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from docling.document_converter import DocumentConverter

source = "./page_1.pdf"

converter = DocumentConverter()
doc = converter.convert(source).document
print(doc.export_to_markdown())

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# ติดตั้ง
$pip install fastapi
$pip install py-pglite
$pip install sqlmodel

$pip install pytest
$pip install coverage


# Run test and coverage

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import base64
import os
from mistralai import Mistral

def encode_pdf(pdf_path):
    """Encode the pdf to base64."""
    try:
        with open(pdf_path, "rb") as pdf_file:
            return base64.b64encode(pdf_file.read()).decode('utf-8')
    except FileNotFoundError:

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*** Variables ***
${AGE: int}    1    

*** Test Cases ***
Use variables
    # FAIL: 1 (integer) != 1 (string)
    Should Be Equal    ${AGE}    1
    # PASS
    Should Be Equal    ${AGE}    ${1}

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# 1. ติดตั้ง

$brew tap microsoft/foundrylocal
$brew install foundrylocal

# 2. ดู Model ที่สนับสนุน
$foundry model list
🟢 Service is Started on http://localhost:5273, PID 28974!
Alias                          Device     Task               File Size    License      Model ID            
-----------------------------------------------------------------------------------------------

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# SAM CLI
$sam --version
SAM CLI, version 1.138.0


# Create Hello Project
$sam init --runtime java11 --dependency-manager maven --app-template hello-world --name sqs-lambda-test

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Cursor's Memory Bank

I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

Memory Bank Structure

The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:

flowchart TD

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#1. Install
$go install github.com/tienanr/docurift/cmd/docurift@latest

# 2. Run docurift proxy server
$docurift -proxy-port 9876 -analyzer-port 9877 -backend-url http://localhost:8080 -max-examples 20

2025/05/12 16:45:00 Starting DocuRift with proxy port 9876 and analyzer port 9877
2025/05/12 16:45:00 Using backend URL: http://localhost:8080
2025/05/12 16:45:00 Starting analyzer server on :9877
2025/05/12 16:45:00 Starting proxy server on :9876

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$git clone https://github.com/The-Pocket/PocketFlow-Tutorial-Codebase-Knowledge demo
$cd demo
$pip install -r requirements.txt

# ใช้งาน GEMINI_API_KEY
$export GEMINI_API_KEY=<your key>

# ลองใช้งาน Pocket Flow Tutorial
python main.py --repo https://github.com/username/repo --include "*.py" "*.js" --exclude "tests/*" --max-size 50000

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# Add datas
$VADD mydata VALUES 4 0.5 1.2 0.75 3.8 "I love dogs"
$VADD mydata VALUES 4 0.5 1.2 0.75 3.8 "I like cats"
$VADD mydata VALUES 4 0.5 1.2 0.75 3.8 "I enjoy birds"

# Show embedding data
$VEMB mydata "I love dogs"
$VEMB mydata "I like cats"
$VEMB mydata "I enjoy birds"