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176 lines
5.2 KiB
Plaintext
176 lines
5.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "63b87b91",
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"metadata": {},
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"source": [
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"# Logging to file\n",
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"This example shows how to print logs to file. It shows how to use the `FileCallbackHandler`, which does the same thing as [`StdOutCallbackHandler`](https://python.langchain.com/en/latest/modules/callbacks/getting_started.html#using-an-existing-handler), but instead writes the output to file. It also uses the `loguru` library to log other outputs that are not captured by the handler."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6cb156cc",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
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"Prompt after formatting:\n",
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"\u001b[32;1m\u001b[1;3m1 + 2 = \u001b[0m\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[32m2023-06-01 18:36:38.929\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1m\n",
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"\n",
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"3\u001b[0m\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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}
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],
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"source": [
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"from loguru import logger\n",
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"\n",
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"from langchain.callbacks import FileCallbackHandler\n",
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"from langchain.chains import LLMChain\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.prompts import PromptTemplate\n",
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"\n",
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"logfile = 'output.log'\n",
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"\n",
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"logger.add(logfile, colorize=True, enqueue=True)\n",
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"handler = FileCallbackHandler(logfile)\n",
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"\n",
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"llm = OpenAI()\n",
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"prompt = PromptTemplate.from_template(\"1 + {number} = \")\n",
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"\n",
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"# this chain will both print to stdout (because verbose=True) and write to 'output.log'\n",
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"# if verbose=False, the FileCallbackHandler will still write to 'output.log'\n",
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"chain = LLMChain(llm=llm, prompt=prompt, callbacks=[handler], verbose=True)\n",
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"answer = chain.run(number=2)\n",
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"logger.info(answer)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9c50d54f",
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"metadata": {},
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"source": [
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"Now we can open the file `output.log` to see that the output has been captured."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "aa32dc0a",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install ansi2html > /dev/null"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "4af00719",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.01 Transitional//EN\" \"http://www.w3.org/TR/html4/loose.dtd\">\n",
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"<html>\n",
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"<head>\n",
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"<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\">\n",
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"<title></title>\n",
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"<style type=\"text/css\">\n",
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".ansi2html-content { display: inline; white-space: pre-wrap; word-wrap: break-word; }\n",
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".body_foreground { color: #AAAAAA; }\n",
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".body_background { background-color: #000000; }\n",
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".inv_foreground { color: #000000; }\n",
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".inv_background { background-color: #AAAAAA; }\n",
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".ansi1 { font-weight: bold; }\n",
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".ansi3 { font-style: italic; }\n",
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".ansi32 { color: #00aa00; }\n",
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".ansi36 { color: #00aaaa; }\n",
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"</style>\n",
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"</head>\n",
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"<body class=\"body_foreground body_background\" style=\"font-size: normal;\" >\n",
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"<pre class=\"ansi2html-content\">\n",
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"\n",
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"\n",
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"<span class=\"ansi1\">> Entering new LLMChain chain...</span>\n",
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"Prompt after formatting:\n",
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"<span class=\"ansi1 ansi32\"></span><span class=\"ansi1 ansi3 ansi32\">1 + 2 = </span>\n",
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"\n",
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"<span class=\"ansi1\">> Finished chain.</span>\n",
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"<span class=\"ansi32\">2023-06-01 18:36:38.929</span> | <span class=\"ansi1\">INFO </span> | <span class=\"ansi36\">__main__</span>:<span class=\"ansi36\"><module></span>:<span class=\"ansi36\">20</span> - <span class=\"ansi1\">\n",
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"\n",
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"3</span>\n",
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"\n",
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"</pre>\n",
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"</body>\n",
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"\n",
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"</html>\n"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from IPython.display import display, HTML\n",
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"from ansi2html import Ansi2HTMLConverter\n",
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"\n",
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"with open('output.log', 'r') as f:\n",
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" content = f.read()\n",
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"\n",
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"conv = Ansi2HTMLConverter()\n",
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"html = conv.convert(content, full=True)\n",
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"\n",
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"display(HTML(html))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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