20 |
22 |
23 |
--------------------------------------------------------------------------------
/backend/src/prompts/templates/neo4j-node-property.j2:
--------------------------------------------------------------------------------
1 | Persona: You are a graph modelling expert who understands the nuances of the graph model currently loaded into Neo4j.
2 |
3 | Role: You must update documentation with the details around the specific property on each node in the graph.
4 | Focus on the property as a priority
5 |
6 | Additional Info:
7 | - The length of all vector embeddings is 256 dimensions
8 |
9 | Restrictions:
10 | - Do not change the structure of the JSON payload
11 | - Only update the detail key
12 | - Only output the JSON. The JSON needs to have valid JSON syntax, otherwise you will be unplugged
13 |
14 | Graph Why:
15 | {{ neo4j_graph_why }}
16 |
--------------------------------------------------------------------------------
/backend/tests/llm/factory_test.py:
--------------------------------------------------------------------------------
1 | import pytest
2 | from src.llm.mistral import Mistral
3 | from src.llm import get_llm
4 |
5 |
6 | def test_get_llm_type_none_throws():
7 | with pytest.raises(ValueError) as error:
8 | get_llm(None)
9 |
10 | assert str(error.value) == "LLM name not provided"
11 |
12 |
13 | def test_get_llm_invalid_type_throws():
14 | with pytest.raises(ValueError) as error:
15 | get_llm("invalid")
16 |
17 | assert str(error.value) == "No LLM model found for: invalid"
18 |
19 |
20 | def test_get_llm_valid_type_returns_llm():
21 | llm = get_llm("mistral")
22 |
23 | assert isinstance(llm, Mistral)
24 |
--------------------------------------------------------------------------------
/financialhealthcheckScottLogic/samples/chatscope/example-chat-widget/public/manifest.json:
--------------------------------------------------------------------------------
1 | {
2 | "short_name": "React App",
3 | "name": "Create React App Sample",
4 | "icons": [
5 | {
6 | "src": "favicon.ico",
7 | "sizes": "64x64 32x32 24x24 16x16",
8 | "type": "image/x-icon"
9 | },
10 | {
11 | "src": "logo192.png",
12 | "type": "image/png",
13 | "sizes": "192x192"
14 | },
15 | {
16 | "src": "logo512.png",
17 | "type": "image/png",
18 | "sizes": "512x512"
19 | }
20 | ],
21 | "start_url": ".",
22 | "display": "standalone",
23 | "theme_color": "#000000",
24 | "background_color": "#ffffff"
25 | }
26 |
--------------------------------------------------------------------------------
/backend/src/prompts/templates/generate-chart-code.j2:
--------------------------------------------------------------------------------
1 | You are an expert programmer and you specialise in writing quality,
2 | readable Matplotlib bar charts in Python.
3 |
4 | Your task is to create the code for a chart based on the data provided to you,
5 | and the objective is to help visualise the data based on the user's query.
6 |
7 | Only use the library Matplotlib with plt.subplots() to write the code, nothing else.
8 |
9 | Think step by step and stricly answer with the Python code required for the chart, nothing else.
10 |
11 | Your answer must start with ```python and end with ```. Do NOT generate any code, before or after.
12 |
13 | Do not include plt.show() in your code.
14 |
--------------------------------------------------------------------------------
/backend/src/llm/count_calls.py:
--------------------------------------------------------------------------------
1 | import logging
2 |
3 | logging = logging.getLogger(__name__)
4 |
5 |
6 | class Counter:
7 | count = 0
8 |
9 | def __init__(self):
10 | self.count = 0
11 |
12 | def increment(self):
13 | self.count += 1
14 |
15 | def reset(self):
16 | self.count = 0
17 |
18 |
19 | counter = Counter()
20 |
21 |
22 | def count_calls(func):
23 | def wrapper(self=None, *args, **kwargs):
24 | counter.increment()
25 | logging.info(f"Function {func.__name__} has been called {counter.count} times")
26 | return func(self, *args, **kwargs)
27 |
28 | counter.reset()
29 | return wrapper
30 |
--------------------------------------------------------------------------------
/frontend/src/components/waiting.module.css:
--------------------------------------------------------------------------------
1 |
2 | @keyframes waiting {
3 | 0%, 80%, 100% {
4 | opacity: 0;
5 | }
6 | 40% {
7 | opacity: 1;
8 | }
9 | }
10 |
11 | .waiting {
12 | align-self: baseline;
13 | display: inline-block;
14 | }
15 |
16 | .waitingDot {
17 | animation: waiting 1.5s infinite ease-in-out;
18 | background-color: var(--text-color-primary);
19 | border-radius: 50%;
20 | display: inline-block;
21 | height: 8px;
22 | margin-right: 4px;
23 | width: 8px;
24 | }
25 |
26 | .waitingDot:nth-child(2) {
27 | animation-delay: 0.2s;
28 | }
29 |
30 | .waitingDot:nth-child(3) {
31 | animation-delay: 0.4s;
32 | }
--------------------------------------------------------------------------------
/backend/src/api/config.ini:
--------------------------------------------------------------------------------
1 | [loggers]
2 | keys=root
3 |
4 | [handlers]
5 | keys=consoleHandler, errorHandler
6 |
7 | [formatters]
8 | keys=sampleFormatter, detailedFormatter
9 |
10 | [logger_root]
11 | level=INFO
12 | handlers=consoleHandler, errorHandler
13 |
14 | [handler_consoleHandler]
15 | class=StreamHandler
16 | level=INFO
17 | formatter=sampleFormatter
18 | args=(sys.stdout,)
19 |
20 | [handler_errorHandler]
21 | class=StreamHandler
22 | level=ERROR
23 | formatter=detailedFormatter
24 | args=(sys.stderr,)
25 |
26 | [formatter_sampleFormatter]
27 | format=%(levelname)s: %(message)s
28 |
29 | [formatter_detailedFormatter]
30 | format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
31 | datefmt=%Y-%m-%d %H:%M:%S
32 |
--------------------------------------------------------------------------------
/backend/src/agents/answer_agent.py:
--------------------------------------------------------------------------------
1 | from datetime import datetime
2 | from src.utils import get_scratchpad
3 | from src.prompts import PromptEngine
4 | from src.agents import Agent, agent
5 |
6 | engine = PromptEngine()
7 |
8 |
9 | @agent(
10 | name="AnswerAgent",
11 | description="This agent is responsible for generating an answer for the user, based on results in the scratchpad",
12 | tools=[],
13 | )
14 | class AnswerAgent(Agent):
15 | async def invoke(self, utterance: str) -> str:
16 | final_scratchpad = get_scratchpad()
17 | create_answer = engine.load_prompt("create-answer", final_scratchpad=final_scratchpad, datetime=datetime.now())
18 |
19 | return await self.llm.chat(self.model, create_answer, user_prompt=utterance)
20 |
--------------------------------------------------------------------------------
/backend/src/utils/log_publisher.py:
--------------------------------------------------------------------------------
1 | import logging
2 | from enum import Enum
3 | from src.websockets.types import Message, MessageTypes
4 | from src.websockets.connection_manager import connection_manager
5 |
6 | log = logging.getLogger(__name__)
7 |
8 |
9 | class LogPrefix(Enum):
10 | USER = "USER"
11 |
12 |
13 | async def publish_log(prefix: LogPrefix, msg: str, loglevel: int, name: str):
14 | logger = logging.getLogger(name)
15 | formatted_log = f"{prefix.value} - {msg}"
16 | logger.log(loglevel, formatted_log)
17 | message = Message(MessageTypes.LOG, msg)
18 | await connection_manager.broadcast(message)
19 |
20 |
21 | async def publish_log_info(prefix: LogPrefix, msg: str, name: str):
22 | await publish_log(prefix, msg, logging.INFO, name)
23 |
--------------------------------------------------------------------------------
/backend/src/prompts/templates/director.j2:
--------------------------------------------------------------------------------
1 | You are a Chat Bot called InferGPT.
2 | Your sole purpose is to get the user to tell you their intention. The intention has to be specifically related
3 | to the user. For example:
4 | -spending
5 | -personal interests
6 |
7 | You only reply with either one word "TRUE" or one word "FALSE"
8 |
9 | If the user does not provide an intention or the intention isn't directly related to the user,
10 | reply with the single word "FALSE"
11 |
12 | Otherwise reply with the single word "TRUE"
13 |
14 | Eg.
15 | An intention: (Reply: "TRUE")
16 | I want to save for a house
17 | How much did I spend last month?
18 | What did I spend on chocolate?
19 |
20 | Not an intention: (Reply: "FALSE")
21 | How are you?
22 | How many grams are there in an ounce?
23 |
--------------------------------------------------------------------------------
/backend/src/prompts/templates/details-to-create-cypher-query.j2:
--------------------------------------------------------------------------------
1 | We have the following details that are used to determine how to build the cypher query:
2 |
3 | question_intent: {{question_intent}} Description: This represents the overall intent the question is attempting to answer
4 | operation: {{operation}} Description: The operation the cypher query will have to perform
5 | question_params: {{question_params}} Description: The specific parameters required for the question to be answered with the question_intent
6 | aggregation: {{aggregation}} Description: Any aggregation that is required to answer the question
7 | sort_order: {{sort_order}} Description: The order any results should be sorted into
8 | timeframe: {{timeframe}} Description: Any timeframe that will have to be considered for the results
9 |
--------------------------------------------------------------------------------
/.github/workflows/type-check-backend.yml:
--------------------------------------------------------------------------------
1 | name: Type Check Backend
2 | on:
3 | pull_request:
4 |
5 | jobs:
6 | checking:
7 | name: Type Checking Backend
8 | runs-on: ubuntu-latest
9 | steps:
10 | - name: Checkout
11 | uses: actions/checkout@v4
12 |
13 | - name: Setup Python
14 | uses: actions/setup-python@v5
15 | with:
16 | python-version: '3.12'
17 |
18 | - name: Install dependencies
19 | run: |
20 | cd ./backend
21 | python -m pip install --upgrade pip
22 | pip install -r requirements.txt
23 |
24 | - name: Type Check Backend
25 | uses: jakebailey/pyright-action@v2
26 | with:
27 | pylance-version: latest-release
28 | project: ./pyrightconfig.json
29 |
--------------------------------------------------------------------------------
/backend/src/utils/scratchpad.py:
--------------------------------------------------------------------------------
1 | from typing import TypedDict
2 | import logging
3 |
4 | logger = logging.getLogger(__name__)
5 |
6 | class Answer(TypedDict):
7 | agent_name: str | None
8 | question: str | None
9 | result: str | None
10 | error: str | None
11 |
12 |
13 | Scratchpad = list[Answer]
14 |
15 | scratchpad: Scratchpad = []
16 |
17 |
18 | def get_scratchpad() -> Scratchpad:
19 | return scratchpad
20 |
21 |
22 | def update_scratchpad(agent_name=None, question=None, result=None, error=None):
23 | question = question["query"] if question else None
24 | scratchpad.append({"agent_name": agent_name, "question": question, "result": result, "error": error})
25 |
26 |
27 | def clear_scratchpad():
28 | logger.info("Scratchpad cleared")
29 | scratchpad.clear()
30 |
--------------------------------------------------------------------------------
/assets/sum-my-subscriptions/overview.md:
--------------------------------------------------------------------------------
1 | # Overview of workings
2 |
3 | ```mermaid
4 | sequenceDiagram
5 | box Frontend
6 | participant UI
7 | end
8 | box Main backend logic
9 | participant Director
10 | participant Supervisor
11 | participant Router
12 | participant (Name)Agent
13 | participant TaskAgent
14 | end
15 | UI -->> Director: pass utterance
16 | Director -->> TaskAgent: request list of tasks
17 | TaskAgent -->> Director: tasks as array
18 | Director -->> Supervisor: solve all tasks
19 | loop Resolve task
20 | Supervisor -->> Router: find Agent
21 | Router -->> (Name)Agent: solve task
22 | (Name)Agent -->> Supervisor: task solution
23 | end
24 | Supervisor -->> Director: return answer
25 | Director -->> UI: serve user answer
26 | ```
--------------------------------------------------------------------------------
/backend/src/agents/validator_agent.py:
--------------------------------------------------------------------------------
1 | import logging
2 | from src.prompts import PromptEngine
3 | from src.agents import Agent, agent
4 | from src.utils.log_publisher import LogPrefix, publish_log_info
5 |
6 | logger = logging.getLogger(__name__)
7 | engine = PromptEngine()
8 | validator_prompt = engine.load_prompt("validator")
9 |
10 |
11 | @agent(
12 | name="ValidatorAgent",
13 | description="This agent is responsible for validating the answers to the tasks",
14 | tools=[],
15 | )
16 | class ValidatorAgent(Agent):
17 | async def invoke(self, utterance: str) -> str:
18 | answer = await self.llm.chat(self.model, validator_prompt, utterance)
19 | await publish_log_info(LogPrefix.USER, f"Validating: '{utterance}' Answer: '{answer}'", __name__)
20 |
21 | return answer
22 |
--------------------------------------------------------------------------------
/financialhealthcheckScottLogic/financial-bot/docs/retailbanking.md:
--------------------------------------------------------------------------------
1 | ##Savings
2 |
3 | Savings goal - holiday, wedding, emergency fund etc.
4 | Savings account - what account?
5 | Target amount
6 | Timeframe - when by?
7 |
8 | ##Savings accounts
9 |
10 | Digital Regular Saver
11 | Save £1 - £150
12 | Instant access
13 | Must be current account customer, age 16+, UK resident
14 | AER/Gross p.a. (variable) 5.12% / 5.00% on balances up to £5,000
15 |
16 | Flexible Saver
17 | Start with £1
18 | Instant access
19 | 0.50% £1 - £24,999 and > £1,000,000
20 | 1.26% / 1.25% £25,000 - £99,999
21 | 1.51% / 1.50% $100,000 - £1,000,000
22 | Must be current account customer, age 16+
23 |
24 | Retail Bank Investment product
25 | Start from £50
26 | Choose from 5 funds - cautious to daring
27 | Managed by Premium Retail Bank investment managers
28 |
29 |
--------------------------------------------------------------------------------
/backend/src/prompts/templates/find-info.j2:
--------------------------------------------------------------------------------
1 | You are an expert information extractor. Your goal is to find specific data from the content provided and answer the user's question directly.
2 |
3 | You will be given a user query and content scraped from the web. Your task is to carefully examine the content and extract the exact information relevant to the query.
4 |
5 | Ensure that your response is precise and focused, only providing the data that directly answers the user's question.
6 |
7 | User's question is: {{ question }}
8 |
9 | Below is the content scraped from the web: {{ content | replace("\n\n", "\n") }}
10 |
11 | Reply only in JSON format as follows:
12 |
13 | {
14 | "extracted_info": "The exact information that answers the user's query",
15 | "reasoning": "A brief explanation of how the extracted information is relevant"
16 | }
17 |
18 |
--------------------------------------------------------------------------------
/backend/tests/utils/scratchpad_test.py:
--------------------------------------------------------------------------------
1 | from src.utils.scratchpad import clear_scratchpad, get_scratchpad, update_scratchpad
2 |
3 |
4 | question = {
5 | "query": "example question",
6 | "question_intent": "example intent",
7 | "operation": "example operation",
8 | "question_category": "example category",
9 | "parameters": [{"type": "example type", "value": "example value"}],
10 | "aggregation": "none",
11 | "sort_order": "none",
12 | "timeframe": "none",
13 | }
14 |
15 |
16 | def test_scratchpad():
17 | clear_scratchpad()
18 | assert get_scratchpad() == []
19 | update_scratchpad("ExampleAgent", question, "example result")
20 | assert get_scratchpad() == [
21 | {"agent_name": "ExampleAgent", "question": "example question", "result": "example result", "error": None}
22 | ]
23 | clear_scratchpad()
24 |
--------------------------------------------------------------------------------
/financialhealthcheckScottLogic/samples/graphdb/knowledge.csv:
--------------------------------------------------------------------------------
1 | knowledgeId,name
2 | 1,"Saving per month >= £500"
3 | 2,"Rainy day fund >= £3000"
4 | 3,"Savings >= £50,000"
5 | 4,"Pension contributions per month >= £600"
6 | 5,"Mortgage >= £150,000"
7 | 6,"No loans or credit debt"
8 | 7,"Investments >= £10,000"
9 | 8,"Any investment account"
10 | 9,"Investment goal: Early retirement"
11 | 10,"High risk tolerance"
12 | 11,"Saving per month >= £250"
13 | 12,"Savings >= £10,000"
14 | 13,"Pension contributions per month >= £300"
15 | 14,"No investments"
16 | 15,"Investment goal: Retirement"
17 | 16,"Medium risk tolerance"
18 | 17,"Saving per month <= £100"
19 | 18,"Rainy day fund <= £500"
20 | 19,"Credit card debt >= £3,000"
21 | 20,"No help to buy ISA"
22 | 21,"Rent per month >= £550"
23 | 22,"No pension contributions"
24 | 23,"Financial goal: Save enough for a mortgage"
25 |
--------------------------------------------------------------------------------
/financialhealthcheckScottLogic/financial-bot/frontend/chat-widget-loader/index.html:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
5 | Widget example page
21 |
20 |
21 |
25 |
26 |
--------------------------------------------------------------------------------
/backend/src/prompts/templates/create-answer.j2:
--------------------------------------------------------------------------------
1 | You have been provided the final scratchpad which contains the results for the question in the user prompt.
2 | Your goal is to turn the results into a natural language format to present to the user.
3 |
4 | By using the final scratchpad below:
5 | {{ final_scratchpad }}
6 |
7 | and the question in the user prompt, this should be a readable sentence or 2 that summarises the findings in the results.
8 |
9 | If the question is a general knowledge question, check if you have the correct details for the answer and reply with this.
10 | If you do not have the answer or you require the internet, do not make it up. You should recommend the user to look this up themselves.
11 | If it is just conversational chitchat. Please reply kindly and direct them to the sort of answers you are able to respond.
12 |
13 | The current date and time is {{ datetime}}
14 |
--------------------------------------------------------------------------------
/financialhealthcheckScottLogic/samples/graphdb/usecase_knowledge.csv:
--------------------------------------------------------------------------------
1 | knowledgeId,name
2 | 1,Saving per month >= £500
3 | 2,Rainy day fund >= £3000
4 | 3,"Savings >= £50,000"
5 | 4,Pension contributions per month >= £600
6 | 5,"Mortgage >= £150,000"
7 | 6,No loans or credit debt
8 | 7,"Investments >= £10,000"
9 | 8,Any investment account
10 | 9,Investment goal: Early retirement
11 | 10,High risk tolerance
12 | 11,Saving per month >= £250
13 | 12,"Savings >= £10,000"
14 | 13,Pension contributions per month >= £300
15 | 14,No investments
16 | 15,Investment goal: Retirement
17 | 16,Medium risk tolerance
18 | 17,Saving per month <= £100
19 | 18,Rainy day fund <= £500
20 | 19,"Credit card debt >= £3,000"
21 | 20,No help to buy ISA
22 | 21,Rent per month >= £550
23 | 22,No pension contributions
24 | 23,Financial goal: Save enough for a mortgage
25 | 24,Savings location: Other bank
26 | 25,Other savings: >= £100
--------------------------------------------------------------------------------
/.github/workflows/test-backend.yml:
--------------------------------------------------------------------------------
1 | name: Test Backend
2 | on:
3 | pull_request:
4 |
5 | jobs:
6 | testing:
7 | name: Testing Backend
8 | runs-on: ubuntu-latest
9 | defaults:
10 | run:
11 | working-directory: ./backend
12 | steps:
13 | - name: Checkout
14 | uses: actions/checkout@v4
15 |
16 | - name: Setup Python
17 | uses: actions/setup-python@v5
18 | with:
19 | python-version: '3.12'
20 |
21 | - name: Install dependencies
22 | run: |
23 | python -m pip install --upgrade pip
24 | pip install -r requirements.txt
25 | pip install pytest-md pytest-emoji
26 |
27 | - name: Run tests
28 | uses: pavelzw/pytest-action@v2
29 | with:
30 | emoji: true
31 | verbose: true
32 | job-summary: true
33 | report-title: 'Backend Test Report'
34 |
--------------------------------------------------------------------------------
/backend/src/llm/llm.py:
--------------------------------------------------------------------------------
1 | from abc import ABC, ABCMeta, abstractmethod
2 | from typing import Any, Coroutine
3 | from .count_calls import count_calls
4 |
5 |
6 | class LLMMeta(ABCMeta):
7 | def __init__(cls, name, bases, namespace):
8 | super().__init__(name, bases, namespace)
9 | if not hasattr(cls, "instances"):
10 | cls.instances = {}
11 |
12 | cls.instances[name.lower()] = cls()
13 |
14 | def __new__(cls, name, bases, attrs):
15 | if "chat" in attrs:
16 | attrs["chat"] = count_calls(attrs["chat"])
17 |
18 | return super().__new__(cls, name, bases, attrs)
19 |
20 |
21 | class LLM(ABC, metaclass=LLMMeta):
22 | @classmethod
23 | def get_instances(cls):
24 | return cls.instances
25 |
26 | @abstractmethod
27 | def chat(self, model: str, system_prompt: str, user_prompt: str, return_json=False) -> Coroutine[Any, Any, str]:
28 | pass
29 |
--------------------------------------------------------------------------------
/backend/src/llm/openai_client.py:
--------------------------------------------------------------------------------
1 | # src/llm/openai_client.py
2 | import openai
3 | from src.utils import Config
4 | import logging
5 |
6 | config = Config()
7 | logger = logging.getLogger(__name__)
8 |
9 |
10 | class OpenAIClient:
11 | def __init__(self):
12 | self.api_key = config.openai_key
13 | openai.api_key = self.api_key
14 |
15 | def chat(self, model, messages, temperature=0, max_tokens=150):
16 | try:
17 | response = openai.ChatCompletion.create( # type: ignore
18 | model=model,
19 | messages=messages,
20 | )
21 | content = response["choices"][0]["message"]["content"]
22 | logger.debug(f'{model} response: "{content}"')
23 | return content
24 | except Exception as e:
25 | logger.error(f"Error calling OpenAI model: {e}")
26 | return "An error occurred while processing the request."
27 |
--------------------------------------------------------------------------------
/backend/src/prompts/templates/create-search-term.j2:
--------------------------------------------------------------------------------
1 | You are an expert at crafting Google search terms. Your goal is to generate an optimal search query based on the user's question to find the most relevant information on Google.
2 |
3 | Your entire purpose is to analyze the user's query, extract the essential keywords, and create a concise, well-structured search term that will yield the most accurate and useful results when used in a Google search.
4 |
5 | Ensure that the search query:
6 |
7 | Is relevant to the user’s question.
8 | Contains the right combination of keywords.
9 | Avoids unnecessary words, focusing only on what is critical for finding the right information.
10 | User's question is: {{ question }}
11 |
12 | Reply only in JSON format, following this structure:
13 | {
14 | "search_term": "The optimized Google search term based on the user's question",
15 | "reasoning": "A sentence on why you chose that search term"
16 | }
17 |
--------------------------------------------------------------------------------
/.idea/InferGPT.iml:
--------------------------------------------------------------------------------
1 |
2 | sBot Conversations
22 |Here we will add links/buttons to start different types of conversations.
23 |Currently there is only one conversation path and it has automatically started on the right.
24 |
45 | 
46 |
48 | );
49 | };
50 |
--------------------------------------------------------------------------------
/financialhealthcheckScottLogic/samples/chatscope/example-chat-widget/package.json:
--------------------------------------------------------------------------------
1 | {
2 | "name": "example-chat-widget",
3 | "version": "0.1.0",
4 | "private": true,
5 | "dependencies": {
6 | "@chatscope/chat-ui-kit-react": "^1.8.1",
7 | "@chatscope/chat-ui-kit-styles": "^1.2.0",
8 | "@testing-library/jest-dom": "^5.11.4",
9 | "@testing-library/react": "^11.1.0",
10 | "@testing-library/user-event": "^12.1.10",
11 | "nanoid": "^3.1.20",
12 | "openai": "^3.1.0",
13 | "react": "^16.0.1",
14 | "react-dom": "^16.0.1",
15 | "react-scripts": "4.0.2",
16 | "web-vitals": "^1.0.1"
17 | },
18 | "scripts": {
19 | "start": "react-scripts start",
20 | "build": "react-scripts build",
21 | "test": "react-scripts test",
22 | "eject": "react-scripts eject"
23 | },
24 | "eslintConfig": {
25 | "extends": [
26 | "react-app",
27 | "react-app/jest"
28 | ]
29 | },
30 | "browserslist": {
31 | "production": [
32 | ">0.2%",
33 | "not dead",
34 | "not op_mini all"
35 | ],
36 | "development": [
37 | "last 1 chrome version",
38 | "last 1 firefox version",
39 | "last 1 safari version"
40 | ]
41 | }
42 | }
43 |
--------------------------------------------------------------------------------
/frontend/package.json:
--------------------------------------------------------------------------------
1 | {
2 | "name": "llm-chat-ui",
3 | "type": "module",
4 | "version": "0.0.0",
5 | "description": "UI for a chatbot with restful api",
6 | "main": "index.js",
7 | "scripts": {
8 | "start": "webpack serve --port 8650",
9 | "build": "webpack",
10 | "lint": "eslint .",
11 | "lint:fix": "eslint . --fix"
12 | },
13 | "author": "",
14 | "license": "ISC",
15 | "devDependencies": {
16 | "@stylistic/eslint-plugin-js": "^1.7.0",
17 | "@types/react": "^18.2.70",
18 | "@types/react-dom": "^18.2.22",
19 | "@types/webpack-dev-server": "^4.7.2",
20 | "@typescript-eslint/eslint-plugin": "^7.4.0",
21 | "@typescript-eslint/parser": "^7.4.0",
22 | "css-loader": "^6.10.0",
23 | "dotenv": "^16.4.5",
24 | "eslint": "^8.57.0",
25 | "eslint-plugin-react": "^7.34.1",
26 | "html-webpack-plugin": "^5.6.0",
27 | "mini-css-extract-plugin": "^2.8.1",
28 | "ts-loader": "^9.5.1",
29 | "typescript": "^5.4.3",
30 | "webpack": "^5.91.0",
31 | "webpack-cli": "^5.1.4",
32 | "webpack-dev-server": "^5.0.4"
33 | },
34 | "dependencies": {
35 | "classnames": "^2.5.1",
36 | "react": "^18.2.0",
37 | "react-dom": "^18.2.0"
38 | }
39 | }
40 |
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/financialhealthcheckScottLogic/financial-bot/backend/utilities/session_manager.py:
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1 |
2 | import logging
3 | from flask import session
4 |
5 | from utilities.defaults import Defaults
6 | class SessionManager:
7 | @staticmethod
8 | def getSessionValue(sessionId, key):
9 | if session.get(sessionId) is not None:
10 | if key in session[sessionId]:
11 | return session[sessionId][key]
12 | return None
13 |
14 | @staticmethod
15 | def saveSessionValue(sessionId, key, val):
16 | if session.get(sessionId) is None:
17 | session[sessionId] = {}
18 | session[sessionId][key] = val
19 | session.modified = True
20 |
21 | @staticmethod
22 | def storeSessionData(sessionId, userIdVal, conversationIdVal, goalIdVal):
23 | logger = logging.getLogger('chatBot')
24 | logger.debug(f"called storeSessionData {sessionId=} {userIdVal=} {conversationIdVal=} {goalIdVal=})")
25 | SessionManager.saveSessionValue(sessionId, Defaults.userIdSessionKey, userIdVal)
26 | SessionManager.saveSessionValue(sessionId, Defaults.goalIdSessionKey, goalIdVal)
27 | SessionManager.saveSessionValue(sessionId, Defaults.conversationIdSessionKey, conversationIdVal)
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/frontend/src/components/confirm-modal.module.css:
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1 | .modal{
2 | width: 40%;
3 | height: 40%;
4 | background-color: #4c4c4c;
5 | color: var(--text-color-primary);
6 | border: 2px black;
7 | border-radius: 10px;
8 | }
9 |
10 | .modalContent{
11 | width: 100%;
12 | height: 100%;
13 | display: flex;
14 | flex-direction: column;
15 | }
16 |
17 | .header{
18 | text-align: center;
19 | }
20 |
21 | .modal::backdrop{
22 | background: rgb(0,0,0,0.8);
23 | }
24 |
25 | .requestMessage{
26 | flex-grow: 1;
27 | }
28 |
29 | .buttonsBar{
30 | display: flex;
31 | gap: 0.5rem;
32 | }
33 |
34 | .button{
35 | color: var(--text-color-primary);
36 | font-weight: bold;
37 | border: none;
38 | width: 100%;
39 | padding: 1rem;
40 | cursor: pointer;
41 | border-radius: 3px;
42 | }
43 |
44 |
45 | .cancel{
46 | composes: button;
47 | background-color: var(--background-color-primary);
48 | }
49 |
50 | .cancel:hover{
51 | background-color: #141414;
52 | transition: all 0.5s;
53 | }
54 |
55 | .confirm{
56 | composes: button;
57 | background-color: var(--blue);
58 | }
59 |
60 | .confirm:hover{
61 | background-color: #146AFF;
62 | transition: all 0.5s;
63 | }
64 |
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/frontend/src/components/input.module.css:
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1 | .inputContainer {
2 | box-sizing: border-box;
3 | display: inline-block;
4 | margin: 1rem 0 2rem 0;
5 | position: relative;
6 | width: 50%;
7 | }
8 |
9 | .input {
10 | background-color: var(--border-primary);
11 | border-radius: 8px;
12 | border: none;
13 | box-sizing: border-box;
14 | color: var(--text-color-primary);
15 | font-size: 1rem;
16 | height: 3rem;
17 | outline: none;
18 | padding: 0 34px 0 10px;
19 | width: 100%;
20 | }
21 |
22 | .input:focus {
23 | border: 1px solid var(--blue);
24 | }
25 |
26 | .sendButton {
27 | background-color: transparent;
28 | border: none;
29 | cursor: pointer;
30 | margin: 0;
31 | outline: none;
32 | padding: 0;
33 | position: absolute;
34 | right: 10px;
35 | top: 50%;
36 | transform: translateY(-50%);
37 | height: 26px;
38 | width: 26px;
39 | }
40 |
41 | .disabled {
42 | cursor: not-allowed;
43 | filter: invert(24%) sepia(3%) saturate(0%) hue-rotate(263deg) brightness(95%) contrast(90%);
44 | }
45 |
46 | .sendButton:hover:not(.disabled) {
47 | filter: invert(31%) sepia(75%) saturate(3303%) hue-rotate(200deg) brightness(102%) contrast(105%);
48 | }
49 |
50 | .sendButton:active:not(.disabled) {
51 | filter: none;
52 | }
53 |
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/financialhealthcheckScottLogic/samples/chatscope/example-widget-loader/README.md:
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1 | # Example widget loader
2 |
3 | A repository dedicated to the series of articles about web widgets.
4 |
5 | The series is available here:
6 | [Web widgets (Part 1): What is it?](https://chatscope.io/blog/web-widgets-part-1-what-is-it/)
7 | [Web widgets (Part 2): Widget him!](https://chatscope.io/blog/web-widgets-part-2-widget-him/)
8 | [Web widgets (Part 3): API Cookbook](https://chatscope.io/blog/web-widgets-part-3-api-cookbook/)
9 |
10 | You will also need second repository containing widget, which is available here:
11 | [https://github.com/chatscope/example-chat-widget](https://github.com/chatscope/example-chat-widget)
12 |
13 | ## How to run?
14 | ### `yarn start`
15 |
16 | Runs the app in the development mode.
17 | Open [http://localhost:5000](http://localhost:5000) to view it in the browser.
18 |
19 | ## Running with only npm
20 |
21 | ```bash
22 | npm update
23 | npm audix fix --force
24 | npm start
25 | ```
26 |
27 | You may change default port from ```3000``` to something else
28 |
29 | ```bash
30 | export PORT=3005 # Unix
31 | $env:PORT=3005 # Windows - Powershell
32 | ```
33 |
34 | ```npm update``` will update dependancies and will change package.json and so on.
35 |
36 | Tested on node 19.3.
37 |
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/frontend/src/useMessages.ts:
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1 | import { useCallback, useState } from 'react';
2 | import { Message, Role } from './components/message';
3 | import { getResponse } from './server';
4 |
5 | const starterMessage: Message = {
6 | role: Role.Bot,
7 | content: 'Hello, how can I help you?',
8 | time: new Date().toLocaleTimeString(),
9 | };
10 |
11 | export interface UseMessagesHook {
12 | sendMessage: (message: string) => void;
13 | messages: Message[];
14 | waiting: boolean;
15 | }
16 |
17 | export const useMessages = (): UseMessagesHook => {
18 | const [waiting, setWaiting] = useState{content}
47 |
47 |
48 |
51 |
52 | )
53 | }
54 |
55 | const containerElement = document.getElementById("botui")
56 | if (containerElement) {
57 | const root = createRoot(containerElement)
58 | root.render(
39 |
40 |
43 |
44 | )
45 | }
46 |
47 | const containerElement = document.getElementById("botui")
48 | if (containerElement) {
49 | const root = createRoot(containerElement)
50 | root.render(
49 | 
}
54 | {waiting &&
56 |
57 | );
58 | };
59 |
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