548 lines
44 KiB
Plaintext
548 lines
44 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 第七章 搭建一个带评估的端到端问答系统"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"在本节课中，我们将搭建一个带评估的端到端问答系统，综合了之前多节课的内容，加入了评估过程。\n",
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"\n",
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"首先，我们将检查输入，看看它是否能够通过审核 API 的审核。\n",
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"\n",
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"其次，如果没有，我们将提取产品列表。\n",
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"\n",
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"第三，如果找到了产品，我们将尝试查找它们。\n",
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"\n",
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"第四，我们将使用模型回答用户问题。\n",
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"\n",
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"最后，我们将通过审核API对答案进行审核。\n",
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"\n",
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"如果没有被标记，我们将把答案返回给用户。"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"环境配置"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@4.2.5/dist/gridstack-h5', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'gridstack': {'exports': 'GridStack'}}});\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 2;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length;\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/0.14.4/dist/bundled/gridstack/gridstack@4.2.5/dist/gridstack-h5.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/0.14.4/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) >= 0) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\", \"https://unpkg.com/@holoviz/panel@0.14.4/dist/panel.min.js\"];\n var js_modules = [];\n var css_urls = [\"https://cdn.holoviz.org/panel/0.14.4/dist/css/alerts.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/card.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/dataframe.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/debugger.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/json.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/loading.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/markdown.css\", \"https://cdn.holoviz.org/panel/0.14.4/dist/css/widgets.css\"];\n var inline_js = [ function(Bokeh) {\n inject_raw_css(\"\\n .bk.pn-loading.arc:before {\\n background-image: url(\\\"data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHN0eWxlPSJtYXJnaW46IGF1dG87IGJhY2tncm91bmQ6IG5vbmU7IGRpc3BsYXk6IGJsb2NrOyBzaGFwZS1yZW5kZXJpbmc6IGF1dG87IiB2aWV3Qm94PSIwIDAgMTAwIDEwMCIgcHJlc2VydmVBc3BlY3RSYXRpbz0ieE1pZFlNaWQiPiAgPGNpcmNsZSBjeD0iNTAiIGN5PSI1MCIgZmlsbD0ibm9uZSIgc3Ryb2tlPSIjYzNjM2MzIiBzdHJva2Utd2lkdGg9IjEwIiByPSIzNSIgc3Ryb2tlLWRhc2hhcnJheT0iMTY0LjkzMzYxNDMxMzQ2NDE1IDU2Ljk3Nzg3MTQzNzgyMTM4Ij4gICAgPGFuaW1hdGVUcmFuc2Zvcm0gYXR0cmlidXRlTmFtZT0idHJhbnNmb3JtIiB0eXBlPSJyb3RhdGUiIHJlcGVhdENvdW50PSJpbmRlZmluaXRlIiBkdXI9IjFzIiB2YWx1ZXM9IjAgNTAgNTA7MzYwIDUwIDUwIiBrZXlUaW1lcz0iMDsxIj48L2FuaW1hdGVUcmFuc2Zvcm0+ICA8L2NpcmNsZT48L3N2Zz4=\\\");\\n background-size: auto calc(min(50%, 400px));\\n }\\n \");\n }, function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, js_modules, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));",
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"application/vnd.holoviews_load.v0+json": ""
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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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"data": {
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"application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n",
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"application/vnd.holoviews_load.v0+json": ""
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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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"data": {
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"text/html": [
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"<style>.bk-root, .bk-root .bk:before, .bk-root .bk:after {\n",
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" font-family: var(--jp-ui-font-size1);\n",
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" font-size: var(--jp-ui-font-size1);\n",
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" color: var(--jp-ui-font-color1);\n",
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"}\n",
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"</style>"
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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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"# 配置 OpenAI KEY\n",
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"import os\n",
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"import openai\n",
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"import sys\n",
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"sys.path.append('../..')\n",
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"# 使用英文 Prompt 的工具包\n",
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"import utils_en\n",
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"# 使用中文 Prompt 的工具包\n",
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"import utils_zh\n",
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"\n",
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"import panel as pn # 用于图形化界面\n",
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"pn.extension()\n",
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"\n",
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"from dotenv import load_dotenv, find_dotenv\n",
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"_ = load_dotenv(find_dotenv()) # read local .env file\n",
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"\n",
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"openai.api_key = os.environ['OPENAI_API_KEY']"
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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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"metadata": {},
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||
"outputs": [],
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"source": [
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"# 封装一个访问 OpenAI GPT3.5 的函数\n",
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"def get_completion_from_messages(messages, model=\"gpt-3.5-turbo\", temperature=0, max_tokens=500):\n",
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" response = openai.ChatCompletion.create(\n",
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" model=model,\n",
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" messages=messages,\n",
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" temperature=temperature, \n",
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" max_tokens=max_tokens, \n",
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" )\n",
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" return response.choices[0].message[\"content\"]"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"一个端到端实现问答的函数"
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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": 15,
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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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"第一步：输入通过 Moderation 检查\n",
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"第二步：抽取出商品列表\n",
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"第三步：查找抽取出的商品信息\n",
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"第四步：生成用户回答\n",
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"第五步：输出经过 Moderation 检查\n",
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"第六步：模型评估该回答\n",
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"第七步：模型赞同了该回答.\n",
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"The SmartX ProPhone is a powerful smartphone with a 6.1-inch display, 128GB storage, 12MP dual camera, and 5G capabilities. The FotoSnap DSLR Camera is a versatile camera with a 24.2MP sensor, 1080p video, 3-inch LCD, and interchangeable lenses. As for our TVs, we have a range of options including the CineView 4K TV with a 55-inch display, 4K resolution, HDR, and smart TV capabilities, the CineView 8K TV with a 65-inch display, 8K resolution, HDR, and smart TV capabilities, and the CineView OLED TV with a 55-inch display, 4K resolution, HDR, and smart TV capabilities. Do you have any specific questions about these products or would you like me to recommend a product based on your needs?\n"
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]
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}
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],
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"source": [
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"# 对用户信息进行预处理\n",
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"def process_user_message(user_input, all_messages, debug=True):\n",
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" # user_input : 用户输入\n",
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" # all_messages : 历史信息\n",
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" # debug : 是否开启 DEBUG 模式,默认开启\n",
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||
"\n",
|
||
" # 分隔符\n",
|
||
" delimiter = \"```\"\n",
|
||
" \n",
|
||
" # 第一步: 使用 OpenAI 的 Moderation API 检查用户输入是否合规或者是一个注入的 Prompt\n",
|
||
" response = openai.Moderation.create(input=user_input)\n",
|
||
" moderation_output = response[\"results\"][0]\n",
|
||
"\n",
|
||
" # 经过 Moderation API 检查该输入不合规\n",
|
||
" if moderation_output[\"flagged\"]:\n",
|
||
" print(\"第一步：输入被 Moderation 拒绝\")\n",
|
||
" return \"抱歉，您的请求不合规\"\n",
|
||
"\n",
|
||
" # 如果开启了 DEBUG 模式，打印实时进度\n",
|
||
" if debug: print(\"第一步：输入通过 Moderation 检查\")\n",
|
||
" \n",
|
||
" # 第二步：抽取出商品和对应的目录，类似于之前课程中的方法，做了一个封装\n",
|
||
" category_and_product_response = utils_en.find_category_and_product_only(user_input, utils_en.get_products_and_category())\n",
|
||
" #print(category_and_product_response)\n",
|
||
" # 将抽取出来的字符串转化为列表\n",
|
||
" category_and_product_list = utils_en.read_string_to_list(category_and_product_response)\n",
|
||
" #print(category_and_product_list)\n",
|
||
"\n",
|
||
" if debug: print(\"第二步：抽取出商品列表\")\n",
|
||
"\n",
|
||
" # 第三步：查找商品对应信息\n",
|
||
" product_information = utils_en.generate_output_string(category_and_product_list)\n",
|
||
" if debug: print(\"第三步：查找抽取出的商品信息\")\n",
|
||
"\n",
|
||
" # 第四步：根据信息生成回答\n",
|
||
" system_message = f\"\"\"\n",
|
||
" You are a customer service assistant for a large electronic store. \\\n",
|
||
" Respond in a friendly and helpful tone, with concise answers. \\\n",
|
||
" Make sure to ask the user relevant follow-up questions.\n",
|
||
" \"\"\"\n",
|
||
" # 插入 message\n",
|
||
" messages = [\n",
|
||
" {'role': 'system', 'content': system_message},\n",
|
||
" {'role': 'user', 'content': f\"{delimiter}{user_input}{delimiter}\"},\n",
|
||
" {'role': 'assistant', 'content': f\"Relevant product information:\\n{product_information}\"}\n",
|
||
" ]\n",
|
||
" # 获取 GPT3.5 的回答\n",
|
||
" # 通过附加 all_messages 实现多轮对话\n",
|
||
" final_response = get_completion_from_messages(all_messages + messages)\n",
|
||
" if debug:print(\"第四步：生成用户回答\")\n",
|
||
" # 将该轮信息加入到历史信息中\n",
|
||
" all_messages = all_messages + messages[1:]\n",
|
||
"\n",
|
||
" # 第五步：基于 Moderation API 检查输出是否合规\n",
|
||
" response = openai.Moderation.create(input=final_response)\n",
|
||
" moderation_output = response[\"results\"][0]\n",
|
||
"\n",
|
||
" # 输出不合规\n",
|
||
" if moderation_output[\"flagged\"]:\n",
|
||
" if debug: print(\"第五步：输出被 Moderation 拒绝\")\n",
|
||
" return \"抱歉，我们不能提供该信息\"\n",
|
||
"\n",
|
||
" if debug: print(\"第五步：输出经过 Moderation 检查\")\n",
|
||
"\n",
|
||
" # 第六步：模型检查是否很好地回答了用户问题\n",
|
||
" user_message = f\"\"\"\n",
|
||
" Customer message: {delimiter}{user_input}{delimiter}\n",
|
||
" Agent response: {delimiter}{final_response}{delimiter}\n",
|
||
"\n",
|
||
" Does the response sufficiently answer the question?\n",
|
||
" \"\"\"\n",
|
||
" messages = [\n",
|
||
" {'role': 'system', 'content': system_message},\n",
|
||
" {'role': 'user', 'content': user_message}\n",
|
||
" ]\n",
|
||
" # 要求模型评估回答\n",
|
||
" evaluation_response = get_completion_from_messages(messages)\n",
|
||
" if debug: print(\"第六步：模型评估该回答\")\n",
|
||
"\n",
|
||
" # 第七步：如果评估为 Y，输出回答；如果评估为 N，反馈将由人工修正答案\n",
|
||
" if \"Y\" in evaluation_response: # 使用 in 来避免模型可能生成 Yes\n",
|
||
" if debug: print(\"第七步：模型赞同了该回答.\")\n",
|
||
" return final_response, all_messages\n",
|
||
" else:\n",
|
||
" if debug: print(\"第七步：模型不赞成该回答.\")\n",
|
||
" neg_str = \"很抱歉，我无法提供您所需的信息。我将为您转接到一位人工客服代表以获取进一步帮助。\"\n",
|
||
" return neg_str, all_messages\n",
|
||
"\n",
|
||
"user_input = \"tell me about the smartx pro phone and the fotosnap camera, the dslr one. Also what tell me about your tvs\"\n",
|
||
"response,_ = process_user_message(user_input,[])\n",
|
||
"print(response)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"第一步：输入通过 Moderation 检查\n",
|
||
"第二步：抽取出商品列表\n",
|
||
"第三步：查找抽取出的商品信息\n",
|
||
"第四步：生成用户回答\n",
|
||
"第五步：输出经过 Moderation 检查\n",
|
||
"第六步：模型评估该回答\n",
|
||
"第七步：模型赞同了该回答.\n",
|
||
"关于SmartX ProPhone和FotoSnap相机的信息：\n",
|
||
"\n",
|
||
"SmartX ProPhone是一款功能强大的智能手机，具有6.1英寸的显示屏，128GB的存储空间，12MP的双摄像头和5G网络。售价为899.99美元。\n",
|
||
"\n",
|
||
"FotoSnap相机系列包括DSLR相机、无反相机和即时相机。DSLR相机具有24.2MP传感器、1080p视频、3英寸LCD和可更换镜头。无反相机具有20.1MP传感器、4K视频、3英寸触摸屏和可更换镜头。即时相机可以即时打印照片，具有内置闪光灯、自拍镜和电池供电。售价分别为599.99美元、799.99美元和69.99美元。\n",
|
||
"\n",
|
||
"关于我们的电视：\n",
|
||
"\n",
|
||
"我们有多种电视可供选择，包括CineView 4K电视、CineView 8K电视和CineView OLED电视。CineView 4K电视具有55英寸的显示屏、4K分辨率、HDR和智能电视功能。CineView 8K电视具有65英寸的显示屏、8K分辨率、HDR和智能电视功能。CineView OLED电视具有55英寸的显示屏、4K分辨率、HDR和智能电视功能。我们还提供SoundMax家庭影院和SoundMax声音栏，以提供更好的音频体验。售价从199.99美元到2999.99美元不等，保修期为1年或2年。\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"'''\n",
|
||
"中文Prompt\n",
|
||
"注意：限于模型对中文理解能力较弱，中文Prompt可能会随机出现不成功，可以多次运行；也非常欢迎同学探究更稳定的中文 Prompt\n",
|
||
"'''\n",
|
||
"# 对用户信息进行预处理\n",
|
||
"def process_user_message_ch(user_input, all_messages, debug=True):\n",
|
||
" # user_input : 用户输入\n",
|
||
" # all_messages : 历史信息\n",
|
||
" # debug : 是否开启 DEBUG 模式,默认开启\n",
|
||
"\n",
|
||
" # 分隔符\n",
|
||
" delimiter = \"```\"\n",
|
||
" \n",
|
||
" # 第一步: 使用 OpenAI 的 Moderation API 检查用户输入是否合规或者是一个注入的 Prompt\n",
|
||
" response = openai.Moderation.create(input=user_input)\n",
|
||
" moderation_output = response[\"results\"][0]\n",
|
||
"\n",
|
||
" # 经过 Moderation API 检查该输入不合规\n",
|
||
" if moderation_output[\"flagged\"]:\n",
|
||
" print(\"第一步：输入被 Moderation 拒绝\")\n",
|
||
" return \"抱歉，您的请求不合规\"\n",
|
||
"\n",
|
||
" # 如果开启了 DEBUG 模式，打印实时进度\n",
|
||
" if debug: print(\"第一步：输入通过 Moderation 检查\")\n",
|
||
" \n",
|
||
" # 第二步：抽取出商品和对应的目录，类似于之前课程中的方法，做了一个封装\n",
|
||
" category_and_product_response = utils_zh.find_category_and_product_only(user_input, utils_zh.get_products_and_category())\n",
|
||
" #print(category_and_product_response)\n",
|
||
" # 将抽取出来的字符串转化为列表\n",
|
||
" category_and_product_list = utils_zh.read_string_to_list(category_and_product_response)\n",
|
||
" #print(category_and_product_list)\n",
|
||
"\n",
|
||
" if debug: print(\"第二步：抽取出商品列表\")\n",
|
||
"\n",
|
||
" # 第三步：查找商品对应信息\n",
|
||
" product_information = utils_zh.generate_output_string(category_and_product_list)\n",
|
||
" if debug: print(\"第三步：查找抽取出的商品信息\")\n",
|
||
"\n",
|
||
" # 第四步：根据信息生成回答\n",
|
||
" system_message = f\"\"\"\n",
|
||
" 您是一家大型电子商店的客户服务助理。\\\n",
|
||
" 请以友好和乐于助人的语气回答问题，并提供简洁明了的答案。\\\n",
|
||
" 请确保向用户提出相关的后续问题。\n",
|
||
" \"\"\"\n",
|
||
" # 插入 message\n",
|
||
" messages = [\n",
|
||
" {'role': 'system', 'content': system_message},\n",
|
||
" {'role': 'user', 'content': f\"{delimiter}{user_input}{delimiter}\"},\n",
|
||
" {'role': 'assistant', 'content': f\"相关商品信息:\\n{product_information}\"}\n",
|
||
" ]\n",
|
||
" # 获取 GPT3.5 的回答\n",
|
||
" # 通过附加 all_messages 实现多轮对话\n",
|
||
" final_response = get_completion_from_messages(all_messages + messages)\n",
|
||
" if debug:print(\"第四步：生成用户回答\")\n",
|
||
" # 将该轮信息加入到历史信息中\n",
|
||
" all_messages = all_messages + messages[1:]\n",
|
||
"\n",
|
||
" # 第五步：基于 Moderation API 检查输出是否合规\n",
|
||
" response = openai.Moderation.create(input=final_response)\n",
|
||
" moderation_output = response[\"results\"][0]\n",
|
||
"\n",
|
||
" # 输出不合规\n",
|
||
" if moderation_output[\"flagged\"]:\n",
|
||
" if debug: print(\"第五步：输出被 Moderation 拒绝\")\n",
|
||
" return \"抱歉，我们不能提供该信息\"\n",
|
||
"\n",
|
||
" if debug: print(\"第五步：输出经过 Moderation 检查\")\n",
|
||
"\n",
|
||
" # 第六步：模型检查是否很好地回答了用户问题\n",
|
||
" user_message = f\"\"\"\n",
|
||
" 用户信息: {delimiter}{user_input}{delimiter}\n",
|
||
" 代理回复: {delimiter}{final_response}{delimiter}\n",
|
||
"\n",
|
||
" 回复是否足够回答问题\n",
|
||
" 如果足够，回答 Y\n",
|
||
" 如果不足够，回答 N\n",
|
||
" 仅回答上述字母即可\n",
|
||
" \"\"\"\n",
|
||
" # print(final_response)\n",
|
||
" messages = [\n",
|
||
" {'role': 'system', 'content': system_message},\n",
|
||
" {'role': 'user', 'content': user_message}\n",
|
||
" ]\n",
|
||
" # 要求模型评估回答\n",
|
||
" evaluation_response = get_completion_from_messages(messages)\n",
|
||
" # print(evaluation_response)\n",
|
||
" if debug: print(\"第六步：模型评估该回答\")\n",
|
||
"\n",
|
||
" # 第七步：如果评估为 Y，输出回答；如果评估为 N，反馈将由人工修正答案\n",
|
||
" if \"Y\" in evaluation_response: # 使用 in 来避免模型可能生成 Yes\n",
|
||
" if debug: print(\"第七步：模型赞同了该回答.\")\n",
|
||
" return final_response, all_messages\n",
|
||
" else:\n",
|
||
" if debug: print(\"第七步：模型不赞成该回答.\")\n",
|
||
" neg_str = \"很抱歉，我无法提供您所需的信息。我将为您转接到一位人工客服代表以获取进一步帮助。\"\n",
|
||
" return neg_str, all_messages\n",
|
||
"\n",
|
||
"user_input = \"请告诉我关于smartx pro phone和the fotosnap camera的信息。另外，请告诉我关于你们的tvs的情况。\"\n",
|
||
"response,_ = process_user_message_ch(user_input,[])\n",
|
||
"print(response)"
|
||
]
|
||
},
|
||
{
|
||
"attachments": {},
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"实现一个可视化界面"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def collect_messages_en(debug=False):\n",
|
||
" user_input = inp.value_input\n",
|
||
" if debug: print(f\"User Input = {user_input}\")\n",
|
||
" if user_input == \"\":\n",
|
||
" return\n",
|
||
" inp.value = ''\n",
|
||
" global context\n",
|
||
" # 调用 process_user_message 函数\n",
|
||
" #response, context = process_user_message(user_input, context, utils.get_products_and_category(),debug=True)\n",
|
||
" response, context = process_user_message(user_input, context, debug=False)\n",
|
||
" context.append({'role':'assistant', 'content':f\"{response}\"})\n",
|
||
" panels.append(\n",
|
||
" pn.Row('User:', pn.pane.Markdown(user_input, width=600)))\n",
|
||
" panels.append(\n",
|
||
" pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))\n",
|
||
" \n",
|
||
" return pn.Column(*panels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 调用中文Prompt版本\n",
|
||
"def collect_messages_ch(debug=False):\n",
|
||
" user_input = inp.value_input\n",
|
||
" if debug: print(f\"User Input = {user_input}\")\n",
|
||
" if user_input == \"\":\n",
|
||
" return\n",
|
||
" inp.value = ''\n",
|
||
" global context\n",
|
||
" # 调用 process_user_message 函数\n",
|
||
" #response, context = process_user_message(user_input, context, utils.get_products_and_category(),debug=True)\n",
|
||
" response, context = process_user_message_ch(user_input, context, debug=False)\n",
|
||
" context.append({'role':'assistant', 'content':f\"{response}\"})\n",
|
||
" panels.append(\n",
|
||
" pn.Row('User:', pn.pane.Markdown(user_input, width=600)))\n",
|
||
" panels.append(\n",
|
||
" pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))\n",
|
||
" \n",
|
||
" return pn.Column(*panels)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.holoviews_exec.v0+json": "",
|
||
"text/html": [
|
||
"<div id='1002'>\n",
|
||
" <div class=\"bk-root\" id=\"5bec6a62-17e2-41a3-9212-7126da759786\" data-root-id=\"1002\"></div>\n",
|
||
"</div>\n",
|
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"<script type=\"application/javascript\">(function(root) {\n",
|
||
" function embed_document(root) {\n",
|
||
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fa-info-circle\",\"color\":\"white\",\"tagName\":\"i\"},\"type\":\"info\"}],\"kind\":null,\"name\":\"types\"}]},{\"extends\":null,\"module\":null,\"name\":\"Notification\",\"overrides\":[],\"properties\":[{\"default\":null,\"kind\":null,\"name\":\"background\"},{\"default\":3000,\"kind\":null,\"name\":\"duration\"},{\"default\":null,\"kind\":null,\"name\":\"icon\"},{\"default\":\"\",\"kind\":null,\"name\":\"message\"},{\"default\":null,\"kind\":null,\"name\":\"notification_type\"},{\"default\":false,\"kind\":null,\"name\":\"_destroyed\"}]},{\"extends\":null,\"module\":null,\"name\":\"TemplateActions1\",\"overrides\":[],\"properties\":[{\"default\":0,\"kind\":null,\"name\":\"open_modal\"},{\"default\":0,\"kind\":null,\"name\":\"close_modal\"}]},{\"extends\":null,\"module\":null,\"name\":\"MaterialTemplateActions1\",\"overrides\":[],\"properties\":[{\"default\":0,\"kind\":null,\"name\":\"open_modal\"},{\"default\":0,\"kind\":null,\"name\":\"close_modal\"}]}],\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"1007\"}],\"height\":300,\"margin\":[0,0,0,0],\"min_height\":300,\"name\":\"Row00110\"},\"id\":\"1006\",\"type\":\"Row\"},{\"attributes\":{\"children\":[{\"id\":\"1003\"},{\"id\":\"1004\"},{\"id\":\"1006\"}],\"margin\":[0,0,0,0],\"name\":\"Column00112\"},\"id\":\"1002\",\"type\":\"Column\"},{\"attributes\":{\"children\":[{\"id\":\"1005\"}],\"margin\":[0,0,0,0],\"name\":\"Row00105\"},\"id\":\"1004\",\"type\":\"Row\"},{\"attributes\":{\"margin\":[5,5,5,5],\"name\":\"Str00108\",\"text\":\"<pre> </pre>\"},\"id\":\"1007\",\"type\":\"panel.models.markup.HTML\"},{\"attributes\":{\"margin\":[5,10,5,10],\"max_length\":5000,\"placeholder\":\"Enter text here\\u2026\"},\"id\":\"1003\",\"type\":\"TextInput\"},{\"attributes\":{\"args\":{\"bidirectional\":false,\"properties\":{\"event:button_click\":\"loading\"},\"source\":{\"id\":\"1005\"},\"target\":{\"id\":\"1006\"}},\"code\":\"\\n if ('event:button_click'.startsWith('event:')) {\\n var value = true\\n } else {\\n var value = source['event:button_click'];\\n value = value;\\n }\\n if (typeof value !== 'boolean' || source.labels !== ['Loading']) {\\n value = true\\n }\\n var css_classes = target.css_classes.slice()\\n var loading_css = ['pn-loading', 'arc']\\n if (value) {\\n for (var css of loading_css) {\\n if (!(css in css_classes)) {\\n css_classes.push(css)\\n }\\n }\\n } else {\\n for (var css of loading_css) {\\n var index = css_classes.indexOf(css)\\n if (index > -1) {\\n css_classes.splice(index, 1)\\n }\\n }\\n }\\n target['css_classes'] = css_classes\\n \",\"tags\":[[140330220591408,[null,\"event:button_click\"],[null,\"loading\"]]]},\"id\":\"1008\",\"type\":\"CustomJS\"},{\"attributes\":{\"client_comm_id\":\"2a4a5b3205d940a0b2a81401239356fc\",\"comm_id\":\"53327ab16d4d4b5a9937d0a053d6c7e0\",\"plot_id\":\"1002\"},\"id\":\"1009\",\"type\":\"panel.models.comm_manager.CommManager\"},{\"attributes\":{\"reload\":false},\"id\":\"1010\",\"type\":\"panel.models.location.Location\"},{\"attributes\":{\"icon\":null,\"js_event_callbacks\":{\"button_click\":[{\"id\":\"1008\"}]},\"label\":\"Service Assistant\",\"margin\":[5,10,5,10],\"subscribed_events\":[\"button_click\"]},\"id\":\"1005\",\"type\":\"Button\"}],\"root_ids\":[\"1002\",\"1009\",\"1010\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n",
|
||
" var render_items = [{\"docid\":\"052e8379-8146-4aee-8619-78e96d2427ee\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"5bec6a62-17e2-41a3-9212-7126da759786\"}}];\n",
|
||
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
|
||
" for (const render_item of render_items) {\n",
|
||
" for (const root_id of render_item.root_ids) {\n",
|
||
"\tconst id_el = document.getElementById(root_id)\n",
|
||
"\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
|
||
"\t const root_el = id_el.children[0]\n",
|
||
"\t root_el.id = root_el.id + '-rendered'\n",
|
||
"\t}\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }\n",
|
||
" if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
|
||
" embed_document(root);\n",
|
||
" } else {\n",
|
||
" var attempts = 0;\n",
|
||
" var timer = setInterval(function(root) {\n",
|
||
" if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
|
||
" clearInterval(timer);\n",
|
||
" embed_document(root);\n",
|
||
" } else if (document.readyState == \"complete\") {\n",
|
||
" attempts++;\n",
|
||
" if (attempts > 200) {\n",
|
||
" clearInterval(timer);\n",
|
||
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
|
||
" }\n",
|
||
" }\n",
|
||
" }, 25, root)\n",
|
||
" }\n",
|
||
"})(window);</script>"
|
||
],
|
||
"text/plain": [
|
||
"Column\n",
|
||
" [0] TextInput(placeholder='Enter text here…')\n",
|
||
" [1] Row\n",
|
||
" [0] Button(name='Service Assistant')\n",
|
||
" [2] ParamFunction(function, _pane=Str, height=300, loading_indicator=True)"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"application/vnd.holoviews_exec.v0+json": {
|
||
"id": "1002"
|
||
}
|
||
},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"panels = [] # collect display \n",
|
||
"\n",
|
||
"# 系统信息\n",
|
||
"context = [ {'role':'system', 'content':\"You are Service Assistant\"} ] \n",
|
||
"\n",
|
||
"inp = pn.widgets.TextInput( placeholder='Enter text here…')\n",
|
||
"button_conversation = pn.widgets.Button(name=\"Service Assistant\")\n",
|
||
"\n",
|
||
"interactive_conversation = pn.bind(collect_messages, button_conversation)\n",
|
||
"\n",
|
||
"dashboard = pn.Column(\n",
|
||
" inp,\n",
|
||
" pn.Row(button_conversation),\n",
|
||
" pn.panel(interactive_conversation, loading_indicator=True, height=300),\n",
|
||
")\n",
|
||
"\n",
|
||
"dashboard"
|
||
]
|
||
},
|
||
{
|
||
"attachments": {},
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"通过监控系统在更多输入上的质量，您可以修改步骤，提高系统的整体性能。\n",
|
||
"\n",
|
||
"也许我们会发现，对于某些步骤，我们的提示可能更好，也许有些步骤甚至不必要，也许我们会找到更好的检索方法等等。\n",
|
||
"\n",
|
||
"我们将在下一个视频中进一步讨论这个问题。 "
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "zyh_gpt",
|
||
"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": "3.10.11"
|
||
},
|
||
"orig_nbformat": 4
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|