\\n \\n ... \n",
"3 \\n \\n ... \n",
"4 \\n ... \n",
"1 [div] AAPL -> CLICK \n",
"2 [checkbox] After School Care -> CLICK \n",
"3 [div] Smile -> CLICK \n",
"4 [img] James Smith -> CLICK \n",
"\n",
" pos_candidates \\\n",
"0 [{'attributes': '{\"backend_node_id\": \"248\", \"b... \n",
"1 [{'attributes': '{\"backend_node_id\": \"14603\", ... \n",
"2 [{'attributes': '{\"backend_node_id\": \"44285\", ... \n",
"3 [{'attributes': '{\"backend_node_id\": \"23642\", ... \n",
"4 [{'attributes': '{\"backend_node_id\": \"117\", \"b... \n",
"\n",
" prev_actions_desc \\\n",
"0 None \n",
"1 ['Enter \"apple\" in the search box to look up t... \n",
"2 ['Click on the \"services for RESIDENTS\" link.'... \n",
"3 ['Search for \"Smile\" in the TV Shows and Movie... \n",
"4 None \n",
"\n",
" prev_actions_reprs \n",
"0 None \n",
"1 ['[textbox] Search for news, symbols or compa... \n",
"2 ['[link] services for RESIDENTS -> CLICK', '[... \n",
"3 ['[textbox] Search TV Shows and Movies... -> ... \n",
"4 None "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd \n",
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"BigAction/mind2web_clean\")\n",
"\n",
"df = dataset[\"train\"].to_pandas()\n",
"df.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Utils"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will define some utility functions for this benchmark.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"import ast\n",
"\n",
"def keep_assignments(code_snippet):\n",
" # Regex to match variable assignments. This pattern assumes variable names are valid Python identifiers\n",
" # and captures typical assignment statements, excluding those that might appear in comments or strings.\n",
" pattern = r'^\\s*[a-zA-Z_][a-zA-Z0-9_]*\\s*=\\s*.+'\n",
"\n",
" # Filter and keep only lines with variable assignments\n",
" assignments = [line for line in code_snippet.split('\\n') if re.match(pattern, line)]\n",
"\n",
" # Join the filtered lines back into a string\n",
" return \"\\n\".join(assignments)\n",
"\n",
"# This function will be used to visit each node in the AST\n",
"class VariableVisitor(ast.NodeVisitor):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.output = []\n",
" \n",
" def visit_Assign(self, node):\n",
" \n",
" # For each assignment, print the targets (variables)\n",
" for target in node.targets:\n",
" if isinstance(target, ast.Name): # Ensure it's a variable assignment\n",
" self.output.append(target.id)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup\n",
"\n",
"def beautify_html(html_string):\n",
" # Use BeautifulSoup to prettify the HTML\n",
" soup = BeautifulSoup(html_string, 'html.parser')\n",
" pretty_html_string = soup.prettify()\n",
" return pretty_html_string\n",
"\n",
"# Function to extract all 'backend_node_id' attributes\n",
"def extract_backend_node_ids(html_content):\n",
" soup = BeautifulSoup(html_content, 'html.parser')\n",
" return set([tag['backend_node_id'] for tag in soup.find_all(attrs={\"backend_node_id\": True})])\n",
"\n",
"def intersection_score(set1, set2):\n",
" intersection_set = set1 & set2\n",
" intersection_len = len(intersection_set)\n",
" ratio1 = intersection_len / len(set1)\n",
" ratio2 = intersection_len / len(set2) \n",
" return max(ratio1, ratio2), min(ratio1, ratio2)\n",
"\n",
"def intersection_backend_node_id(ground_truth_outer_html, context_str):\n",
" ground_truth_ids = extract_backend_node_ids(ground_truth_outer_html)\n",
" context_ids = extract_backend_node_ids(context_str)\n",
" recall, precision = intersection_score(ground_truth_ids, context_ids)\n",
" return recall, precision\n",
"\n",
"def load_html(html, driver):\n",
" \"\"\"Loads a specific HTML content into the browser.\"\"\"\n",
" file_path = 'sample_page.html'\n",
"\n",
" with open(file_path, 'w', encoding='utf-8') as file:\n",
" file.write(html)\n",
" \n",
" abs_file_path = os.path.abspath(\"sample_page.html\")\n",
"\n",
" # Use the file:/// protocol to load the local HTML file\n",
" driver.get(f\"file:///{abs_file_path}\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"config.json: 100%|██████████| 743/743 [00:00<00:00, 4.57MB/s]\n",
"model.safetensors: 100%|██████████| 133M/133M [00:00<00:00, 494MB/s] \n",
"tokenizer_config.json: 100%|██████████| 366/366 [00:00<00:00, 2.34MB/s]\n",
"vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 41.4MB/s]\n",
"tokenizer.json: 100%|██████████| 711k/711k [00:00<00:00, 35.5MB/s]\n",
"special_tokens_map.json: 100%|██████████| 125/125 [00:00<00:00, 871kB/s]\n"
]
}
],
"source": [
"\n",
"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
"from llama_index.core.node_parser import LangchainNodeParser, CodeSplitter\n",
"from llama_index.retrievers.bm25 import BM25Retriever\n",
"from llama_index.core import VectorStoreIndex, Document\n",
"from lavague.defaults import DefaultEmbedder\n",
"\n",
"def get_retriever_recursive(embed, html):\n",
" \n",
" K = 3\n",
" \n",
" text_list = [html]\n",
" documents = [Document(text=t) for t in text_list]\n",
" \n",
" splitter = LangchainNodeParser(lc_splitter=RecursiveCharacterTextSplitter.from_language(\n",
" language=\"html\",\n",
" ))\n",
" \n",
" nodes = splitter.get_nodes_from_documents(documents)\n",
" nodes = [node for node in nodes if node.text]\n",
"\n",
" index = VectorStoreIndex(nodes, embed_model=embed)\n",
" retriever_recursive = BM25Retriever.from_defaults(index = index, similarity_top_k=K)\n",
" return retriever_recursive\n",
"\n",
"def get_retriever_code(embed, html):\n",
" \n",
" K = 3\n",
" \n",
" text_list = [html]\n",
" documents = [Document(text=t) for t in text_list]\n",
" \n",
" splitter = CodeSplitter(\n",
" language=\"html\",\n",
" chunk_lines=50, # lines per chunk\n",
" chunk_lines_overlap=15, # lines overlap between chunks\n",
" max_chars=2000, # max chars per chunk\n",
" )\n",
" \n",
" nodes = splitter.get_nodes_from_documents(documents)\n",
" nodes = [node for node in nodes if node.text]\n",
"\n",
" index = VectorStoreIndex(nodes, embed_model=embed)\n",
" retriever_code = BM25Retriever.from_defaults(index = index, similarity_top_k=K)\n",
" return retriever_code\n",
"\n",
"def get_nodes(query, html, get_retriever, embedder): \n",
" retriever = get_retriever(embedder, html)\n",
" source_nodes = retriever.retrieve(query)\n",
" source_nodes = [node.text for node in source_nodes]\n",
" return source_nodes\n",
"\n",
"embedder = DefaultEmbedder()\n",
"\n",
"import re\n",
"\n",
"def extract_first_python_code(markdown_text: str):\n",
" # Pattern to match the first ```python ``` code block\n",
" pattern = r\"```python(.*?)```\"\n",
"\n",
" # Using re.DOTALL to make '.' match also newlines\n",
" match = re.search(pattern, markdown_text, re.DOTALL)\n",
" if match:\n",
" # Return the first matched group, which is the code inside the ```python ```\n",
" return match.group(1).strip()\n",
" else:\n",
" # Return None if no match is found\n",
" return None\n",
"\n",
"decontaminate_html = lambda x: re.sub(r' backend_node_id=\"\\d+\"', '', x)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"192it [06:13, 1.94s/it]\n"
]
},
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute 'split'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[47], line 42\u001b[0m\n\u001b[1;32m 39\u001b[0m generated_code \u001b[38;5;241m=\u001b[39m extract_first_python_code(response)\n\u001b[1;32m 41\u001b[0m \u001b[38;5;66;03m# Keep only the variable assignments in the generated code\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m code \u001b[38;5;241m=\u001b[39m \u001b[43mkeep_assignments\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgenerated_code\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 44\u001b[0m \u001b[38;5;66;03m# Split the code into lines and keep only the first assignment\u001b[39;00m\n\u001b[1;32m 45\u001b[0m code \u001b[38;5;241m=\u001b[39m code\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n",
"Cell \u001b[0;32mIn[5], line 10\u001b[0m, in \u001b[0;36mkeep_assignments\u001b[0;34m(code_snippet)\u001b[0m\n\u001b[1;32m 7\u001b[0m pattern \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m^\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*[a-zA-Z_][a-zA-Z0-9_]*\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*=\u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124ms*.+\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# Filter and keep only lines with variable assignments\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m assignments \u001b[38;5;241m=\u001b[39m [line \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m \u001b[43mcode_snippet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m re\u001b[38;5;241m.\u001b[39mmatch(pattern, line)]\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# Join the filtered lines back into a string\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(assignments)\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'split'"
]
}
],
"source": [
"from tqdm import tqdm\n",
"\n",
"outputs = []\n",
"\n",
"get_retriever = get_retriever_recursive\n",
"retriever_name = get_retriever.__name__\n",
"\n",
"for index, row in tqdm(df.iterrows()):\n",
" \n",
" if row[\"action_uid\"] in results[\"action_uid\"].values:\n",
" continue\n",
" \n",
" ground_truth_code = row[\"code\"]\n",
" html = row[\"cleaned_html\"]\n",
" query = row[\"cur_actions_desc\"]\n",
" action_uid = row[\"action_uid\"]\n",
"\n",
" backend_node_id = ground_truth_code.split(\"backend_node_id\")[1].split(\"']\")[0].replace(\"='\",\"\")\n",
"\n",
" load_html(html, driver)\n",
"\n",
" source_nodes = get_nodes(query, html, get_retriever, embedder)\n",
" backend_node_id = ground_truth_code.split(\"backend_node_id\")[1].split(\"']\")[0].replace(\"='\",\"\")\n",
"\n",
" ground_truth_element = driver.find_element(By.XPATH, f'//*[@backend_node_id=\"{backend_node_id}\"]')\n",
" ground_truth_outer_html = driver.execute_script(\"return arguments[0].outerHTML;\", ground_truth_element)\n",
" \n",
" context_str = \"\\n\".join(source_nodes)\n",
" recall_retriever, precision_retriever = intersection_backend_node_id(ground_truth_outer_html, context_str)\n",
" \n",
" # We remove the backend node ids to ensure the LLM does not use them\n",
" decontaminated_context_str = decontaminate_html(context_str)\n",
" prompt = DEFAULT_PROMPT.format(context_str=decontaminated_context_str, query_str=query)\n",
"\n",
" response = llm.complete(prompt).text\n",
"\n",
" generated_code = extract_first_python_code(response)\n",
"\n",
" # Keep only the variable assignments in the generated code\n",
" code = keep_assignments(generated_code)\n",
"\n",
" # Split the code into lines and keep only the first assignment\n",
" code = code.split(\"\\n\")[0]\n",
" parsed_code = ast.parse(code)\n",
"\n",
" recall_llm, precision_llm = None, None\n",
" execution_error = \"\"\n",
"\n",
" try:\n",
" # Create a visitor instance and use it to visit the nodes in the parsed AST\n",
" visitor = VariableVisitor()\n",
" visitor.visit(parsed_code)\n",
" variable_name = visitor.output[0]\n",
"\n",
" # Execute the code to define the first variable\n",
" exec(code)\n",
"\n",
" # Assign the variable to the target_element variable which will be used afterwards to compute score\n",
" target_element = None\n",
" exec(f\"\"\"target_element = {variable_name}\"\"\")\n",
"\n",
" target_outer_html = driver.execute_script(\"return arguments[0].outerHTML;\", target_element)\n",
" recall_llm, precision_llm = intersection_backend_node_id(ground_truth_outer_html, target_outer_html)\n",
" except Exception as e:\n",
" target_outer_html = \"\"\n",
" execution_error = str(e)\n",
"\n",
" output = {\n",
" \"query\": query,\n",
" \"recall_retriever\": recall_retriever,\n",
" \"precision_retriever\": precision_retriever,\n",
" \"recall_llm\": recall_llm,\n",
" \"precision_llm\": precision_llm,\n",
" \"html\": html,\n",
" \"action_uid\": action_uid,\n",
" \"source_nodes\": (\"-\"*10).join(source_nodes),\n",
" \"ground_truth_code\": f\"\"\"ground_truth_element = driver.find_element(By.XPATH, f'//*[@backend_node_id=\"{backend_node_id}\"]')\"\"\",\n",
" \"ground_truth_outer_html\": ground_truth_outer_html,\n",
" \"generated_code\": generated_code,\n",
" \"target_outer_html\": target_outer_html,\n",
" \"execution_error\": execution_error,\n",
" \"retriever\": retriever_name,\n",
" \"model\": model,\n",
" }\n",
" outputs.append(output)\n",
" \n",
" results = pd.DataFrame(outputs)\n",
"\n",
" results.to_csv(f\"benchmark_retriever_{retriever_name}_model_{model}.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"results = pd.read_csv(\"benchmark_retriever_get_retriever_recursive_model_gpt-35-turbo.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" query \n",
" recall_retriever \n",
" precision_retriever \n",
" recall_llm \n",
" precision_llm \n",
" html \n",
" action_uid \n",
" source_nodes \n",
" ground_truth_code \n",
" ground_truth_outer_html \n",
" generated_code \n",
" target_outer_html \n",
" execution_error \n",
" retriever \n",
" model \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Enter \"Rock And Roll Over\" in the text box to ... \n",
" 0.0 \n",
" 0.000000 \n",
" 0.0 \n",
" 0.000000 \n",
" <html backend_node_id=\"338\">\\n <body>\\n <d... \n",
" 96238fb3-bc46-4dff-95c0-4b2d4ecc70ea \n",
" <div backend_node_id=\"2662\">\\n ... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <input backend_node_id=\"248\" type=\"text\" name=... \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <text backend_node_id=\"2663\">Hi ! I have a Iba... \n",
" NaN \n",
" get_retriever_recursive \n",
" NaN \n",
" \n",
" \n",
" 1 \n",
" Click on \"AAPL\" to look up the visitors trend ... \n",
" 1.0 \n",
" 0.114943 \n",
" 1.0 \n",
" 0.100000 \n",
" <html backend_node_id=\"14307\">\\n <div backend... \n",
" 8043e8e8-bb7a-4521-80bf-246bc16e883c \n",
" <div backend_node_id=\"14708\">\\n ... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <li backend_node_id=\"14603\" role=\"option\" titl... \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <text backend_node_id=\"14606\">AAPL</text> \n",
" NaN \n",
" get_retriever_recursive \n",
" NaN \n",
" \n",
" \n",
" 2 \n",
" Select the After School Care checkbox. \n",
" 1.0 \n",
" 0.008772 \n",
" 1.0 \n",
" 1.000000 \n",
" <html backend_node_id=\"44287\">\\n <body>\\n ... \n",
" b416d08b-90c4-43e1-b3d6-9d4a05e56a06 \n",
" <span backend_node_id=\"44871\">\\n ... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <input backend_node_id=\"44285\" type=\"checkbox\"... \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <input backend_node_id=\"44285\" type=\"checkbox\"... \n",
" NaN \n",
" get_retriever_recursive \n",
" NaN \n",
" \n",
" \n",
" 3 \n",
" Click on \"Smile.\" \n",
" 1.0 \n",
" 0.050000 \n",
" 1.0 \n",
" 0.333333 \n",
" <html backend_node_id=\"23177\">\\n <body>\\n ... \n",
" fd713700-3876-44a8-80ab-4da898beab42 \n",
" <div backend_node_id=\"23101\">\\n ... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <li backend_node_id=\"23642\" role=\"option\">\\n ... \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <text backend_node_id=\"23648\">Smile</text> \n",
" NaN \n",
" get_retriever_recursive \n",
" NaN \n",
" \n",
" \n",
" 4 \n",
" Click on James Smith's profile image. \n",
" 1.0 \n",
" 0.011236 \n",
" 1.0 \n",
" 1.000000 \n",
" <html backend_node_id=\"123\">\\n <body backend_... \n",
" 01342a5e-5cc3-45e7-bd9d-b522611fc7bf \n",
" <body backend_node_id=\"187\">\\n <div backend... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <img backend_node_id=\"117\" alt=\"James Smith\"> \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <img backend_node_id=\"117\" alt=\"James Smith\"> \n",
" NaN \n",
" get_retriever_recursive \n",
" NaN \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" query recall_retriever \\\n",
"0 Enter \"Rock And Roll Over\" in the text box to ... 0.0 \n",
"1 Click on \"AAPL\" to look up the visitors trend ... 1.0 \n",
"2 Select the After School Care checkbox. 1.0 \n",
"3 Click on \"Smile.\" 1.0 \n",
"4 Click on James Smith's profile image. 1.0 \n",
"\n",
" precision_retriever recall_llm precision_llm \\\n",
"0 0.000000 0.0 0.000000 \n",
"1 0.114943 1.0 0.100000 \n",
"2 0.008772 1.0 1.000000 \n",
"3 0.050000 1.0 0.333333 \n",
"4 0.011236 1.0 1.000000 \n",
"\n",
" html \\\n",
"0 \\n \\n
\\n \\n \\n ... \n",
"3 \\n \\n ... \n",
"4 \\n \\n ... \n",
"1
\\n ... \n",
"2
\\n ... \n",
"3 \\n ... \n",
"4 \\n
\\n ... \n",
"4
\n",
"\n",
" generated_code \\\n",
"0 # Let's proceed step by step.\\n# First we need... \n",
"1 # Let's proceed step by step.\\n# First we need... \n",
"2 # Let's proceed step by step.\\n# First we need... \n",
"3 # Let's proceed step by step.\\n# First we need... \n",
"4 # Let's proceed step by step.\\n# First we need... \n",
"\n",
" target_outer_html execution_error \\\n",
"0
Hi ! I have a Iba... NaN \n",
"1 AAPL NaN \n",
"2 Smile NaN \n",
"4
NaN \n",
"\n",
" retriever model \n",
"0 get_retriever_recursive NaN \n",
"1 get_retriever_recursive NaN \n",
"2 get_retriever_recursive NaN \n",
"3 get_retriever_recursive NaN \n",
"4 get_retriever_recursive NaN "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = results['recall_retriever']\n",
"y = results['recall_llm']\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Filter results on recall retriever == 1.0\n",
"filtered_results = results[results['recall_retriever'] == 1.0]\n",
"\n",
"# Plot distribution of recall_llm\n",
"plt.hist(filtered_results['recall_llm'], bins=10)\n",
"plt.xlabel('recall_llm')\n",
"plt.ylabel('Frequency')\n",
"plt.title('Distribution of recall_llm when retriever has perfect recall')\n",
"plt.show()\n",
"\n",
"filtered_results = results[results['recall_retriever'] < 1.0]\n",
"\n",
"# Plot distribution of recall_llm\n",
"plt.hist(filtered_results['recall_llm'], bins=10)\n",
"plt.xlabel('recall_llm')\n",
"plt.ylabel('Frequency')\n",
"plt.title('Distribution of recall_llm when retriever has imperfect recall')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Retriever performance: 0.8177083333333334\n",
"LLM performance: 0.5572916666666666\n",
"Deperdition rate: 0.6815286624203821\n"
]
}
],
"source": [
"print(\"Retriever performance: \", (results.recall_retriever == 1.0).mean())\n",
"print(\"LLM performance: \", (results.recall_llm == 1.0).mean())\n",
"print(\"Deperdition rate: \", (results.recall_llm == 1.0).mean() / (results.recall_retriever == 1.0).mean())"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6242038216560509"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"perfect_retriever = results.loc[results.recall_retriever == 1.0]\n",
"(perfect_retriever.recall_llm == 1.0).mean()\n",
"# perfect_retriever = perfect_retriever.loc[~perfect_retriever.recall_llm.isna()]"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
"\n",
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\n",
" \n",
" \n",
" \n",
" query \n",
" recall_retriever \n",
" precision_retriever \n",
" recall_llm \n",
" precision_llm \n",
" html \n",
" action_uid \n",
" source_nodes \n",
" ground_truth_code \n",
" ground_truth_outer_html \n",
" generated_code \n",
" target_outer_html \n",
" execution_error \n",
" retriever \n",
" model \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" Enter \"Rock And Roll Over\" in the text box to ... \n",
" 0.000000 \n",
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" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
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" # Let's proceed step by step.\\n# First we need... \n",
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" \n",
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" \n",
" \n",
" 1 \n",
" Click on \"AAPL\" to look up the visitors trend ... \n",
" 1.000000 \n",
" 0.114943 \n",
" 1.0 \n",
" 0.100000 \n",
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" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <li backend_node_id=\"14603\" role=\"option\" titl... \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <text backend_node_id=\"14606\">AAPL</text> \n",
" \n",
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" \n",
" \n",
" 2 \n",
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" <span backend_node_id=\"44871\">\\n ... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
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" # Let's proceed step by step.\\n# First we need... \n",
" <input backend_node_id=\"44285\" type=\"checkbox\"... \n",
" \n",
" get_retriever_recursive \n",
" gpt-35-turbo \n",
" \n",
" \n",
" 3 \n",
" Click on \"Smile.\" \n",
" 1.000000 \n",
" 0.050000 \n",
" 1.0 \n",
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" fd713700-3876-44a8-80ab-4da898beab42 \n",
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" <li backend_node_id=\"23642\" role=\"option\">\\n ... \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <text backend_node_id=\"23648\">Smile</text> \n",
" \n",
" get_retriever_recursive \n",
" gpt-35-turbo \n",
" \n",
" \n",
" 4 \n",
" Click on James Smith's profile image. \n",
" 1.000000 \n",
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" 1.0 \n",
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" 01342a5e-5cc3-45e7-bd9d-b522611fc7bf \n",
" <body backend_node_id=\"187\">\\n <div backend... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <img backend_node_id=\"117\" alt=\"James Smith\"> \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <img backend_node_id=\"117\" alt=\"James Smith\"> \n",
" \n",
" get_retriever_recursive \n",
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" \n",
" \n",
" ... \n",
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" ... \n",
" ... \n",
" ... \n",
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" 187 \n",
" Click on the search option to get the best rat... \n",
" 0.000000 \n",
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" <html backend_node_id=\"15457\">\\n <body backen... \n",
" 5036b341-d933-4f21-9bde-af116b2f78e2 \n",
" <div backend_node_id=\"16067\">\\n ... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <svg backend_node_id=\"15442\" class=\"icon\"></svg> \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <form backend_node_id=\"15635\" name=\"search-for... \n",
" \n",
" get_retriever_recursive \n",
" gpt-35-turbo \n",
" \n",
" \n",
" 188 \n",
" Click on the \"Benefits and financial support f... \n",
" 1.000000 \n",
" 0.016260 \n",
" 1.0 \n",
" 0.500000 \n",
" <html backend_node_id=\"4953\">\\n <body>\\n <... \n",
" bf65fe94-df6e-4817-8530-93bf6b9800a3 \n",
" <div backend_node_id=\"5365\">\\n <f... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <a backend_node_id=\"4947\">\\n ... \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <text backend_node_id=\"5568\">Benefits and fina... \n",
" \n",
" get_retriever_recursive \n",
" gpt-35-turbo \n",
" \n",
" \n",
" 189 \n",
" Click on the link labeled \"Thrill Rides.\" \n",
" 0.000000 \n",
" 0.000000 \n",
" 0.0 \n",
" 0.000000 \n",
" <html backend_node_id=\"64176\">\\n <body backen... \n",
" c7cd25d6-0721-488d-9a01-975f6b1bc76d \n",
" <p backend_node_id=\"67582\">\\n ... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <a backend_node_id=\"64159\">\\n ... \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <text backend_node_id=\"66373\">Thrill Rides</text> \n",
" \n",
" get_retriever_recursive \n",
" gpt-35-turbo \n",
" \n",
" \n",
" 190 \n",
" Enter \"Ohio\" in the search box for the location. \n",
" 1.000000 \n",
" 0.012346 \n",
" 1.0 \n",
" 1.000000 \n",
" <html backend_node_id=\"1531\">\\n <body>\\n <... \n",
" 370ca9f4-e6ee-4576-bcf1-ea418704debf \n",
" <div backend_node_id=\"1948\">\\n ... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <input backend_node_id=\"1528\" type=\"text\" name... \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <input backend_node_id=\"1528\" type=\"text\" name... \n",
" \n",
" get_retriever_recursive \n",
" gpt-35-turbo \n",
" \n",
" \n",
" 191 \n",
" Click on the \"Price\" heading. \n",
" 0.916667 \n",
" 0.277311 \n",
" 1.0 \n",
" 0.055556 \n",
" <html backend_node_id=\"329709\">\\n <body backe... \n",
" 3bb445bc-1794-4365-8d2e-7eaea29db4e3 \n",
" <div backend_node_id=\"344655\">\\n ... \n",
" driver.find_element(By.XPATH, f'//*[@backend_n... \n",
" <li backend_node_id=\"335800\">\\n ... \n",
" # Let's proceed step by step.\\n# First we need... \n",
" <h2 backend_node_id=\"329691\" title=\"Expand Pri... \n",
" \n",
" get_retriever_recursive \n",
" gpt-35-turbo \n",
" \n",
" \n",
"
\n",
"
192 rows × 15 columns
\n",
"
"
],
"text/plain": [
" query recall_retriever \\\n",
"0 Enter \"Rock And Roll Over\" in the text box to ... 0.000000 \n",
"1 Click on \"AAPL\" to look up the visitors trend ... 1.000000 \n",
"2 Select the After School Care checkbox. 1.000000 \n",
"3 Click on \"Smile.\" 1.000000 \n",
"4 Click on James Smith's profile image. 1.000000 \n",
".. ... ... \n",
"187 Click on the search option to get the best rat... 0.000000 \n",
"188 Click on the \"Benefits and financial support f... 1.000000 \n",
"189 Click on the link labeled \"Thrill Rides.\" 0.000000 \n",
"190 Enter \"Ohio\" in the search box for the location. 1.000000 \n",
"191 Click on the \"Price\" heading. 0.916667 \n",
"\n",
" precision_retriever recall_llm precision_llm \\\n",
"0 0.000000 0.0 0.000000 \n",
"1 0.114943 1.0 0.100000 \n",
"2 0.008772 1.0 1.000000 \n",
"3 0.050000 1.0 0.333333 \n",
"4 0.011236 1.0 1.000000 \n",
".. ... ... ... \n",
"187 0.000000 0.0 0.000000 \n",
"188 0.016260 1.0 0.500000 \n",
"189 0.000000 0.0 0.000000 \n",
"190 0.012346 1.0 1.000000 \n",
"191 0.277311 1.0 0.055556 \n",
"\n",
" html \\\n",
"0 \\n \\n \\n \\n \\n ... \n",
"3 \\n \\n ... \n",
"4 \\n \\n \\n \\n <... \n",
"189 \\n \\n \\n <... \n",
"191 \\n \\n ... \n",
"1
\\n ... \n",
"2
\\n ... \n",
"3 \\n ... \n",
"4 \\n
\\n ... \n",
"188
\\n
\\n ... \n",
"190 \\n ... \n",
"191
\\n ... \n",
"\n",
" ground_truth_code \\\n",
"0 driver.find_element(By.XPATH, f'//*[@backend_n... \n",
"1 driver.find_element(By.XPATH, f'//*[@backend_n... \n",
"2 driver.find_element(By.XPATH, f'//*[@backend_n... \n",
"3 driver.find_element(By.XPATH, f'//*[@backend_n... \n",
"4 driver.find_element(By.XPATH, f'//*[@backend_n... \n",
".. ... \n",
"187 driver.find_element(By.XPATH, f'//*[@backend_n... \n",
"188 driver.find_element(By.XPATH, f'//*[@backend_n... \n",
"189 driver.find_element(By.XPATH, f'//*[@backend_n... \n",
"190 driver.find_element(By.XPATH, f'//*[@backend_n... \n",
"191 driver.find_element(By.XPATH, f'//*[@backend_n... \n",
"\n",
" ground_truth_outer_html \\\n",
"0
\\n ... \n",
"4
\n",
".. ... \n",
"187
\n",
"188
\\n ... \n",
"189 \\n ... \n",
"190 \\n ... \n",
"\n",
" generated_code \\\n",
"0 # Let's proceed step by step.\\n# First we need... \n",
"1 # Let's proceed step by step.\\n# First we need... \n",
"2 # Let's proceed step by step.\\n# First we need... \n",
"3 # Let's proceed step by step.\\n# First we need... \n",
"4 # Let's proceed step by step.\\n# First we need... \n",
".. ... \n",
"187 # Let's proceed step by step.\\n# First we need... \n",
"188 # Let's proceed step by step.\\n# First we need... \n",
"189 # Let's proceed step by step.\\n# First we need... \n",
"190 # Let's proceed step by step.\\n# First we need... \n",
"191 # Let's proceed step by step.\\n# First we need... \n",
"\n",
" target_outer_html execution_error \\\n",
"0 Hi ! I have a Iba... \n",
"1 AAPL \n",
"2 Smile \n",
"4 \n",
".. ... ... \n",
"187