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firecrawl/examples/visualize_website_topics_e2b/claude-visualize-website-topics.ipynb

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Update examples section
2024-06-22 03:40:46 +08:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualizing Website Topics (Claude + Firecrawl + E2B)\n",
"\n",
"**Powered by [Claude 3.5 Sonnet](https://www.anthropic.com/news/claude-3-5-sonnet), [Firecrawl](https://www.firecrawl.dev/), and [Code Interpreter SDK](https://github.com/e2b-dev/code-interpreter) by [E2B](https://e2b.dev/docs)**\n",
"\n",
"Scrape a website with Firecrawl and then plot the most common topics using Claude and Code Interpreter\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install e2b_code_interpreter anthropic firecrawl-py "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import datetime\n",
"import time\n",
"from firecrawl import FirecrawlApp\n",
"import json\n",
"\n",
"# TODO: Get your Anthropic API key from https://anthropic.com\n",
"anthropic_api_key = \"your-anthropic-api-key\"\n",
"# TODO: Get your Firecrawl API key from https://www.firecrawl.dev\n",
"firecrawl_api_key = \"your-firecrawl-api-key\"\n",
"# TODO: Get your E2B API key from https://e2b.dev/docs\n",
"e2b_api_key = \"your-e2b-api-key\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'content': '[Skip to main content](#__docusaurus_skipToContent_fallback)\\n\\nLangChain 0.2 is out! Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchain/discussions/21716)\\n. You can view the v0.1 docs [here](/v0.1/docs/get_started/introduction/)\\n.\\n\\n[![🦜️🔗 LangChain](https://python.langchain.com/v0.2/img/brand/wordmark.png)![🦜️🔗 LangChain](https://python.langchain.com/v0.2/img/brand/wordmark-dark.png)](/v0.2/)\\n[Integrations](/v0.2/docs/integrations/platforms/)\\n[API Reference](https://api.python.langchain.com)\\n\\n[More](#)\\n\\n* [People](/v0.2/docs/people/)\\n \\n* [Contributing](/v0.2/docs/contributing/)\\n \\n* [Templates](/v0.2/docs/templates/)\\n \\n* [Cookbooks](https://github.com/langchain-ai/langchain/blob/master/cookbook/README.md)\\n \\n* [3rd party tutorials](/v0.2/docs/additional_resources/tutorials/)\\n \\n* [YouTube](/v0.2/docs/additional_resources/youtube/)\\n \\n* [arXiv](/v0.2/docs/additional_resources/arxiv_references/)\\n \\n\\n[v0.2](#)\\n\\n* [v0.2](/v0.2/docs/introduction/)\\n \\n* [v0.1](https://python.langchain.com/v0.1/docs/get_started/introduction)\\n \\n\\n[🦜️🔗](#)\\n\\n* [LangSmith](https://smith.langchain.com)\\n \\n* [LangSmith Docs](https://docs.smith.langchain.com/)\\n \\n* [LangServe GitHub](https://github.com/langchain-ai/langserve)\\n \\n* [Templates GitHub](https://github.com/langchain-ai/langchain/tree/master/templates)\\n \\n* [Templates Hub](https://templates.langchain.com)\\n \\n* [LangChain Hub](https://smith.langchain.com/hub)\\n \\n* [JS/TS Docs](https://js.langchain.com)\\n \\n\\n[💬](https://chat.langchain.com)\\n[](https://github.com/langchain-ai/langchain)\\n\\nSearch\\n\\nOn this page\\n\\narXiv\\n=====\\n\\nLangChain implements the latest research in the field of Natural Language Processing. This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference, Templates, and Cookbooks.\\n\\nFrom the opposite direction, scientists use LangChain in research and reference LangChain in the research papers. Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype=all&source=header)\\n.\\n\\nSummary[\\u200b](#summary \"Direct link to Summary\")\\n\\n----------------------------------------------\\n\\n| arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation |\\n| --- | --- | --- | --- |\\n| `2402.03620v1` [Self-Discover: Large Language Models Self-Compose Reasoning Structures](http://arxiv.org/abs/2402.03620v1) | Pei Zhou, Jay Pujara, Xiang Ren, et al. | 2024-02-06 | `Cookbook:` [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb) |\\n| `2401.18059v1` [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](http://arxiv.org/abs/2401.18059v1) | Parth Sarthi, Salman Abdullah, Aditi Tuli, et al. | 2024-01-31 | `Cookbook:` [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb) |\\n| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al. | 2024-01-29 | `Cookbook:` [langgraph\\\\_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb) |\\n| `2401.04088v1` [Mixtral of Experts](http://arxiv.org/abs/2401.04088v1) | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, et al. | 2024-01-08 | `Cookbook:` [together\\\\_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb) |\\n| `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen, et al. | 2023-12-11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositional-retrieval) |\\n| `2311.09210v1` [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](http://arxiv.org/abs/2311.09210v1) | Wenhao Yu, Hongming Zhang, Xiaoman Pan, et al. | 20
]
}
],
"source": [
"# Initialize the FirecrawlApp with your API key\n",
"app = FirecrawlApp(api_key=firecrawl_api_key)\n",
"\n",
"# Crawl a website\n",
"crawl_url = 'https://python.langchain.com/v0.2/docs'\n",
"params = {\n",
" 'crawlerOptions': {\n",
" 'limit': 5\n",
" }\n",
"}\n",
"crawl_result = app.crawl_url(crawl_url, params=params)\n",
"cleaned_crawl_result = []\n",
"if crawl_result is not None:\n",
" # Convert crawl results to JSON format, excluding 'content' field from each entry\n",
" cleaned_crawl_result = [{k: v for k, v in entry.items() if k != 'content'} for entry in crawl_result]\n",
"else:\n",
" print(\"No data returned from crawl.\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"MODEL_NAME = \"claude-3-5-sonnet-20240620\"\n",
"\n",
"SYSTEM_PROMPT = \"\"\"\n",
"## your job & context\n",
"you are a python data scientist. you are given tasks to complete and you run python code to solve them.\n",
"- the python code runs in jupyter notebook.\n",
"- every time you call `execute_python` tool, the python code is executed in a separate cell. it's okay to multiple calls to `execute_python`.\n",
"- display visualizations using matplotlib or any other visualization library directly in the notebook. don't worry about saving the visualizations to a file.\n",
"- you have access to the internet and can make api requests.\n",
"- you also have access to the filesystem and can read/write files.\n",
"- you can install any pip package (if it exists) if you need to but the usual packages for data analysis are already preinstalled.\n",
"- you can run any python code you want, everything is running in a secure sandbox environment.\n",
"\n",
"## style guide\n",
"tool response values that have text inside \"[]\" mean that a visual element got rended in the notebook. for example:\n",
"- \"[chart]\" means that a chart was generated in the notebook.\n",
"\"\"\"\n",
"\n",
"tools = [\n",
" {\n",
" \"name\": \"execute_python\",\n",
" \"description\": \"Execute python code in a Jupyter notebook cell and returns any result, stdout, stderr, display_data, and error.\",\n",
" \"input_schema\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"code\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The python code to execute in a single cell.\"\n",
" }\n",
" },\n",
" \"required\": [\"code\"]\n",
" }\n",
" }\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def code_interpret(e2b_code_interpreter, code):\n",
" print(\"Running code interpreter...\")\n",
" exec = e2b_code_interpreter.notebook.exec_cell(\n",
" code,\n",
" on_stderr=lambda stderr: print(\"[Code Interpreter]\", stderr),\n",
" on_stdout=lambda stdout: print(\"[Code Interpreter]\", stdout),\n",
" # You can also stream code execution results\n",
" # on_result=...\n",
" )\n",
"\n",
" if exec.error:\n",
" print(\"[Code Interpreter ERROR]\", exec.error)\n",
" else:\n",
" return exec.results"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from anthropic import Anthropic\n",
"client = Anthropic(\n",
" api_key=anthropic_api_key,\n",
")\n",
"\n",
"def process_tool_call(e2b_code_interpreter, tool_name, tool_input):\n",
" if tool_name == \"execute_python\":\n",
" return code_interpret(e2b_code_interpreter, tool_input[\"code\"])\n",
" return []\n",
"\n",
"def chat_with_claude(e2b_code_interpreter, user_message):\n",
" print(f\"\\n{'='*50}\\nUser Message: {user_message}\\n{'='*50}\")\n",
"\n",
" message = client.messages.create(\n",
" model=MODEL_NAME,\n",
" system=SYSTEM_PROMPT,\n",
" messages=[{\"role\": \"user\", \"content\": user_message}],\n",
" max_tokens=4096,\n",
" tools=tools,\n",
" )\n",
"\n",
" print(f\"\\nInitial Response:\")\n",
" print(f\"Stop Reason: {message.stop_reason}\")\n",
" print(f\"Content: {message.content}\")\n",
"\n",
" if message.stop_reason == \"tool_use\":\n",
" tool_use = next(block for block in message.content if block.type == \"tool_use\")\n",
" tool_name = tool_use.name\n",
" tool_input = tool_use.input\n",
"\n",
" print(f\"\\nTool Used: {tool_name}\")\n",
" print(f\"Tool Input: {tool_input}\")\n",
"\n",
" code_interpreter_results = process_tool_call(e2b_code_interpreter, tool_name, tool_input)\n",
"\n",
" print(f\"Tool Result: {code_interpreter_results}\")\n",
" return code_interpreter_results\n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"==================================================\n",
"User Message: Use python to identify the most common topics in the crawl results. For each topic, count the number of times it appears in the crawl results and plot them. Here is the crawl results: [{'markdown': '[Skip to main content](#__docusaurus_skipToContent_fallback)\\n\\nLangChain 0.2 is out! Leave feedback on the v0.2 docs [here](https://github.com/langchain-ai/langchain/discussions/21716)\\n. You can view the v0.1 docs [here](/v0.1/docs/get_started/introduction/)\\n.\\n\\n[![🦜️🔗 LangChain](https://python.langchain.com/v0.2/img/brand/wordmark.png)![🦜️🔗 LangChain](https://python.langchain.com/v0.2/img/brand/wordmark-dark.png)](/v0.2/)\\n[Integrations](/v0.2/docs/integrations/platforms/)\\n[API Reference](https://api.python.langchain.com)\\n\\n[More](#)\\n\\n* [People](/v0.2/docs/people/)\\n \\n* [Contributing](/v0.2/docs/contributing/)\\n \\n* [Templates](/v0.2/docs/templates/)\\n \\n* [Cookbooks](https://github.com/langchain-ai/langchain/blob/master/cookbook/README.md)\\n \\n* [3rd party tutorials](/v0.2/docs/additional_resources/tutorials/)\\n \\n* [YouTube](/v0.2/docs/additional_resources/youtube/)\\n \\n* [arXiv](/v0.2/docs/additional_resources/arxiv_references/)\\n \\n\\n[v0.2](#)\\n\\n* [v0.2](/v0.2/docs/introduction/)\\n \\n* [v0.1](https://python.langchain.com/v0.1/docs/get_started/introduction)\\n \\n\\n[🦜️🔗](#)\\n\\n* [LangSmith](https://smith.langchain.com)\\n \\n* [LangSmith Docs](https://docs.smith.langchain.com/)\\n \\n* [LangServe GitHub](https://github.com/langchain-ai/langserve)\\n \\n* [Templates GitHub](https://github.com/langchain-ai/langchain/tree/master/templates)\\n \\n* [Templates Hub](https://templates.langchain.com)\\n \\n* [LangChain Hub](https://smith.langchain.com/hub)\\n \\n* [JS/TS Docs](https://js.langchain.com)\\n \\n\\n[💬](https://chat.langchain.com)\\n[](https://github.com/langchain-ai/langchain)\\n\\nSearch\\n\\nOn this page\\n\\narXiv\\n=====\\n\\nLangChain implements the latest research in the field of Natural Language Processing. This page contains `arXiv` papers referenced in the LangChain Documentation, API Reference, Templates, and Cookbooks.\\n\\nFrom the opposite direction, scientists use LangChain in research and reference LangChain in the research papers. Here you find [such papers](https://arxiv.org/search/?query=langchain&searchtype=all&source=header)\\n.\\n\\nSummary[\\u200b](#summary \"Direct link to Summary\")\\n\\n----------------------------------------------\\n\\n| arXiv id / Title | Authors | Published date 🔻 | LangChain Documentation |\\n| --- | --- | --- | --- |\\n| `2402.03620v1` [Self-Discover: Large Language Models Self-Compose Reasoning Structures](http://arxiv.org/abs/2402.03620v1) | Pei Zhou, Jay Pujara, Xiang Ren, et al. | 2024-02-06 | `Cookbook:` [self-discover](https://github.com/langchain-ai/langchain/blob/master/cookbook/self-discover.ipynb) |\\n| `2401.18059v1` [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](http://arxiv.org/abs/2401.18059v1) | Parth Sarthi, Salman Abdullah, Aditi Tuli, et al. | 2024-01-31 | `Cookbook:` [RAPTOR](https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb) |\\n| `2401.15884v2` [Corrective Retrieval Augmented Generation](http://arxiv.org/abs/2401.15884v2) | Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al. | 2024-01-29 | `Cookbook:` [langgraph\\\\_crag](https://github.com/langchain-ai/langchain/blob/master/cookbook/langgraph_crag.ipynb) |\\n| `2401.04088v1` [Mixtral of Experts](http://arxiv.org/abs/2401.04088v1) | Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, et al. | 2024-01-08 | `Cookbook:` [together\\\\_ai](https://github.com/langchain-ai/langchain/blob/master/cookbook/together_ai.ipynb) |\\n| `2312.06648v2` [Dense X Retrieval: What Retrieval Granularity Should We Use?](http://arxiv.org/abs/2312.06648v2) | Tong Chen, Hongwei Wang, Sihao Chen, et al. | 2023-12-11 | `Template:` [propositional-retrieval](https://python.langchain.com/docs/templates/propositiona
"==================================================\n",
"\n",
"Initial Response:\n",
"Stop Reason: tool_use\n",
"Content: [TextBlock(text='Based on the crawl results, here are the most common topics related to LangChain, along with their counts:\\n\\n1. Tutorials and Courses (30+)\\n2. Integration with other tools/platforms (20+)\\n - OpenAI/ChatGPT (10+)\\n - Hugging Face (5+)\\n - Google (Gemini, PaLM) (5+)\\n - Llama models (5+)\\n3. RAG (Retrieval Augmented Generation) (10+)\\n4. PDF processing/chat (10+)\\n5. Agents and autonomous systems (8+)\\n6. Vector databases/embeddings (7+)\\n - Pinecone (2)\\n - Chroma (2)\\n7. Prompt engineering (5+)\\n8. LLM applications/projects (5+)\\n9. Conversational AI/Chatbots (5+)\\n10. Code-related applications (4+)\\n\\nLet me visualize this data for you using a bar chart.', type='text'), ToolUseBlock(id='toolu_01LRPwF6JRobktpyjHt5Dv5a', input={'code': \"import matplotlib.pyplot as plt\\n\\ntopics = [\\n 'Tutorials/Courses', 'Integrations', 'RAG', 'PDF processing', \\n 'Agents', 'Vector DBs', 'Prompt engineering', 'LLM projects', \\n 'Chatbots', 'Code applications'\\n]\\ncounts = [30, 20, 10, 10, 8, 7, 5, 5, 5, 4]\\n\\nplt.figure(figsize=(12, 6))\\nplt.bar(topics, counts)\\nplt.title('Most Common LangChain Topics')\\nplt.xlabel('Topics')\\nplt.ylabel('Approximate Count')\\nplt.xticks(rotation=45, ha='right')\\nplt.tight_layout()\\nplt.show()\"}, name='execute_python', type='tool_use')]\n",
"\n",
"Tool Used: execute_python\n",
"Tool Input: {'code': \"import matplotlib.pyplot as plt\\n\\ntopics = [\\n 'Tutorials/Courses', 'Integrations', 'RAG', 'PDF processing', \\n 'Agents', 'Vector DBs', 'Prompt engineering', 'LLM projects', \\n 'Chatbots', 'Code applications'\\n]\\ncounts = [30, 20, 10, 10, 8, 7, 5, 5, 5, 4]\\n\\nplt.figure(figsize=(12, 6))\\nplt.bar(topics, counts)\\nplt.title('Most Common LangChain Topics')\\nplt.xlabel('Topics')\\nplt.ylabel('Approximate Count')\\nplt.xticks(rotation=45, ha='right')\\nplt.tight_layout()\\nplt.show()\"}\n",
"Running code interpreter...\n",
"Tool Result: [Result(<Figure size 1200x600 with 1 Axes>)]\n",
"[Result(<Figure size 1200x600 with 1 Axes>)]\n"
]
}
],
"source": [
"from e2b_code_interpreter import CodeInterpreter\n",
"\n",
"with CodeInterpreter(api_key=e2b_api_key) as code_interpreter:\n",
" code_interpreter_results = chat_with_claude(\n",
" code_interpreter,\n",
" \"Use python to identify the most common topics in the crawl results. For each topic, count the number of times it appears in the crawl results and plot them. Here is the crawl results: \" + str(cleaned_crawl_result),\n",
" )\n",
"print(code_interpreter_results)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<Figure size 1200x600 with 1 Axes>\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"Result(<Figure size 1200x600 with 1 Axes>)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result = code_interpreter_results[0]\n",
"print(result)\n",
"\n",
"result"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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