今天我学习了DeepLearning.AI的 Building Systems with LLM 的在线课程，我想和大家一起分享一下该门课程的一些主要内容。之前我们已经学习了下面这些知识：
- 使用大型语言模(LLM)构建系统(一)：分类
- 使用大型语言模(LLM)构建系统(二):内容审核、预防Prompt注入
- 使用大型语言模(LLM)构建系统(三)：思维链推理
- 使用大型语言模(LLM)构建系统(四)：链式提示
- 使用大型语言模(LLM)构建系统(五)：输出结果检查
用于处理用户查询的链式提示系统
今天我们要把这些知识串在一起来开发一个简单端到端系统的应用系统。下面是我们访问LLM模型的主要代码：
import openai
#您的openai的api key
openai.api_key ='YOUR-OPENAI-API-KEY'
def get_completion_from_messages(messages,
model="gpt-3.5-turbo",
temperature=0,
max_tokens=500):
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
return response.choices[0].message["content"]我们的系统大体流程是这样的：
系统是一个机器人问答系统，客户提问有关电子产品的信息，机器人负责从系统内置的产品信息中搜索相关信息，并回复给客户，系统从接受客户提问开始要经过7个步骤，最终会产生一个给客户的回复信息。下面我们看看主要的功能函数process_user_message：
def process_user_message(user_input, all_messages, debug=True):
delimiter = "```"
# Step 1: Check input to see if it flags the Moderation API or is a prompt injection
response = openai.Moderation.create(input=user_input)
moderation_output = response["results"][0]
if moderation_output["flagged"]:
print("Step 1: Input flagged by Moderation API.")
return "Sorry, we cannot process this request."
if debug: print("Step 1: Input passed moderation check.")
category_and_product_response = utils.find_category_and_product_only(user_input, utils.get_products_and_category())
#print(print(category_and_product_response)
# Step 2: Extract the list of products
category_and_product_list = utils.read_string_to_list(category_and_product_response)
#print(category_and_product_list)
if debug: print("Step 2: Extracted list of products.")
# Step 3: If products are found, look them up
product_information = utils.generate_output_string(category_and_product_list)
if debug: print("Step 3: Looked up product information.")
# Step 4: Answer the user question
system_message = f"""
You are a customer service assistant for a large electronic store. \
Respond in a friendly and helpful tone, with concise answers. \
Make sure to ask the user relevant follow-up questions.
"""
messages = [
{'role': 'system', 'content': system_message},
{'role': 'user', 'content': f"{delimiter}{user_input}{delimiter}"},
{'role': 'assistant', 'content': f"Relevant product information:\n{product_information}"}
]
final_response = get_completion_from_messages(all_messages + messages)
if debug:print("Step 4: Generated response to user question.")
all_messages = all_messages + messages[1:]
# Step 5: Put the answer through the Moderation API
response = openai.Moderation.create(input=final_response)
moderation_output = response["results"][0]
if moderation_output["flagged"]:
if debug: print("Step 5: Response flagged by Moderation API.")
return "Sorry, we cannot provide this information."
if debug: print("Step 5: Response passed moderation check.")
# Step 6: Ask the model if the response answers the initial user query well
user_message = f"""
Customer message: {delimiter}{user_input}{delimiter}
Agent response: {delimiter}{final_response}{delimiter}
Does the response sufficiently answer the question?
"""
messages = [
{'role': 'system', 'content': system_message},
{'role': 'user', 'content': user_message}
]
evaluation_response = get_completion_from_messages(messages)
if debug: print("Step 6: Model evaluated the response.")
# Step 7: If yes, use this answer; if not, say that you will connect the user to a human
if "Y" in evaluation_response: # Using "in" instead of "==" to be safer for model output variation (e.g., "Y." or "Yes")
if debug: print("Step 7: Model approved the response.")
return final_response, all_messages
else:
if debug: print("Step 7: Model disapproved the response.")
neg_str = "I'm unable to provide the information you're looking for. I'll connect you with a human representative for further assistance."
return neg_str, all_messagesprocess_user_message函数定义了处理用户消息的所有7个步骤，其中：
step1:内容审查1
对客户的问题进行内容审查，这里调用openai的Moderation api函数，可以返回用户信息是否属于违规的标记flagged，Moderation api的返回的具体信息的含义在使用大型语言模(LLM)构建系统(二):内容审核、预防Prompt注入这篇博客已经介绍过了，这里不再赘述了。
step2:查询产品目录清单，string转list
查询产品目录清单和string转list 这两个方法的功能我在使用大型语言模(LLM)构建系统(四)：链式提示这篇博客中已经介绍过了，这里不在赘述。
Step3.查询产品信息
通过查询到的产品目录信息，再去查询具体的产品信息，这里会调用generate_output_string函数，该函数在使用大型语言模(LLM)构建系统(四)：链式提示这篇博客中已经介绍过了，这里不在赘述。
Step4.产生最终回复
在得到了具体的产品信息后，我们需要将此产品信息和客户的问题结合起来，让LLM产生最终的回复信息，这里具体的实现在使用大型语言模(LLM)构建系统(四)：链式提示这篇博客中已经介绍过了，这里不再赘述。
step5:内容审查2
这里我们任然需要对LLM产生的最终回复内容进行审查，以免出现违规内容。内容审查我们任然调用openai的Moderation api函数来完成。
Step6.检验最终回复
这里的检验最终回复的目的在于确认最终回复是否可以真的满足客户问题需求，避免出现答非所问，驴唇不对马嘴的情况。这里我们会把用户的问题和最终回复结合在一起发送给LLM，这样LLM才能识别出最终回复是否符合要求。
Step7.输出最终回复
根据Step6的检验结果，来决定是否输出最终回复，如果检验结果中包含字母"Y",则输出最终回复，否则输出婉言拒绝的信息。这里有个小小的疑惑是 只用字母“Y”来判断是否通过“检验最终回复”似乎有点不太严谨，因为检验的结果是一长串英语的句子如下面所示：
这里无法确定的是如果没有通过“检验最终回复”这一关，得到的答复是否也包含字母“Y”, 如果某一产品中也包含字母“Y”,它正好也出现在检验的结果中，这样就有可能出现问题。
测试
下面我们对上述process_user_message函数进行测试，我们尝试用各种问题来考考LLM，看看LLM最终会怎么回答。首先我们询问一些具体的产品问题：
user_input = "tell me about the smartx pro phone and the fotosnap camera, the dslr one. Also what tell me about your tvs"
response,_ = process_user_message(user_input,[])
print(response)
这里我们向LLM询问了3个具体产品和一个产品类别(tvs)的问题,从上面的结果上看process_user_message内部的7个步骤都被执行且都通过了，最后我们得到了3个具体产品和一个类别的具体信息。
接下来我们向LLM询问有哪些类别(category):
user_input2= "what specific categorys do you have?"
response,_ = process_user_message(user_input2,[])
print(response)
当我们询问所有类别时，系统报错了，报错的位置是在执行了Step3以后开始的，从报错的内容上看，似乎是上下文长度超过了4096个tokens, 这里需要说明的是，当我们询问所有类别时，LLM会去获取系统里所有的类别和每个类别下所有的产品信息。这将导致Step3的结果的长度超标,因此会在执行Step4时报错。
下面我们询问一个具体的类别：
user_input2= "tell me about BlueWave Chromebook"
response,_ = process_user_message(user_input2,[])
print(response)
下面我们询问一个具体的产品：
user_input2= "tell me about TechPro Ultrabook"
response,_ = process_user_message(user_input2,[])
print(response) 
接下来我们询问一个不存在的产品：
user_input2= "tell me about your cars"
response,_ = process_user_message(user_input2,[])
print(response)
打造一个聊天机器人
接下来我们来打造一个聊天机器人的应用系统，让LLM扮演客服来回答用户关于相关产品的问题，下面我全程用中文和机器人聊天，尽管我们所有的产品信息和system message消息都是英文的，但是LLM仍然可以用中文来回复我的所有问题：
def collect_messages(debug=True):
user_input = inp.value_input
if debug: print(f"User Input = {user_input}")
if user_input == "":
return
inp.value = ''
global context
#response, context = process_user_message(user_input, context, utils.get_products_and_category(),debug=True)
response, context = process_user_message(user_input, context, debug=True)
context.append({'role':'assistant', 'content':f"{response}"})
panels.append(
pn.Row('User:', pn.pane.Markdown(user_input, width=600)))
panels.append(
pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))
return pn.Column(*panels)
panels = [] # collect display
context = [ {'role':'system', 'content':"You are Service Assistant"} ]
inp = pn.widgets.TextInput( placeholder='Enter text here…')
button_conversation = pn.widgets.Button(name="Service Assistant")
interactive_conversation = pn.bind(collect_messages, button_conversation)
dashboard = pn.Column(
inp,
pn.Row(button_conversation),
pn.panel(interactive_conversation, loading_indicator=True, height=300),
)
dashboard
有趣的是LLM把“苹果”误认为是苹果公司的电子产品，但是苹果的电子产品并未包含在系统的所有电子产品的名单里，也就是说系统的产品清单里根本没有苹果的产品，这说明LLM此时产生了“幻觉”，它编造了一些不存在的产品。最后我询问有关电动汽车的时候，LLM能够给出正确的答案。
下面是utils.py里面包含了所有的产品信息，以及一些主要的调用函数：
import json
import openai
from collections import defaultdict
products_file = 'products.json'
categories_file = 'categories.json'
delimiter = "####"
step_2_system_message_content = f"""
You will be provided with customer service a conversation. \
The most recent user query will be delimited with \
{delimiter} characters.
Output a python list of objects, where each object has \
the following format:
'category': <one of Computers and Laptops, \
Smartphones and Accessories, \
Televisions and Home Theater Systems, \
Gaming Consoles and Accessories,
Audio Equipment, Cameras and Camcorders>,
OR
'products': <a list of products that must \
be found in the allowed products below>
Where the categories and products must be found in \
the customer service query.
If a product is mentioned, it must be associated with \
the correct category in the allowed products list below.
If no products or categories are found, output an \
empty list.
Only list products and categories that have not already \
been mentioned and discussed in the earlier parts of \
the conversation.
Allowed products:
Computers and Laptops category:
TechPro Ultrabook
BlueWave Gaming Laptop
PowerLite Convertible
TechPro Desktop
BlueWave Chromebook
Smartphones and Accessories category:
SmartX ProPhone
MobiTech PowerCase
SmartX MiniPhone
MobiTech Wireless Charger
SmartX EarBuds
Televisions and Home Theater Systems category:
CineView 4K TV
SoundMax Home Theater
CineView 8K TV
SoundMax Soundbar
CineView OLED TV
Gaming Consoles and Accessories category:
GameSphere X
ProGamer Controller
GameSphere Y
ProGamer Racing Wheel
GameSphere VR Headset
Audio Equipment category:
AudioPhonic Noise-Canceling Headphones
WaveSound Bluetooth Speaker
AudioPhonic True Wireless Earbuds
WaveSound Soundbar
AudioPhonic Turntable
Cameras and Camcorders category:
FotoSnap DSLR Camera
ActionCam 4K
FotoSnap Mirrorless Camera
ZoomMaster Camcorder
FotoSnap Instant Camera
Only output the list of objects, with nothing else.
"""
step_2_system_message = {'role':'system', 'content': step_2_system_message_content}
step_4_system_message_content = f"""
You are a customer service assistant for a large electronic store. \
Respond in a friendly and helpful tone, with VERY concise answers. \
Make sure to ask the user relevant follow-up questions.
"""
step_4_system_message = {'role':'system', 'content': step_4_system_message_content}
step_6_system_message_content = f"""
You are an assistant that evaluates whether \
customer service agent responses sufficiently \
answer customer questions, and also validates that \
all the facts the assistant cites from the product \
information are correct.
The conversation history, product information, user and customer \
service agent messages will be delimited by \
3 backticks, i.e. ```.
Respond with a Y or N character, with no punctuation:
Y - if the output sufficiently answers the question \
AND the response correctly uses product information
N - otherwise
Output a single letter only.
"""
step_6_system_message = {'role':'system', 'content': step_6_system_message_content}
def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0, max_tokens=500):
response = openai.ChatCompletion.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
)
return response.choices[0].message["content"]
def create_categories():
categories_dict = {
'Billing': [
'Unsubscribe or upgrade',
'Add a payment method',
'Explanation for charge',
'Dispute a charge'],
'Technical Support':[
'General troubleshooting'
'Device compatibility',
'Software updates'],
'Account Management':[
'Password reset'
'Update personal information',
'Close account',
'Account security'],
'General Inquiry':[
'Product information'
'Pricing',
'Feedback',
'Speak to a human']
}
with open(categories_file, 'w') as file:
json.dump(categories_dict, file)
return categories_dict
def get_categories():
with open(categories_file, 'r') as file:
categories = json.load(file)
return categories
def get_product_list():
"""
Used in L4 to get a flat list of products
"""
products = get_products()
product_list = []
for product in products.keys():
product_list.append(product)
return product_list
def get_products_and_category():
"""
Used in L5
"""
products = get_products()
products_by_category = defaultdict(list)
for product_name, product_info in products.items():
category = product_info.get('category')
if category:
products_by_category[category].append(product_info.get('name'))
return dict(products_by_category)
def get_products():
with open(products_file, 'r') as file:
products = json.load(file)
return products
def find_category_and_product(user_input,products_and_category):
delimiter = "####"
system_message = f"""
You will be provided with customer service queries. \
The customer service query will be delimited with {delimiter} characters.
Output a python list of json objects, where each object has the following format:
'category': <one of Computers and Laptops, Smartphones and Accessories, Televisions and Home Theater Systems, \
Gaming Consoles and Accessories, Audio Equipment, Cameras and Camcorders>,
OR
'products': <a list of products that must be found in the allowed products below>
Where the categories and products must be found in the customer service query.
If a product is mentioned, it must be associated with the correct category in the allowed products list below.
If no products or categories are found, output an empty list.
The allowed products are provided in JSON format.
The keys of each item represent the category.
The values of each item is a list of products that are within that category.
Allowed products: {products_and_category}
"""
messages = [
{'role':'system', 'content': system_message},
{'role':'user', 'content': f"{delimiter}{user_input}{delimiter}"},
]
return get_completion_from_messages(messages)
def find_category_and_product_only(user_input,products_and_category):
delimiter = "####"
system_message = f"""
You will be provided with customer service queries. \
The customer service query will be delimited with {delimiter} characters.
Output a python list of objects, where each object has the following format:
'category': <one of Computers and Laptops, Smartphones and Accessories, Televisions and Home Theater Systems, \
Gaming Consoles and Accessories, Audio Equipment, Cameras and Camcorders>,
OR
'products': <a list of products that must be found in the allowed products below>
Where the categories and products must be found in the customer service query.
If a product is mentioned, it must be associated with the correct category in the allowed products list below.
If no products or categories are found, output an empty list.
Allowed products:
Computers and Laptops category:
TechPro Ultrabook
BlueWave Gaming Laptop
PowerLite Convertible
TechPro Desktop
BlueWave Chromebook
Smartphones and Accessories category:
SmartX ProPhone
MobiTech PowerCase
SmartX MiniPhone
MobiTech Wireless Charger
SmartX EarBuds
Televisions and Home Theater Systems category:
CineView 4K TV
SoundMax Home Theater
CineView 8K TV
SoundMax Soundbar
CineView OLED TV
Gaming Consoles and Accessories category:
GameSphere X
ProGamer Controller
GameSphere Y
ProGamer Racing Wheel
GameSphere VR Headset
Audio Equipment category:
AudioPhonic Noise-Canceling Headphones
WaveSound Bluetooth Speaker
AudioPhonic True Wireless Earbuds
WaveSound Soundbar
AudioPhonic Turntable
Cameras and Camcorders category:
FotoSnap DSLR Camera
ActionCam 4K
FotoSnap Mirrorless Camera
ZoomMaster Camcorder
FotoSnap Instant Camera
Only output the list of objects, nothing else.
"""
messages = [
{'role':'system', 'content': system_message},
{'role':'user', 'content': f"{delimiter}{user_input}{delimiter}"},
]
return get_completion_from_messages(messages)
def get_products_from_query(user_msg):
"""
Code from L5, used in L8
"""
products_and_category = get_products_and_category()
delimiter = "####"
system_message = f"""
You will be provided with customer service queries. \
The customer service query will be delimited with {delimiter} characters.
Output a python list of json objects, where each object has the following format:
'category': <one of Computers and Laptops, Smartphones and Accessories, Televisions and Home Theater Systems, \
Gaming Consoles and Accessories, Audio Equipment, Cameras and Camcorders>,
OR
'products': <a list of products that must be found in the allowed products below>
Where the categories and products must be found in the customer service query.
If a product is mentioned, it must be associated with the correct category in the allowed products list below.
If no products or categories are found, output an empty list.
The allowed products are provided in JSON format.
The keys of each item represent the category.
The values of each item is a list of products that are within that category.
Allowed products: {products_and_category}
"""
messages = [
{'role':'system', 'content': system_message},
{'role':'user', 'content': f"{delimiter}{user_msg}{delimiter}"},
]
category_and_product_response = get_completion_from_messages(messages)
return category_and_product_response
# product look up (either by category or by product within category)
def get_product_by_name(name):
products = get_products()
return products.get(name, None)
def get_products_by_category(category):
products = get_products()
return [product for product in products.values() if product["category"] == category]
def get_mentioned_product_info(data_list):
"""
Used in L5 and L6
"""
product_info_l = []
if data_list is None:
return product_info_l
for data in data_list:
try:
if "products" in data:
products_list = data["products"]
for product_name in products_list:
product = get_product_by_name(product_name)
if product:
product_info_l.append(product)
else:
print(f"Error: Product '{product_name}' not found")
elif "category" in data:
category_name = data["category"]
category_products = get_products_by_category(category_name)
for product in category_products:
product_info_l.append(product)
else:
print("Error: Invalid object format")
except Exception as e:
print(f"Error: {e}")
return product_info_l
def read_string_to_list(input_string):
if input_string is None:
return None
try:
input_string = input_string.replace("'", "\"") # Replace single quotes with double quotes for valid JSON
data = json.loads(input_string)
return data
except json.JSONDecodeError:
print("Error: Invalid JSON string")
return None
def generate_output_string(data_list):
output_string = ""
if data_list is None:
return output_string
for data in data_list:
try:
if "products" in data:
products_list = data["products"]
for product_name in products_list:
product = get_product_by_name(product_name)
if product:
output_string += json.dumps(product, indent=4) + "\n"
else:
print(f"Error: Product '{product_name}' not found")
elif "category" in data:
category_name = data["category"]
category_products = get_products_by_category(category_name)
for product in category_products:
output_string += json.dumps(product, indent=4) + "\n"
else:
print("Error: Invalid object format")
except Exception as e:
print(f"Error: {e}")
return output_string
# Example usage:
#product_information_for_user_message_1 = generate_output_string(category_and_product_list)
#print(product_information_for_user_message_1)
def answer_user_msg(user_msg,product_info):
"""
Code from L5, used in L6
"""
delimiter = "####"
system_message = f"""
You are a customer service assistant for a large electronic store. \
Respond in a friendly and helpful tone, with concise answers. \
Make sure to ask the user relevant follow up questions.
"""
# user_msg = f"""
# tell me about the smartx pro phone and the fotosnap camera, the dslr one. Also what tell me about your tvs"""
messages = [
{'role':'system', 'content': system_message},
{'role':'user', 'content': f"{delimiter}{user_msg}{delimiter}"},
{'role':'assistant', 'content': f"Relevant product information:\n{product_info}"},
]
response = get_completion_from_messages(messages)
return response
def create_products():
"""
Create products dictionary and save it to a file named products.json
"""
# product information
# fun fact: all these products are fake and were generated by a language model
products = {
"TechPro Ultrabook": {
"name": "TechPro Ultrabook",
"category": "Computers and Laptops",
"brand": "TechPro",
"model_number": "TP-UB100",
"warranty": "1 year",
"rating": 4.5,
"features": ["13.3-inch display", "8GB RAM", "256GB SSD", "Intel Core i5 processor"],
"description": "A sleek and lightweight ultrabook for everyday use.",
"price": 799.99
},
"BlueWave Gaming Laptop": {
"name": "BlueWave Gaming Laptop",
"category": "Computers and Laptops",
"brand": "BlueWave",
"model_number": "BW-GL200",
"warranty": "2 years",
"rating": 4.7,
"features": ["15.6-inch display", "16GB RAM", "512GB SSD", "NVIDIA GeForce RTX 3060"],
"description": "A high-performance gaming laptop for an immersive experience.",
"price": 1199.99
},
"PowerLite Convertible": {
"name": "PowerLite Convertible",
"category": "Computers and Laptops",
"brand": "PowerLite",
"model_number": "PL-CV300",
"warranty": "1 year",
"rating": 4.3,
"features": ["14-inch touchscreen", "8GB RAM", "256GB SSD", "360-degree hinge"],
"description": "A versatile convertible laptop with a responsive touchscreen.",
"price": 699.99
},
"TechPro Desktop": {
"name": "TechPro Desktop",
"category": "Computers and Laptops",
"brand": "TechPro",
"model_number": "TP-DT500",
"warranty": "1 year",
"rating": 4.4,
"features": ["Intel Core i7 processor", "16GB RAM", "1TB HDD", "NVIDIA GeForce GTX 1660"],
"description": "A powerful desktop computer for work and play.",
"price": 999.99
},
"BlueWave Chromebook": {
"name": "BlueWave Chromebook",
"category": "Computers and Laptops",
"brand": "BlueWave",
"model_number": "BW-CB100",
"warranty": "1 year",
"rating": 4.1,
"features": ["11.6-inch display", "4GB RAM", "32GB eMMC", "Chrome OS"],
"description": "A compact and affordable Chromebook for everyday tasks.",
"price": 249.99
},
"SmartX ProPhone": {
"name": "SmartX ProPhone",
"category": "Smartphones and Accessories",
"brand": "SmartX",
"model_number": "SX-PP10",
"warranty": "1 year",
"rating": 4.6,
"features": ["6.1-inch display", "128GB storage", "12MP dual camera", "5G"],
"description": "A powerful smartphone with advanced camera features.",
"price": 899.99
},
"MobiTech PowerCase": {
"name": "MobiTech PowerCase",
"category": "Smartphones and Accessories",
"brand": "MobiTech",
"model_number": "MT-PC20",
"warranty": "1 year",
"rating": 4.3,
"features": ["5000mAh battery", "Wireless charging", "Compatible with SmartX ProPhone"],
"description": "A protective case with built-in battery for extended usage.",
"price": 59.99
},
"SmartX MiniPhone": {
"name": "SmartX MiniPhone",
"category": "Smartphones and Accessories",
"brand": "SmartX",
"model_number": "SX-MP5",
"warranty": "1 year",
"rating": 4.2,
"features": ["4.7-inch display", "64GB storage", "8MP camera", "4G"],
"description": "A compact and affordable smartphone for basic tasks.",
"price": 399.99
},
"MobiTech Wireless Charger": {
"name": "MobiTech Wireless Charger",
"category": "Smartphones and Accessories",
"brand": "MobiTech",
"model_number": "MT-WC10",
"warranty": "1 year",
"rating": 4.5,
"features": ["10W fast charging", "Qi-compatible", "LED indicator", "Compact design"],
"description": "A convenient wireless charger for a clutter-free workspace.",
"price": 29.99
},
"SmartX EarBuds": {
"name": "SmartX EarBuds",
"category": "Smartphones and Accessories",
"brand": "SmartX",
"model_number": "SX-EB20",
"warranty": "1 year",
"rating": 4.4,
"features": ["True wireless", "Bluetooth 5.0", "Touch controls", "24-hour battery life"],
"description": "Experience true wireless freedom with these comfortable earbuds.",
"price": 99.99
},
"CineView 4K TV": {
"name": "CineView 4K TV",
"category": "Televisions and Home Theater Systems",
"brand": "CineView",
"model_number": "CV-4K55",
"warranty": "2 years",
"rating": 4.8,
"features": ["55-inch display", "4K resolution", "HDR", "Smart TV"],
"description": "A stunning 4K TV with vibrant colors and smart features.",
"price": 599.99
},
"SoundMax Home Theater": {
"name": "SoundMax Home Theater",
"category": "Televisions and Home Theater Systems",
"brand": "SoundMax",
"model_number": "SM-HT100",
"warranty": "1 year",
"rating": 4.4,
"features": ["5.1 channel", "1000W output", "Wireless subwoofer", "Bluetooth"],
"description": "A powerful home theater system for an immersive audio experience.",
"price": 399.99
},
"CineView 8K TV": {
"name": "CineView 8K TV",
"category": "Televisions and Home Theater Systems",
"brand": "CineView",
"model_number": "CV-8K65",
"warranty": "2 years",
"rating": 4.9,
"features": ["65-inch display", "8K resolution", "HDR", "Smart TV"],
"description": "Experience the future of television with this stunning 8K TV.",
"price": 2999.99
},
"SoundMax Soundbar": {
"name": "SoundMax Soundbar",
"category": "Televisions and Home Theater Systems",
"brand": "SoundMax",
"model_number": "SM-SB50",
"warranty": "1 year",
"rating": 4.3,
"features": ["2.1 channel", "300W output", "Wireless subwoofer", "Bluetooth"],
"description": "Upgrade your TV's audio with this sleek and powerful soundbar.",
"price": 199.99
},
"CineView OLED TV": {
"name": "CineView OLED TV",
"category": "Televisions and Home Theater Systems",
"brand": "CineView",
"model_number": "CV-OLED55",
"warranty": "2 years",
"rating": 4.7,
"features": ["55-inch display", "4K resolution", "HDR", "Smart TV"],
"description": "Experience true blacks and vibrant colors with this OLED TV.",
"price": 1499.99
},
"GameSphere X": {
"name": "GameSphere X",
"category": "Gaming Consoles and Accessories",
"brand": "GameSphere",
"model_number": "GS-X",
"warranty": "1 year",
"rating": 4.9,
"features": ["4K gaming", "1TB storage", "Backward compatibility", "Online multiplayer"],
"description": "A next-generation gaming console for the ultimate gaming experience.",
"price": 499.99
},
"ProGamer Controller": {
"name": "ProGamer Controller",
"category": "Gaming Consoles and Accessories",
"brand": "ProGamer",
"model_number": "PG-C100",
"warranty": "1 year",
"rating": 4.2,
"features": ["Ergonomic design", "Customizable buttons", "Wireless", "Rechargeable battery"],
"description": "A high-quality gaming controller for precision and comfort.",
"price": 59.99
},
"GameSphere Y": {
"name": "GameSphere Y",
"category": "Gaming Consoles and Accessories",
"brand": "GameSphere",
"model_number": "GS-Y",
"warranty": "1 year",
"rating": 4.8,
"features": ["4K gaming", "500GB storage", "Backward compatibility", "Online multiplayer"],
"description": "A compact gaming console with powerful performance.",
"price": 399.99
},
"ProGamer Racing Wheel": {
"name": "ProGamer Racing Wheel",
"category": "Gaming Consoles and Accessories",
"brand": "ProGamer",
"model_number": "PG-RW200",
"warranty": "1 year",
"rating": 4.5,
"features": ["Force feedback", "Adjustable pedals", "Paddle shifters", "Compatible with GameSphere X"],
"description": "Enhance your racing games with this realistic racing wheel.",
"price": 249.99
},
"GameSphere VR Headset": {
"name": "GameSphere VR Headset",
"category": "Gaming Consoles and Accessories",
"brand": "GameSphere",
"model_number": "GS-VR",
"warranty": "1 year",
"rating": 4.6,
"features": ["Immersive VR experience", "Built-in headphones", "Adjustable headband", "Compatible with GameSphere X"],
"description": "Step into the world of virtual reality with this comfortable VR headset.",
"price": 299.99
},
"AudioPhonic Noise-Canceling Headphones": {
"name": "AudioPhonic Noise-Canceling Headphones",
"category": "Audio Equipment",
"brand": "AudioPhonic",
"model_number": "AP-NC100",
"warranty": "1 year",
"rating": 4.6,
"features": ["Active noise-canceling", "Bluetooth", "20-hour battery life", "Comfortable fit"],
"description": "Experience immersive sound with these noise-canceling headphones.",
"price": 199.99
},
"WaveSound Bluetooth Speaker": {
"name": "WaveSound Bluetooth Speaker",
"category": "Audio Equipment",
"brand": "WaveSound",
"model_number": "WS-BS50",
"warranty": "1 year",
"rating": 4.5,
"features": ["Portable", "10-hour battery life", "Water-resistant", "Built-in microphone"],
"description": "A compact and versatile Bluetooth speaker for music on the go.",
"price": 49.99
},
"AudioPhonic True Wireless Earbuds": {
"name": "AudioPhonic True Wireless Earbuds",
"category": "Audio Equipment",
"brand": "AudioPhonic",
"model_number": "AP-TW20",
"warranty": "1 year",
"rating": 4.4,
"features": ["True wireless", "Bluetooth 5.0", "Touch controls", "18-hour battery life"],
"description": "Enjoy music without wires with these comfortable true wireless earbuds.",
"price": 79.99
},
"WaveSound Soundbar": {
"name": "WaveSound Soundbar",
"category": "Audio Equipment",
"brand": "WaveSound",
"model_number": "WS-SB40",
"warranty": "1 year",
"rating": 4.3,
"features": ["2.0 channel", "80W output", "Bluetooth", "Wall-mountable"],
"description": "Upgrade your TV's audio with this slim and powerful soundbar.",
"price": 99.99
},
"AudioPhonic Turntable": {
"name": "AudioPhonic Turntable",
"category": "Audio Equipment",
"brand": "AudioPhonic",
"model_number": "AP-TT10",
"warranty": "1 year",
"rating": 4.2,
"features": ["3-speed", "Built-in speakers", "Bluetooth", "USB recording"],
"description": "Rediscover your vinyl collection with this modern turntable.",
"price": 149.99
},
"FotoSnap DSLR Camera": {
"name": "FotoSnap DSLR Camera",
"category": "Cameras and Camcorders",
"brand": "FotoSnap",
"model_number": "FS-DSLR200",
"warranty": "1 year",
"rating": 4.7,
"features": ["24.2MP sensor", "1080p video", "3-inch LCD", "Interchangeable lenses"],
"description": "Capture stunning photos and videos with this versatile DSLR camera.",
"price": 599.99
},
"ActionCam 4K": {
"name": "ActionCam 4K",
"category": "Cameras and Camcorders",
"brand": "ActionCam",
"model_number": "AC-4K",
"warranty": "1 year",
"rating": 4.4,
"features": ["4K video", "Waterproof", "Image stabilization", "Wi-Fi"],
"description": "Record your adventures with this rugged and compact 4K action camera.",
"price": 299.99
},
"FotoSnap Mirrorless Camera": {
"name": "FotoSnap Mirrorless Camera",
"category": "Cameras and Camcorders",
"brand": "FotoSnap",
"model_number": "FS-ML100",
"warranty": "1 year",
"rating": 4.6,
"features": ["20.1MP sensor", "4K video", "3-inch touchscreen", "Interchangeable lenses"],
"description": "A compact and lightweight mirrorless camera with advanced features.",
"price": 799.99
},
"ZoomMaster Camcorder": {
"name": "ZoomMaster Camcorder",
"category": "Cameras and Camcorders",
"brand": "ZoomMaster",
"model_number": "ZM-CM50",
"warranty": "1 year",
"rating": 4.3,
"features": ["1080p video", "30x optical zoom", "3-inch LCD", "Image stabilization"],
"description": "Capture life's moments with this easy-to-use camcorder.",
"price": 249.99
},
"FotoSnap Instant Camera": {
"name": "FotoSnap Instant Camera",
"category": "Cameras and Camcorders",
"brand": "FotoSnap",
"model_number": "FS-IC10",
"warranty": "1 year",
"rating": 4.1,
"features": ["Instant prints", "Built-in flash", "Selfie mirror", "Battery-powered"],
"description": "Create instant memories with this fun and portable instant camera.",
"price": 69.99
}
}
products_file = 'products.json'
with open(products_file, 'w') as file:
json.dump(products, file)
return products总结
今天我们学习了如何构造一个端对端对机器人问答系统，该系统包含了大型语言模型(LLM) 处理用户信息的完整流程，总共包含7个主要的步骤：内容审核1，查询产品目录清单，查询产品信息，生产最终回复，内容审核2，检验最终回复，输出最终回复。这里面每一个步骤的主要功能和实现方法在本博客和之前的博客中都有详细介绍，最后我们开发了一个聊天机器人程序，并对有关产品的问题进行了测试，总的来说机器人的回复还算正确，但偶尔也会产生“幻觉”，并且编造一些根本不存在的产品出来(如苹果的产品）。希望今天的内容能帮助到大家。