今日はDeepLearning.AIのBuilding Systems with LLMのオンラインコースを受講したので、その主な内容の一部を共有したいと思います。私たちはこれまでに次の知識を学びました。
- 大規模言語モデル (LLM) を使用したシステムの構築パート 1: 分類
- 大規模言語モデル (LLM) を使用したシステム構築 (2): コンテンツのレビュー、プロンプト インジェクションの防止
- 大規模言語モデル (LLM) を使用したシステムの構築 (3): 思考連鎖推論
- 大規模言語モデル (LLM) を使用したシステムの構築 (4): 連鎖したヒント
- 大規模言語モデル(LLM)を使ったシステム構築(5)：出力チェック
ユーザーのクエリを処理するための連鎖ヒント システム
今日は、この知識を組み合わせて、シンプルなエンドツーエンドのシステム アプリケーション システムを開発します。LLM モデルにアクセスするためのメイン コードは次のとおりです。
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"]弊社システムの大まかな流れは以下の通りです。
本システムは、ロボットによる質疑応答システムです お客様から電気製品に関する質問があり、ロボットはシステムに組み込まれた製品情報から関連情報を検索し、お客様に回答する役割を果たします システムは開始から7つのステップを経ます顧客の質問を受け付け、最終的に顧客への回答を生成する顧客の返信メッセージ。以下のメイン関数 process_user_message を見てみましょう。
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 関数は、ユーザー メッセージを処理するための 7 つのステップすべてを定義します。
step1: コンテンツレビュー1
顧客の質問の内容を確認します。ここでは、openai のモデレーション API 関数を呼び出して、ユーザー情報に違反フラグが設定されているかどうかを返します。モデレーション API によって返される特定の情報の意味は、大規模な言語モデルの使用にあります ( LLM) によるシステム構築 (2) : コンテンツのレビューとプロンプト インジェクションの防止については、すでにこのブログで紹介しているので、ここでは詳しく説明しません。
ステップ 2: 製品カタログ リストをクエリし、文字列をリストに追加します。
製品カタログのリストをクエリする方法と文字列をリストに変換する 2 つのメソッドの機能については、このブログですでに紹介されているため、ここでは詳しく説明しません。
Step3. 製品情報の問い合わせ
クエリされた製品カタログ情報を通じて、次に特定の製品情報をクエリするために、generate_output_string 関数がここで呼び出されます。この関数は、ラージ言語モデル (LLM) を使用したシステムの構築 (4): チェーン プロンプトのブログで紹介されています。ここでは詳細には触れません。
ステップ4. 最終応答を生成する
特定の製品情報を取得した後、製品情報と顧客の質問を組み合わせて、LLM に最終応答情報を生成させる必要があります。ここでの具体的な実装は、大規模言語モデル (LLM) を使用してシステムを構築することです (4) 。チェーンのヒントこのブログではすでに紹介していますので、ここでは繰り返しません。
step5: コンテンツレビュー2
ここでも、違法なコンテンツを避けるために、LLM によって生成された最終応答コンテンツを確認する必要があります。コンテンツのレビューを完了するには、引き続き openai のモデレーション API 関数を呼び出します。
Step6. 最終返信を確認する
ここで最終回答をテストする目的は、最終回答が顧客の質問のニーズを本当に満たせるかどうかを確認し、質問の内容と異なる答えが返ってロバの口が間違っているという事態を避けることです。ここでは、ユーザーの質問と最終応答を組み合わせて LLM に送信し、LLM が最終応答が要件を満たしているかどうかを識別できるようにします。
Step7. 最終応答を出力する
Step6のテスト結果に応じて、最終応答を出力するかどうかを決定し、テスト結果に文字「Y」が含まれる場合は最終応答を出力し、そうでない場合は丁寧な拒否のメッセージを出力します。ここで少し疑問があるのは、テストの結果は以下に示すような長い英語の文章のリストであるため、「テストの最終回答」の合否を判断するために文字「Y」だけを使用するのは少し不正確ではないかと思われることです。
ここで判断できないのは、「テスト最終回答」が不合格となった場合、その回答に「Y」の文字が含まれているかどうかですが、ある製品に「Y」の文字が含まれている場合、たまたまそれがテスト結果に表示されることになります。問題が発生する可能性がある場所です。
テスト
次に、上記の process_user_message 関数をテストし、さまざまな質問で LLM をテストし、最終的に LLM がどのように応答するかを確認します。まず、製品に関する具体的な質問をいくつかします。
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)
ここでは、LLM に 3 つの特定の製品と 1 つの製品カテゴリ (テレビ) について問い合わせました。上記の結果から、process_user_message 内の 7 つのステップがすべて実行され、通過したことがわかります。最終的に、3 つの特定の製品と 1 つのカテゴリ固有の情報を取得しました。
次に、LLM にどのようなカテゴリがあるかを尋ねます。
user_input2= "what specific categorys do you have?"
response,_ = process_user_message(user_input2,[])
print(response)
すべてのカテゴリについて尋ねたところ、システムはエラーを報告しました。エラーの場所は手順 3 の実行後に始まりました。エラー レポートの内容から、コンテキスト長が 4096 トークンを超えているようです。ここで説明する必要があるのは、カテゴリを指定すると、LLM はシステム内のすべてのカテゴリと各カテゴリのすべての製品情報を取得します。これにより、ステップ 3 の結果の長さが制限を超えるため、ステップ 4 の実行時にエラーが報告されます。
以下では、特定のカテゴリについて質問します。
user_input2= "tell me about BlueWave Chromebook"
response,_ = process_user_message(user_input2,[])
print(response)
特定の製品について質問してみましょう。
user_input2= "tell me about TechPro Ultrabook"
response,_ = process_user_message(user_input2,[])
print(response) 
次に、存在しない製品を要求します。
user_input2= "tell me about your cars"
response,_ = process_user_message(user_input2,[])
print(response)
チャットボットを構築する
次に、チャット ロボット アプリケーション システムを構築しましょう。LLM が関連製品に関するユーザーの質問に答えるカスタマー サービスの役割を果たします。次に、プロセス全体を通してロボットと中国語でチャットします。製品情報やシステム メッセージはすべて中国語で行われます。は英語ですが、LLM は私のすべての質問に中国語で答えることができます。
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
興味深いのは、LLMが「Apple」をAppleの電子製品と間違えたのですが、システム内のすべての電子製品のリストにAppleの電子製品が含まれていなかったこと、つまりシステムの製品リストにApple製品が存在しないことです。は、LLMがこの時点で「幻想」を持っており、存在しないいくつかの製品を捏造したことを示しています。最後に、電気自動車について質問したところ、LLM は正しい答えを返すことができました。
以下は、すべての製品情報といくつかの主要な呼び出し関数を含む utils.py です。
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要約する
今日、私たちはエンドツーエンドのロボット質問応答システムを構築する方法を学びました。これには、ユーザー情報を処理する大規模言語モデル (LLM) の完全なプロセスが含まれます。これには、コンテンツ レビュー 1、製品カタログ リストのクエリという合計 7 つの主要なステップが含まれます。 、クエリ製品情報、生産最終応答、内容レビュー 2、検査最終応答、出力最終応答。主な機能と各ステップの実装方法については、このブログと以前のブログで詳しく説明しています 最後に、チャット ロボット プログラムを開発し、製品に関する質問をテストしました ロボットの応答は概ね正しいですが、場合によっては「幻覚」が発生することがあります存在しない製品（Apple製品など）を作り上げます。今日の内容があなたのお役に立てば幸いです。