feat: TelegramBot with OpenAI assistant support

pull/1/head
Artem-Darius Weber 1 year ago
parent 075bc6d862
commit cd2529816b

BIN
.DS_Store vendored

Binary file not shown.

@ -0,0 +1,16 @@
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<option name="HIGHLIGHT_CONFLICTS" value="true" />
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<option name="RECENT_TEMPLATES">
<list>
<option value="TypeScript File" />
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<component name="MarkdownSettingsMigration">
<option name="stateVersion" value="1" />
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<component name="ProjectLevelVcsManager">
<ConfirmationsSetting value="1" id="Add" />
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<option name="hideEmptyMiddlePackages" value="true" />
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@ -32,6 +63,7 @@
"RunOnceActivity.OpenProjectViewOnStart": "true",
"RunOnceActivity.ShowReadmeOnStart": "true",
"WebServerToolWindowFactoryState": "false",
"last_opened_file_path": "/Users/darius/Desktop/ASC/CyberGarden/kempt-kinkajou/weather_platform/apps",
"node.js.detected.package.eslint": "true",
"node.js.detected.package.tslint": "true",
"node.js.selected.package.eslint": "(autodetect)",
@ -41,6 +73,12 @@
"vue.rearranger.settings.migration": "true"
}
}]]></component>
<component name="RecentsManager">
<key name="CopyFile.RECENT_KEYS">
<recent name="$PROJECT_DIR$/weather_platform/apps" />
<recent name="$PROJECT_DIR$/weather_platform/ui-modules/src/lib/navbar" />
</key>
</component>
<component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
<component name="TaskManager">
<task active="true" id="Default" summary="Default task">
@ -49,7 +87,8 @@
<option name="number" value="Default" />
<option name="presentableId" value="Default" />
<updated>1696663237589</updated>
<workItem from="1696663238626" duration="1098000" />
<workItem from="1696663238626" duration="5030000" />
<workItem from="1696670312018" duration="20121000" />
</task>
<servers />
</component>

@ -74,6 +74,10 @@ Run all services:
nx run-many --parallel --target=serve --projects=crud,agw,agregators-service,measures-service,sensors-service
```
```bash
nx run py-tg-bot-weather-agent:serve
```
## Структура репозитория
Репозиторий содержит следующие основные каталоги и файлы:

@ -8,9 +8,9 @@ export class AgregatorCreateDTOClass {
@ApiProperty({description: 'The agregator name'})
name: string;
@ApiProperty({description: 'The agregator latitude'})
let: number;
lat: string;
@ApiProperty({description: 'The agregator longitude'})
lng: number;
lng: string;
@ApiProperty({description: 'The agregator height from the ground'})
height: number;
@ApiProperty({description: 'The agregator country'})

@ -27,8 +27,8 @@ export type AgregatorCreateDTO = {
sendedInDate: string;
creatorUUID: string;
name: string;
let: number;
lng: number;
lat: string;
lng: string;
height: number;
country: string;
city: string;
@ -102,6 +102,8 @@ export class AppController {
uuid: uuid,
};
console.log(updatedWhere);
const agregator = await this.appService.create(updatedWhere);
return agregator;
}

@ -44,6 +44,7 @@ export class AgregatorController {
async getAgregators(
@Body() params: AgregatorGetDTO,
): Promise<Partial<Agregator[]> | null> {
console.log(params);
return await this.agregatorService.get(params);
}
@ -63,6 +64,7 @@ export class AgregatorController {
async createAgregator(
@Body() sectionData: AgregatorCreateDTO,
): Promise<Partial<Agregator> | null> {
console.log(sectionData);
return await this.agregatorService.create(sectionData);
}

@ -6,10 +6,12 @@ import { Agregator, Prisma } from "@weather-platform/prisma-clients/Agregators";
export class AgregatorService {
constructor(private readonly httpService: HttpService) {}
AGREGATOR_SERVICE_URL = (process.env.AGREGATOR_SERVICE_PROTOCOL || 'http://') +
(process.env.AGREGATOR_SERVICE_HOST || 'localhost') + ':' +
(process.env.AGREGATOR_SERVICE_PORT || 3000) +
'/api/';
// AGREGATOR_SERVICE_URL = (process.env.AGREGATOR_SERVICE_PROTOCOL || 'http://') +
// (process.env.AGREGATOR_SERVICE_HOST || 'localhost') + ':' +
// (process.env.AGREGATOR_SERVICE_PORT || 3000) +
// '/api/';
AGREGATOR_SERVICE_URL = 'http://localhost:8047/api/';
async get(data: Prisma.AgregatorFindManyArgs): Promise<Partial<Agregator[]> | null> {
try {

@ -1,10 +1,33 @@
import { Controller, Get } from '@nestjs/common';
import { AppService } from './app.service';
import {AgregatorService} from "../agregator/agregator.service";
// eslint-disable-next-line @nx/enforce-module-boundaries
import {AgregatorCreateDTOClass} from "../../../agregators-service/src/DTO/AgregatorCreateDTOClass.dto";
@Controller()
export class AppController {
constructor(private readonly appService: AppService) {}
constructor(private readonly appService: AppService,
private readonly agregatorService: AgregatorService,
) {}
@Get('/seed/test/data')
setSeedTestData() {
const data: AgregatorCreateDTOClass = {
sendedInDate: new Date().toISOString(),
creatorUUID: "test",
name: "agregator 1",
lat: "45",
lng: "76",
height: 1,
country: "russia",
city: "taganrog",
region: "rostov",
street: "street",
}
this.agregatorService.create(data);
return "success";
}
@Get()
getData() {

@ -1,4 +1,4 @@
import { Body, Controller, Post } from "@nestjs/common";
import {Body, Controller, Get, Post} from "@nestjs/common";
import { MeasureCreateDTOLocalClass, MeasuresService } from "./measures.service";
import {
ApiInternalServerErrorResponse,
@ -12,6 +12,11 @@ import {
MeasuresCreateSuccessResponse,
MeasuresDataClass
} from "./DTO/CreateMeasuresClass.dto";
// eslint-disable-next-line @nx/enforce-module-boundaries
import {AgregatorGetDTO} from "../../../agregators-service/src/app/app.controller";
import {Measures} from "@weather-platform/prisma-clients/Measures";
// eslint-disable-next-line @nx/enforce-module-boundaries
import {MeasureGetDTOClass} from "../../../measures-service/src/DTO/MeasureGetDTOClass.dto";
@Controller('measures')
export class MeasuresController {
@ -50,6 +55,8 @@ export class MeasuresController {
const res = await this.measuresService.create(measure_create_data);
console.log(res);
if (!res) {
check = true;
}
@ -57,4 +64,43 @@ export class MeasuresController {
return check ? { code: '200' } : { code: '500' };
}
@ApiOperation({
summary: 'Get measures data with Prisma params', tags: ['Measures'], description: 'Get measures with Prisma params', operationId: 'getWithParams',
})
@ApiResponse({ status: 200, type: MeasureGetDTOClass, headers: { 'X-Request-Id': { schema: { type: 'string' } } }, description: 'The found measure', })
@ApiResponse({ status: 500, type: Error, description: 'Internal server error', headers: { 'X-Request-Id': { schema: { type: 'string' } } }, })
@ApiOkResponse({
description: 'Retrieved measures successfully',
type: MeasureGetDTOClass, status: 200, headers: { 'X-Request-Id': { schema: { type: 'string' } } },
})
@ApiNotFoundResponse({
description: 'No measures found for this request', type: Error, status: 404, headers: { 'X-Request-Id': { schema: { type: 'string' } } },
})
@ApiInternalServerErrorResponse({
description: 'Internal server error', status: 500, headers: { 'X-Request-Id': { schema: { type: 'string' } } }, type: Error,
})
@Post('get-with-params')
async getAgregators(
@Body() params: MeasureGetDTOClass,
): Promise<Partial<Measures[]> | null> {
return await this.measuresService.get(params);
}
@Get('get-for-ai')
async getAgregatorsForAI() {
const res = await this.measuresService.get({});
const newArray = res.map(item => ({
sensor_uuid: item.sensor_uuid,
agregator_uuid: item.agregator_uuid,
type: item.type,
value: item.value,
time: item.time
}));
return newArray;
}
}

@ -34,10 +34,7 @@ export class MeasureCreateDTOLocalClass implements MeasureCreateLocalDTO {
export class MeasuresService {
constructor(private readonly httpService: HttpService) {}
MEASURES_SERVICE_URL = (process.env.MEASURES_SERVICE_PROTOCOL || 'http://') +
(process.env.MEASURES_SERVICE_HOST || 'localhost') + ':' +
(process.env.MEASURES_SERVICE_PORT || 3000) +
'/api/';
MEASURES_SERVICE_URL = "http://localhost:8048/api/"
async get(data: Prisma.MeasuresFindManyArgs): Promise<Partial<Measures[]> | null> {
try {
@ -52,11 +49,11 @@ export class MeasuresService {
async create(data: MeasureCreateDTOLocalClass): Promise<Partial<Measures> | null> {
// eslint-disable-next-line no-useless-catch
try {
const updated_data: MeasureCreateDTOClass = {
const updated_data: any = {
...data,
creatorUUID: '1',
}
const response = await this.httpService.post(this.MEASURES_SERVICE_URL + 'create', data).toPromise();
};
console.log(updated_data);
const response = await this.httpService.post(this.MEASURES_SERVICE_URL + 'create', updated_data).toPromise();
return response.data;
} catch (error) {
throw error;

@ -6,10 +6,7 @@ import { Sensor, Prisma } from "@weather-platform/prisma-clients/Sensors";
export class SensorsService {
constructor(private readonly httpService: HttpService) {}
SENSORS_SERVICE_URL = (process.env.SENSORS_SERVICE_PROTOCOL || 'http://') +
(process.env.SENSORS_SERVICE_HOST || 'localhost') + ':' +
(process.env.SENSORS_SERVICE_PORT || 3000) +
'/api/';
SENSORS_SERVICE_URL = 'http://localhost:8046/api/';
async get(data: Prisma.SensorFindManyArgs): Promise<Partial<Sensor[]> | null> {
try {

@ -1,13 +1,19 @@
// eslint-disable-next-line @typescript-eslint/no-unused-vars
import styles from './app.module.scss';
import {BrowserRouter as Router, Route, Routes} from 'react-router-dom';
import NxWelcome from './nx-welcome';
import {AgregatorsPage, Dashboard, SensorPage} from "@weather-platform/pages";
export function App() {
return (
<div>
<NxWelcome title="crud" />
</div>
<Router>
<Routes>
<Route path="/" element={<Dashboard/>} />
<Route path="/agregators" element={<AgregatorsPage/>} />
<Route path="/sensors" element={<SensorPage/>} />
</Routes>
</Router>
);
}

@ -2,7 +2,6 @@ import { ApiProperty } from "@nestjs/swagger";
type MeasureCreateDTO = {
sendedInDate: string;
creatorUUID: string;
sensor_uuid: string;
agregator_uuid: string;
time: string;
@ -13,8 +12,6 @@ type MeasureCreateDTO = {
export class MeasureCreateDTOClass implements MeasureCreateDTO {
@ApiProperty({description: 'The date when the was sent'})
sendedInDate: string;
@ApiProperty({description: 'The UUID of the creator'})
creatorUUID: string;
@ApiProperty({description: 'The sensor uuid'})
sensor_uuid: string;
@ApiProperty({description: 'The agregator uuid'})

@ -0,0 +1,23 @@
import {Prisma} from "@weather-platform/prisma-clients/Measures";
import {ApiProperty} from "@nestjs/swagger";
type MeasureGetDTO = {
skip?: number;
take?: number;
cursor?: Prisma.MeasuresWhereUniqueInput;
where?: Prisma.MeasuresWhereInput;
orderBy?: Prisma.MeasuresOrderByWithRelationInput;
};
export class MeasureGetDTOClass implements MeasureGetDTO {
@ApiProperty({description: 'The number of items to skip'})
skip?: number;
@ApiProperty({description: 'The number of items to take'})
take?: number;
@ApiProperty({description: 'The cursor'})
cursor?: Prisma.MeasuresWhereUniqueInput;
@ApiProperty({description: 'The where'})
where?: Prisma.MeasuresWhereInput;
@ApiProperty({description: 'The orderBy'})
orderBy?: Prisma.MeasuresOrderByWithRelationInput;
}

@ -12,27 +12,7 @@ import { Measures, Prisma } from '@weather-platform/prisma-clients/Measures';
import { AppService } from './app.service';
import { MeasureCreateDTOClass } from "../DTO/MeasureCreateDTOClass.dto";
export type MeasureGetDTO = {
skip?: number;
take?: number;
cursor?: Prisma.MeasuresWhereUniqueInput;
where?: Prisma.MeasuresWhereInput;
orderBy?: Prisma.MeasuresOrderByWithRelationInput;
};
class MeasureGetDTOClass {
@ApiProperty({description: 'The number of items to skip'})
skip?: number;
@ApiProperty({description: 'The number of items to take'})
take?: number;
@ApiProperty({description: 'The cursor'})
cursor?: Prisma.MeasuresWhereUniqueInput;
@ApiProperty({description: 'The where'})
where?: Prisma.MeasuresWhereInput;
@ApiProperty({description: 'The orderBy'})
orderBy?: Prisma.MeasuresOrderByWithRelationInput;
}
import {MeasureGetDTOClass} from "../DTO/MeasureGetDTOClass.dto";
type MeasureUpdateDTO = {
where: Prisma.MeasuresWhereUniqueInput;
@ -68,7 +48,7 @@ export class AppController {
})
@Post('get-with-params')
async getMeasures(
@Body() params: MeasureGetDTO,
@Body() params: MeasureGetDTOClass,
): Promise<Measures[]> {
const { skip, take, cursor, where, orderBy } = params;
@ -105,6 +85,8 @@ export class AppController {
@Body() sectionData: MeasureCreateDTOClass,
): Promise<Measures> {
console.log("calles");
const { uuid } = uuidv4();
const updatedWhere: Prisma.MeasuresCreateInput = {

@ -0,0 +1,8 @@
# Default ignored files
/shelf/
/workspace.xml
# Editor-based HTTP Client requests
/httpRequests/
# Datasource local storage ignored files
/dataSources/
/dataSources.local.xml

@ -0,0 +1,17 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="DataSourceManagerImpl" format="xml" multifile-model="true">
<data-source source="LOCAL" name="events" uuid="11434940-57f3-4269-9681-6bd8a7b5fcfc">
<driver-ref>sqlite.xerial</driver-ref>
<synchronize>true</synchronize>
<jdbc-driver>org.sqlite.JDBC</jdbc-driver>
<jdbc-url>jdbc:sqlite:$PROJECT_DIR$/examples/events.db</jdbc-url>
<working-dir>$ProjectFileDir$</working-dir>
<libraries>
<library>
<url>file://$APPLICATION_CONFIG_DIR$/jdbc-drivers/Xerial SQLiteJDBC/3.39.2/sqlite-jdbc-3.39.2.jar</url>
</library>
</libraries>
</data-source>
</component>
</project>

@ -0,0 +1,7 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="DiscordProjectSettings">
<option name="show" value="ASK" />
<option name="description" value="" />
</component>
</project>

@ -0,0 +1,19 @@
<component name="InspectionProjectProfileManager">
<profile version="1.0">
<option name="myName" value="Project Default" />
<inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
<option name="ignoredErrors">
<list>
<option value="N803" />
</list>
</option>
</inspection_tool>
<inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
<option name="ignoredIdentifiers">
<list>
<option value="random.random.choice" />
</list>
</option>
</inspection_tool>
</profile>
</component>

@ -0,0 +1,6 @@
<component name="InspectionProjectProfileManager">
<settings>
<option name="USE_PROJECT_PROFILE" value="false" />
<version value="1.0" />
</settings>
</component>

@ -0,0 +1,4 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
</project>

@ -0,0 +1,8 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/py-multy-agent-interpreter.iml" filepath="$PROJECT_DIR$/.idea/py-multy-agent-interpreter.iml" />
</modules>
</component>
</project>

@ -0,0 +1,10 @@
<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/venv" />
</content>
<orderEntry type="jdk" jdkName="Python 3.9" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

@ -0,0 +1,7 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$/../../.." vcs="Git" />
<mapping directory="$PROJECT_DIR$/../.." vcs="Git" />
</component>
</project>

@ -0,0 +1,37 @@
Win:
venv v\Scripts\activate
Mac:
source venv/bin/activate
deactivate
# Run
pip install -r requirements.txt
## LLaMA 2 install
model:
Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/botato/point-alpaca-ggml-model-q4_0
if you want to clone without large files just their pointers
prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1
mac:
CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install llama-cpp-python
win:
git clone --recursive -j8 https://github.com/abetlen/llama-cpp-python.git
set FORCE_CMAKE=1
set CMAKE_ARGS=-DLLAMA_CUBLAS=OFF
python setup.py clean
python setup.py install
pip freeze > requirements.txt

@ -0,0 +1,13 @@
from langchain.agents.agent_toolkits import create_python_agent
from langchain.tools.python.tool import PythonREPLTool
from langchain.python import PythonREPL
from langchain.llms.openai import OpenAI
OPENAI_API = "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"
agent_executor = create_python_agent(
llm=OpenAI(temperature=0, max_tokens=1000, openai_api_key=OPENAI_API),
tool=PythonREPLTool(),
verbose=True
)
agent_executor.run("Find the roots (zeros) if the quadratic function 3 * x**2 + 2*x - 1")

@ -0,0 +1,32 @@
from langchain.llms import OpenAI
from langchain.schema import (AIMessage, HumanMessage, SystemMessage)
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate
from langchain.chains import LLMChain, SimpleSequentialChain
OPENAI_API = "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"
llm = OpenAI(model_name="text-davinci-003", openai_api_key=OPENAI_API)
template = """
You are an expert data scientist with an expertise in building deep learning models.
Explain the concept of {concept} in a couple of lines
"""
prompt = PromptTemplate(
input_variables=['concept'],
template=template,
)
chain = LLMChain(llm=llm, prompt=prompt)
print(chain.run("autoencoder"))
second_prompt = PromptTemplate(
input_variables=["ml_concept"],
template="Turn the concept description of {ml_concept} and explain it to me like I'm five",
)
chain_two = LLMChain(llm=llm, prompt=second_prompt)
overall_chain = SimpleSequentialChain(chains=[chain, chain_two], verbose=True)
explanation = overall_chain.run("autoencoder")
print(explanation)

@ -0,0 +1,15 @@
from langchain.schema import (AIMessage, HumanMessage, SystemMessage)
from langchain.chat_models import ChatOpenAI
OPENAI_API = "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"
chat = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0.3, openai_api_key=OPENAI_API)
messages = [
SystemMessage(content="You are an expert data scientist and neuroscience"),
HumanMessage(content="объясни как выполняется сортировка входных данных в таламусе человека по шагам")
]
response=chat(messages)
print(response.content, end='\n')

@ -0,0 +1,43 @@
import pandas as pd
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.tools.base import StructuredTool
from typing import Optional
import requests
OPENAI_API = "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"
# ONPU AGW
AGW_PORT = 3000
AGW_HOST = 'localhost'
AGW_URL = f"http://{AGW_HOST}:{AGW_PORT}/"
def fetch_get_devices(params={}):
try:
response = requests.post(AGW_URL + 'api/matrix/get', json=params)
response.raise_for_status()
data = response.json()
return data
except requests.exceptions.RequestException as e:
print('Error fetching data:', e)
return None
def get_devices_insight() -> str:
"""Tool that counts the number of items for a given Laboratory Brains data object"""
data = fetch_get_devices()
# return str(len(data))
return data
device_insights_tool = StructuredTool.from_function(get_devices_insight)
chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.2, openai_api_key=OPENAI_API)
tools = [device_insights_tool]
agent_chain = initialize_agent(
tools,
chat,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
print(agent_chain("how many devices with online status?"))

@ -0,0 +1,43 @@
Rank,Paper Title,Score,Data preprocessing,Data augmentation,Imbalance,End-to-end,Ensemble,Multi-binary classification,Feature engineering,Hand Features,Temporal,Frequent,Demographic Features,DNN,CNN,RNN/Transformer,Attention,Backbone,Others
1,A Wide & Deep Transformer Neural Network for 12-Lead ECG Classification,0.533,Sampling rate 500Hz; Bandwidth 3 - 45 Hz; Value -1 - 1; Window size 15s 0-padding,No,No,Yes,Top 3 best models from 10 folds CV,Yes,Yes,No,No,No,"age,sex + 20 top identified features by random forest model",Yes,Yes,Transformer,Inherently in Transformer (location wise),ResNet + Transformer + hand features + ensemble,None
2,Adaptive lead weighted ResNet trained with different duration signals for classifying 12-lead ECGs,0.52,Sampling rate 257 Hz; Value -1 - 1; Window size 4096(16s) 0-padding,No,Thresholding,Yes,Ensemble 5 CV models,Yes,Yes,No,No,No,only age and sex,Yes,Yes,No,Squeeze-And-Excitation (channel wise),ResNet + SE,None
3,Classification of Cardiac Abnormalities from ECG Signals using SE-ResNet,0.514,Sampling rate 500Hz; Window size 30s 0-padding; Exclude 4 leads; Wavelet denoise,With external data: Hefei,Sign loss,Yes,Rule model for bradycardia,Yes,No,No,No,No,No,Yes,Yes,No,Squeeze-And-Excitation (channel wise),ResNet + SE,None
4,Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification,0.485,Sampling rate 500Hz; Value -1 - 1; Window size 10240(20.48 s) ,Add noise and drift,No,Yes,No,Yes,No,No,No,No,No,Yes,Yes,Transformer,Inherently in Transformer (location wise),ResNet + Transformer,None
5,Classification of 12-lead ECG Signals with Adversarial Multi-Source Domain Generalization,0.437,Sampling rate 250Hz; Value normalized,"Add or filter frequency components; Substitute, shuffle, invert, filt and scale lead data",No,Yes,No,Yes,Yes,No,No,No,only age and sex,Yes,Yes,Yes,No,ResNet + RNN,Domain-adversarial training
6,Bag of Tricks for Electrocardiogram Classification with Deep Neural Networks,0.42,Sampling rate 500Hz; Bandwith 50-60Hz; Value use mean and std; Exlude PTB and StPetersburg datasets,Manifold Mixup,"Weighted loss, Thresholding",Yes,Ensemble 10 CV models,Yes,No,No,No,No,No,Yes,Yes,No,No,ResNet,None
7,Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks,0.417,Sampling rate 500Hz,With external data: UMCU,Weighted loss (focal),Yes,No,Yes,No,No,No,No,No,Yes,Yes,No,No,ResNet,None
8,SE-ECGNet : Multi-scale SE-Net for Multi-lead ECG Data,0.411,Window sizw 10s,Random shift signals; Add Gaussian noise,Thresholding,Yes,No,Yes,Yes,No,No,No,only age and sex,Yes,Yes,No,Squeeze-And-Excitation (channel wise),ResNet + SE,None
9,Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals,0.382,Window size 10s 0-padding,No,Thresholding,Yes,No,Yes,No,No,No,No,No,Yes,Yes,Yes,No,ResNet + BiLSTM,NAS
10,Automatic Detection and Classification of 12-lead ECGs Using a Deep Neural Network,0.359,Sampling rate 500Hz; Window size 10s 0-padding,No,No,Yes,No,Yes,Yes,No,No,No,only age and sex,Yes,Yes,No,Squeeze-And-Excitation (channel wise),ResNet + SE,None
11,Cardiac Pathologies Detection and Classification in 12-lead ECG,0.354,No,No,No,No,No,Yes,Yes,Yes,Yes,Yes,No,No,No,No,No,Rule based,None
12,Rule-Based Method and Deep Learning Networks for Automatic Classification of ECG,0.298,Sampling rate 500 Hz,No,"At most N_max
instances",No,No,Yes,Yes,Yes,Yes,Yes,No,Yes,Yes,No,No,Rule,CWT transforms signal to scalogram image
13,Convolutional Recurrent Neural Network and LightGBM Ensemble Model for 12-lead ECG Classification,0.281,Samping rate 500Hz; Bandwidth 0.01 - 20 Hz; Window size 10s ,No,SMOTE,No,Yes,Yes,Yes,Yes,Yes,Yes,only age and sex,Yes,Yes,Yes,Yes,CRNN + LightGBM,None
14,12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes,0.279,"Samping rate 100Hz, Window size 10 s; Median filter with 5 samples; Subtract mean of 50 samples",No,No,Yes,No,Yes,No,No,No,No,No,Yes,Yes,No,No,CNN,None
15,Cardiac Abnormality Detection in 12-lead ECGs With Deep Convolutional Neural Networks Using Data Augmentation,0.27,Sampling rate 500 Hz; Bandwidth 0.5 - 45 Hz; Uniform filter with 50 samples,Signal slicing; CutOut; Channel shifting; Additive noise; Dropout,No,Yes,No,No,No,No,No,NO,NO,Yes,Yes,No,No,ResNet,Data augmentation
16,A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis Using ECG Signal and Deep Learning,0.26,Cardiac cycle 400 samples; Bandwidth 3 - 45 Hz;,No,No,Yes,No,No,No,No,No,No,No,Yes,Yes,LSTM,No,CNN+LSTM,None
17,Multi-label Arrhythmia Classification From 12-Lead Electrocardiograms,0.24,Bandwidth 12 - 50 Hz; Median filter with 10ms; ,No,Undersample the majority class,No (Feature extraction),No,Yes,Yes,Yes,Yes,Yes,No,Yes,Yes,Yes,No,"AlexNet, LSTM, SVM… for different classes",Feature Map Generation algorithm design
18,MADNN : A Multi-scale Attention Deep Neural Network for Arrhythmia Classification Data preprocessing,0.24,"Sampling rate 500 Hz, Window size 30000(60 s) copy-padding",Romdomly crop,Focal loss; Balance factor to filter samples,Yes,"VGG, Xception",No,No,No,No,No,No,Yes,Yes,No,Yes,ResNet+SE+SK,Data augmentation
19,Classification of 12-lead ECGs Using Gradient Boosting on Features Acquired With Domain-Specific and Domain-Agnostic Methods,0.23,Bandwidth 0-80 Hz; Median filter with 200 ms and 600 ms,No,Re-weight positive samples,No (Feature extraction),No,No,Yes,Yes,Yes,Yes,No,No,No,No,No,XGBoost,Feature extraction
20,Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs,0.21,Samping rate 500Hz; Window size 5000(10s) 0-padding,No,No,Yes,No,No,Yes,No,No,No,only age and sex,Yes,Yes,No,No,CNN+Rule-based model (HRV-score),RMSSD
21,Automated Classification of Electrocardiograms Using Wavelet Analysis and Deep Learning,0.21,No,No,Undersample the majority class,No,No,Yes,Yes,Yes,No,Yes,No,Yes,Yes,No,No,"SqueezeNet, 12 models for each lead with voting scheme",None
22,ECG Abnormalities Recognition Using Convolutional Network With Global Skip Connections and Custom Loss Function,0.2,Sampling rate 125 Hz,No,WCE loss function,Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,ResNet+Custom Loss Function,None
23,Multi-Class Classification of Pathologies Found on Short ECG Signals,0.19,Samping rate250Hz,No,No,Yes,No,Yes,Yes,Yes,Yes,No,No,No,No,No,No,"ECOC, Bagging",None
24,ECG Classification with a Convolutional Recurrent Neural Network,0.17,"Samping rate 257 Hz, Bandwidth 0.5- Hz",Random offset,Focal loss; Oversampling; MLSMOTE; An ensemble of binary and multi-label deep models,Yes,No,No,No,No,No,No,No,Yes,Yes,GRU,No,CRNN,Time Augmentation
25,Interpretable XGBoost Based Classification of 12-lead ECGs Applying Information Theory Measures From Neuroscience,0.16,Bandwidth 1.5-25 Hz; db4,No,Undersample the majority class,No (Feature extraction),No,Yes,Yes,Yes,Yes,Yes,known patient metadata.,No,No,No,No,XGBoost,None
26,Identification of Cardiac Arrhythmias from 12-lead ECG using Beat-wise Analysis and a Combination of CNN and LSTM,0.15,"Sampling rate 500 Hz; Window size 10s 0-padding, Bandwith 0-50 Hz; MODWT; db5; LOESS",No,Select most significant beats number,Yes,No,No,No,No,No,No,No,Yes,Yes,LSTM,No,CNN+BiLSTM,None
27,Classification of 12-Lead Electrocardiograms Using Residual Neural Networks and Transfer Learning,0.14,Sampling rate 250Hz; Window size 10s,With external data: MUSE,No,Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,ResNet,None
28,ECG Segmentation Using a Neural Network as the Basis for Detection of Cardiac Pathologies,0.14,Sampling rate 500 Hz; Bandwith 0.05-42 Hz; db4,No,Focal loss,No (Feature extraction),No,Yes,Yes,Yes,Yes,No,No,Yes,Yes,No,No,Unet+XGBoost,None
29,Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble,0.132,Sampling rate 400hz; Window size 4096 0-padding,No,Correlation factor,Yes,7 convolutional models ,No,No,No,No,No,No,Yes,Yes,No,No,ResNet,None
30,Utilization of Residual CNN-GRU With Attention Mechanism for Classification of 12-lead ECG,0.122,Sampling rate 250 Hz,Power envelopes,No,Yes,Yes,No,No,No,No,No,No,Yes,Yes,GRU,No,ResNet + RNN,None
31,Selected Features for Classification of 12-lead ECGs,0.102,Bandwidth 1- Hz; Median filter ,No,No,No,Bootstrap-aggregated ,No,Yes,Yes,Yes,No,only age and sex,No,No,No,No,Decision tree,Featre extraction
32,Classification of 12 Lead ECG Signal Using 1D-Convolutional Neural Network With Class Dependent Threshold,0.077,Sampling rate 500 Hz; Window size 10000 copy-padding; Converted to physical unit(mV),No,No,Yes,No,Yes,No,No,No,No,No,Yes,Yes,No,No,CNN,None
33,Deep Multi-Label Multi-Instance Classification on 12-Lead ECG,0.001,Bandwidth 0.5 - 50 Hz,Randomly sampled,BCELoss,Yes,No,Yes,No,No,No,No,No,Yes,Yes,No,Yes,ResNet Encoder+attention-based MIC+Decoder,None
34,Diagnostic of Multiple Cardiac Disorders from 12-lead ECGs Using Graph Convolutional Network Based Multi-label Classification,-0.012,Sampling rate 500 Hz; Window size 18s 0-padding,No,No,Yes,Yes,Yes,No,No,No,No,No,Yes,Yes,GRU,No,CNN+BiGRU+GCN,CNN+BiGRU+GCN(GRN for label correlations)
35,Automatic Concurrent Arrhythmia Classification Using Deep Residual Neural Networks,-0.035,Window size 2500,No,No,Yes,Yes,No,No,No,No,No,No,Yes,Yes,RNN GRU LSTM,Yes,ResNet,None
36,Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble,-0.08,"Sampling rate 0-500 Hz; Window middle 2,000 samples(R:-0.35-0.5s); Average filter with 0.02s; ",No,Oversampling; Re-weight,No,Yes,Yes,Yes,Yes,Yes,No,No,No,No,No,No,XGboost,None
37,A Topology Informed Random Forest Classifier for ECG Classification,-0.113,1800 time points,No,No,No,No,No,Yes,Yes,Yes,No,demographic data,No,No,No,No,random forest,point cloud embedding
38,Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks,-0.128,Window size 3000 0-padding,No,Thresholding,Yes,No,No,No,No,No,No,No,Yes,Yes,No,No,VGG16,None
39,Multi-Stream Deep Neural Network For 12-Lead ECG Classification,-0.29,Bandwidth 0.1-30 Hz ,No,No,No (Feature extraction),Yes,No,Yes,Yes,Yes,No,No,Yes,Yes,No,No,CNN,CNN+feature extraction
40,Classification of 12-lead ECG with an Ensemble Machine Learning Approach,-0.476,Sampling rate 500 Hz; Bandwidth 0.67-30 Hz,Vector magnitude for detect beats,No,No (Feature extraction),Yes,Yes,Yes,Yes,Yes,No,No,Yes,Yes,No,No,CNN,3 CNNs for signal + FFNN for phythm + PVC detection
41,Classification of 12-lead ECGs using digital biomarkersand representation learning,-0.658,Sampling rate 500 Hz; Bandwidth 0.13-88 Hz; bSQ>0.8;,No,Oversampling,No,Yes,No,Yes,Yes,Yes,No,only age and sex,Yes,Yes,GRU,No,CNN+GRU,feature engineering + CNN
1 Rank Paper Title Score Data preprocessing Data augmentation Imbalance End-to-end Ensemble Multi-binary classification Feature engineering Hand Features Temporal Frequent Demographic Features DNN CNN RNN/Transformer Attention Backbone Others
2 1 A Wide & Deep Transformer Neural Network for 12-Lead ECG Classification 0.533 Sampling rate 500Hz; Bandwidth 3 - 45 Hz; Value -1 - 1; Window size 15s 0-padding No No Yes Top 3 best models from 10 folds CV Yes Yes No No No age,sex + 20 top identified features by random forest model Yes Yes Transformer Inherently in Transformer (location wise) ResNet + Transformer + hand features + ensemble None
3 2 Adaptive lead weighted ResNet trained with different duration signals for classifying 12-lead ECGs 0.52 Sampling rate 257 Hz; Value -1 - 1; Window size 4096(16s) 0-padding No Thresholding Yes Ensemble 5 CV models Yes Yes No No No only age and sex Yes Yes No Squeeze-And-Excitation (channel wise) ResNet + SE None
4 3 Classification of Cardiac Abnormalities from ECG Signals using SE-ResNet 0.514 Sampling rate 500Hz; Window size 30s 0-padding; Exclude 4 leads; Wavelet denoise With external data: Hefei Sign loss Yes Rule model for bradycardia Yes No No No No No Yes Yes No Squeeze-And-Excitation (channel wise) ResNet + SE None
5 4 Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification 0.485 Sampling rate 500Hz; Value -1 - 1; Window size 10240(20.48 s) Add noise and drift No Yes No Yes No No No No No Yes Yes Transformer Inherently in Transformer (location wise) ResNet + Transformer None
6 5 Classification of 12-lead ECG Signals with Adversarial Multi-Source Domain Generalization 0.437 Sampling rate 250Hz; Value normalized Add or filter frequency components; Substitute, shuffle, invert, filt and scale lead data No Yes No Yes Yes No No No only age and sex Yes Yes Yes No ResNet + RNN Domain-adversarial training
7 6 Bag of Tricks for Electrocardiogram Classification with Deep Neural Networks 0.42 Sampling rate 500Hz; Bandwith 50-60Hz; Value use mean and std; Exlude PTB and StPetersburg datasets Manifold Mixup Weighted loss, Thresholding Yes Ensemble 10 CV models Yes No No No No No Yes Yes No No ResNet None
8 7 Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks 0.417 Sampling rate 500Hz With external data: UMCU Weighted loss (focal) Yes No Yes No No No No No Yes Yes No No ResNet None
9 8 SE-ECGNet : Multi-scale SE-Net for Multi-lead ECG Data 0.411 Window sizw 10s Random shift signals; Add Gaussian noise Thresholding Yes No Yes Yes No No No only age and sex Yes Yes No Squeeze-And-Excitation (channel wise) ResNet + SE None
10 9 Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals 0.382 Window size 10s 0-padding No Thresholding Yes No Yes No No No No No Yes Yes Yes No ResNet + BiLSTM NAS
11 10 Automatic Detection and Classification of 12-lead ECGs Using a Deep Neural Network 0.359 Sampling rate 500Hz; Window size 10s 0-padding No No Yes No Yes Yes No No No only age and sex Yes Yes No Squeeze-And-Excitation (channel wise) ResNet + SE None
12 11 Cardiac Pathologies Detection and Classification in 12-lead ECG 0.354 No No No No No Yes Yes Yes Yes Yes No No No No No Rule based None
13 12 Rule-Based Method and Deep Learning Networks for Automatic Classification of ECG 0.298 Sampling rate 500 Hz No At most N_max instances No No Yes Yes Yes Yes Yes No Yes Yes No No Rule CWT transforms signal to scalogram image
14 13 Convolutional Recurrent Neural Network and LightGBM Ensemble Model for 12-lead ECG Classification 0.281 Samping rate 500Hz; Bandwidth 0.01 - 20 Hz; Window size 10s No SMOTE No Yes Yes Yes Yes Yes Yes only age and sex Yes Yes Yes Yes CRNN + LightGBM None
15 14 12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes 0.279 Samping rate 100Hz, Window size 10 s; Median filter with 5 samples; Subtract mean of 50 samples No No Yes No Yes No No No No No Yes Yes No No CNN None
16 15 Cardiac Abnormality Detection in 12-lead ECGs With Deep Convolutional Neural Networks Using Data Augmentation 0.27 Sampling rate 500 Hz; Bandwidth 0.5 - 45 Hz; Uniform filter with 50 samples Signal slicing; CutOut; Channel shifting; Additive noise; Dropout No Yes No No No No No NO NO Yes Yes No No ResNet Data augmentation
17 16 A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis Using ECG Signal and Deep Learning 0.26 Cardiac cycle 400 samples; Bandwidth 3 - 45 Hz; No No Yes No No No No No No No Yes Yes LSTM No CNN+LSTM None
18 17 Multi-label Arrhythmia Classification From 12-Lead Electrocardiograms 0.24 Bandwidth 12 - 50 Hz; Median filter with 10ms; No Undersample the majority class No (Feature extraction) No Yes Yes Yes Yes Yes No Yes Yes Yes No AlexNet, LSTM, SVM… for different classes Feature Map Generation algorithm design
19 18 MADNN : A Multi-scale Attention Deep Neural Network for Arrhythmia Classification Data preprocessing 0.24 Sampling rate 500 Hz, Window size 30000(60 s) copy-padding Romdomly crop Focal loss; Balance factor to filter samples Yes VGG, Xception No No No No No No Yes Yes No Yes ResNet+SE+SK Data augmentation
20 19 Classification of 12-lead ECGs Using Gradient Boosting on Features Acquired With Domain-Specific and Domain-Agnostic Methods 0.23 Bandwidth 0-80 Hz; Median filter with 200 ms and 600 ms No Re-weight positive samples No (Feature extraction) No No Yes Yes Yes Yes No No No No No XGBoost Feature extraction
21 20 Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs 0.21 Samping rate 500Hz; Window size 5000(10s) 0-padding No No Yes No No Yes No No No only age and sex Yes Yes No No CNN+Rule-based model (HRV-score) RMSSD
22 21 Automated Classification of Electrocardiograms Using Wavelet Analysis and Deep Learning 0.21 No No Undersample the majority class No No Yes Yes Yes No Yes No Yes Yes No No SqueezeNet, 12 models for each lead with voting scheme None
23 22 ECG Abnormalities Recognition Using Convolutional Network With Global Skip Connections and Custom Loss Function 0.2 Sampling rate 125 Hz No WCE loss function Yes No No No No No No No Yes Yes No No ResNet+Custom Loss Function None
24 23 Multi-Class Classification of Pathologies Found on Short ECG Signals 0.19 Samping rate250Hz No No Yes No Yes Yes Yes Yes No No No No No No ECOC, Bagging None
25 24 ECG Classification with a Convolutional Recurrent Neural Network 0.17 Samping rate 257 Hz, Bandwidth 0.5- Hz Random offset Focal loss; Oversampling; MLSMOTE; An ensemble of binary and multi-label deep models Yes No No No No No No No Yes Yes GRU No CRNN Time Augmentation
26 25 Interpretable XGBoost Based Classification of 12-lead ECGs Applying Information Theory Measures From Neuroscience 0.16 Bandwidth 1.5-25 Hz; db4 No Undersample the majority class No (Feature extraction) No Yes Yes Yes Yes Yes known patient metadata. No No No No XGBoost None
27 26 Identification of Cardiac Arrhythmias from 12-lead ECG using Beat-wise Analysis and a Combination of CNN and LSTM 0.15 Sampling rate 500 Hz; Window size 10s 0-padding, Bandwith 0-50 Hz; MODWT; db5; LOESS No Select most significant beats number Yes No No No No No No No Yes Yes LSTM No CNN+BiLSTM None
28 27 Classification of 12-Lead Electrocardiograms Using Residual Neural Networks and Transfer Learning 0.14 Sampling rate 250Hz; Window size 10s With external data: MUSE No Yes No No No No No No No Yes Yes No No ResNet None
29 28 ECG Segmentation Using a Neural Network as the Basis for Detection of Cardiac Pathologies 0.14 Sampling rate 500 Hz; Bandwith 0.05-42 Hz; db4 No Focal loss No (Feature extraction) No Yes Yes Yes Yes No No Yes Yes No No Unet+XGBoost None
30 29 Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble 0.132 Sampling rate 400hz; Window size 4096 0-padding No Correlation factor Yes 7 convolutional models  No No No No No No Yes Yes No No ResNet None
31 30 Utilization of Residual CNN-GRU With Attention Mechanism for Classification of 12-lead ECG 0.122 Sampling rate 250 Hz Power envelopes No Yes Yes No No No No No No Yes Yes GRU No ResNet + RNN None
32 31 Selected Features for Classification of 12-lead ECGs 0.102 Bandwidth 1- Hz; Median filter No No No Bootstrap-aggregated No Yes Yes Yes No only age and sex No No No No Decision tree Featre extraction
33 32 Classification of 12 Lead ECG Signal Using 1D-Convolutional Neural Network With Class Dependent Threshold 0.077 Sampling rate 500 Hz; Window size 10000 copy-padding; Converted to physical unit(mV) No No Yes No Yes No No No No No Yes Yes No No CNN None
34 33 Deep Multi-Label Multi-Instance Classification on 12-Lead ECG 0.001 Bandwidth 0.5 - 50 Hz Randomly sampled BCELoss Yes No Yes No No No No No Yes Yes No Yes ResNet Encoder+attention-based MIC+Decoder None
35 34 Diagnostic of Multiple Cardiac Disorders from 12-lead ECGs Using Graph Convolutional Network Based Multi-label Classification -0.012 Sampling rate 500 Hz; Window size 18s 0-padding No No Yes Yes Yes No No No No No Yes Yes GRU No CNN+BiGRU+GCN CNN+BiGRU+GCN(GRN for label correlations)
36 35 Automatic Concurrent Arrhythmia Classification Using Deep Residual Neural Networks -0.035 Window size 2500 No No Yes Yes No No No No No No Yes Yes RNN GRU LSTM Yes ResNet None
37 36 Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble -0.08 Sampling rate 0-500 Hz; Window middle 2,000 samples(R:-0.35-0.5s); Average filter with 0.02s; No Oversampling; Re-weight No Yes Yes Yes Yes Yes No No No No No No XGboost None
38 37 A Topology Informed Random Forest Classifier for ECG Classification -0.113 1800 time points No No No No No Yes Yes Yes No demographic data No No No No random forest point cloud embedding
39 38 Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks -0.128 Window size 3000 0-padding No Thresholding Yes No No No No No No No Yes Yes No No VGG16 None
40 39 Multi-Stream Deep Neural Network For 12-Lead ECG Classification -0.29 Bandwidth 0.1-30 Hz No No No (Feature extraction) Yes No Yes Yes Yes No No Yes Yes No No CNN CNN+feature extraction
41 40 Classification of 12-lead ECG with an Ensemble Machine Learning Approach -0.476 Sampling rate 500 Hz; Bandwidth 0.67-30 Hz Vector magnitude for detect beats No No (Feature extraction) Yes Yes Yes Yes Yes No No Yes Yes No No CNN 3 CNNs for signal + FFNN for phythm + PVC detection
42 41 Classification of 12-lead ECGs using digital biomarkersand representation learning -0.658 Sampling rate 500 Hz; Bandwidth 0.13-88 Hz; bSQ>0.8; No Oversampling No Yes No Yes Yes Yes No only age and sex Yes Yes GRU No CNN+GRU feature engineering + CNN

@ -0,0 +1,35 @@
from langchain.llms import LlamaCpp
from langchain import PromptTemplate, LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
template = """Question: {question}
Answer: Let's work this out in a step by step way to be sure we have the right answer."""
prompt = PromptTemplate(template=template, input_variables=["question"])
# Callbacks support token-wise streaming
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
# Verbose is required to pass to the callback manager
n_gpu_layers = 1 # Metal set to 1 is enough.
n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.
# Make sure the model path is correct for your system!
llm = LlamaCpp(
model_path="ggml-model-q4_0.bin",
n_gpu_layers=n_gpu_layers,
n_ctx=2048,
ctx=2048,
n_batch=n_batch,
f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls
callback_manager=callback_manager,
verbose=True,
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.run(question)

@ -0,0 +1,9 @@
from langchain.llms import OpenAI
OPENAI_API = "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"
llm = OpenAI(model_name="text-davinci-003", openai_api_key=OPENAI_API)
msg = llm("explain large language models in one sentance")
print(msg)

@ -0,0 +1,30 @@
import numpy as np
import faiss
# Generate random vectors for demonstration
num_vectors = 1000
vector_dim = 128
np.random.seed(42)
vectors = np.random.rand(num_vectors, vector_dim).astype('float32')
# Indexing the vectors using Faiss
index = faiss.IndexFlatL2(vector_dim)
index.add(vectors)
# Querying the index
query_vector = np.random.rand(1, vector_dim).astype('float32') # Random query vector
k = 5 # Number of nearest neighbors to retrieve
distances, indices = index.search(query_vector, k)
print("Query Vector:")
print(query_vector)
print("Nearest Neighbors:")
print(vectors[indices[0]])
print("Distances:")
print(distances[0])
# pip install faiss-cpu # for CPU version
# or
# pip install faiss-gpu # for GPU version

@ -0,0 +1,29 @@
from langchain import SQLDatabase
from langchain.chat_models import ChatOpenAI
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from sqlalchemy import create_engine
OPENAI_API = "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"
engine = create_engine('sqlite:///events.db')
db = SQLDatabase(engine)
chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.2, openai_api_key=OPENAI_API)
toolkit = SQLDatabaseToolkit(db=db, llm=chat)
db_agent_toolkit = create_sql_agent(
llm=chat,
toolkit=toolkit,
max_iterations=3,
verbose=True
)
# ТУТ НАПИШИ СВОГО ТГ БОТА
YOUT_REQUEST = "get my events"
LLM_RESPONCE = db_agent_toolkit(YOUT_REQUEST)
print(LLM_RESPONCE)

@ -0,0 +1,17 @@
import pandas as pd
from langchain.chat_models import ChatOpenAI
from langchain.agents import create_pandas_dataframe_agent
OPENAI_API = "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"
df = pd.read_csv('./files/extracted_data.csv')
print(df)
chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0, openai_api_key=OPENAI_API)
agent = create_pandas_dataframe_agent(chat, df, verbose=True)
msg = agent.run("What anomalies exist in the data?")
print(msg)

@ -0,0 +1,18 @@
from langchain.llms import OpenAI
from langchain import PromptTemplate
OPENAI_API = "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"
template = """
You are an expert data scientist with an expertise in building deep learning models.
Explain the concept of {concept} in a couple of lines
"""
prompt = PromptTemplate(
input_variables=['concept'],
template=template,
)
llm = OpenAI(model_name="text-davinci-003", openai_api_key=OPENAI_API)
msg = llm(prompt.format(concept="regularization"))
print(msg)

@ -0,0 +1,26 @@
import sqlite3
# Connect to the SQLite database (creates the file if it doesn't exist)
conn = sqlite3.connect("events.db")
# Create a cursor object to execute SQL commands
cursor = conn.cursor()
# Define the SQL command to create the "events" table
create_table_query = """
CREATE TABLE IF NOT EXISTS events (
id INTEGER PRIMARY KEY,
name TEXT NOT NULL,
date TEXT NOT NULL,
location TEXT
);
"""
# Execute the SQL command to create the table
cursor.execute(create_table_query)
# Commit the changes and close the connection
conn.commit()
conn.close()
print("Database created with the 'events' table.")

@ -0,0 +1,86 @@
from langchain.llms import OpenAI
from langchain.schema import (AIMessage, HumanMessage, SystemMessage)
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate
from langchain.chains import LLMChain, SimpleSequentialChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
import faiss
import numpy as np
class LocalVectorDB:
def __init__(self):
self.index = None
self.vector_dim = None
def index_vectors(self, vectors):
self.vector_dim = vectors.shape[1]
self.index = faiss.IndexFlatL2(self.vector_dim)
self.index.add(vectors)
def search(self, query_vector, k):
distances, indices = self.index.search(np.array([query_vector], dtype=np.float32), k)
return distances[0], indices[0]
OPENAI_API = "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"
template = """
You are an expert data scientist with an expertise in building deep learning models.
Explain the concept of {concept} in a couple of lines
"""
prompt = PromptTemplate(
input_variables=['concept'],
template=template,
)
llm = OpenAI(model_name="text-davinci-003", openai_api_key=OPENAI_API)
# msg = llm("explain large language models in one sentance")
# msg = llm(prompt.format(concept="regularization"))
# print(msg)
chain = LLMChain(llm=llm, prompt=prompt)
print("First Chain: ", chain.run("autoencoder"))
second_prompt = PromptTemplate(
input_variables=["ml_concept"],
template="Turn the concept description of {ml_concept} and explain it to me like I'm five",
)
chain_two = LLMChain(llm=llm, prompt=second_prompt)
overall_chain = SimpleSequentialChain(chains=[chain, chain_two], verbose=True)
explanation = overall_chain.run("autoencoder")
print("Second Chain: ", explanation)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=100,
chunk_overlap=0,
)
texts = text_splitter.create_documents([explanation])
print("Page content: ", texts[0].page_content)
embeddings = OpenAIEmbeddings(model_name="ada", openai_api_key=OPENAI_API)
query_result = embeddings.embed_query(texts[0].page_content)
print("Vector Query Result: ", query_result)
# all_embeddings = []
# for text in texts:
# embedding = embeddings.embed_query(text.page_content)
# all_embeddings.append(embedding)
#
# all_embeddings = np.array(all_embeddings)
all_embeddings = []
all_embeddings.append(query_result)
all_embeddings = np.array(all_embeddings)
local_db = LocalVectorDB()
local_db.index_vectors(all_embeddings)
distances, indices = local_db.search(query_result, 5)
print(print(embeddings[indices]))
print(distances)

@ -0,0 +1,11 @@
/* eslint-disable */
export default {
displayName: 'py-tg-bot-weather-agent',
preset: '../../jest.preset.js',
testEnvironment: 'node',
transform: {
'^.+\\.[tj]s$': ['ts-jest', { tsconfig: '<rootDir>/tsconfig.spec.json' }],
},
moduleFileExtensions: ['ts', 'js', 'html', 'py'],
coverageDirectory: '../../coverage/apps/py-tg-bot-weather-agent',
};

@ -0,0 +1,133 @@
import pandas as pd
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType, AgentOutputParser
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish, OutputParserException
from langchain.tools.base import StructuredTool
from typing import Optional
import requests
import telebot
OPENAI_API = "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"
BOT_KEY = '6415742729:AAHVyDkHHF57ZsVd9gJjVtXjKE2M9CydzPk'
WELCOME_MSG = """"
Привет! Мы K-Lab, Команда, занимающаяся разработкой роботов 🤖 и машинным обучением и мы рады видеть тебя в нашей системе управления метеоданными!
Спроси что-нибудь у нашего бота 🙂
"""
# Weather AGW
AGW_PORT = 8045
AGW_HOST = 'localhost'
AGW_URL = f"http://{AGW_HOST}:{AGW_PORT}/"
bot = telebot.TeleBot(BOT_KEY)
def fetch_get_sensors(params={}):
try:
response = requests.post(AGW_URL + 'api/v1/sensors/get-with-params', json=params)
response.raise_for_status()
data = response.json()
return data
except requests.exceptions.RequestException as e:
print('Error fetching data:', e)
return None
def fetch_get_agregator(params={}):
try:
response = requests.post(AGW_URL + 'api/v1/agregator/get-with-params', json=params)
response.raise_for_status()
data = response.json()
return data
except requests.exceptions.RequestException as e:
print('Error fetching data:', e)
return None
def fetch_get_weather_data(params={}):
try:
response = requests.get(AGW_URL + 'api/v1/measures/get-for-ai')
response.raise_for_status()
data = response.json()
return data
except requests.exceptions.RequestException as e:
print('Error fetching data:', e)
return None
def get_sensors_insight() -> str:
"""Tool that using for get meteo weather sensors"""
data = fetch_get_sensors()
return data
def get_agregators_insight() -> str:
"""Tool that using for get meteo weather sensors"""
data = fetch_get_agregator()
return data
def get_weather_data_history_insight() -> str:
"""Tool that using for get meteo weather history from sensors using in the meteo system"""
data = fetch_get_weather_data()
return data
sensors_insights_tool = StructuredTool.from_function(get_sensors_insight)
agregators_insights_tool = StructuredTool.from_function(get_agregators_insight)
weather_history_insights_tool = StructuredTool.from_function(get_weather_data_history_insight)
chat = ChatOpenAI(model_name="gpt-3.5-turbo-16k", temperature=0.2, openai_api_key=OPENAI_API)
tools = [sensors_insights_tool, agregators_insights_tool, weather_history_insights_tool]
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
final_answer = llm_output.split("Final Answer:")[-1].strip()
print("final is - " + final_answer)
return AgentFinish(
return_values={"output": final_answer},
log=llm_output,
)
# Parse out the action and action input
regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
# Return the action and action input
return AgentAction(
tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output
)
output_parser = CustomOutputParser()
agent_chain = initialize_agent(
tools,
chat,
max_iterations=3,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
output_parser=output_parser
)
# print(get_weather_data_history_insight())
@bot.message_handler(commands=['start', 'hello'])
def send_welcome(message):
bot.reply_to(message, WELCOME_MSG)
@bot.message_handler(func=lambda msg: True)
def echo_all(message):
user_id = message.from_user.id
print(message.text)
bot.reply_to(message, "AI думает... 🤔")
result = agent_chain(message.text)
if (result):
final_answer = result['output']
print(final_answer)
bot.reply_to(message, str(final_answer))
bot.infinity_polling()

@ -0,0 +1,16 @@
{
"root": "apps/py-tg-bot-weather-agent",
"sourceRoot": "apps/py-tg-bot-weather-agent",
"$schema": "../../node_modules/nx/schemas/project-schema.json",
"projectType": "application",
"generators": {},
"name": "py-tg-bot-weather-agent",
"targets": {
"serve": {
"executor": "nx:run-commands",
"options": {
"command": "python3 apps/py-tg-bot-weather-agent/main.py"
}
}
}
}

@ -0,0 +1,198 @@
aiofiles==23.2.1
aiohttp==3.8.5
aiosignal==1.3.1
altair==5.0.1
anyio==3.7.1
appdirs==1.4.4
appnope==0.1.3
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
asttokens==2.2.1
async-lru==2.0.4
async-timeout==4.0.3
attrs==23.1.0
Babel==2.12.1
backcall==0.2.0
beautifulsoup4==4.12.2
biopython==1.81
bleach==6.0.0
bowler==0.9.0
certifi==2023.7.22
cffi==1.15.1
charset-normalizer==3.2.0
click==8.1.7
cloudpickle==2.2.1
colorama==0.4.6
comm==0.1.4
contourpy==1.1.0
cycler==0.11.0
Cython==3.0.2
dask==2023.8.0
dataclasses-json==0.6.1
debugpy==1.6.7.post1
decorator==5.1.1
defusedxml==0.7.1
dill==0.3.7
executing==1.2.0
fastapi==0.101.1
fastavro==1.8.2
fastjsonschema==2.18.0
feast==0.33.1
ffmpy==0.3.1
filelock==3.12.2
fisher==0.1.10
fissix==21.11.13
fonttools==4.42.0
fqdn==1.5.1
frozenlist==1.4.0
fsspec==2023.6.0
gradio==3.40.1
gradio_client==0.4.0
grpcio==1.57.0
grpcio-reflection==1.57.0
gunicorn==21.2.0
h11==0.14.0
httpcore==0.17.3
httptools==0.6.0
httpx==0.24.1
huggingface-hub==0.16.4
idna==3.4
importlib-metadata==6.8.0
importlib-resources==6.0.1
ipykernel==6.25.1
ipython==8.14.0
ipywidgets==8.1.0
isoduration==20.11.0
jedi==0.19.0
Jinja2==3.1.2
json5==0.9.14
jsonpatch==1.33
jsonpointer==2.4
jsonschema==4.19.0
jsonschema-specifications==2023.7.1
jupyter-events==0.7.0
jupyter-lsp==2.2.0
jupyter_client==8.3.0
jupyter_core==5.3.1
jupyter_server==2.7.2
jupyter_server_terminals==0.4.4
jupyterlab==4.0.5
jupyterlab-pygments==0.2.2
jupyterlab-widgets==3.0.8
jupyterlab_server==2.24.0
kiwisolver==1.4.4
langchain==0.0.304
langsmith==0.0.41
linkify-it-py==2.0.2
locket==1.0.0
Markdown==3.4.4
markdown-it-py==2.2.0
MarkupSafe==2.1.3
marshmallow==3.20.1
matplotlib==3.7.2
matplotlib-inline==0.1.6
mdit-py-plugins==0.3.3
mdurl==0.1.2
methplotlib==0.20.1
mistune==3.0.1
mmh3==4.0.1
moreorless==0.4.0
mpmath==1.3.0
multidict==6.0.4
mypy==1.5.1
mypy-extensions==1.0.0
natsort==8.4.0
nbclient==0.8.0
nbconvert==7.7.4
nbformat==5.9.2
ncls==0.0.68
nest-asyncio==1.5.7
notebook==7.0.2
notebook_shim==0.2.3
numexpr==2.8.7
numpy==1.25.2
openai==0.28.1
orjson==3.9.5
overrides==7.4.0
packaging @ file:///private/tmp/sip--packaging-20230802-5907-msmw2b/packaging-23.1
pandas==1.5.3
pandavro==1.5.2
pandocfilters==1.5.0
parso==0.8.3
partd==1.4.0
pexpect==4.8.0
pickleshare==0.7.5
Pillow==10.0.0
platformdirs==3.10.0
plotly==5.16.1
ply @ file:///private/tmp/sip--ply-20230802-5907-1daiooy/ply-3.11
prometheus-client==0.17.1
prompt-toolkit==3.0.39
proto-plus==1.22.3
protobuf==4.24.0
psutil==5.9.5
ptyprocess==0.7.0
pure-eval==0.2.2
pyarrow==11.0.0
pycparser==2.21
pydantic==1.10.12
pydub==0.25.1
pyfaidx==0.7.2.1
Pygments==2.16.1
pyparsing==3.0.9
pyranges==0.0.129
pysam==0.21.0
pyTelegramBotAPI==4.14.0
python-dateutil==2.8.2
python-dotenv==1.0.0
python-json-logger==2.0.7
python-multipart==0.0.6
pytz==2023.3
PyYAML==6.0.1
pyzmq==25.1.1
referencing==0.30.2
requests==2.31.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rpds-py==0.9.2
semantic-version==2.10.0
Send2Trash==1.8.2
sip @ file:///private/tmp/sip-20230802-5907-xvci02/sip-6.7.11
six==1.16.0
sklearn==0.0.post9
sniffio==1.3.0
sorted-nearest==0.0.39
soupsieve==2.4.1
SQLAlchemy==1.4.49
sqlalchemy2-stubs==0.0.2a35
stack-data==0.6.2
starlette==0.27.0
sympy==1.12
tabulate==0.9.0
tenacity==8.2.3
terminado==0.17.1
tinycss2==1.2.1
toml==0.10.2
toolz==0.12.0
tornado==6.3.3
tqdm==4.66.1
traitlets==5.9.0
typeguard==2.13.3
typing-inspect==0.9.0
typing_extensions==4.7.1
uc-micro-py==1.0.2
uri-template==1.3.0
urllib3==2.0.4
uvicorn==0.23.2
uvloop==0.17.0
volatile==2.1.0
watchfiles==0.19.0
wcwidth==0.2.6
webcolors==1.13
webencodings==0.5.1
websocket-client==1.6.1
websockets==11.0.3
widgetsnbextension==4.0.8
yarl==1.9.2
zipp==3.16.2

@ -0,0 +1,241 @@
<#
.Synopsis
Activate a Python virtual environment for the current PowerShell session.
.Description
Pushes the python executable for a virtual environment to the front of the
$Env:PATH environment variable and sets the prompt to signify that you are
in a Python virtual environment. Makes use of the command line switches as
well as the `pyvenv.cfg` file values present in the virtual environment.
.Parameter VenvDir
Path to the directory that contains the virtual environment to activate. The
default value for this is the parent of the directory that the Activate.ps1
script is located within.
.Parameter Prompt
The prompt prefix to display when this virtual environment is activated. By
default, this prompt is the name of the virtual environment folder (VenvDir)
surrounded by parentheses and followed by a single space (ie. '(.venv) ').
.Example
Activate.ps1
Activates the Python virtual environment that contains the Activate.ps1 script.
.Example
Activate.ps1 -Verbose
Activates the Python virtual environment that contains the Activate.ps1 script,
and shows extra information about the activation as it executes.
.Example
Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv
Activates the Python virtual environment located in the specified location.
.Example
Activate.ps1 -Prompt "MyPython"
Activates the Python virtual environment that contains the Activate.ps1 script,
and prefixes the current prompt with the specified string (surrounded in
parentheses) while the virtual environment is active.
.Notes
On Windows, it may be required to enable this Activate.ps1 script by setting the
execution policy for the user. You can do this by issuing the following PowerShell
command:
PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
For more information on Execution Policies:
https://go.microsoft.com/fwlink/?LinkID=135170
#>
Param(
[Parameter(Mandatory = $false)]
[String]
$VenvDir,
[Parameter(Mandatory = $false)]
[String]
$Prompt
)
<# Function declarations --------------------------------------------------- #>
<#
.Synopsis
Remove all shell session elements added by the Activate script, including the
addition of the virtual environment's Python executable from the beginning of
the PATH variable.
.Parameter NonDestructive
If present, do not remove this function from the global namespace for the
session.
#>
function global:deactivate ([switch]$NonDestructive) {
# Revert to original values
# The prior prompt:
if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) {
Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt
Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT
}
# The prior PYTHONHOME:
if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) {
Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME
Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME
}
# The prior PATH:
if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) {
Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH
Remove-Item -Path Env:_OLD_VIRTUAL_PATH
}
# Just remove the VIRTUAL_ENV altogether:
if (Test-Path -Path Env:VIRTUAL_ENV) {
Remove-Item -Path env:VIRTUAL_ENV
}
# Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether:
if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) {
Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force
}
# Leave deactivate function in the global namespace if requested:
if (-not $NonDestructive) {
Remove-Item -Path function:deactivate
}
}
<#
.Description
Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the
given folder, and returns them in a map.
For each line in the pyvenv.cfg file, if that line can be parsed into exactly
two strings separated by `=` (with any amount of whitespace surrounding the =)
then it is considered a `key = value` line. The left hand string is the key,
the right hand is the value.
If the value starts with a `'` or a `"` then the first and last character is
stripped from the value before being captured.
.Parameter ConfigDir
Path to the directory that contains the `pyvenv.cfg` file.
#>
function Get-PyVenvConfig(
[String]
$ConfigDir
) {
Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg"
# Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue).
$pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue
# An empty map will be returned if no config file is found.
$pyvenvConfig = @{ }
if ($pyvenvConfigPath) {
Write-Verbose "File exists, parse `key = value` lines"
$pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath
$pyvenvConfigContent | ForEach-Object {
$keyval = $PSItem -split "\s*=\s*", 2
if ($keyval[0] -and $keyval[1]) {
$val = $keyval[1]
# Remove extraneous quotations around a string value.
if ("'""".Contains($val.Substring(0, 1))) {
$val = $val.Substring(1, $val.Length - 2)
}
$pyvenvConfig[$keyval[0]] = $val
Write-Verbose "Adding Key: '$($keyval[0])'='$val'"
}
}
}
return $pyvenvConfig
}
<# Begin Activate script --------------------------------------------------- #>
# Determine the containing directory of this script
$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition
$VenvExecDir = Get-Item -Path $VenvExecPath
Write-Verbose "Activation script is located in path: '$VenvExecPath'"
Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)"
Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)"
# Set values required in priority: CmdLine, ConfigFile, Default
# First, get the location of the virtual environment, it might not be
# VenvExecDir if specified on the command line.
if ($VenvDir) {
Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values"
}
else {
Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir."
$VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/")
Write-Verbose "VenvDir=$VenvDir"
}
# Next, read the `pyvenv.cfg` file to determine any required value such
# as `prompt`.
$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir
# Next, set the prompt from the command line, or the config file, or
# just use the name of the virtual environment folder.
if ($Prompt) {
Write-Verbose "Prompt specified as argument, using '$Prompt'"
}
else {
Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value"
if ($pyvenvCfg -and $pyvenvCfg['prompt']) {
Write-Verbose " Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'"
$Prompt = $pyvenvCfg['prompt'];
}
else {
Write-Verbose " Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virutal environment)"
Write-Verbose " Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'"
$Prompt = Split-Path -Path $venvDir -Leaf
}
}
Write-Verbose "Prompt = '$Prompt'"
Write-Verbose "VenvDir='$VenvDir'"
# Deactivate any currently active virtual environment, but leave the
# deactivate function in place.
deactivate -nondestructive
# Now set the environment variable VIRTUAL_ENV, used by many tools to determine
# that there is an activated venv.
$env:VIRTUAL_ENV = $VenvDir
if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) {
Write-Verbose "Setting prompt to '$Prompt'"
# Set the prompt to include the env name
# Make sure _OLD_VIRTUAL_PROMPT is global
function global:_OLD_VIRTUAL_PROMPT { "" }
Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT
New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt
function global:prompt {
Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) "
_OLD_VIRTUAL_PROMPT
}
}
# Clear PYTHONHOME
if (Test-Path -Path Env:PYTHONHOME) {
Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME
Remove-Item -Path Env:PYTHONHOME
}
# Add the venv to the PATH
Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH
$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"

@ -0,0 +1,66 @@
# This file must be used with "source bin/activate" *from bash*
# you cannot run it directly
deactivate () {
# reset old environment variables
if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then
PATH="${_OLD_VIRTUAL_PATH:-}"
export PATH
unset _OLD_VIRTUAL_PATH
fi
if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then
PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}"
export PYTHONHOME
unset _OLD_VIRTUAL_PYTHONHOME
fi
# This should detect bash and zsh, which have a hash command that must
# be called to get it to forget past commands. Without forgetting
# past commands the $PATH changes we made may not be respected
if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
hash -r 2> /dev/null
fi
if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then
PS1="${_OLD_VIRTUAL_PS1:-}"
export PS1
unset _OLD_VIRTUAL_PS1
fi
unset VIRTUAL_ENV
if [ ! "${1:-}" = "nondestructive" ] ; then
# Self destruct!
unset -f deactivate
fi
}
# unset irrelevant variables
deactivate nondestructive
VIRTUAL_ENV="/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv"
export VIRTUAL_ENV
_OLD_VIRTUAL_PATH="$PATH"
PATH="$VIRTUAL_ENV/bin:$PATH"
export PATH
# unset PYTHONHOME if set
# this will fail if PYTHONHOME is set to the empty string (which is bad anyway)
# could use `if (set -u; : $PYTHONHOME) ;` in bash
if [ -n "${PYTHONHOME:-}" ] ; then
_OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}"
unset PYTHONHOME
fi
if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then
_OLD_VIRTUAL_PS1="${PS1:-}"
PS1="(venv) ${PS1:-}"
export PS1
fi
# This should detect bash and zsh, which have a hash command that must
# be called to get it to forget past commands. Without forgetting
# past commands the $PATH changes we made may not be respected
if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then
hash -r 2> /dev/null
fi

@ -0,0 +1,25 @@
# This file must be used with "source bin/activate.csh" *from csh*.
# You cannot run it directly.
# Created by Davide Di Blasi <davidedb@gmail.com>.
# Ported to Python 3.3 venv by Andrew Svetlov <andrew.svetlov@gmail.com>
alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; test "\!:*" != "nondestructive" && unalias deactivate'
# Unset irrelevant variables.
deactivate nondestructive
setenv VIRTUAL_ENV "/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv"
set _OLD_VIRTUAL_PATH="$PATH"
setenv PATH "$VIRTUAL_ENV/bin:$PATH"
set _OLD_VIRTUAL_PROMPT="$prompt"
if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then
set prompt = "(venv) $prompt"
endif
alias pydoc python -m pydoc
rehash

@ -0,0 +1,64 @@
# This file must be used with "source <venv>/bin/activate.fish" *from fish*
# (https://fishshell.com/); you cannot run it directly.
function deactivate -d "Exit virtual environment and return to normal shell environment"
# reset old environment variables
if test -n "$_OLD_VIRTUAL_PATH"
set -gx PATH $_OLD_VIRTUAL_PATH
set -e _OLD_VIRTUAL_PATH
end
if test -n "$_OLD_VIRTUAL_PYTHONHOME"
set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME
set -e _OLD_VIRTUAL_PYTHONHOME
end
if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
functions -e fish_prompt
set -e _OLD_FISH_PROMPT_OVERRIDE
functions -c _old_fish_prompt fish_prompt
functions -e _old_fish_prompt
end
set -e VIRTUAL_ENV
if test "$argv[1]" != "nondestructive"
# Self-destruct!
functions -e deactivate
end
end
# Unset irrelevant variables.
deactivate nondestructive
set -gx VIRTUAL_ENV "/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv"
set -gx _OLD_VIRTUAL_PATH $PATH
set -gx PATH "$VIRTUAL_ENV/bin" $PATH
# Unset PYTHONHOME if set.
if set -q PYTHONHOME
set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
set -e PYTHONHOME
end
if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
# fish uses a function instead of an env var to generate the prompt.
# Save the current fish_prompt function as the function _old_fish_prompt.
functions -c fish_prompt _old_fish_prompt
# With the original prompt function renamed, we can override with our own.
function fish_prompt
# Save the return status of the last command.
set -l old_status $status
# Output the venv prompt; color taken from the blue of the Python logo.
printf "%s%s%s" (set_color 4B8BBE) "(venv) " (set_color normal)
# Restore the return status of the previous command.
echo "exit $old_status" | .
# Output the original/"old" prompt.
_old_fish_prompt
end
set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
end

@ -0,0 +1,134 @@
#!/usr/bin/env python
import json
import os
import re
import struct
import sys
from typing import Any, Dict, Sequence, TextIO
import torch
from convert import DATA_TYPE_TO_FTYPE, NUMPY_TYPE_TO_DATA_TYPE, DataType
HF_SUBLAYER_TO_GGML = {
"self_attn.q_proj": "attention.wq",
"self_attn.k_proj": "attention.wk",
"self_attn.v_proj": "attention.wv",
"self_attn.o_proj": "attention.wo",
"mlp.gate_proj": "feed_forward.w1",
"mlp.down_proj": "feed_forward.w2",
"mlp.up_proj": "feed_forward.w3",
"input_layernorm": "attention_norm",
"post_attention_layernorm": "ffn_norm",
# "norm": "norm",
# "embed_tokens": "tok_embeddings",
# "lm_head": "output",
}
def translate_tensor_name(t: str) -> str:
match = re.match(r".*layers\.(\d+)\.(\w+\.\w+)\.lora_(A|B)\.weight", t)
if match:
nn = match.group(1)
sub_layer = match.group(2)
lora_type = match.group(3)
sub_layer_renamed = HF_SUBLAYER_TO_GGML.get(sub_layer)
if sub_layer_renamed is None:
print(f"Error: unrecognized sub-layer {sub_layer} in tensor {t}")
sys.exit(1)
output_string = (
f"layers.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
)
return output_string
else:
print(f"Error: unrecognized tensor {t}")
sys.exit(1)
def write_file_header(fout: TextIO, params: Dict[str, Any]) -> None:
fout.write(b"ggla"[::-1]) # magic (ggml lora)
fout.write(struct.pack("i", 1)) # file version
fout.write(struct.pack("i", params["r"]))
# https://opendelta.readthedocs.io/en/latest/modules/deltas.html says that `lora_alpha` is an int
# but some models ship a float value instead
# let's convert to int, but fail if lossless conversion is not possible
assert int(params["lora_alpha"]) == params["lora_alpha"], "cannot convert float to int losslessly"
fout.write(struct.pack("i", int(params["lora_alpha"])))
def write_tensor_header(
self, name: str, shape: Sequence[int], data_type: DataType
) -> None:
sname = name.encode("utf-8")
fout.write(
struct.pack(
"iii",
len(shape),
len(sname),
DATA_TYPE_TO_FTYPE[NUMPY_TYPE_TO_DATA_TYPE[data_type]],
)
)
fout.write(struct.pack("i" * len(shape), *shape[::-1]))
fout.write(sname)
fout.seek((fout.tell() + 31) & -32)
if len(sys.argv) != 2:
print(f"Usage: python {sys.argv[0]} <path>")
print(
"Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
)
sys.exit(1)
input_json = os.path.join(sys.argv[1], "adapter_config.json")
input_model = os.path.join(sys.argv[1], "adapter_model.bin")
output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
model = torch.load(input_model, map_location="cpu")
with open(input_json, "r") as f:
params = json.load(f)
if params["peft_type"] != "LORA":
print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
sys.exit(1)
if params["fan_in_fan_out"] is True:
print("Error: param fan_in_fan_out is not supported")
sys.exit(1)
if params["bias"] is not None and params["bias"] != "none":
print("Error: param bias is not supported")
sys.exit(1)
# TODO: these seem to be layers that have been trained but without lora.
# doesn't seem widely used but eventually should be supported
if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
print("Error: param modules_to_save is not supported")
sys.exit(1)
with open(output_path, "wb") as fout:
fout.truncate()
write_file_header(fout, params)
for k, v in model.items():
if k.endswith(".default.weight"):
k = k.replace(".default.weight", ".weight")
if k in ["llama_proj.weight", "llama_proj.bias"]:
continue
if k.endswith("lora_A.weight"):
if v.dtype != torch.float16 and v.dtype != torch.float32:
v = v.float()
v = v.T
else:
v = v.float()
t = v.detach().numpy()
tname = translate_tensor_name(k)
print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
write_tensor_header(fout, tname, t.shape, t.dtype)
t.tofile(fout)
print(f"Converted {input_json} and {input_model} to {output_path}")

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from numpy.f2py.f2py2e import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from numpy.f2py.f2py2e import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from numpy.f2py.f2py2e import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from langchain.server import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from langsmith.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from charset_normalizer.cli.normalizer import cli_detect
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(cli_detect())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from openai._openai_scripts import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pip._internal.cli.main import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from pyllamacpp.cli import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,8 @@
#!/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from tqdm.cli import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())

@ -0,0 +1,20 @@
Copyright (c) 2017-2021 Ingy döt Net
Copyright (c) 2006-2016 Kirill Simonov
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
of the Software, and to permit persons to whom the Software is furnished to do
so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

@ -0,0 +1,46 @@
Metadata-Version: 2.1
Name: PyYAML
Version: 6.0.1
Summary: YAML parser and emitter for Python
Home-page: https://pyyaml.org/
Download-URL: https://pypi.org/project/PyYAML/
Author: Kirill Simonov
Author-email: xi@resolvent.net
License: MIT
Project-URL: Bug Tracker, https://github.com/yaml/pyyaml/issues
Project-URL: CI, https://github.com/yaml/pyyaml/actions
Project-URL: Documentation, https://pyyaml.org/wiki/PyYAMLDocumentation
Project-URL: Mailing lists, http://lists.sourceforge.net/lists/listinfo/yaml-core
Project-URL: Source Code, https://github.com/yaml/pyyaml
Platform: Any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Text Processing :: Markup
Requires-Python: >=3.6
License-File: LICENSE
YAML is a data serialization format designed for human readability
and interaction with scripting languages. PyYAML is a YAML parser
and emitter for Python.
PyYAML features a complete YAML 1.1 parser, Unicode support, pickle
support, capable extension API, and sensible error messages. PyYAML
supports standard YAML tags and provides Python-specific tags that
allow to represent an arbitrary Python object.
PyYAML is applicable for a broad range of tasks from complex
configuration files to object serialization and persistence.

@ -0,0 +1,43 @@
../../../../../../../../../Library/Caches/com.apple.python/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/lib/python3.9/site-packages/_yaml/__init__.cpython-39.pyc,,
../../../../../../../../../Library/Caches/com.apple.python/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/lib/python3.9/site-packages/yaml/__init__.cpython-39.pyc,,
../../../../../../../../../Library/Caches/com.apple.python/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/lib/python3.9/site-packages/yaml/composer.cpython-39.pyc,,
../../../../../../../../../Library/Caches/com.apple.python/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/lib/python3.9/site-packages/yaml/constructor.cpython-39.pyc,,
../../../../../../../../../Library/Caches/com.apple.python/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/lib/python3.9/site-packages/yaml/cyaml.cpython-39.pyc,,
../../../../../../../../../Library/Caches/com.apple.python/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/lib/python3.9/site-packages/yaml/dumper.cpython-39.pyc,,
../../../../../../../../../Library/Caches/com.apple.python/Users/darius/Desktop/ASC/onpu/apps/py-multy-agent-interpreter/venv/lib/python3.9/site-packages/yaml/emitter.cpython-39.pyc,,
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Copyright 2005-2023 SQLAlchemy authors and contributors <see AUTHORS file>.
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
of the Software, and to permit persons to whom the Software is furnished to do
so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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Name: SQLAlchemy
Version: 2.0.19
Summary: Database Abstraction Library
Home-page: https://www.sqlalchemy.org
Author: Mike Bayer
Author-email: mike_mp@zzzcomputing.com
License: MIT
Project-URL: Documentation, https://docs.sqlalchemy.org
Project-URL: Issue Tracker, https://github.com/sqlalchemy/sqlalchemy/
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Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
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Classifier: Programming Language :: Python :: 3
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SQLAlchemy
==========
|PyPI| |Python| |Downloads|
.. |PyPI| image:: https://img.shields.io/pypi/v/sqlalchemy
:target: https://pypi.org/project/sqlalchemy
:alt: PyPI
.. |Python| image:: https://img.shields.io/pypi/pyversions/sqlalchemy
:target: https://pypi.org/project/sqlalchemy
:alt: PyPI - Python Version
.. |Downloads| image:: https://img.shields.io/pypi/dm/sqlalchemy
:target: https://pypi.org/project/sqlalchemy
:alt: PyPI - Downloads
The Python SQL Toolkit and Object Relational Mapper
Introduction
-------------
SQLAlchemy is the Python SQL toolkit and Object Relational Mapper
that gives application developers the full power and
flexibility of SQL. SQLAlchemy provides a full suite
of well known enterprise-level persistence patterns,
designed for efficient and high-performing database
access, adapted into a simple and Pythonic domain
language.
Major SQLAlchemy features include:
* An industrial strength ORM, built
from the core on the identity map, unit of work,
and data mapper patterns. These patterns
allow transparent persistence of objects
using a declarative configuration system.
Domain models
can be constructed and manipulated naturally,
and changes are synchronized with the
current transaction automatically.
* A relationally-oriented query system, exposing
the full range of SQL's capabilities
explicitly, including joins, subqueries,
correlation, and most everything else,
in terms of the object model.
Writing queries with the ORM uses the same
techniques of relational composition you use
when writing SQL. While you can drop into
literal SQL at any time, it's virtually never
needed.
* A comprehensive and flexible system
of eager loading for related collections and objects.
Collections are cached within a session,
and can be loaded on individual access, all
at once using joins, or by query per collection
across the full result set.
* A Core SQL construction system and DBAPI
interaction layer. The SQLAlchemy Core is
separate from the ORM and is a full database
abstraction layer in its own right, and includes
an extensible Python-based SQL expression
language, schema metadata, connection pooling,
type coercion, and custom types.
* All primary and foreign key constraints are
assumed to be composite and natural. Surrogate
integer primary keys are of course still the
norm, but SQLAlchemy never assumes or hardcodes
to this model.
* Database introspection and generation. Database
schemas can be "reflected" in one step into
Python structures representing database metadata;
those same structures can then generate
CREATE statements right back out - all within
the Core, independent of the ORM.
SQLAlchemy's philosophy:
* SQL databases behave less and less like object
collections the more size and performance start to
matter; object collections behave less and less like
tables and rows the more abstraction starts to matter.
SQLAlchemy aims to accommodate both of these
principles.
* An ORM doesn't need to hide the "R". A relational
database provides rich, set-based functionality
that should be fully exposed. SQLAlchemy's
ORM provides an open-ended set of patterns
that allow a developer to construct a custom
mediation layer between a domain model and
a relational schema, turning the so-called
"object relational impedance" issue into
a distant memory.
* The developer, in all cases, makes all decisions
regarding the design, structure, and naming conventions
of both the object model as well as the relational
schema. SQLAlchemy only provides the means
to automate the execution of these decisions.
* With SQLAlchemy, there's no such thing as
"the ORM generated a bad query" - you
retain full control over the structure of
queries, including how joins are organized,
how subqueries and correlation is used, what
columns are requested. Everything SQLAlchemy
does is ultimately the result of a developer-initiated
decision.
* Don't use an ORM if the problem doesn't need one.
SQLAlchemy consists of a Core and separate ORM
component. The Core offers a full SQL expression
language that allows Pythonic construction
of SQL constructs that render directly to SQL
strings for a target database, returning
result sets that are essentially enhanced DBAPI
cursors.
* Transactions should be the norm. With SQLAlchemy's
ORM, nothing goes to permanent storage until
commit() is called. SQLAlchemy encourages applications
to create a consistent means of delineating
the start and end of a series of operations.
* Never render a literal value in a SQL statement.
Bound parameters are used to the greatest degree
possible, allowing query optimizers to cache
query plans effectively and making SQL injection
attacks a non-issue.
Documentation
-------------
Latest documentation is at:
https://www.sqlalchemy.org/docs/
Installation / Requirements
---------------------------
Full documentation for installation is at
`Installation <https://www.sqlalchemy.org/docs/intro.html#installation>`_.
Getting Help / Development / Bug reporting
------------------------------------------
Please refer to the `SQLAlchemy Community Guide <https://www.sqlalchemy.org/support.html>`_.
Code of Conduct
---------------
Above all, SQLAlchemy places great emphasis on polite, thoughtful, and
constructive communication between users and developers.
Please see our current Code of Conduct at
`Code of Conduct <https://www.sqlalchemy.org/codeofconduct.html>`_.
License
-------
SQLAlchemy is distributed under the `MIT license
<https://www.opensource.org/licenses/mit-license.php>`_.

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@ -0,0 +1,5 @@
Wheel-Version: 1.0
Generator: bdist_wheel (0.40.0)
Root-Is-Purelib: false
Tag: cp39-cp39-macosx_11_0_arm64

@ -0,0 +1,128 @@
import sys
import os
import re
import importlib
import warnings
is_pypy = '__pypy__' in sys.builtin_module_names
warnings.filterwarnings('ignore',
r'.+ distutils\b.+ deprecated',
DeprecationWarning)
def warn_distutils_present():
if 'distutils' not in sys.modules:
return
if is_pypy and sys.version_info < (3, 7):
# PyPy for 3.6 unconditionally imports distutils, so bypass the warning
# https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
return
warnings.warn(
"Distutils was imported before Setuptools, but importing Setuptools "
"also replaces the `distutils` module in `sys.modules`. This may lead "
"to undesirable behaviors or errors. To avoid these issues, avoid "
"using distutils directly, ensure that setuptools is installed in the "
"traditional way (e.g. not an editable install), and/or make sure "
"that setuptools is always imported before distutils.")
def clear_distutils():
if 'distutils' not in sys.modules:
return
warnings.warn("Setuptools is replacing distutils.")
mods = [name for name in sys.modules if re.match(r'distutils\b', name)]
for name in mods:
del sys.modules[name]
def enabled():
"""
Allow selection of distutils by environment variable.
"""
which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'stdlib')
return which == 'local'
def ensure_local_distutils():
clear_distutils()
distutils = importlib.import_module('setuptools._distutils')
distutils.__name__ = 'distutils'
sys.modules['distutils'] = distutils
# sanity check that submodules load as expected
core = importlib.import_module('distutils.core')
assert '_distutils' in core.__file__, core.__file__
def do_override():
"""
Ensure that the local copy of distutils is preferred over stdlib.
See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
for more motivation.
"""
if enabled():
warn_distutils_present()
ensure_local_distutils()
class DistutilsMetaFinder:
def find_spec(self, fullname, path, target=None):
if path is not None:
return
method_name = 'spec_for_{fullname}'.format(**locals())
method = getattr(self, method_name, lambda: None)
return method()
def spec_for_distutils(self):
import importlib.abc
import importlib.util
class DistutilsLoader(importlib.abc.Loader):
def create_module(self, spec):
return importlib.import_module('setuptools._distutils')
def exec_module(self, module):
pass
return importlib.util.spec_from_loader('distutils', DistutilsLoader())
def spec_for_pip(self):
"""
Ensure stdlib distutils when running under pip.
See pypa/pip#8761 for rationale.
"""
if self.pip_imported_during_build():
return
clear_distutils()
self.spec_for_distutils = lambda: None
@staticmethod
def pip_imported_during_build():
"""
Detect if pip is being imported in a build script. Ref #2355.
"""
import traceback
return any(
frame.f_globals['__file__'].endswith('setup.py')
for frame, line in traceback.walk_stack(None)
)
DISTUTILS_FINDER = DistutilsMetaFinder()
def add_shim():
sys.meta_path.insert(0, DISTUTILS_FINDER)
def remove_shim():
try:
sys.meta_path.remove(DISTUTILS_FINDER)
except ValueError:
pass

@ -0,0 +1,33 @@
# This is a stub package designed to roughly emulate the _yaml
# extension module, which previously existed as a standalone module
# and has been moved into the `yaml` package namespace.
# It does not perfectly mimic its old counterpart, but should get
# close enough for anyone who's relying on it even when they shouldn't.
import yaml
# in some circumstances, the yaml module we imoprted may be from a different version, so we need
# to tread carefully when poking at it here (it may not have the attributes we expect)
if not getattr(yaml, '__with_libyaml__', False):
from sys import version_info
exc = ModuleNotFoundError if version_info >= (3, 6) else ImportError
raise exc("No module named '_yaml'")
else:
from yaml._yaml import *
import warnings
warnings.warn(
'The _yaml extension module is now located at yaml._yaml'
' and its location is subject to change. To use the'
' LibYAML-based parser and emitter, import from `yaml`:'
' `from yaml import CLoader as Loader, CDumper as Dumper`.',
DeprecationWarning
)
del warnings
# Don't `del yaml` here because yaml is actually an existing
# namespace member of _yaml.
__name__ = '_yaml'
# If the module is top-level (i.e. not a part of any specific package)
# then the attribute should be set to ''.
# https://docs.python.org/3.8/library/types.html
__package__ = ''

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Copyright aio-libs contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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Metadata-Version: 2.1
Name: aiohttp
Version: 3.8.5
Summary: Async http client/server framework (asyncio)
Home-page: https://github.com/aio-libs/aiohttp
Maintainer: aiohttp team <team@aiohttp.org>
Maintainer-email: team@aiohttp.org
License: Apache 2
Project-URL: Chat: Matrix, https://matrix.to/#/#aio-libs:matrix.org
Project-URL: Chat: Matrix Space, https://matrix.to/#/#aio-libs-space:matrix.org
Project-URL: CI: GitHub Actions, https://github.com/aio-libs/aiohttp/actions?query=workflow%3ACI
Project-URL: Coverage: codecov, https://codecov.io/github/aio-libs/aiohttp
Project-URL: Docs: Changelog, https://docs.aiohttp.org/en/stable/changes.html
Project-URL: Docs: RTD, https://docs.aiohttp.org
Project-URL: GitHub: issues, https://github.com/aio-libs/aiohttp/issues
Project-URL: GitHub: repo, https://github.com/aio-libs/aiohttp
Classifier: Development Status :: 5 - Production/Stable
Classifier: Framework :: AsyncIO
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Internet :: WWW/HTTP
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
License-File: LICENSE.txt
Requires-Dist: attrs (>=17.3.0)
Requires-Dist: charset-normalizer (<4.0,>=2.0)
Requires-Dist: multidict (<7.0,>=4.5)
Requires-Dist: async-timeout (<5.0,>=4.0.0a3)
Requires-Dist: yarl (<2.0,>=1.0)
Requires-Dist: frozenlist (>=1.1.1)
Requires-Dist: aiosignal (>=1.1.2)
Requires-Dist: idna-ssl (>=1.0) ; python_version < "3.7"
Requires-Dist: asynctest (==0.13.0) ; python_version < "3.8"
Requires-Dist: typing-extensions (>=3.7.4) ; python_version < "3.8"
Provides-Extra: speedups
Requires-Dist: aiodns ; extra == 'speedups'
Requires-Dist: Brotli ; extra == 'speedups'
Requires-Dist: cchardet ; (python_version < "3.10") and extra == 'speedups'
==================================
Async http client/server framework
==================================
.. image:: https://raw.githubusercontent.com/aio-libs/aiohttp/master/docs/aiohttp-plain.svg
:height: 64px
:width: 64px
:alt: aiohttp logo
|
.. image:: https://github.com/aio-libs/aiohttp/workflows/CI/badge.svg
:target: https://github.com/aio-libs/aiohttp/actions?query=workflow%3ACI
:alt: GitHub Actions status for master branch
.. image:: https://codecov.io/gh/aio-libs/aiohttp/branch/master/graph/badge.svg
:target: https://codecov.io/gh/aio-libs/aiohttp
:alt: codecov.io status for master branch
.. image:: https://badge.fury.io/py/aiohttp.svg
:target: https://pypi.org/project/aiohttp
:alt: Latest PyPI package version
.. image:: https://readthedocs.org/projects/aiohttp/badge/?version=latest
:target: https://docs.aiohttp.org/
:alt: Latest Read The Docs
.. image:: https://img.shields.io/matrix/aio-libs:matrix.org?label=Discuss%20on%20Matrix%20at%20%23aio-libs%3Amatrix.org&logo=matrix&server_fqdn=matrix.org&style=flat
:target: https://matrix.to/#/%23aio-libs:matrix.org
:alt: Matrix Room — #aio-libs:matrix.org
.. image:: https://img.shields.io/matrix/aio-libs-space:matrix.org?label=Discuss%20on%20Matrix%20at%20%23aio-libs-space%3Amatrix.org&logo=matrix&server_fqdn=matrix.org&style=flat
:target: https://matrix.to/#/%23aio-libs-space:matrix.org
:alt: Matrix Space — #aio-libs-space:matrix.org
Key Features
============
- Supports both client and server side of HTTP protocol.
- Supports both client and server Web-Sockets out-of-the-box and avoids
Callback Hell.
- Provides Web-server with middlewares and plugable routing.
Getting started
===============
Client
------
To get something from the web:
.. code-block:: python
import aiohttp
import asyncio
async def main():
async with aiohttp.ClientSession() as session:
async with session.get('http://python.org') as response:
print("Status:", response.status)
print("Content-type:", response.headers['content-type'])
html = await response.text()
print("Body:", html[:15], "...")
asyncio.run(main())
This prints:
.. code-block::
Status: 200
Content-type: text/html; charset=utf-8
Body: <!doctype html> ...
Coming from `requests <https://requests.readthedocs.io/>`_ ? Read `why we need so many lines <https://aiohttp.readthedocs.io/en/latest/http_request_lifecycle.html>`_.
Server
------
An example using a simple server:
.. code-block:: python
# examples/server_simple.py
from aiohttp import web
async def handle(request):
name = request.match_info.get('name', "Anonymous")
text = "Hello, " + name
return web.Response(text=text)
async def wshandle(request):
ws = web.WebSocketResponse()
await ws.prepare(request)
async for msg in ws:
if msg.type == web.WSMsgType.text:
await ws.send_str("Hello, {}".format(msg.data))
elif msg.type == web.WSMsgType.binary:
await ws.send_bytes(msg.data)
elif msg.type == web.WSMsgType.close:
break
return ws
app = web.Application()
app.add_routes([web.get('/', handle),
web.get('/echo', wshandle),
web.get('/{name}', handle)])
if __name__ == '__main__':
web.run_app(app)
Documentation
=============
https://aiohttp.readthedocs.io/
Demos
=====
https://github.com/aio-libs/aiohttp-demos
External links
==============
* `Third party libraries
<http://aiohttp.readthedocs.io/en/latest/third_party.html>`_
* `Built with aiohttp
<http://aiohttp.readthedocs.io/en/latest/built_with.html>`_
* `Powered by aiohttp
<http://aiohttp.readthedocs.io/en/latest/powered_by.html>`_
Feel free to make a Pull Request for adding your link to these pages!
Communication channels
======================
*aio-libs discourse group*: https://aio-libs.discourse.group
*gitter chat* https://gitter.im/aio-libs/Lobby
We support `Stack Overflow
<https://stackoverflow.com/questions/tagged/aiohttp>`_.
Please add *aiohttp* tag to your question there.
Requirements
============
- Python >= 3.6
- async-timeout_
- attrs_
- charset-normalizer_
- multidict_
- yarl_
- frozenlist_
Optionally you may install the cChardet_ and aiodns_ libraries (highly
recommended for sake of speed).
.. _charset-normalizer: https://pypi.org/project/charset-normalizer
.. _aiodns: https://pypi.python.org/pypi/aiodns
.. _attrs: https://github.com/python-attrs/attrs
.. _multidict: https://pypi.python.org/pypi/multidict
.. _frozenlist: https://pypi.org/project/frozenlist/
.. _yarl: https://pypi.python.org/pypi/yarl
.. _async-timeout: https://pypi.python.org/pypi/async_timeout
.. _cChardet: https://pypi.python.org/pypi/cchardet
License
=======
``aiohttp`` is offered under the Apache 2 license.
Keepsafe
========
The aiohttp community would like to thank Keepsafe
(https://www.getkeepsafe.com) for its support in the early days of
the project.
Source code
===========
The latest developer version is available in a GitHub repository:
https://github.com/aio-libs/aiohttp
Benchmarks
==========
If you are interested in efficiency, the AsyncIO community maintains a
list of benchmarks on the official wiki:
https://github.com/python/asyncio/wiki/Benchmarks

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Tag: cp39-cp39-macosx_11_0_arm64

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