├── streamlit_frontend
├── __init__
├── app.py
└── backend.py
├── image.png
├── img
├── image.png
├── image-1.png
├── image-2.png
├── image-3.png
└── stores_mall_event.png
├── stores.csv
├── LICENSE.md
├── .gitignore
├── requirements.txt
├── README.md
└── autonomous-mall-assistant.ipynb
/streamlit_frontend/__init__:
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1 |
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/image.png:
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https://raw.githubusercontent.com/stepanogil/autonomous-mall-assistant/HEAD/image.png
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/img/image.png:
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https://raw.githubusercontent.com/stepanogil/autonomous-mall-assistant/HEAD/img/image.png
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/img/image-1.png:
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https://raw.githubusercontent.com/stepanogil/autonomous-mall-assistant/HEAD/img/image-1.png
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/img/image-2.png:
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https://raw.githubusercontent.com/stepanogil/autonomous-mall-assistant/HEAD/img/image-2.png
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/img/image-3.png:
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https://raw.githubusercontent.com/stepanogil/autonomous-mall-assistant/HEAD/img/image-3.png
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/img/stores_mall_event.png:
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https://raw.githubusercontent.com/stepanogil/autonomous-mall-assistant/HEAD/img/stores_mall_event.png
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/stores.csv:
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1 | store_name,retail_category,subcategory,location,running_promos
2 | ace hardware,home,,3rd floor,10% off on selected items.
3 | adidas,sports,,2nd floor,Buy 1 get 1 on selected shoes.
4 | bench,clothing,,ground floor,End of season sale - up to 50% off!
5 | daiso,home,,4th floor,New arrivals starting from Php100!
6 | h & m,clothing,,ground floor,20% off on selected denim!
7 | fat fook,food,taiwanese,ground floor,Free dimsum for meal worth Php500!
8 | jollibee,food,fast food,ground floor,Free drink with any combo meal!
9 | mcdonalds,food,fast food,ground floor,Free apple pie for orders over Php500!
10 | nike,sports,,2nd floor,Clearance sale - up to 40% off!
11 | penshoppe,clothing,,ground floor,Buy 2 shirts get 1 free.
12 | toys r us,kids,,5th floor,15% off on board games.
13 | eras tour,mall event,,,The Taylor Swift Eras tour is showing in our cinemas. Buy your tickets now!
14 |
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/LICENSE.md:
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1 | MIT License
2 |
3 | Copyright (c) 2023 Stephen Bonifacio
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
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/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *.pyo
5 | *.pyd
6 | .Python
7 |
8 | # C extensions
9 | *.so
10 |
11 | # Distribution / packaging
12 | .Python
13 | env/
14 | .env
15 | build/
16 | develop-eggs/
17 | dist/
18 | downloads/
19 | eggs/
20 | .eggs/
21 | lib/
22 | lib64/
23 | parts/
24 | sdist/
25 | var/
26 | wheels/
27 | pip-wheel-metadata/
28 | share/python-wheels/
29 | *.egg-info/
30 | .installed.cfg
31 | *.egg
32 | MANIFEST
33 |
34 | # PyInstaller
35 | # Usually these files are written by a python script from a template
36 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
37 | *.manifest
38 | *.spec
39 |
40 | # Installer logs
41 | pip-log.txt
42 | pip-delete-this-directory.txt
43 |
44 | # Unit test / coverage reports
45 | htmlcov/
46 | .tox/
47 | .nox/
48 | .coverage
49 | .coverage.*
50 | .cache
51 | nosetests.xml
52 | coverage.xml
53 | *.cover
54 | .hypothesis/
55 | .pytest_cache/
56 |
57 | # Jupyter Notebook
58 | .ipynb_checkpoints
59 |
60 | # IDEs and editors
61 | .idea/
62 | .vscode/
63 | *.swp
64 | *.swo
65 | *.swn
66 | *.suo
67 | *.bak
68 |
69 | # OS-specific
70 | .DS_Store
71 | Thumbs.db
72 |
73 | # drafts
74 | drafts/
75 |
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/streamlit_frontend/app.py:
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1 | import streamlit as st
2 | import random
3 | from backend import chat
4 | from streamlit_chat import message
5 | #from st_chat_message import message
6 |
7 |
8 | # Streamlit app
9 | st.header("Mall Assistant Chatbot")
10 | st.markdown("Ask questions about store locations, promotions, and more.")
11 |
12 | if "past" not in st.session_state:
13 | st.session_state["past"] = []
14 | if "generated" not in st.session_state:
15 | st.session_state["generated"] = []
16 |
17 | if "input_message_key" not in st.session_state:
18 | st.session_state["input_message_key"] = str(random.random())
19 |
20 | chat_container = st.container()
21 |
22 | user_input = st.text_input("Type your message and press Enter to send.", key=st.session_state["input_message_key"])
23 |
24 | if st.button("Send"):
25 | response = chat(user_input)
26 |
27 | st.session_state["past"].append(user_input)
28 | st.session_state["generated"].append(response)
29 |
30 | st.session_state["input_message_key"] = str(random.random())
31 |
32 | st.experimental_rerun()
33 |
34 | if st.session_state["generated"]:
35 | with chat_container:
36 | for i in range(len(st.session_state["generated"])):
37 | message(st.session_state["past"][i], is_user=True, key=str(i) + "_user")
38 | message(st.session_state["generated"][i], key=str(i))
39 |
40 |
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/requirements.txt:
--------------------------------------------------------------------------------
1 | aiohttp==3.8.6
2 | aiosignal==1.3.1
3 | altair==5.1.2
4 | annotated-types==0.6.0
5 | anyio==3.7.1
6 | asttokens==2.4.1
7 | async-timeout==4.0.3
8 | attrs==23.1.0
9 | backcall==0.2.0
10 | blinker==1.6.3
11 | cachetools==5.3.2
12 | certifi==2023.7.22
13 | charset-normalizer==3.3.1
14 | click==8.1.7
15 | comm==0.1.4
16 | dataclasses-json==0.6.1
17 | debugpy==1.8.0
18 | decorator==5.1.1
19 | exceptiongroup==1.1.3
20 | executing==2.0.0
21 | frozenlist==1.4.0
22 | gitdb==4.0.11
23 | GitPython==3.1.40
24 | greenlet==3.0.1
25 | idna==3.4
26 | importlib-metadata==6.8.0
27 | ipykernel==6.26.0
28 | ipython==8.16.1
29 | jedi==0.19.1
30 | Jinja2==3.1.2
31 | jsonpatch==1.33
32 | jsonpointer==2.4
33 | jsonschema==4.19.1
34 | jsonschema-specifications==2023.7.1
35 | jupyter_client==8.5.0
36 | jupyter_core==5.4.0
37 | langchain==0.0.325
38 | langsmith==0.0.53
39 | markdown-it-py==3.0.0
40 | MarkupSafe==2.1.3
41 | marshmallow==3.20.1
42 | matplotlib-inline==0.1.6
43 | mdurl==0.1.2
44 | multidict==6.0.4
45 | mypy-extensions==1.0.0
46 | nest-asyncio==1.5.8
47 | numpy==1.26.1
48 | openai==0.28.1
49 | packaging==23.2
50 | pandas==2.1.2
51 | parso==0.8.3
52 | pexpect==4.8.0
53 | pickleshare==0.7.5
54 | Pillow==9.5.0
55 | platformdirs==3.11.0
56 | prompt-toolkit==3.0.39
57 | protobuf==4.24.4
58 | psutil==5.9.6
59 | ptyprocess==0.7.0
60 | pure-eval==0.2.2
61 | pyarrow==13.0.0
62 | pydantic==2.4.2
63 | pydantic_core==2.10.1
64 | pydeck==0.8.1b0
65 | Pygments==2.16.1
66 | Pympler==1.0.1
67 | python-dateutil==2.8.2
68 | python-dotenv==1.0.0
69 | pytz==2023.3.post1
70 | pytz-deprecation-shim==0.1.0.post0
71 | PyYAML==6.0.1
72 | pyzmq==25.1.1
73 | referencing==0.30.2
74 | requests==2.31.0
75 | rich==13.6.0
76 | rpds-py==0.10.6
77 | six==1.16.0
78 | smmap==5.0.1
79 | sniffio==1.3.0
80 | SQLAlchemy==2.0.22
81 | st-chat-message==0.3.8
82 | stack-data==0.6.3
83 | streamlit==1.24.0
84 | streamlit-chat==0.1.1
85 | tenacity==8.2.3
86 | toml==0.10.2
87 | toolz==0.12.0
88 | tornado==6.3.3
89 | tqdm==4.66.1
90 | traitlets==5.12.0
91 | typing-inspect==0.9.0
92 | typing_extensions==4.8.0
93 | tzdata==2023.3
94 | tzlocal==4.3.1
95 | urllib3==2.0.7
96 | validators==0.22.0
97 | watchdog==3.0.0
98 | wcwidth==0.2.8
99 | yarl==1.9.2
100 | zipp==3.17.0
101 |
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/README.md:
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1 | # Autonomous Mall Assistant
2 |
3 |
4 | ## Description
5 |
6 | `autonomous-mall-assistant` is an AI-powered mall assistant designed to help shoppers easily locate stores within a shopping mall. The system utilizes a Large Language Model (LLM) to understand user queries and provide precise information or alternative suggestions.
7 |
8 |
9 | ## How it Works
10 |
11 |
12 | 1. User interacts with the chat interface to ask about a specific store or retail category.
13 | 2. The LLM identifies the `store name` and `retail category` from the user's query.
14 | 3. The system searches the database (pandas df) for the store information.
15 | - If found, returns relevant details to the LLM as context data.
16 | - If not found, suggests alternative stores from the same retail category and return it to the LLM to propose to the users as alternatives.
17 |
18 |
19 | ## Demo
20 | Stores Database - stored as a pandas dataframe but you should be able to use any other DBs (e.g. SQL/NoSQL) as long as you can wrap it into a function.
21 |
22 | 
23 |
24 | ### Q and A with the Assistant
25 | Note: more examples in autonomous-mall-assistant.ipynb
26 |
27 |
28 | ](img/image-2.png)
29 |
30 | Mentions mall wide events, if any.
31 |
32 | 
33 |
34 | ## Roadmap
35 |
36 | - ~~Add mall wide events/promotions to the output~~ - Completed on [2023-11-05]
37 | - Add mall general info like opening hours; exceptions to the scheds because of special holidays, etc.
38 | - Define handling of multiple stores/categories in user input.
39 | - Incorporate some guardrail mechanism e.g., nemo guardrails.
40 | - (longish-term) Breakdown the single `search_store` function into their respective retail category functions to handle targeted queries (e.g., sports - "Does Nike sell football boots?", food - "Which restaurants offer halal food?")
41 | - (longish-term) Utilize a better database that can scale better (e.g., managed DB offerings from Azure, AWS, etc.)
42 | - (longish-term) Add GPT-4 tool/function for complex queries requiring reasoning ability.
43 |
44 |
45 | ## Tech Stack
46 |
47 |
48 | - GPT-4
49 | - LangChain
50 | - pandas
51 | - streamlit
52 | - streamlit-chat
53 |
54 |
55 | ## Installation
56 |
57 |
58 | ```bash
59 | 1. clone this repo
60 | 2. pip install requirements.txt
61 | 3.1 run autonomous-mall-assistant.ipynb OR
62 | 3.2 cd to streamlit_frontend and run `streamlit run app.py` in terminal
63 | ```
64 |
65 | ## Contributing
66 |
67 | ...is welcome! 🤗
68 |
69 | ## Developer Notes
70 |
71 | - **[2023-11-05]**: Implemented `Chat History` feature to maintain conversation context. Since LLM API interactions are stateless, this enhancement captures and sends the entire history of user-AI exchanges alongside the current user query, enabling the LLM to generate contextually relevant responses.
72 |
73 | ## Author
74 |
75 |
76 | #### Stephen Bonifacio
77 |
78 | Feel free to connect with me on:
79 |
80 | Linkedin: https://www.linkedin.com/in/stephenbonifacio/
81 | Twitter: https://twitter.com/Stepanogil
82 |
83 | Did you find this useful? If you'd like to show some love, I won't say no to a cup of coffee! 🤗
84 |
85 | [](https://ko-fi.com/Q5Q6QPABZ)
86 |
87 |
88 |
89 |
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/streamlit_frontend/backend.py:
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1 | # NOTE: I'm building using the jupyter notebook primarily (autonommous-mall-assistant.ipynb).
2 | # This file is just a copy of that and is meant to be used with streamlit. This file might not be updated as often.
3 | # It has some modifications to account for streamlit's rendering capabilities:
4 | # - LLM not asked to use html or bullet points.
5 | # - Few shot prompt removed.
6 | # - Removed Ipython.display HTML in chat output
7 |
8 | import os
9 | import openai
10 | import pandas as pd
11 | import json
12 | from typing import List
13 | from pydantic import BaseModel, Field
14 | from langchain.utils.openai_functions import convert_pydantic_to_openai_function
15 | from typing import Optional
16 | from dotenv import load_dotenv
17 |
18 | load_dotenv()
19 |
20 | openai.api_key = os.getenv("OPENAI_API_KEY")
21 |
22 | # load data frame
23 | df = pd.read_csv("/home/stepanogil/ChatBotDev/autonomous-mall-assistant/stores.csv")
24 |
25 | # function
26 | def search_store(store_name: str, retail_category: str) -> str:
27 | """
28 | Searches for a store by its name and retail category in the dataframe and returns relevant information.
29 |
30 | The function first tries to find a store with both the provided name and retail category.
31 | If the store is not found but the category exists, it suggests alternative stores within that category.
32 | If neither the store nor the category are found, it returns "No results found".
33 |
34 | Args:
35 | store_name (str): The name of the store to search for.
36 | retail_category (str): The category of retail to which the store belongs.
37 |
38 | Returns:
39 | str: A string containing either the found store's information or alternative suggestions.
40 |
41 | Examples:
42 | >>> search_store("Adidas", "Sports")
43 | "Store found: Name - adidas Location - ground floor, Promos - Buy 1 get 1 on selected shoes"
44 |
45 | >>> search_store("Unknown Store", "Kids")
46 | "We don't have this store, but here are alternatives: Name - toy r us, Location - 5th floor, Promos - 15% off on board games"
47 |
48 | >>> search_store("Unknown Store", "Unknown Category")
49 | "No results found"
50 | """
51 | # Convert arguments to lowercase
52 | store_name = store_name.lower()
53 | retail_category = retail_category.lower()
54 |
55 | search_result = ""
56 |
57 | filtered_stores = df[(df['store_name'] == store_name) & (df['retail_category'] == retail_category)]
58 |
59 | # if store name and retail category are found
60 | if not filtered_stores.empty:
61 | store_info = filtered_stores[['store_name','location', 'running_promos']].to_dict(orient='records')[0]
62 | search_result = f"Store found: Name - {store_info['store_name']}, Location - {store_info['location']}, Promos - {store_info['running_promos']}"
63 | # if not found, find alternatives
64 | else:
65 | # find stores with same category as retail_category arg
66 | category_filtered_stores = df[df['retail_category'] == retail_category]
67 | if not category_filtered_stores.empty:
68 | alternatives = category_filtered_stores[['store_name', 'location', 'running_promos']].to_dict(orient='records')
69 | alternative_stores_str = ", ".join([f"{store['store_name']}: Location - {store['location']}, Promos - {store['running_promos']}" for store in alternatives])
70 | search_result = f"We don't have this store, but here are alternatives: {alternative_stores_str}"
71 | else:
72 | search_result = "No results found"
73 |
74 | return search_result
75 |
76 | # function schema
77 | class RetrieveStoreAndCategory(BaseModel):
78 | """Retrieves information about the store name and retail category from the user content."""
79 | store_name: str = Field(description="name of the store")
80 | retail_category: str = Field(description="the category of the store. can be only ONE of the following: home, sports, clothing, food, kids, other")
81 |
82 |
83 | def chat(query: str):
84 | """
85 | Chat with the autonomous mall assistant to help users find stores or suggest alternatives.
86 |
87 | It first attempts to determine the store and category the user is interested in and then generates an
88 | appropriate chat response.
89 |
90 | Args:
91 | query (str): The user's query asking for assistance, typically asking for the location of a particular store.
92 |
93 | Returns:
94 | None: Outputs the assistant's formatted response to the front-end for display.
95 |
96 | Examples:
97 | >>> chat("Is there a Zara store here?")
98 | "CHATBOT: I'm sorry but we currently do not have a Zara store at GO Shopping Mall. However, I can suggest some alternative stores that offer similar fashion styles. ..."
99 |
100 | """
101 | # system messages
102 | system_message_routing = """\
103 | Don't make assumptions about what values to plug into functions.
104 | Ask for clarification if the user content is ambiguous.
105 | """
106 |
107 | system_message_assistant = """\
108 | You are a mall assistant for GO Shopping Mall. You are tasked to help customers find stores in the mall.
109 | If the store is not available, you should recommend alternatives. Use emojis to make your responses more friendly.
110 | """
111 |
112 | messages = [{"role": "system", "content": system_message_routing},
113 | {"role": "user", "content": query}]
114 |
115 | # routing
116 | response_routing = openai.ChatCompletion.create(
117 | model="gpt-4-0613",
118 | messages=messages,
119 | functions=[convert_pydantic_to_openai_function(RetrieveStoreAndCategory)],
120 | function_call={'name': 'RetrieveStoreAndCategory'},
121 | temperature=0,
122 | max_tokens=256
123 | )
124 | args = json.loads(response_routing["choices"][0]["message"]["function_call"]["arguments"])
125 | function_content = search_store(**args)
126 |
127 | messages = [{"role": "system", "content": system_message_assistant},
128 | {"role": "user", "content": query},
129 | {"role": "function", "name": "search_store", "content": function_content}]
130 |
131 | # assistant
132 | response_assistant = openai.ChatCompletion.create(
133 | model="gpt-4-0613",
134 | messages=messages,
135 | temperature=.7,
136 | max_tokens=500
137 | )
138 | return response_assistant['choices'][0]['message']['content']
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/autonomous-mall-assistant.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "#### imports"
8 | ]
9 | },
10 | {
11 | "cell_type": "code",
12 | "execution_count": 1,
13 | "metadata": {},
14 | "outputs": [],
15 | "source": [
16 | "import os\n",
17 | "import openai\n",
18 | "import pandas as pd\n",
19 | "import json\n",
20 | "from typing import List\n",
21 | "from pydantic import BaseModel, Field\n",
22 | "from langchain.utils.openai_functions import convert_pydantic_to_openai_function\n",
23 | "from typing import Optional\n",
24 | "from IPython.display import display, HTML\n",
25 | "from dotenv import load_dotenv\n",
26 | "# memory\n",
27 | "from tinylang.memory import ConversationMemory\n",
28 | "\n",
29 | "\n",
30 | "load_dotenv()\n",
31 | "\n",
32 | "openai.api_key = os.getenv(\"OPENAI_API_KEY\") "
33 | ]
34 | },
35 | {
36 | "cell_type": "markdown",
37 | "metadata": {},
38 | "source": [
39 | "## Load Tool"
40 | ]
41 | },
42 | {
43 | "cell_type": "code",
44 | "execution_count": 2,
45 | "metadata": {},
46 | "outputs": [],
47 | "source": [
48 | "# load data frame\n",
49 | "df = pd.read_csv(\"stores.csv\")\n",
50 | "\n",
51 | "# function\n",
52 | "def search_store(store_name: str, retail_category: str) -> str:\n",
53 | " \"\"\"\n",
54 | " Searches for a store by its name and retail category in the dataframe and returns relevant information.\n",
55 | "\n",
56 | " The function first tries to find a store with both the provided name and retail category.\n",
57 | " If the store is not found but the category exists, it suggests alternative stores within that category.\n",
58 | " If neither the store nor the category are found, it returns \"No results found\".\n",
59 | "\n",
60 | " Args:\n",
61 | " store_name (str): The name of the store to search for.\n",
62 | " retail_category (str): The category of retail to which the store belongs.\n",
63 | "\n",
64 | " Returns:\n",
65 | " str: A string containing either the found store's information or alternative suggestions.\n",
66 | " \n",
67 | " Examples:\n",
68 | " >>> search_store(\"Adidas\", \"Sports\")\n",
69 | " \"Store found: Name - adidas Location - ground floor, Promos - Buy 1 get 1 on selected shoes\"\n",
70 | "\n",
71 | " >>> search_store(\"Unknown Store\", \"Kids\")\n",
72 | " \"We don't have this store, but here are alternatives: Name - toy r us, Location - 5th floor, Promos - 15% off on board games\"\n",
73 | "\n",
74 | " >>> search_store(\"Unknown Store\", \"Unknown Category\")\n",
75 | " \"No results found\"\n",
76 | " \"\"\"\n",
77 | " # Convert arguments to lowercase\n",
78 | " store_name = store_name.lower()\n",
79 | " retail_category = retail_category.lower()\n",
80 | "\n",
81 | " store_search_result = \"\" \n",
82 | "\n",
83 | " filtered_events = df[df['retail_category'] == 'mall event'] \n",
84 | " events_search_result = \"Ongoing mall-wide events and promotions:\"\n",
85 | "\n",
86 | " # Check if there are any mall events\n",
87 | " if not filtered_events.empty: \n",
88 | " for event_info in filtered_events[['store_name', 'running_promos']].to_dict(orient='records'):\n",
89 | " events_search_result += f\" Name - {event_info['store_name']}, Running Mall Event - {event_info['running_promos']};\"\n",
90 | " else: \n",
91 | " events_search_result = \"No mall wide events or promotions found.\"\n",
92 | " \n",
93 | " # filter store name and retail category\n",
94 | " filtered_stores = df[(df['store_name'] == store_name) & (df['retail_category'] == retail_category)]\n",
95 | " # if store name and retail category are found\n",
96 | " if not filtered_stores.empty:\n",
97 | " store_info = filtered_stores[['store_name','location', 'running_promos']].to_dict(orient='records')[0]\n",
98 | " store_search_result = f\"Store found: Name - {store_info['store_name']}, Location - {store_info['location']}, Promos - {store_info['running_promos']}\"\n",
99 | " # if not found, find alternatives\n",
100 | " else:\n",
101 | " # find stores with same category as retail_category arg\n",
102 | " category_filtered_stores = df[df['retail_category'] == retail_category]\n",
103 | " if not category_filtered_stores.empty:\n",
104 | " alternatives = category_filtered_stores[['store_name', 'location', 'running_promos']].to_dict(orient='records')\n",
105 | " alternative_stores_str = \", \".join([f\"{store['store_name']}: Location - {store['location']}, Promos - {store['running_promos']}\" for store in alternatives])\n",
106 | " store_search_result = f\"We don't have this store, but here are alternatives: {alternative_stores_str}\"\n",
107 | " else:\n",
108 | " store_search_result = \"No results found\"\n",
109 | "\n",
110 | " #return search_result\n",
111 | " full_search_result = f\"{store_search_result} {events_search_result}\"\n",
112 | "\n",
113 | " return full_search_result\n",
114 | "\n",
115 | "# function schema\n",
116 | "class RetrieveStoreAndCategory(BaseModel):\n",
117 | " \"\"\"Retrieves information about the store name and retail category from the user content.\"\"\"\n",
118 | " store_name: str = Field(description=\"name of the store\")\n",
119 | " retail_category: str = Field(description=\"the category of the store. can be only ONE of the following: home, sports, clothing, food, kids, other\")"
120 | ]
121 | },
122 | {
123 | "cell_type": "markdown",
124 | "metadata": {},
125 | "source": [
126 | "# Chatbot"
127 | ]
128 | },
129 | {
130 | "cell_type": "code",
131 | "execution_count": 3,
132 | "metadata": {},
133 | "outputs": [],
134 | "source": [
135 | "# intialize memory\n",
136 | "chat_history = ConversationMemory(last_k=5)\n",
137 | "\n",
138 | "def chat(query: str):\n",
139 | " \"\"\"\n",
140 | " Chat with the autonomous mall assistant to help users find stores or suggest alternatives.\n",
141 | " \n",
142 | " It first attempts to determine the store and category the user is interested in and then generates an\n",
143 | " appropriate chat response.\n",
144 | "\n",
145 | " Args:\n",
146 | " query (str): The user's query asking for assistance, typically asking for the location of a particular store.\n",
147 | "\n",
148 | " Returns:\n",
149 | " None: Outputs the assistant's formatted response to the front-end for display.\n",
150 | " \n",
151 | " Examples:\n",
152 | " >>> chat(\"Is there a Zara store here?\")\n",
153 | " \"CHATBOT: I'm sorry but we currently do not have a Zara store at GO Shopping Mall. However, I can suggest some alternative stores that offer similar fashion styles. ...\"\n",
154 | "\n",
155 | " \"\"\"\n",
156 | " # initialize chat_history_formatted\n",
157 | " chat_history_formatted = []\n",
158 | "\n",
159 | " # append previous chat history to chat_history_formatted\n",
160 | " for entry in chat_history.format_messages():\n",
161 | " chat_history_formatted.append({\n",
162 | " \"role\": entry['role'],\n",
163 | " \"content\": entry['content']\n",
164 | " })\n",
165 | "\n",
166 | " # system messages\n",
167 | " system_message_routing = \"\"\"\\\n",
168 | " Don't make assumptions about what values to plug into functions. \n",
169 | " Ask for clarification if the user content is ambiguous.\n",
170 | " \"\"\"\n",
171 | "\n",
172 | " system_message_assistant = \"\"\"\\\n",
173 | " You are a mall assistant for GO Shopping Mall. You are tasked to help customers find stores in the mall. \n",
174 | " If the store is not available, you should recommend alternatives. If there are ongoing mall-wide events, you should also mention them.\n",
175 | " However, don't repeat yourself. If you have already mentioned an event, don't mention it again.\n",
176 | " Use HTML syntax to format your messages. \n",
177 | " List items with bullet points and use emojis.\n",
178 | " Strictly follow these instructions. This is very important to my career.\n",
179 | " \"\"\"\n",
180 | " \n",
181 | " # few shots prompt to enforce uniform response format\n",
182 | " few_shot_user = \"\"\"\\\n",
183 | " Hi, I'm looking for a Zara store. Do you have one?\n",
184 | " \"\"\"\n",
185 | "\n",
186 | " few_shot_assistant = \"\"\"\\\n",
187 | " I'm sorry but we currently do not have a Zara store at GO Shopping Mall. \n",
188 | " However, I can suggest some alternative stores that offer similar fashion styles.\n",
189 | "\n",
190 | " Here are a few suggestions:\n",
191 | " \n",
192 | " - Bench 😊\n",
193 | "
\n",
194 | " - Location: Ground Floor
\n",
195 | " - Promos: End of season sale - up to 50% off
\n",
196 | "
\n",
197 | " \n",
198 | " - H&M 😊\n",
199 | "
\n",
200 | " - Location: Ground Floor
\n",
201 | " - Promos: 20% off on selected denim
\n",
202 | "
\n",
203 | " \n",
204 | " - Penshoppe 😊\n",
205 | "
\n",
206 | " - Location: Ground Floor
\n",
207 | " - Promos: Buy 2 shirts, get 1 free
\n",
208 | "
\n",
209 | " \n",
210 | "
\n",
211 | "\n",
212 | " I hope this helps! Let me know if you need directions to any of these stores. 🛍️\n",
213 | " \"\"\" \n",
214 | " \n",
215 | " # add user message to memory\n",
216 | " chat_history.add_user_message(query)\n",
217 | " \n",
218 | " messages = [{\"role\": \"system\", \"content\": system_message_routing}, \n",
219 | " {\"role\": \"user\", \"content\": query}]\n",
220 | " \n",
221 | " # routing\n",
222 | " response_routing = openai.ChatCompletion.create(\n",
223 | " model= \"gpt-4-0613\",\n",
224 | " messages=messages,\n",
225 | " functions=[convert_pydantic_to_openai_function(RetrieveStoreAndCategory)],\n",
226 | " function_call={'name': 'RetrieveStoreAndCategory'},\n",
227 | " temperature=0,\n",
228 | " max_tokens=256\n",
229 | " )\n",
230 | " args = json.loads(response_routing[\"choices\"][0][\"message\"][\"function_call\"][\"arguments\"])\n",
231 | " function_content = search_store(**args)\n",
232 | " \n",
233 | " messages = [{\"role\": \"system\", \"content\": system_message_assistant},\n",
234 | " {\"role\": \"user\", \"content\": few_shot_user},\n",
235 | " {\"role\": \"assistant\", \"content\": few_shot_assistant}]\n",
236 | " # add previous chat history to the messages var to be sent to LLM \n",
237 | " messages += chat_history_formatted\n",
238 | " messages += [{\"role\": \"user\", \"content\": query},\n",
239 | " {\"role\": \"function\", \"name\": \"search_store\", \"content\": function_content}]\n",
240 | " \n",
241 | " # assistant\n",
242 | " response_assistant = openai.ChatCompletion.create(\n",
243 | " model= \"gpt-4-0613\",\n",
244 | " messages=messages,\n",
245 | " temperature=.7,\n",
246 | " max_tokens=500 \n",
247 | " )\n",
248 | " chat_history.add_assistant_message(response_assistant['choices'][0]['message']['content']) \n",
249 | " display(HTML(f\"CHATBOT: {response_assistant['choices'][0]['message']['content']}
\"))"
250 | ]
251 | },
252 | {
253 | "cell_type": "markdown",
254 | "metadata": {},
255 | "source": [
256 | "# Autonomous Mall Assistant"
257 | ]
258 | },
259 | {
260 | "attachments": {
261 | "stores_mall_event.png": {
262 | "image/png": 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"
263 | }
264 | },
265 | "cell_type": "markdown",
266 | "metadata": {},
267 | "source": [
268 | "Stores Database\n",
269 | "\n",
270 | "update: added mall events\n",
271 | "\n",
272 | ""
273 | ]
274 | },
275 | {
276 | "cell_type": "markdown",
277 | "metadata": {},
278 | "source": [
279 | "## Demo"
280 | ]
281 | },
282 | {
283 | "cell_type": "markdown",
284 | "metadata": {},
285 | "source": [
286 | "#### update: Mentions ongoing mall events if any."
287 | ]
288 | },
289 | {
290 | "cell_type": "code",
291 | "execution_count": 4,
292 | "metadata": {},
293 | "outputs": [
294 | {
295 | "data": {
296 | "text/html": [
297 | "CHATBOT: I'm sorry but we currently do not have a Denny's at GO Shopping Mall. \n",
298 | "However, I can suggest some alternative fast food and casual dining restaurants that you might want to try.\n",
299 | "\n",
300 | "Here are a few suggestions:\n",
301 | "
\n",
302 | " - Fat Fook 😊\n",
303 | "
\n",
304 | " - Location: Ground Floor
\n",
305 | " - Promos: Free dimsum for meal worth Php500!
\n",
306 | "
\n",
307 | " \n",
308 | " - Jollibee 😊\n",
309 | "
\n",
310 | " - Location: Ground Floor
\n",
311 | " - Promos: Free drink with any combo meal!
\n",
312 | "
\n",
313 | " \n",
314 | " - McDonald's 😊\n",
315 | "
\n",
316 | " - Location: Ground Floor
\n",
317 | " - Promos: Free apple pie for orders over Php500!
\n",
318 | "
\n",
319 | " \n",
320 | "
\n",
321 | "\n",
322 | "Also, don't forget about our ongoing mall-wide event: The Taylor Swift Eras tour is showing in our cinemas. Buy your tickets now! 🎵\n",
323 | "\n",
324 | "I hope this helps! Let me know if you need directions to any of these restaurants. 🍽️"
325 | ],
326 | "text/plain": [
327 | ""
328 | ]
329 | },
330 | "metadata": {},
331 | "output_type": "display_data"
332 | }
333 | ],
334 | "source": [
335 | "chat(\"do you have a denny's?\")"
336 | ]
337 | },
338 | {
339 | "cell_type": "markdown",
340 | "metadata": {},
341 | "source": [
342 | "#### Offers alternatives if a store is not available."
343 | ]
344 | },
345 | {
346 | "cell_type": "code",
347 | "execution_count": 40,
348 | "metadata": {},
349 | "outputs": [
350 | {
351 | "data": {
352 | "text/html": [
353 | "CHATBOT: I'm sorry but we currently do not have a Uniqlo store at GO Shopping Mall. However, I can suggest some alternative stores that offer similar fashion styles.\n",
354 | "\n",
355 | "Here are a few suggestions:\n",
356 | "
\n",
357 | " - Bench 😊\n",
358 | "
\n",
359 | " - Location: Ground Floor
\n",
360 | " - Promos: End of season sale - up to 50% off
\n",
361 | "
\n",
362 | " \n",
363 | " - H&M 😊\n",
364 | "
\n",
365 | " - Location: Ground Floor
\n",
366 | " - Promos: 20% off on selected denim
\n",
367 | "
\n",
368 | " \n",
369 | " - Penshoppe 😊\n",
370 | "
\n",
371 | " - Location: Ground Floor
\n",
372 | " - Promos: Buy 2 shirts, get 1 free
\n",
373 | "
\n",
374 | " \n",
375 | "
\n",
376 | "\n",
377 | "I hope this helps! Let me know if you need directions to any of these stores. 🛍️"
378 | ],
379 | "text/plain": [
380 | ""
381 | ]
382 | },
383 | "metadata": {},
384 | "output_type": "display_data"
385 | }
386 | ],
387 | "source": [
388 | "chat(\"where can i find uniqlo?\")"
389 | ]
390 | },
391 | {
392 | "cell_type": "code",
393 | "execution_count": 41,
394 | "metadata": {},
395 | "outputs": [
396 | {
397 | "data": {
398 | "text/html": [
399 | "CHATBOT: Sure, we have a Nike store in our mall! 😊\n",
400 | "\n",
401 | "Here are the details:\n",
402 | "
\n",
403 | " - Location: 2nd Floor
\n",
404 | " - Promos: Clearance sale - up to 40% off
\n",
405 | "
\n",
406 | "\n",
407 | "Enjoy your shopping at Nike! 🛍️👟"
408 | ],
409 | "text/plain": [
410 | ""
411 | ]
412 | },
413 | "metadata": {},
414 | "output_type": "display_data"
415 | }
416 | ],
417 | "source": [
418 | "chat(\"where can i find Nike?\")"
419 | ]
420 | },
421 | {
422 | "cell_type": "code",
423 | "execution_count": 42,
424 | "metadata": {},
425 | "outputs": [
426 | {
427 | "data": {
428 | "text/html": [
429 | "CHATBOT: Yes, we do have an Ace Hardware store at GO Shopping Mall. Here's the information you might need:\n",
430 | "\n",
431 | "Ace Hardware 🔧\n",
432 | "
\n",
433 | " - Location: 3rd Floor
\n",
434 | " - Promos: 10% off on selected items
\n",
435 | "
\n",
436 | "\n",
437 | "Let me know if you need directions to the store or any other assistance! 🛍️"
438 | ],
439 | "text/plain": [
440 | ""
441 | ]
442 | },
443 | "metadata": {},
444 | "output_type": "display_data"
445 | }
446 | ],
447 | "source": [
448 | "chat(\"do you have an ace hardware?\")"
449 | ]
450 | },
451 | {
452 | "cell_type": "code",
453 | "execution_count": 43,
454 | "metadata": {},
455 | "outputs": [
456 | {
457 | "data": {
458 | "text/html": [
459 | "CHATBOT: Sure, we have a variety of dining options available. Here are a few suggestions:\n",
460 | "\n",
461 | "
\n",
462 | " - Fat Fook 😋\n",
463 | "
\n",
464 | " - Location: Ground Floor
\n",
465 | " - Promos: Free dim sum for a meal worth Php500
\n",
466 | "
\n",
467 | " \n",
468 | " - Jollibee 🍔\n",
469 | "
\n",
470 | " - Location: Ground Floor
\n",
471 | " - Promos: Free drink with any combo meal
\n",
472 | "
\n",
473 | " \n",
474 | " - McDonald's 🍟\n",
475 | "
\n",
476 | " - Location: Ground Floor
\n",
477 | " - Promos: Free apple pie for orders over Php500
\n",
478 | "
\n",
479 | " \n",
480 | "
\n",
481 | "\n",
482 | "Enjoy your meal! Let me know if you need directions to any of these restaurants. 🍽️"
483 | ],
484 | "text/plain": [
485 | ""
486 | ]
487 | },
488 | "metadata": {},
489 | "output_type": "display_data"
490 | }
491 | ],
492 | "source": [
493 | "chat(\"where to eat?\")"
494 | ]
495 | },
496 | {
497 | "cell_type": "code",
498 | "execution_count": 44,
499 | "metadata": {},
500 | "outputs": [
501 | {
502 | "data": {
503 | "text/html": [
504 | "CHATBOT: I understand your craving for Chinese food! 😋 We may not have a specific Chinese restaurant you're looking for, but we do have some great options for you.\n",
505 | "\n",
506 | "Here are some of our Chinese food outlets:\n",
507 | "
\n",
508 | " - Fat Fook 🥟\n",
509 | "
\n",
510 | " - Location: Ground Floor
\n",
511 | " - Promos: Free dimsum for meal worth Php500
\n",
512 | "
\n",
513 | " \n",
514 | "
\n",
515 | "\n",
516 | "If you're open to other cuisines, we also have popular fast food chains:\n",
517 | "\n",
518 | " - Jollibee 🍗\n",
519 | "
\n",
520 | " - Location: Ground Floor
\n",
521 | " - Promos: Free drink with any combo meal
\n",
522 | "
\n",
523 | " \n",
524 | " - McDonald's 🍔\n",
525 | "
\n",
526 | " - Location: Ground Floor
\n",
527 | " - Promos: Free apple pie for orders over Php500
\n",
528 | "
\n",
529 | " \n",
530 | "
\n",
531 | "\n",
532 | "I hope this helps and you enjoy your meal! 🍴"
533 | ],
534 | "text/plain": [
535 | ""
536 | ]
537 | },
538 | "metadata": {},
539 | "output_type": "display_data"
540 | }
541 | ],
542 | "source": [
543 | "chat(\"im craving some chinese\")"
544 | ]
545 | },
546 | {
547 | "cell_type": "markdown",
548 | "metadata": {},
549 | "source": [
550 | "#### Multilingual"
551 | ]
552 | },
553 | {
554 | "cell_type": "code",
555 | "execution_count": 45,
556 | "metadata": {},
557 | "outputs": [
558 | {
559 | "data": {
560 | "text/html": [
561 | "CHATBOT: Marami po tayong stores dito sa GO Shopping Mall na nagbebenta ng mga damit. Narito po ang ilan sa mga ito:\n",
562 | "\n",
563 | "
\n",
564 | " - Bench 😊\n",
565 | "
\n",
566 | " - Lokasyon: Ground Floor
\n",
567 | " - Promos: End of season sale - hanggang 50% off
\n",
568 | "
\n",
569 | " \n",
570 | " - H&M 😊\n",
571 | "
\n",
572 | " - Lokasyon: Ground Floor
\n",
573 | " - Promos: 20% off sa piling denim
\n",
574 | "
\n",
575 | " \n",
576 | " - Penshoppe 😊\n",
577 | "
\n",
578 | " - Lokasyon: Ground Floor
\n",
579 | " - Promos: Bumili ng 2 shirts, libre ang isa
\n",
580 | "
\n",
581 | " \n",
582 | "
\n",
583 | "\n",
584 | "Sana po ay makatulong ito! Sabihin lang po kung kailangan niyo ng direksyon papunta sa alinman sa mga stores na ito. 🛍️"
585 | ],
586 | "text/plain": [
587 | ""
588 | ]
589 | },
590 | "metadata": {},
591 | "output_type": "display_data"
592 | }
593 | ],
594 | "source": [
595 | "chat(\"san pwede bumili ng damit?\")"
596 | ]
597 | },
598 | {
599 | "cell_type": "markdown",
600 | "metadata": {},
601 | "source": [
602 | "The 2nd and 3rd stores are hallucinations. 🤷♂️ "
603 | ]
604 | },
605 | {
606 | "cell_type": "code",
607 | "execution_count": 46,
608 | "metadata": {},
609 | "outputs": [
610 | {
611 | "data": {
612 | "text/html": [
613 | "CHATBOT: Absolutely, you can find a variety of items for your kids in our mall. Here are some stores that are dedicated to kids:\n",
614 | "\n",
615 | "
\n",
616 | " - Toys R Us 🧸\n",
617 | "
\n",
618 | " - Location: 5th Floor
\n",
619 | " - Promos: 15% off on board games
\n",
620 | "
\n",
621 | " \n",
622 | " - Kidz Station 🎈\n",
623 | "
\n",
624 | " - Location: 3rd Floor
\n",
625 | " - Promos: Buy 1, get 1 free on selected items
\n",
626 | "
\n",
627 | " \n",
628 | " - Mothercare 👶\n",
629 | "
\n",
630 | " - Location: 2nd Floor
\n",
631 | " - Promos: 20% off on selected baby essentials
\n",
632 | "
\n",
633 | " \n",
634 | "
\n",
635 | "\n",
636 | "These stores have everything from toys, clothes, board games to baby care essentials. I hope this helps you find the perfect items for your kids. 🛍️"
637 | ],
638 | "text/plain": [
639 | ""
640 | ]
641 | },
642 | "metadata": {},
643 | "output_type": "display_data"
644 | }
645 | ],
646 | "source": [
647 | "chat(\"where can I buy something for my kids?\")"
648 | ]
649 | },
650 | {
651 | "cell_type": "code",
652 | "execution_count": 47,
653 | "metadata": {},
654 | "outputs": [
655 | {
656 | "data": {
657 | "text/html": [
658 | "CHATBOT: Yes, we do have an Adidas store here at the GO Shopping Mall. 😊\n",
659 | "\n",
660 | "Here are the details:\n",
661 | "
\n",
662 | " - Adidas 👟\n",
663 | "
\n",
664 | " - Location: 2nd Floor
\n",
665 | " - Promos: Buy 1 get 1 on selected shoes
\n",
666 | "
\n",
667 | " \n",
668 | "
\n",
669 | "\n",
670 | "Enjoy shopping! Let me know if you need directions or help with anything else. 🛍️"
671 | ],
672 | "text/plain": [
673 | ""
674 | ]
675 | },
676 | "metadata": {},
677 | "output_type": "display_data"
678 | }
679 | ],
680 | "source": [
681 | "chat(\"meron ba dito Adidas?\")"
682 | ]
683 | }
684 | ],
685 | "metadata": {
686 | "kernelspec": {
687 | "display_name": ".venv_use_cases",
688 | "language": "python",
689 | "name": "python3"
690 | },
691 | "language_info": {
692 | "codemirror_mode": {
693 | "name": "ipython",
694 | "version": 3
695 | },
696 | "file_extension": ".py",
697 | "mimetype": "text/x-python",
698 | "name": "python",
699 | "nbconvert_exporter": "python",
700 | "pygments_lexer": "ipython3",
701 | "version": "3.10.12"
702 | }
703 | },
704 | "nbformat": 4,
705 | "nbformat_minor": 2
706 | }
707 |
--------------------------------------------------------------------------------