From b6bf8bbba5986c392b52481aa258174fce3b3b14 Mon Sep 17 00:00:00 2001
From: Laurentius von Liechti <432717+kurisu@users.noreply.github.com>
Date: Fri, 25 Oct 2024 23:53:10 -0700
Subject: [PATCH] chore: Cleaning up code and documentation

---
 README.md          |  43 +++++----
 app.py             |  20 +++--
 benchmarking.ipynb | 213 +++++++++++++++++++++++++++++----------------
 3 files changed, 179 insertions(+), 97 deletions(-)

diff --git a/README.md b/README.md
index 34c929c..34920b6 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,8 @@ python_version: 3.11.9
 
 The project is built using Transformers Agents 2.0, and uses the Stanford SQuAD dataset for training. The chatbot is designed to answer questions about the dataset, while also incorporating conversational context and various tools to provide a more natural and engaging conversational experience.
 
+At the time of writing, the project is available on [Hugging Face Spaces](https://huggingface.co/spaces/kaiokendall/SQuAD_Agent_Experiment).
+
 ## Getting Started
 
 1. Install dependencies:
@@ -23,13 +25,16 @@ pip install -r pre-requirements.txt
 pip install -r requirements.txt
 ```
 
-1. Set up required keys:
+2. Set up required keys:
+
+Create a `.env` file and set the following environment variables:
 
 ```bash
 HF_TOKEN=<your token>
+OPENAI_API_KEY=<your key>
 ```
 
-1. Run the app:
+3. Run the app:
 
 ```bash
 python app.py
@@ -39,37 +44,37 @@ python app.py
 
 1. SQuAD Dataset: The dataset used for training the chatbot is the Stanford SQuAD dataset, which contains over 100,000 questions and answers extracted from 500+ articles.
 2. RAG: RAG is a technique used to improve the accuracy of chatbots by using a custom knowledge base. In this project, the Stanford SQuAD dataset is used as the knowledge base.
-3. Llama 3.1: Llama 3.1 is a large language model used to generate responses to user questions. It is used in this project to generate responses to user questions, while also incorporating conversational context.
-4. Transformers Agents 2.0: Transformers Agents 2.0 is a framework for building conversational AI systems. It is used in this project to build the chatbot.
-5. Created a SquadRetrieverTool to integrate a fine-tuned BERT model into the agent, along with a TextToImageTool for a playful way to engage with the question-answering agent.
+3. Transformers Agents 2.0: Transformers Agents 2.0 is a framework for building conversational AI systems. It is used in this project to build the chatbot.
+4. Created a SquadRetrieverTool to integrate a fine-tuned BERT model into the agent, along with a TextToImageTool for a playful way to engage with the question-answering agent.
+5. Gradio: Gradio is used to create the chatbot interface, in `app.py`.
 
 ## Evaluation
 
-* [Agent Reasoning Benchmark](https://github.com/aymeric-roucher/agent_reasoning_benchmark)
-* [Hugging Face Blog: Open Source LLMs as Agents](https://huggingface.co/blog/open-source-llms-as-agents)
-* [Benchmarking Transformers Agents](https://github.com/aymeric-roucher/agent_reasoning_benchmark/blob/main/benchmark_transformers_agents.ipynb)
+SemScore is used in this project to evaluate the chatbot's responses in the notebook `benchmarking.ipynb`.
 
-## Results
+See [SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity](https://doi.org/10.48550/arXiv.2401.17072)
 
-TBD
+In this experiment, the agent is evaluated with 3 different system prompting approaches:
 
-## Limitations
+1. The default prompting approach, which is just the default system prompt used in Hugging Face Transformers Agents 2.0, with only an example of using the `squad_retriever` tool added.
+2. A succinct prompting approach, which guides the agent to be concise if possible while still answering the question.
+3. A focused prompting approach, which reframes the entire chatbots purpose to focus more on the specific task of answering questions about the SQuAD dataset, while still being open to exploring other topics.
+
+## Results
 
-TBD
 
-## Related Research
 
-* [Retro: A Generalist Agent for Science](https://arxiv.org/abs/2112.04426)
-* [RETRO-pytorch](https://github.com/lucidrains/RETRO-pytorch)
-* [Why isn't Retro mainstream? State-of-the-art within reach](https://www.reddit.com/r/MachineLearning/comments/1cffgkt/d_why_isnt_retro_mainstream_stateoftheart_within/)
+## Limitations
 
-TBD
+* This experiment is not designed for multiple users. While it has in-session memory, simply refreshing the browser will reset the chat history, which is convenient for experimentation.
+* Some of the agent's underlying engines, models, and tools use keys that have usage limits, so the app may not work if those limits have been reached.
+  * It is recommended to clone the repo and run the code using your own keys, to avoid running into those limits.
 
 ## Acknowledgments
 
-* [Agents 2.0](https://github.com/huggingface/transformers/tree/main/src/transformers/agents)
+* [Hugging Face Transformers Agents 2.0](https://huggingface.co/docs/transformers/en/main_classes/agent)
 * [SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity](https://arxiv.org/abs/2401.17072)
+* `semscore.py` from [geronimi73/semscore](https://github.com/geronimi73/semscore/blob/main/semscore.py)
 * [SemScore](https://huggingface.co/blog/g-ronimo/semscore)
 * [Stanford SQuAD](https://rajpurkar.github.io/SQuAD-explorer/)
-* [llama 3.1](https://github.com/meta-llama/Meta-Llama)
 * [Gradio](https://www.gradio.app/)
diff --git a/app.py b/app.py
index 16feae4..f558fe1 100644
--- a/app.py
+++ b/app.py
@@ -40,6 +40,12 @@
     else "http://localhost:1234/v1"
 )
 
+"""
+The ImageQuestionAnsweringTool from Transformers Agents 2.0 has a bug where 
+it said it accepts the path to an image, but it does not. 
+This class uses the adapter pattern to fix the issue, in a way that may be 
+compatible with future versions of the tool even if the bug is fixed.
+"""
 class FixImageQuestionAnsweringTool(ImageQuestionAnsweringTool):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -49,6 +55,13 @@ def encode(self, image: "Image | str", question: str):
             image = Image.open(image)
         return super().encode(image, question)
 
+"""
+The app version of the agent has access to additional tools that are not available
+during benchmarking. We chose this approach to focus benchmarking on the agent's
+ability to solve questions about the SQuAD dataset, without the help of general 
+knowledge available on the web.  For the purposes of the project, the demo 
+app has access to additional tools to provide a more interactive and engaging experience.
+"""
 ADDITIONAL_TOOLS = [
     DuckDuckGoSearchTool(),
     VisitWebpageTool(),
@@ -62,7 +75,7 @@ def encode(self, image: "Image | str", question: str):
 # Add image tools to the default task solving toolbox, for a more visually interactive experience
 TASK_SOLVING_TOOLBOX = DEFAULT_TASK_SOLVING_TOOLBOX + ADDITIONAL_TOOLS
 
-# system_prompt = DEFAULT_SQUAD_REACT_CODE_SYSTEM_PROMPT
+# Using the focused prompt, which was the top-performing prompt during benchmarking
 system_prompt = FOCUSED_SQUAD_REACT_CODE_SYSTEM_PROMPT
 
 agent = get_agent(
@@ -72,9 +85,6 @@ def encode(self, image: "Image | str", question: str):
     use_openai=True,  # Use OpenAI instead of a local or HF model as the base LLM engine
 )
 
-app = None
-
-
 def append_example_message(x: gr.SelectData, messages):
     if x.value["text"] is not None:
         message = x.value["text"]
@@ -197,7 +207,7 @@ def _postprocess_content(
                     "text": "What is on top of the Notre Dame building?",
                 },
                 {
-                    "text": "Tell me what's on top of the Notre Dame building, and draw a picture of it.",
+                    "text": "What is the Olympic Torch made of?",
                 },
                 {
                     "text": "Draw a picture of whatever is on top of the Notre Dame building.",
diff --git a/benchmarking.ipynb b/benchmarking.ipynb
index 75439df..c2e54fb 100644
--- a/benchmarking.ipynb
+++ b/benchmarking.ipynb
@@ -37,25 +37,34 @@
     "from statistics import mean\n",
     "from agent import get_agent\n",
     "from openai import OpenAI\n",
-    "from prompts import SUCCINCT_SQUAD_REACT_CODE_SYSTEM_PROMPT, FOCUSED_SQUAD_REACT_CODE_SYSTEM_PROMPT\n",
+    "from prompts import (\n",
+    "    SUCCINCT_SQUAD_REACT_CODE_SYSTEM_PROMPT,\n",
+    "    FOCUSED_SQUAD_REACT_CODE_SYSTEM_PROMPT,\n",
+    "    DEFAULT_SQUAD_REACT_CODE_SYSTEM_PROMPT,\n",
+    ")\n",
     "import re\n",
     "from string import punctuation\n",
     "from nltk.corpus import stopwords\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "from dotenv import load_dotenv\n",
+    "\n",
     "load_dotenv()  # Load OPENAI_API_KEY from .env (not included in repo)\n",
     "\n",
     "SAMPLES_DIR = \"samples\"\n",
     "BENCHMARKS_DIR = \"benchmarks\"\n",
-    "STOP_WORDS = set(stopwords.words('english'))\n",
+    "STOP_WORDS = set(stopwords.words(\"english\"))\n",
+    "\n",
     "\n",
     "def display_text_df(df):\n",
-    "    display(df.style.set_properties(**{'white-space': 'pre-wrap'}).set_table_styles(\n",
-    "        [{'selector': 'th', 'props': [('text-align', 'left')]},\n",
-    "         {'selector': 'td', 'props': [('text-align', 'left')]}\n",
-    "        ]\n",
-    "    ))\n"
+    "    display(\n",
+    "        df.style.set_properties(**{\"white-space\": \"pre-wrap\"}).set_table_styles(\n",
+    "            [\n",
+    "                {\"selector\": \"th\", \"props\": [(\"text-align\", \"left\")]},\n",
+    "                {\"selector\": \"td\", \"props\": [(\"text-align\", \"left\")]},\n",
+    "            ]\n",
+    "        )\n",
+    "    )"
    ]
   },
   {
@@ -512,6 +521,110 @@
     "    return dfAnswers\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Retro-active Diversion - Llama Index's Chat Engine\n",
+    "\n",
+    "* After completing this notebook, including the benchmark comparison towards the end, I realized that Llama Index's chat engine is a good example of how to use a vector database to power a QA chatbot with minimal code.\n",
+    "* So I decided to quickly benchmark it to see if I should include it in the final version of the project.\n",
+    "* The default chat engine uses a retriever, just like my agent, then uses an LLM to answer questions using that retriever as a tool. \n",
+    "* I'll use the same approach to benchmark this alternative agent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43661bb4be16494388350e1a0cea1082",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "chat_engine = data.index.as_chat_engine()\n",
+    "answers_ref, answers_pred = [], []\n",
+    "\n",
+    "for title, context, question, answer, synthesized_question in tqdm(dfSample.values):\n",
+    "    prompt = synthesized_question\n",
+    "    final_answer = chat_engine.chat(prompt)\n",
+    "    answers_ref.append(answer)\n",
+    "    answers_pred.append(final_answer)\n",
+    "\n",
+    "answers_ref = [str(answer) for answer in answers_ref]\n",
+    "answers_pred = [str(answer) for answer in answers_pred]\n",
+    "\n",
+    "dfAnswersCE = dfSample.copy()\n",
+    "dfAnswersCE[\"Predicted Answer\"] = answers_pred\n",
+    "\n",
+    "dfAnswersCE = dfSample.copy()\n",
+    "dfAnswersCE[\"Predicted Answer\"] = answers_pred\n",
+    "\n",
+    "# Remove stop words and punctuation from answers\n",
+    "answers_ref = [clean_text(answer) for answer in answers_ref]\n",
+    "answers_pred = [clean_text(answer) for answer in answers_pred]\n",
+    "\n",
+    "dfAnswersCE[\"Cleaned Answer\"] = answers_ref\n",
+    "dfAnswersCE[\"Cleaned Predicted Answer\"] = answers_pred\n",
+    "\n",
+    "em = EmbeddingModelWrapper()\n",
+    "similarities = em.get_similarities(\n",
+    "    em.get_embeddings( answers_pred ),\n",
+    "    em.get_embeddings( answers_ref ),\n",
+    ")\n",
+    "\n",
+    "dfAnswersCE[\"Similarity\"] = similarities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "#### Llama Index Chat Engine Mean Similarity: 0.45"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(\n",
+    "    Markdown(\n",
+    "        f\"#### Llama Index Chat Engine Mean Similarity: {round(dfAnswersCE['Similarity'].mean(), 2)}\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Observations\n",
+    "\n",
+    "* The Llama Index Chat Engine has roughly the same mean semantic similarity as the `baseline` agent.\n",
+    "* It doesn't seem like including it would add much value, so I'll stick with the agent variations I built using Transformers Agents 2.0.\n",
+    "* Getting back to the original benchmarks:\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -589,7 +702,7 @@
    "source": [
     "# Create the agents to be benchmarked\n",
     "benchmarks = [\n",
-    "    {\"agent\": get_agent(), \"name\": \"baseline\"}, # Baseline agent with default settings\n",
+    "    {\"agent\": get_agent(system_prompt=DEFAULT_SQUAD_REACT_CODE_SYSTEM_PROMPT), \"name\": \"baseline\"}, # Baseline agent with default settings\n",
     "    {\"agent\": get_agent(system_prompt=SUCCINCT_SQUAD_REACT_CODE_SYSTEM_PROMPT), \"name\": \"succinct\"}, # Succinct agent\n",
     "    {\"agent\": get_agent(system_prompt=FOCUSED_SQUAD_REACT_CODE_SYSTEM_PROMPT), \"name\": \"focused\"}, # Focused agent\n",
     "]\n",
@@ -620,14 +733,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 227,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1200x600 with 2 Axes>"
+       "<Figure size 900x450 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -646,7 +759,7 @@
     "# Plot the mean similarity for each benchmark\n",
     "mean_similarities = [benchmark[\"mean_similarity\"] for benchmark in benchmarks]\n",
     "\n",
-    "fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(9, 4.5))\n",
     "axs[0].bar(benchmark_names, mean_similarities)\n",
     "for i, v in enumerate(mean_similarities):\n",
     "    axs[0].text(i, v + 0.01, str(round(v, 2)), ha=\"center\", va=\"bottom\")\n",
@@ -765,67 +878,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 224,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/markdown": [
-       "#### Number of answers with Similarity == 1.0 for baseline"
-      ],
+      "image/png": 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",
       "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Number of answers with Similarity == 1.0 for succinct"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "#### Number of answers with Similarity == 1.0 for focused"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "45"
+       "<Figure size 600x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -834,9 +894,16 @@
    ],
    "source": [
     "# Show how many answers have Similarity == 1.0 exactly\n",
+    "# Plot the number of answers with Similarity == 1.0 for each benchmark on a horizontal bar chart with seaborn\n",
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
     "for benchmark in benchmarks:\n",
-    "    display(Markdown(f\"#### Number of answers with Similarity == 1.0 for {benchmark['name']}\"))\n",
-    "    display(benchmark['data'][benchmark['data']['Similarity'] == 1.0].shape[0])"
+    "    ax.barh(benchmark['name'], benchmark['data'][benchmark['data']['Similarity'] == 1.0].shape[0])\n",
+    "ax.set_xlabel('Number of answers')\n",
+    "ax.set_ylabel('Benchmark')\n",
+    "ax.set_title('Number of answers with Similarity == 1.0')\n",
+    "for container in ax.containers:\n",
+    "    ax.bar_label(container, label_type='edge')\n",
+    "plt.show()"
    ]
   },
   {
@@ -1608,13 +1675,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## BenchmarkingConclusion\n",
+    "## Conclusion\n",
     "\n",
     "Overall, the semantic similarity metric differentiates between good and bad answers, but is prone to penalizing agents for elaborating on answers when the reference answer is brief.  \n",
     "\n",
-    "## BenchmarkingFuture Work\n",
+    "## Future Work\n",
     "\n",
-    "* It may be interesting to establish both concise and contextualized acceptable answers, and then take the max sementic similarity score between the predicted answer and the acceptable answers.\n",
+    "* It may be interesting to have human-generated, contextualized acceptable answers to go with the concise answers, and then take the max sementic similarity score between the predicted answer and the acceptable answers, to avoid penalizing agents for providing relevant context.\n",
     "* It would also be interesting to look for and include the other acceptable answers found in the SQuAD dataset. "
    ]
   }