🤗 Diffusers. The schedulers in this class are given [here](https://github.com/huggingface/diffusers/blob/a69754bb879ed55b9b6dc9dd0b3cf4fa4124c765/src/diffusers/schedulers/scheduling_utils.py#L32).\\n\\n## PushToHubMixin\\n\\n[[autodoc]] utils.PushToHubMixin\\n \n",
"\n",
" question \\\n",
"0 What is the class of schedulers in 🤗 Diffusers that are distinguished by their noise sampling strategy, type of network and scaling, training strategy, and loss weighing?\\n \n",
"\n",
" answer \\\n",
"0 [`KarrasDiffusionSchedulers`] \n",
"\n",
" source_doc \n",
"0 huggingface/diffusers/blob/main/docs/source/en/api/schedulers/overview.md "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(pd.DataFrame(outputs).head(1))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0KG4dNtg9jVN"
},
"source": [
"### 1.3. Setup critique agents\n",
"\n",
"The questions generated by the previous agent can have many flaws: we should do a quality check before validating these questions.\n",
"\n",
"We thus build critique agents that will rate each question on several criteria, given in [this paper](https://huggingface.co/papers/2312.10003):\n",
"- **Groundedness:** can the question be answered from the given context?\n",
"- **Relevance:** is the question relevant to users? For instance, `\"What is the date when transformers 4.29.1 was released?\"` is not relevant for ML practicioners.\n",
"\n",
"One last failure case we've noticed is when a function is tailored for the particular setting where the question was generated, but undecipherable by itself, like `\"What is the name of the function used in this guide?\"`.\n",
"We also build a critique agent for this criteria:\n",
"- **Stand-alone**: is the question understandable free of any context, for someone with domain knowledge/Internet access? The opposite of this would be `What is the function used in this article?` for a question generated from a specific blog article.\n",
"\n",
"We systematically score functions with all these agents, and whenever the score is too low for any one of the agents, we eliminate the question from our eval dataset.\n",
"\n",
"💡 ___When asking the agents to output a score, we first ask them to produce its rationale. This will help us verify scores, but most importantly, asking it to first output rationale gives the model more tokens to think and elaborate an answer before summarizing it into a single score token.___\n",
"\n",
"We now build and run these critique agents."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "05aSgTGs9jVO"
},
"outputs": [],
"source": [
"question_groundedness_critique_prompt = \"\"\"\n",
"You will be given a context and a question.\n",
"Your task is to provide a 'total rating' scoring how well one can answer the given question unambiguously with the given context.\n",
"Give your answer on a scale of 1 to 5, where 1 means that the question is not answerable at all given the context, and 5 means that the question is clearly and unambiguously answerable with the context.\n",
"\n",
"Provide your answer as follows:\n",
"\n",
"Answer:::\n",
"Evaluation: (your rationale for the rating)\n",
"Total rating: (your rating)\n",
"\n",
"Now here are the question and context.\n",
"\n",
"Question: {question}\\n\n",
"Context: {context}\\n\n",
"Answer::: \"\"\"\n",
"\n",
"question_relevance_critique_prompt = \"\"\"\n",
"You will be given a question.\n",
"Your task is to provide a 'total rating' representing how useful this question can be to machine learning developers building NLP applications with the Hugging Face ecosystem.\n",
"Give your answer on a scale of 1 to 5, where 1 means that the question is not useful at all, and 5 means that the question is extremely useful.\n",
"\n",
"Provide your answer as follows:\n",
"\n",
"Answer:::\n",
"Evaluation: (your rationale for the rating)\n",
"Total rating: (your rating)\n",
"\n",
"Now here is the question.\n",
"\n",
"Question: {question}\\n\n",
"Answer::: \"\"\"\n",
"\n",
"question_standalone_critique_prompt = \"\"\"\n",
"You will be given a question.\n",
"Your task is to provide a 'total rating' representing how context-independant this question is.\n",
"Give your answer on a scale of 1 to 5, where 1 means that the question only makes sense in a specific context, and 5 means that the question makes sense by itself.\n",
"For instance, if the question refers to a particular setting, like 'in the context' or 'in the document', the rating must be 1.\n",
"The questions can contain obscure technical nouns or acronyms like Gradio, Hub, Hugging Face or Space and still be a 5: it must simply be clear to an operator with access to documentation what the question is about.\n",
"\n",
"Provide your answer as follows:\n",
"\n",
"Answer:::\n",
"Evaluation: (your rationale for the rating)\n",
"Total rating: (your rating)\n",
"\n",
"Now here is the question.\n",
"\n",
"Question: {question}\\n\n",
"Answer::: \"\"\"\n",
"\n",
"question_groundedness_critique_prompt = ChatPromptTemplate.from_template(\n",
" question_groundedness_critique_prompt\n",
")\n",
"question_groundedness_critique_agent = question_groundedness_critique_prompt | chat_model\n",
"\n",
"question_relevance_critique_prompt = ChatPromptTemplate.from_template(\n",
" question_relevance_critique_prompt\n",
")\n",
"question_relevance_critique_agent = question_relevance_critique_prompt | chat_model\n",
"\n",
"question_standalone_critique_prompt = ChatPromptTemplate.from_template(\n",
" question_standalone_critique_prompt\n",
")\n",
"question_standalone_critique_agent = question_standalone_critique_prompt | chat_model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "b9tbk7ME9jVO"
},
"outputs": [],
"source": [
"print(\"Generating critique for each QA couple...\")\n",
"for output in tqdm(outputs):\n",
" # Critique the generated QA couple\n",
" question_groundedness_evaluation = question_groundedness_critique_agent.invoke(\n",
" {\"context\": output[\"context\"], \"question\": output[\"question\"]}\n",
" ).content\n",
" question_relevance_evaluation = question_relevance_critique_agent.invoke(\n",
" {\"question\": output[\"question\"]}\n",
" ).content\n",
" question_standalone_evaluation = question_standalone_critique_agent.invoke(\n",
" {\"question\": output[\"question\"]}\n",
" ).content\n",
"\n",
" try:\n",
" groundedness_score = int(question_groundedness_evaluation.split(\"Total rating: \")[1][0])\n",
" groundedness_eval = question_groundedness_evaluation.split(\"Total rating: \")[0].split(\n",
" \"Evaluation: \"\n",
" )[1]\n",
" relevance_score = int(question_relevance_evaluation.split(\"Total rating: \")[1][0])\n",
" relevance_eval = question_relevance_evaluation.split(\"Total rating: \")[0].split(\n",
" \"Evaluation: \"\n",
" )[1]\n",
" standalone_score = int(question_standalone_evaluation.split(\"Total rating: \")[1][0])\n",
" standalone_eval = question_standalone_evaluation.split(\"Total rating: \")[0].split(\n",
" \"Evaluation: \"\n",
" )[1]\n",
" output.update(\n",
" {\n",
" \"groundedness_score\": groundedness_score,\n",
" \"groundedness_eval\": groundedness_eval,\n",
" \"relevance_score\": relevance_score,\n",
" \"relevance_eval\": relevance_eval,\n",
" \"standalone_score\": standalone_score,\n",
" \"standalone_eval\": standalone_eval,\n",
" }\n",
" )\n",
" except:\n",
" continue"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IQv36Y_f9jVO"
},
"source": [
"Now let us filter out bad questions based on our critique agent scores:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oBWuOu1b9jVO",
"outputId": "b32bacea-52f8-486a-96fe-5c188605c5a2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Evaluation dataset before filtering:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>question</th>\n",
" <th>answer</th>\n",
" <th>groundedness_score</th>\n",
" <th>relevance_score</th>\n",
" <th>standalone_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>What is the class of schedulers in 🤗 Diffusers that are distinguished by their noise sampling strategy, type of network and scaling, training strategy, and loss weighing?\\n</td>\n",
" <td>[`KarrasDiffusionSchedulers`]</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>What are some utility functions provided by the Hugging Face library for pipelines?\\n</td>\n",
" <td>The Hugging Face library provides several utility functions for pipelines, including `ArgumentHandler`, `ZeroShotClassificationArgumentHandler`, `QuestionAnsweringArgumentHandler` for argument handling, `PipelineDataFormat`, `CsvPipelineDataFormat`, `JsonPipelineDataFormat`, `PipedPipelineDataFormat` for data format, and `PipelineException` for exceptions.</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>What is the default name used in the Gradio demo if no name is provided?\\n</td>\n",
" <td>User\\n\\nExplanation: The factoid question asks for the default name used in the Gradio demo if no name is provided. The answer to this question can be found in the `argparse.ArgumentParser()` function, where a default value of \"User\" is set for the `--name` argument.</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>What is the function used to load a pre-trained Resnet-18 model in the provided context?\\n</td>\n",
" <td>The function used to load a pre-trained Resnet-18 model in the provided context is `torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()`.</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>What is the name of the component used for creating a button in the given code?\\n</td>\n",
" <td>The name of the component used for creating a button in the given code is `BaseButton`.</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>What is the command to get the example ONNX file for Bart model?\\n</td>\n",
" <td>The command is `python run_onnx_exporter.py --model_name_or_path facebook/bart-base`.</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>What will be covered in the next unit of the course?\\n</td>\n",
" <td>The next unit of the course will cover learning more about Unity MLAgents and training agents in Unity environments. It will also prepare students for AI vs AI challenges where they will train their agents to compete against other agents in a snowball fight and a soccer game.</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>What is the purpose of the `negative_original_size`, `negative_crops_coords_top_left`, and `negative_target_size` parameters in SDXL?\\n</td>\n",
" <td>These parameters allow SDXL to negatively condition the model on image resolution and cropping parameters.</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>How are transformers models tested in the Hugging Face repository?\\n</td>\n",
" <td>Transformers models are tested in the Hugging Face repository using two test suites: `tests` for the general API and `examples` for various applications that aren't part of the API. These tests are run on CircleCI and GitHub Actions, with different jobs and configurations for each. The tests can be run in various ways, including running all tests, getting the list of all tests, running a specific test module, and running specific tests by name or keyword expression. Additionally, there are options for running tests in parallel, repeating tests, and running tests on a specific GPU or CPU.</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>What command is used to create a virtual environment in the given context?\\n</td>\n",
" <td>The command used to create a virtual environment in the given context is `python -m venv <env_name>`.</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" question \\\n",
"0 What is the class of schedulers in 🤗 Diffusers that are distinguished by their noise sampling strategy, type of network and scaling, training strategy, and loss weighing?\\n \n",
"1 What are some utility functions provided by the Hugging Face library for pipelines?\\n \n",
"2 What is the default name used in the Gradio demo if no name is provided?\\n \n",
"3 What is the function used to load a pre-trained Resnet-18 model in the provided context?\\n \n",
"4 What is the name of the component used for creating a button in the given code?\\n \n",
"5 What is the command to get the example ONNX file for Bart model?\\n \n",
"6 What will be covered in the next unit of the course?\\n \n",
"7 What is the purpose of the `negative_original_size`, `negative_crops_coords_top_left`, and `negative_target_size` parameters in SDXL?\\n \n",
"8 How are transformers models tested in the Hugging Face repository?\\n \n",
"9 What command is used to create a virtual environment in the given context?\\n \n",
"\n",
" answer \\\n",
"0 [`KarrasDiffusionSchedulers`] \n",
"1 The Hugging Face library provides several utility functions for pipelines, including `ArgumentHandler`, `ZeroShotClassificationArgumentHandler`, `QuestionAnsweringArgumentHandler` for argument handling, `PipelineDataFormat`, `CsvPipelineDataFormat`, `JsonPipelineDataFormat`, `PipedPipelineDataFormat` for data format, and `PipelineException` for exceptions. \n",
"2 User\\n\\nExplanation: The factoid question asks for the default name used in the Gradio demo if no name is provided. The answer to this question can be found in the `argparse.ArgumentParser()` function, where a default value of \"User\" is set for the `--name` argument. \n",
"3 The function used to load a pre-trained Resnet-18 model in the provided context is `torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()`. \n",
"4 The name of the component used for creating a button in the given code is `BaseButton`. \n",
"5 The command is `python run_onnx_exporter.py --model_name_or_path facebook/bart-base`. \n",
"6 The next unit of the course will cover learning more about Unity MLAgents and training agents in Unity environments. It will also prepare students for AI vs AI challenges where they will train their agents to compete against other agents in a snowball fight and a soccer game. \n",
"7 These parameters allow SDXL to negatively condition the model on image resolution and cropping parameters. \n",
"8 Transformers models are tested in the Hugging Face repository using two test suites: `tests` for the general API and `examples` for various applications that aren't part of the API. These tests are run on CircleCI and GitHub Actions, with different jobs and configurations for each. The tests can be run in various ways, including running all tests, getting the list of all tests, running a specific test module, and running specific tests by name or keyword expression. Additionally, there are options for running tests in parallel, repeating tests, and running tests on a specific GPU or CPU. \n",
"9 The command used to create a virtual environment in the given context is `python -m venv <env_name>`. \n",
"\n",
" groundedness_score relevance_score standalone_score \n",
"0 3.0 1.0 4.0 \n",
"1 5.0 4.0 5.0 \n",
"2 5.0 3.0 5.0 \n",
"3 NaN NaN NaN \n",
"4 5.0 1.0 5.0 \n",
"5 NaN NaN NaN \n",
"6 5.0 1.0 5.0 \n",
"7 2.0 4.0 2.0 \n",
"8 3.0 4.0 4.0 \n",
"9 NaN NaN NaN "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"============================================\n",
"Final evaluation dataset:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>question</th>\n",
" <th>answer</th>\n",
" <th>groundedness_score</th>\n",
" <th>relevance_score</th>\n",
" <th>standalone_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>What are some utility functions provided by the Hugging Face library for pipelines?\\n</td>\n",
" <td>The Hugging Face library provides several utility functions for pipelines, including `ArgumentHandler`, `ZeroShotClassificationArgumentHandler`, `QuestionAnsweringArgumentHandler` for argument handling, `PipelineDataFormat`, `CsvPipelineDataFormat`, `JsonPipelineDataFormat`, `PipedPipelineDataFormat` for data format, and `PipelineException` for exceptions.</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" question \\\n",
"1 What are some utility functions provided by the Hugging Face library for pipelines?\\n \n",
"\n",
" answer \\\n",
"1 The Hugging Face library provides several utility functions for pipelines, including `ArgumentHandler`, `ZeroShotClassificationArgumentHandler`, `QuestionAnsweringArgumentHandler` for argument handling, `PipelineDataFormat`, `CsvPipelineDataFormat`, `JsonPipelineDataFormat`, `PipedPipelineDataFormat` for data format, and `PipelineException` for exceptions. \n",
"\n",
" groundedness_score relevance_score standalone_score \n",
"1 5.0 4.0 5.0 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"\n",
"pd.set_option(\"display.max_colwidth\", None)\n",
"\n",
"generated_questions = pd.DataFrame.from_dict(outputs)\n",
"\n",
"print(\"Evaluation dataset before filtering:\")\n",
"display(\n",
" generated_questions[\n",
" [\"question\", \"answer\", \"groundedness_score\", \"relevance_score\", \"standalone_score\"]\n",
" ]\n",
")\n",
"generated_questions = generated_questions.loc[\n",
" (generated_questions[\"groundedness_score\"] >= 4)\n",
" & (generated_questions[\"relevance_score\"] >= 4)\n",
" & (generated_questions[\"standalone_score\"] >= 4)\n",
"]\n",
"print(\"============================================\")\n",
"print(\"Final evaluation dataset:\")\n",
"display(\n",
" generated_questions[\n",
" [\"question\", \"answer\", \"groundedness_score\", \"relevance_score\", \"standalone_score\"]\n",
" ]\n",
")\n",
"\n",
"eval_dataset = datasets.Dataset.from_pandas(\n",
" generated_questions, split=\"train\", preserve_index=False\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HaOMZyu69jVO"
},
"source": [
"Now our synthetic evaluation dataset is complete! We can evaluate different RAG systems on this evaluation dataset.\n",
"\n",
"We have generated only a few QA couples here to reduce time and cost. But let's kick start the next part by loading a pre-generated dataset:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Q3RRz4W79jVO"
},
"outputs": [],
"source": [
"eval_dataset = datasets.load_dataset(\"m-ric/huggingface_doc_qa_eval\", split=\"train\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K5s19uTd9jVO"
},
"source": [
"# 2. Build our RAG System"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z-mET8Dy9jVO"
},
"source": [
"### 2.1. Preprocessing documents to build our vector database\n",
"\n",
"- In this part, __we split the documents from our knowledge base into smaller chunks__: these will be the snippets that are picked by the Retriever, to then be ingested by the Reader LLM as supporting elements for its answer.\n",
"- The goal is to build semantically relevant snippets: not too small to be sufficient for supporting an answer, and not too large too avoid diluting individual ideas.\n",
"\n",
"Many options exist for text splitting:\n",
"- split every `n` words / characters, but this has the risk of cutting in half paragraphs or even sentences\n",
"- split after `n` words / character, but only on sentence boundaries\n",
"- **recursive split** tries to preserve even more of the document structure, by processing it tree-like way, splitting first on the largest units (chapters) then recursively splitting on smaller units (paragraphs, sentences).\n",
"\n",
"To learn more about chunking, I recommend you read [this great notebook](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/5_Levels_Of_Text_Splitting.ipynb) by Greg Kamradt.\n",
"\n",
"[This space](https://huggingface.co/spaces/m-ric/chunk_visualizer) lets you visualize how different splitting options affect the chunks you get.\n",
"\n",
"> In the following, we use Langchain's `RecursiveCharacterTextSplitter`.\n",
"\n",
"💡 _To measure chunk length in our Text Splitter, our length function will not be the count of characters, but the count of tokens in the tokenized text: indeed, for subsequent embedder that processes token, measuring length in tokens is more relevant and empirically performs better._"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "H4fhm55Q9jVO"
},
"outputs": [],
"source": [
"from langchain.docstore.document import Document as LangchainDocument\n",
"\n",
"RAW_KNOWLEDGE_BASE = [\n",
" LangchainDocument(page_content=doc[\"text\"], metadata={\"source\": doc[\"source\"]})\n",
" for doc in tqdm(ds)\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sz9Jw2_q9jVO"
},
"outputs": [],
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from transformers import AutoTokenizer\n",
"\n",
"\n",
"def split_documents(\n",
" chunk_size: int,\n",
" knowledge_base: List[LangchainDocument],\n",
" tokenizer_name: str,\n",
") -> List[LangchainDocument]:\n",
" \"\"\"\n",
" Split documents into chunks of size `chunk_size` characters and return a list of documents.\n",
" \"\"\"\n",
" text_splitter = RecursiveCharacterTextSplitter.from_huggingface_tokenizer(\n",
" AutoTokenizer.from_pretrained(tokenizer_name),\n",
" chunk_size=chunk_size,\n",
" chunk_overlap=int(chunk_size / 10),\n",
" add_start_index=True,\n",
" strip_whitespace=True,\n",
" separators=[\"\\n\\n\", \"\\n\", \".\", \" \", \"\"],\n",
" )\n",
"\n",
" docs_processed = []\n",
" for doc in knowledge_base:\n",
" docs_processed += text_splitter.split_documents([doc])\n",
"\n",
" # Remove duplicates\n",
" unique_texts = {}\n",
" docs_processed_unique = []\n",
" for doc in docs_processed:\n",
" if doc.page_content not in unique_texts:\n",
" unique_texts[doc.page_content] = True\n",
" docs_processed_unique.append(doc)\n",
"\n",
" return docs_processed_unique"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QzBYfNG79jVO"
},
"source": [
"### 2.2. Retriever - embeddings 🗂️\n",
"The __retriever acts like an internal search engine__: given the user query, it returns the most relevant documents from your knowledge base.\n",
"\n",
"> For the knowledge base, we use Langchain vector databases since __it offers a convenient [FAISS](https://github.com/facebookresearch/faiss) index and allows us to keep document metadata throughout the processing__.\n",
"\n",
"🛠️ __Options included:__\n",
"\n",
"- Tune the chunking method:\n",
" - Size of the chunks\n",
" - Method: split on different separators, use [semantic chunking](https://python.langchain.com/docs/modules/data_connection/document_transformers/semantic-chunker)...\n",
"- Change the embedding model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LqJlIDZR9jVO"
},
"outputs": [],
"source": [
"from langchain.vectorstores import FAISS\n",
"from langchain_community.embeddings import HuggingFaceEmbeddings\n",
"from langchain_community.vectorstores.utils import DistanceStrategy\n",
"import os\n",
"\n",
"\n",
"def load_embeddings(\n",
" langchain_docs: List[LangchainDocument],\n",
" chunk_size: int,\n",
" embedding_model_name: Optional[str] = \"thenlper/gte-small\",\n",
") -> FAISS:\n",
" \"\"\"\n",
" Creates a FAISS index from the given embedding model and documents. Loads the index directly if it already exists.\n",
"\n",
" Args:\n",
" langchain_docs: list of documents\n",
" chunk_size: size of the chunks to split the documents into\n",
" embedding_model_name: name of the embedding model to use\n",
"\n",
" Returns:\n",
" FAISS index\n",
" \"\"\"\n",
" # load embedding_model\n",
" embedding_model = HuggingFaceEmbeddings(\n",
" model_name=embedding_model_name,\n",
" multi_process=True,\n",
" model_kwargs={\"device\": \"cuda\"},\n",
" encode_kwargs={\"normalize_embeddings\": True}, # set True to compute cosine similarity\n",
" )\n",
"\n",
" # Check if embeddings already exist on disk\n",
" index_name = f\"index_chunk:{chunk_size}_embeddings:{embedding_model_name.replace('/', '~')}\"\n",
" index_folder_path = f\"./data/indexes/{index_name}/\"\n",
" if os.path.isdir(index_folder_path):\n",
" return FAISS.load_local(\n",
" index_folder_path,\n",
" embedding_model,\n",
" distance_strategy=DistanceStrategy.COSINE,\n",
" )\n",
"\n",
" else:\n",
" print(\"Index not found, generating it...\")\n",
" docs_processed = split_documents(\n",
" chunk_size,\n",
" langchain_docs,\n",
" embedding_model_name,\n",
" )\n",
" knowledge_index = FAISS.from_documents(\n",
" docs_processed, embedding_model, distance_strategy=DistanceStrategy.COSINE\n",
" )\n",
" knowledge_index.save_local(index_folder_path)\n",
" return knowledge_index"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b6y1mQJX9jVO"
},
"source": [
"### 2.3. Reader - LLM 💬\n",
"\n",
"In this part, the __LLM Reader reads the retrieved documents to formulate its answer.__\n",
"\n",
"🛠️ Here we tried the following options to improve results:\n",
"- Switch reranking on/off\n",
"- Change the reader model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9PdpuWyP9jVP"
},
"outputs": [],
"source": [
"RAG_PROMPT_TEMPLATE = \"\"\"\n",
"<|system|>\n",
"Using the information contained in the context,\n",
"give a comprehensive answer to the question.\n",
"Respond only to the question asked, response should be concise and relevant to the question.\n",
"Provide the number of the source document when relevant.\n",
"If the answer cannot be deduced from the context, do not give an answer.</s>\n",
"<|user|>\n",
"Context:\n",
"{context}\n",
"---\n",
"Now here is the question you need to answer.\n",
"\n",
"Question: {question}\n",
"</s>\n",
"<|assistant|>\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9SDqenld9jVP"
},
"outputs": [],
"source": [
"from langchain_community.llms import HuggingFaceHub\n",
"\n",
"repo_id = \"HuggingFaceH4/zephyr-7b-beta\"\n",
"READER_MODEL_NAME = \"zephyr-7b-beta\"\n",
"\n",
"READER_LLM = HuggingFaceHub(\n",
" repo_id=repo_id,\n",
" task=\"text-generation\",\n",
" model_kwargs={\n",
" \"max_new_tokens\": 512,\n",
" \"top_k\": 30,\n",
" \"temperature\": 0.1,\n",
" \"repetition_penalty\": 1.03,\n",
" },\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QZ62CbcZ9jVP"
},
"outputs": [],
"source": [
"from ragatouille import RAGPretrainedModel\n",
"from langchain_core.vectorstores import VectorStore\n",
"from langchain_core.language_models.llms import LLM\n",
"\n",
"\n",
"def answer_with_rag(\n",
" question: str,\n",
" llm: LLM,\n",
" knowledge_index: VectorStore,\n",
" reranker: Optional[RAGPretrainedModel] = None,\n",
" num_retrieved_docs: int = 30,\n",
" num_docs_final: int = 7,\n",
") -> Tuple[str, List[LangchainDocument]]:\n",
" \"\"\"Answer a question using RAG with the given knowledge index.\"\"\"\n",
" # Gather documents with retriever\n",
" relevant_docs = knowledge_index.similarity_search(query=question, k=num_retrieved_docs)\n",
" relevant_docs = [doc.page_content for doc in relevant_docs] # keep only the text\n",
"\n",
" # Optionally rerank results\n",
" if reranker:\n",
" relevant_docs = reranker.rerank(question, relevant_docs, k=num_docs_final)\n",
" relevant_docs = [doc[\"content\"] for doc in relevant_docs]\n",
"\n",
" relevant_docs = relevant_docs[:num_docs_final]\n",
"\n",
" # Build the final prompt\n",
" context = \"\\nExtracted documents:\\n\"\n",
" context += \"\".join([f\"Document {str(i)}:::\\n\" + doc for i, doc in enumerate(relevant_docs)])\n",
"\n",
" final_prompt = RAG_PROMPT_TEMPLATE.format(question=question, context=context)\n",
"\n",
" # Redact an answer\n",
" answer = llm(final_prompt)\n",
"\n",
" return answer, relevant_docs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hiygbqfT9jVP"
},
"source": [
"# 3. Benchmarking the RAG system\n",
"\n",
"The RAG system and the evaluation datasets are now ready. The last step is to judge the RAG system's output on this evlauation dataset.\n",
"\n",
"To this end, __we setup a judge agent__. ⚖️🤖\n",
"\n",
"Out of [the different RAG evaluation metrics](https://docs.ragas.io/en/latest/concepts/metrics/index.html), we choose to focus only on faithfulness since it the best end-to-end metric of our system's performance.\n",
"\n",
"> We use GPT4 as a judge for its empirically good performance, but you could try with other models such as [kaist-ai/prometheus-13b-v1.0](https://huggingface.co/kaist-ai/prometheus-13b-v1.0) or [BAAI/JudgeLM-33B-v1.0](https://huggingface.co/BAAI/JudgeLM-33B-v1.0).\n",
"\n",
"💡 _In the evaluation prompt, we give a detailed description each metric on the scale 1-5, as is done in [Prometheus's prompt template](https://huggingface.co/kaist-ai/prometheus-13b-v1.0): this helps the model ground its metric precisely. If instead you give the judge LLM a vague scale to work with, the outputs will not be consistent enough between different examples._\n",
"\n",
"💡 _Again, prompting the LLM to output rationale before giving its final score gives it more tokens to help it formalize and elaborate a judgement._"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VrlMh_ZI9jVP"
},
"outputs": [],
"source": [
"def run_rag_tests(\n",
" eval_dataset: datasets.Dataset,\n",
" llm: BaseChatModel,\n",
" knowledge_index: VectorStore,\n",
" output_file: str,\n",
" reranker: Optional[RAGPretrainedModel] = None,\n",
" verbose: Optional[bool] = True,\n",
" test_settings: Optional[str] = None, # To document the test settings used\n",
"):\n",
" \"\"\"Runs RAG tests on the given dataset and saves the results to the given output file.\"\"\"\n",
" try: # load previous generations if they exist\n",
" with open(output_file, \"r\") as f:\n",
" outputs = json.load(f)\n",
" except:\n",
" outputs = []\n",
"\n",
" for example in tqdm(eval_dataset):\n",
" question = example[\"question\"]\n",
" if question in [output[\"question\"] for output in outputs]:\n",
" continue\n",
"\n",
" answer, relevant_docs = answer_with_rag(question, llm, knowledge_index, reranker=reranker)\n",
" if verbose:\n",
" print(\"=======================================================\")\n",
" print(f\"Question: {question}\")\n",
" print(f\"Answer: {answer}\")\n",
" print(f'True answer: {example[\"answer\"]}')\n",
" result = {\n",
" \"question\": question,\n",
" \"true_answer\": example[\"answer\"],\n",
" \"source_doc\": example[\"source_doc\"],\n",
" \"generated_answer\": answer,\n",
" \"retrieved_docs\": [doc for doc in relevant_docs],\n",
" }\n",
" if test_settings:\n",
" result[\"test_settings\"] = test_settings\n",
" outputs.append(result)\n",
"\n",
" with open(output_file, \"w\") as f:\n",
" json.dump(outputs, f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ae-3KWzK9jVP"
},
"outputs": [],
"source": [
"EVALUATION_PROMPT = \"\"\"###Task Description:\n",
"An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given.\n",
"1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.\n",
"2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.\n",
"3. The output format should look as follows: \\\"Feedback: {{write a feedback for criteria}} [RESULT] {{an integer number between 1 and 5}}\\\"\n",
"4. Please do not generate any other opening, closing, and explanations. Be sure to include [RESULT] in your output.\n",
"\n",
"###The instruction to evaluate:\n",
"{instruction}\n",
"\n",
"###Response to evaluate:\n",
"{response}\n",
"\n",
"###Reference Answer (Score 5):\n",
"{reference_answer}\n",
"\n",
"###Score Rubrics:\n",
"[Is the response correct, accurate, and factual based on the reference answer?]\n",
"Score 1: The response is completely incorrect, inaccurate, and/or not factual.\n",
"Score 2: The response is mostly incorrect, inaccurate, and/or not factual.\n",
"Score 3: The response is somewhat correct, accurate, and/or factual.\n",
"Score 4: The response is mostly correct, accurate, and factual.\n",
"Score 5: The response is completely correct, accurate, and factual.\n",
"\n",
"###Feedback:\"\"\"\n",
"\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"from langchain.schema import SystemMessage\n",
"\n",
"\n",
"evaluation_prompt_template = ChatPromptTemplate.from_messages(\n",
" [\n",
" SystemMessage(content=\"You are a fair evaluator language model.\"),\n",
" HumanMessagePromptTemplate.from_template(EVALUATION_PROMPT),\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ia9Mvn859jVP"
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"\n",
"eval_chat_model = ChatOpenAI(model=\"gpt-4-1106-preview\", temperature=0)\n",
"evaluator_name = \"GPT4\"\n",
"\n",
"\n",
"def evaluate_answers(\n",
" answer_path: str,\n",
" eval_chat_model: BaseChatModel,\n",
" evaluator_name: str,\n",
" evaluation_prompt_template: ChatPromptTemplate,\n",
") -> None:\n",
" \"\"\"Evaluates generated answers. Modifies the given answer file in place for better checkpointing.\"\"\"\n",
" answers = []\n",
" if os.path.isfile(answer_path): # load previous generations if they exist\n",
" answers = json.load(open(answer_path, \"r\"))\n",
"\n",
" for experiment in tqdm(answers):\n",
" if f\"eval_score_{evaluator_name}\" in experiment:\n",
" continue\n",
"\n",
" eval_prompt = evaluation_prompt_template.format_messages(\n",
" instruction=experiment[\"question\"],\n",
" response=experiment[\"generated_answer\"],\n",
" reference_answer=experiment[\"true_answer\"],\n",
" )\n",
" eval_result = eval_chat_model.invoke(eval_prompt)\n",
" feedback, score = [item.strip() for item in eval_result.content.split(\"[RESULT]\")]\n",
" experiment[f\"eval_score_{evaluator_name}\"] = score\n",
" experiment[f\"eval_feedback_{evaluator_name}\"] = feedback\n",
"\n",
" with open(answer_path, \"w\") as f:\n",
" json.dump(answers, f)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EXH-szLe9jVP"
},
"source": [
"🚀 Let's run the tests and evaluate answers!👇"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jW2nnvUT9jVQ"
},
"outputs": [],
"source": [
"if not os.path.exists(\"./output\"):\n",
" os.mkdir(\"./output\")\n",
"\n",
"for chunk_size in [200]: # Add other chunk sizes (in tokens) as needed\n",
" for embeddings in [\"thenlper/gte-small\"]: # Add other embeddings as needed\n",
" for rerank in [True, False]:\n",
" settings_name = f\"chunk:{chunk_size}_embeddings:{embeddings.replace('/', '~')}_rerank:{rerank}_reader-model:{READER_MODEL_NAME}\"\n",
" output_file_name = f\"./output/rag_{settings_name}.json\"\n",
"\n",
" print(f\"Running evaluation for {settings_name}:\")\n",
"\n",
" print(\"Loading knowledge base embeddings...\")\n",
" knowledge_index = load_embeddings(\n",
" RAW_KNOWLEDGE_BASE,\n",
" chunk_size=chunk_size,\n",
" embedding_model_name=embeddings,\n",
" )\n",
"\n",
" print(\"Running RAG...\")\n",
" reranker = (\n",
" RAGPretrainedModel.from_pretrained(\"colbert-ir/colbertv2.0\") if rerank else None\n",
" )\n",
" run_rag_tests(\n",
" eval_dataset=eval_dataset,\n",
" llm=READER_LLM,\n",
" knowledge_index=knowledge_index,\n",
" output_file=output_file_name,\n",
" reranker=reranker,\n",
" verbose=False,\n",
" test_settings=settings_name,\n",
" )\n",
"\n",
" print(\"Running evaluation...\")\n",
" evaluate_answers(\n",
" output_file_name,\n",
" eval_chat_model,\n",
" evaluator_name,\n",
" evaluation_prompt_template,\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tytXV5-h9jVT"
},
"source": [
"### Inspect results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "D4YDSfmr9jVT"
},
"outputs": [],
"source": [
"import glob\n",
"\n",
"outputs = []\n",
"for file in glob.glob(\"./output/*.json\"):\n",
" output = pd.DataFrame(json.load(open(file, \"r\")))\n",
" output[\"settings\"] = file\n",
" outputs.append(output)\n",
"result = pd.concat(outputs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CdkXMNvS9jVT"
},
"outputs": [],
"source": [
"result[\"eval_score_GPT4\"] = result[\"eval_score_GPT4\"].apply(\n",
" lambda x: int(x) if isinstance(x, str) else 1\n",
")\n",
"result[\"eval_score_GPT4\"] = (result[\"eval_score_GPT4\"] - 1) / 4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lgxBpid29jVT",
"outputId": "9a3bcf32-4b0c-4df1-c76c-3ebbca82929d"
},
"outputs": [
{
"data": {
"text/plain": [
"settings\n",
"./output/rag_chunk:200_embeddings:thenlper~gte-small_rerank:False_reader-model:zephyr-7b-beta.json 0.884328\n",
"./output/rag_chunk:200_embeddings:BAAI~bge-base-en-v1.5_rerank:False_reader-model:zephyr-7b-beta.json 0.906716\n",
"./output/rag_chunk:200_embeddings:BAAI~bge-base-en-v1.5_rerank:True_reader-model:zephyr-7b-beta.json 0.906716\n",
"./output/rag_chunk:200_embeddings:thenlper~gte-small_rerank:True_reader-model:mixtral.json 0.906716\n",
"./output/rag_chunk:200_embeddings:thenlper~gte-small_rerank:True_reader-model:zephyr-7b-beta.json 0.921642\n",
"./output/rag_chunk:200_embeddings:thenlper~gte-small_rerank:True_reader-model:mixtral0.json 0.947761\n",
"Name: eval_score_GPT4, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"average_scores = result.groupby(\"settings\")[\"eval_score_GPT4\"].mean()\n",
"average_scores.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pSPH9DYI9jVT"
},
"source": [
"## Example results\n",
"\n",
"Let us load the results that I obtained by tweaking the different options available in this notebook.\n",
"For more detail on why these options could work on not, see the notebook on [advanced_RAG](advanced_rag).\n",
"\n",
"As you can see in the graph below, some tweaks do not bring any improvement, some give huge performance boosts.\n",
"\n",
"➡️ ___There is no single good recipe: you should try several different directions when tuning your RAG systems.___\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RVOxatv99jVT"
},
"outputs": [],
"source": [
"import plotly.express as px\n",
"\n",
"scores = datasets.load_dataset(\"m-ric/rag_scores_cookbook\", split=\"train\")\n",
"scores = pd.Series(scores[\"score\"], index=scores[\"settings\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vqK0Dg2Q9jVT"
},
"outputs": [],
"source": [
"fig = px.bar(\n",
" scores,\n",
" color=scores,\n",
" labels={\n",
" \"value\": \"Accuracy\",\n",
" \"settings\": \"Configuration\",\n",
" },\n",
" color_continuous_scale=\"bluered\",\n",
")\n",
"fig.update_layout(w\n",
" width=1000,\n",
" height=600,\n",
" barmode=\"group\",\n",
" yaxis_range=[0, 100],\n",
" title=\"<b>Accuracy of different RAG configurations</b>\",\n",
" xaxis_title=\"RAG settings\",\n",
" font=dict(size=15),\n",
")\n",
"fig.layout.yaxis.ticksuffix = \"%\"\n",
"fig.update_coloraxes(showscale=False)\n",
"fig.update_traces(texttemplate=\"%{y:.1f}\", textposition=\"outside\")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dPUOMWGk9jVT"
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"source": [
"<img src=\"https://huggingface.co/datasets/huggingface/cookbook-images/resolve/main/RAG_settings_accuracy.png\" height=\"500\" width=\"800\">\n",
"\n",
"As you can see, these had varying impact on performance. In particular, tuning the chunk size is both easy and very impactful.\n",
"\n",
"But this is our case: your results could be very different: now that you have a robust evaluation pipeline, you can set on to explore other options! 🗺️"
]
}
],
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|