11 | 12 | Reference: https://github.com/kennethleungty/Llama-2-Open-Source-LLM-CPU-Inference 13 | 14 | -------------------------------------------------------------------------------- /llama-2-local/assets/diagram_flow.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/singlestore-labs/webinar-code-examples/70e860e51e3346d044134f187bd16bf4bfef614e/llama-2-local/assets/diagram_flow.png -------------------------------------------------------------------------------- /llama-2-local/assets/qa_output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/singlestore-labs/webinar-code-examples/70e860e51e3346d044134f187bd16bf4bfef614e/llama-2-local/assets/qa_output.png -------------------------------------------------------------------------------- /llama-2-local/config/config.yml: -------------------------------------------------------------------------------- 1 | RETURN_SOURCE_DOCUMENTS: True 2 | VECTOR_COUNT: 2 3 | CHUNK_SIZE: 500 4 | CHUNK_OVERLAP: 50 5 | DATA_PATH: 'data/' 6 | DB_FAISS_PATH: 'vectorstore/db_faiss' 7 | # MODEL_TYPE: 'mpt' 8 | # MODEL_BIN_PATH: 'models/mpt-7b-instruct.ggmlv3.q8_0.bin' 9 | MODEL_TYPE: 'llama' 10 | MODEL_BIN_PATH: 'models/llama-2-7b-chat.ggmlv3.q8_0.bin' 11 | MAX_NEW_TOKENS: 256 12 | TEMPERATURE: 0.01 -------------------------------------------------------------------------------- /llama-2-local/data/manu-20f-2022-09-24.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/singlestore-labs/webinar-code-examples/70e860e51e3346d044134f187bd16bf4bfef614e/llama-2-local/data/manu-20f-2022-09-24.pdf -------------------------------------------------------------------------------- /llama-2-local/db_build.py: -------------------------------------------------------------------------------- 1 | # ========================= 2 | # Module: Vector DB Build 3 | # ========================= 4 | import box 5 | import yaml 6 | from langchain.vectorstores import FAISS 7 | from langchain.text_splitter import RecursiveCharacterTextSplitter 8 | from langchain.document_loaders import PyPDFLoader, DirectoryLoader 9 | from langchain.embeddings import HuggingFaceEmbeddings 10 | 11 | # Import config vars 12 | with open('config/config.yml', 'r', encoding='utf8') as ymlfile: 13 | cfg = box.Box(yaml.safe_load(ymlfile)) 14 | 15 | 16 | # Build vector database 17 | def run_db_build(): 18 | loader = DirectoryLoader(cfg.DATA_PATH, 19 | glob='*.pdf', 20 | loader_cls=PyPDFLoader) 21 | documents = loader.load() 22 | text_splitter = RecursiveCharacterTextSplitter(chunk_size=cfg.CHUNK_SIZE, 23 | chunk_overlap=cfg.CHUNK_OVERLAP) 24 | texts = text_splitter.split_documents(documents) 25 | 26 | embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', 27 | model_kwargs={'device': 'cpu'}) 28 | 29 | vectorstore = FAISS.from_documents(texts, embeddings) 30 | vectorstore.save_local(cfg.DB_FAISS_PATH) 31 | 32 | if __name__ == "__main__": 33 | run_db_build() 34 | -------------------------------------------------------------------------------- /llama-2-local/main.py: -------------------------------------------------------------------------------- 1 | from urllib import response 2 | import box 3 | import timeit 4 | import yaml 5 | import argparse 6 | from dotenv import find_dotenv, load_dotenv 7 | from src.utils import setup_dbqa 8 | 9 | # Load environment variables from .env file 10 | load_dotenv(find_dotenv()) 11 | 12 | # Import config vars 13 | with open('config/config.yml', 'r', encoding='utf8') as ymlfile: 14 | cfg = box.Box(yaml.safe_load(ymlfile)) 15 | 16 | 17 | if __name__ == "__main__": 18 | parser = argparse.ArgumentParser() 19 | parser.add_argument('input', 20 | type=str, 21 | default='How much is the minimum guarantee payable by adidas?', 22 | help='Enter the query to pass into the LLM') 23 | args = parser.parse_args() 24 | 25 | # Setup DBQA 26 | start = timeit.default_timer() 27 | dbqa = setup_dbqa() 28 | response = dbqa({'query': args.input}) 29 | end = timeit.default_timer() 30 | 31 | # Process source documents 32 | source_docs = response['source_documents'] 33 | for i, doc in enumerate(source_docs): 34 | print(f'\nSource Document {i+1}\n') 35 | print(f'Source Text: {doc.page_content}') 36 | print(f'Document Name: {doc.metadata["source"]}') 37 | print(f'Page Number: {doc.metadata["page"]}\n') 38 | print('='* 60) 39 | 40 | print(f'\nAnswer: {response["result"]}') 41 | print('='*50) 42 | 43 | print(f"Time to retrieve response: {end - start}") 44 | dbqa = setup_dbqa() 45 | response = dbqa({'query': 'How much is the minimum guarantee payable by adidas?'}) 46 | -------------------------------------------------------------------------------- /llama-2-local/requirements.txt: -------------------------------------------------------------------------------- 1 | aiofiles==23.1.0 2 | aiohttp==3.8.5 3 | aiosignal==1.3.1 4 | anyio==3.7.1 5 | appnope==0.1.3 6 | asttokens==2.2.1 7 | async-timeout==4.0.2 8 | attrs==23.1.0 9 | backcall==0.2.0 10 | blinker==1.5 11 | brotlipy==0.7.0 12 | build==0.10.0 13 | certifi==2023.5.7 14 | cffi==1.15.1 15 | charset-normalizer==2.0.4 16 | click==8.1.3 17 | comm==0.1.3 18 | cryptography==39.0.1 19 | ctransformers==0.2.5 20 | dataclasses-json==0.5.13 21 | debugpy==1.6.7 22 | decorator==5.1.1 23 | diskcache==5.6.1 24 | executing==1.2.0 25 | faiss-cpu==1.7.4 26 | fastapi==0.100.0 27 | filelock==3.12.2 28 | frozenlist==1.4.0 29 | fsspec==2023.6.0 30 | h11==0.14.0 31 | h2==4.1.0 32 | hpack==4.0.0 33 | huggingface-hub==0.16.4 34 | hypercorn==0.14.3 35 | hyperframe==6.0.1 36 | idna==3.4 37 | ipykernel==6.25.0 38 | ipython==8.14.0 39 | itsdangerous==2.1.2 40 | jedi==0.18.2 41 | Jinja2==3.1.2 42 | joblib==1.3.1 43 | jupyter_client==8.3.0 44 | jupyter_core==5.3.1 45 | langchain==0.0.236 46 | langchainplus-sdk==0.0.20 47 | llama-cpp-python==0.1.77 48 | MarkupSafe==2.1.2 49 | marshmallow==3.20.1 50 | matplotlib-inline==0.1.6 51 | mpmath==1.3.0 52 | multidict==6.0.4 53 | mypy-extensions==1.0.0 54 | nest-asyncio==1.5.6 55 | networkx==3.1 56 | nltk==3.8.1 57 | numexpr==2.8.4 58 | numpy==1.25.1 59 | openapi-schema-pydantic==1.2.4 60 | packaging==23.1 61 | parso==0.8.3 62 | pexpect==4.8.0 63 | pickleshare==0.7.5 64 | Pillow==10.0.0 65 | pip==23.0.1 66 | platformdirs==3.9.1 67 | priority==2.0.0 68 | prompt-toolkit==3.0.39 69 | psutil==5.9.5 70 | ptyprocess==0.7.0 71 | pure-eval==0.2.2 72 | pycparser==2.21 73 | pydantic==1.10.12 74 | Pygments==2.15.1 75 | PyJWT==2.8.0 76 | PyMySQL==1.1.0 77 | pyOpenSSL==23.0.0 78 | pypdf==3.8.1 79 | pyproject_hooks==1.0.0 80 | PySocks==1.7.1 81 | python-box==7.0.1 82 | python-dateutil==2.8.2 83 | python-dotenv==1.0.0 84 | PyYAML==6.0.1 85 | pyzmq==25.1.0 86 | quart==0.18.4 87 | quart-cors==0.6.0 88 | regex==2023.6.3 89 | requests==2.29.0 90 | safetensors==0.3.1 91 | scikit-learn==1.3.0 92 | scipy==1.11.1 93 | sentence-transformers==2.2.2 94 | sentencepiece==0.1.99 95 | setuptools==66.0.0 96 | singlestoredb==0.8.1 97 | six==1.16.0 98 | sniffio==1.3.0 99 | SQLAlchemy==2.0.19 100 | sqlparams==5.1.0 101 | stack-data==0.6.2 102 | starlette==0.27.0 103 | sympy==1.12 104 | tenacity==8.2.2 105 | threadpoolctl==3.2.0 106 | tokenizers==0.13.3 107 | toml==0.10.2 108 | tomlkit==0.12.1 109 | torch==2.0.1 110 | torchvision==0.15.2 111 | tornado==6.3.2 112 | tqdm==4.65.0 113 | traitlets==5.9.0 114 | transformers==4.31.0 115 | typing_extensions==4.7.1 116 | typing-inspect==0.9.0 117 | urllib3==1.26.15 118 | uvicorn==0.23.1 119 | wcwidth==0.2.6 120 | Werkzeug==2.3.4 121 | wheel==0.38.4 122 | wsproto==1.2.0 123 | yarl==1.9.2 124 | -------------------------------------------------------------------------------- /llama-2-local/src/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/singlestore-labs/webinar-code-examples/70e860e51e3346d044134f187bd16bf4bfef614e/llama-2-local/src/__init__.py -------------------------------------------------------------------------------- /llama-2-local/src/llm.py: -------------------------------------------------------------------------------- 1 | ''' 2 | =========================================== 3 | Module: Open-source LLM Setup 4 | =========================================== 5 | ''' 6 | from langchain.llms import CTransformers, LlamaCpp 7 | from dotenv import find_dotenv, load_dotenv 8 | import box 9 | import yaml 10 | 11 | # Load environment variables from .env file 12 | load_dotenv(find_dotenv()) 13 | 14 | # Import config vars 15 | with open('config/config.yml', 'r', encoding='utf8') as ymlfile: 16 | cfg = box.Box(yaml.safe_load(ymlfile)) 17 | 18 | 19 | def build_llm(): 20 | n_gpu_layers = 1 # Metal set to 1 is enough. 21 | n_batch = 1024 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip. 22 | 23 | # Make sure the model path is correct for your system! 24 | llm = LlamaCpp( 25 | model_path="./models/llama-2-7b-chat.ggmlv3.q2_K.bin", 26 | n_gpu_layers=n_gpu_layers, 27 | n_batch=n_batch, 28 | f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls 29 | verbose=False, 30 | ) 31 | 32 | return llm 33 | -------------------------------------------------------------------------------- /llama-2-local/src/prompts.py: -------------------------------------------------------------------------------- 1 | ''' 2 | =========================================== 3 | Module: Prompts collection 4 | =========================================== 5 | ''' 6 | # Note: Precise formatting of spacing and indentation of the prompt template is important for Llama-2-7B-Chat, 7 | # as it is highly sensitive to whitespace changes. For example, it could have problems generating 8 | # a summary from the pieces of context if the spacing is not done correctly 9 | 10 | qa_template = """Use the following pieces of information to answer the user's question. 11 | If you don't know the answer, just say that you don't know, don't try to make up an answer. 12 | 13 | Context: {context} 14 | Question: {question} 15 | 16 | Only return the helpful answer below and nothing else. 17 | Helpful answer: 18 | """ 19 | -------------------------------------------------------------------------------- /llama-2-local/src/utils.py: -------------------------------------------------------------------------------- 1 | ''' 2 | =========================================== 3 | Module: Util functions 4 | =========================================== 5 | ''' 6 | import box 7 | import yaml 8 | import os 9 | 10 | from langchain import PromptTemplate 11 | from langchain.chains import RetrievalQA 12 | from langchain.embeddings import HuggingFaceEmbeddings 13 | from langchain.vectorstores import FAISS, SingleStoreDB 14 | from langchain.text_splitter import RecursiveCharacterTextSplitter 15 | from langchain.document_loaders import PyPDFLoader, DirectoryLoader 16 | from src.prompts import qa_template 17 | from src.llm import build_llm 18 | 19 | # Import config vars 20 | with open('config/config.yml', 'r', encoding='utf8') as ymlfile: 21 | cfg = box.Box(yaml.safe_load(ymlfile)) 22 | 23 | 24 | def set_qa_prompt(): 25 | """ 26 | Prompt template for QA retrieval for each vectorstore 27 | """ 28 | prompt = PromptTemplate(template=qa_template, 29 | input_variables=['context', 'question']) 30 | return prompt 31 | 32 | 33 | def build_retrieval_qa(llm, prompt, vectordb): 34 | dbqa = RetrievalQA.from_chain_type(llm=llm, 35 | chain_type='stuff', 36 | retriever=vectordb.as_retriever(search_kwargs={'k': cfg.VECTOR_COUNT}), 37 | return_source_documents=cfg.RETURN_SOURCE_DOCUMENTS, 38 | chain_type_kwargs={'prompt': prompt} 39 | ) 40 | return dbqa 41 | 42 | 43 | def setup_dbqa(): 44 | embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", 45 | model_kwargs={'device': 'cpu'}) 46 | 47 | loader = DirectoryLoader(cfg.DATA_PATH, 48 | glob='*.pdf', 49 | loader_cls=PyPDFLoader) 50 | documents = loader.load() 51 | text_splitter = RecursiveCharacterTextSplitter(chunk_size=cfg.CHUNK_SIZE, 52 | chunk_overlap=cfg.CHUNK_OVERLAP) 53 | texts = text_splitter.split_documents(documents) 54 | 55 | os.environ["SINGLESTOREDB_URL"] = "placeholder" 56 | vectorstore = SingleStoreDB(embeddings, distance_strategy="DOT_PRODUCT", table_name="demo0") 57 | 58 | # if you want to build a new vectorstore, use the following line instead of the above 59 | # vectorstore = SingleStoreDB.from_documents(texts, embeddings, distance_strategy="DOT_PRODUCT", table_name="demo0") 60 | 61 | llm = build_llm() 62 | qa_prompt = set_qa_prompt() 63 | dbqa = build_retrieval_qa(llm, qa_prompt, vectorstore) 64 | 65 | return dbqa 66 | -------------------------------------------------------------------------------- /mktg-email-flow/README.md: -------------------------------------------------------------------------------- 1 | # Beyond Vectors: How to Build a ChatGPT Super App 2 | 3 | Join us for this upcoming webinar exploring how sales and marketing technology has evolved over time, and what’s now possible in today’s era of LLMs and Generative AI. 4 | 5 | From the age-old "spray and pray" approach to sophisticated personalization strategies driven by AI and vector databases, our Head of Solution Engineering Sarung Tripathi will present an overview of how versatile general-purpose databases (with vector capabilities) can enhance prospect engagement and conversion rates. Sarung will also showcase how multi-model capabilities (covering JSON, NoSQL, and other data types) within LLMs can offer stronger contextual insights from various data stores (website and app data, etc) for even more personalized communications. 6 | 7 | The spotlight of the webinar, however, will be Sarung’s demo of a modern general-purpose database application that can use vector embeddings, structured data and unstructured data to generate mass personalization at scale in milliseconds. In this insightful session, you'll grasp the real-world applications and advantages of this technology, setting a new standard for real-time, data-driven decision making. 8 | 9 | ## Discussion Topics 10 | 11 | - Discover Generative AI and LLM capabilities for application development, focusing on user experience, engagement, and conversion rates optimization. 12 | - Understand how to harness vector embeddings for mass personalization at scale, delivering real-time, tailored experiences. 13 | - Navigate the critical considerations around privacy and security in AI-powered applications, ensuring data integrity and robustness. 14 | 15 | Sign up for a [free trial](http://bit.ly/beyond-vectors-raffle) of SingleStoreDB with a $600 Credit today! 16 | -------------------------------------------------------------------------------- /mktg-email-flow/docs/add-tracking-to-website.md: -------------------------------------------------------------------------------- 1 | # Add Tracking to Your Website 2 | 3 | This page guides you through the process of adding the Awesome Web Analytics tracking code to your website. It provides code examples and instructions for popular platforms and frameworks like WordPress, Django, and Node.js. It also includes troubleshooting tips in case you encounter any issues during the implementation. 4 | 5 | ## Getting Started 6 | 7 | ### Add Tracking to Your Website 8 | 9 | This page provides a step-by-step guide on how to add the Awesome Web Analytics tracking code to your website. By following these instructions, you will be able to start tracking valuable data and gaining insights into your website's performance. 10 | 11 | #### Why Tracking is Important 12 | 13 | Before we dive into the implementation process, let's briefly understand why tracking is crucial for your website. Awesome Web Analytics allows you to gather valuable data that can help you make informed decisions about your website's design, content, and marketing strategies. By tracking user behavior, you can identify areas of improvement, optimize conversion rates, and ultimately enhance the user experience. 14 | 15 | #### Implementation Instructions 16 | 17 | To get started with tracking your website using Awesome Web Analytics, follow the instructions below. We have provided code examples and instructions for popular platforms and frameworks like WordPress, Django, and Node.js to ensure compatibility with your chosen technology stack. 18 | 19 | 1. **WordPress:** 20 | 21 | If you are using WordPress, follow these steps to add the Awesome Web Analytics tracking code: 22 | 23 | - Login to your WordPress admin dashboard. 24 | - Navigate to the "Appearance" section and click on "Editor." 25 | - Locate your theme's header.php file and open it for editing. 26 | - Find the closing head tag (``) and paste the Awesome Web Analytics tracking code just before it. 27 | - Save the changes, and you're done! Awesome Web Analytics is now tracking your WordPress website. 28 | 29 | 2. **Django:** 30 | 31 | For Django websites, the process of adding the Awesome Web Analytics tracking code is as follows: 32 | 33 | - Open your Django project's base.html file. 34 | - Locate the closing head tag (``) and insert the Awesome Web Analytics tracking code just before it. 35 | - Save the file, and you're all set! Awesome Web Analytics will now track your Django website. 36 | 37 | 3. **Node.js:** 38 | 39 | If you're using Node.js as your backend framework, here's how you can add the Awesome Web Analytics tracking code: 40 | 41 | - Open your main server file (e.g., `server.js` or `app.js`). 42 | - Require the Awesome Web Analytics module at the top of your file. 43 | - Initialize the Awesome Web Analytics tracking code with your unique tracking ID. 44 | - Add the middleware function to track incoming requests. 45 | - Save the file, and your Node.js website is now being tracked by Awesome Web Analytics. 46 | 47 | #### Troubleshooting Tips 48 | 49 | If you encounter any issues during the implementation process or if the tracking code is not functioning correctly, refer to the troubleshooting tips below: 50 | 51 | - Double-check that you have correctly copied and pasted the Awesome Web Analytics tracking code. Even a small typo or missing character can cause issues. 52 | - Ensure that the tracking code is placed within the `` section of your HTML document. 53 | - Verify that you have a stable internet connection to ensure the tracking data is being sent to the Awesome Web Analytics servers. 54 | - Clear your browser cache and test the website again to rule out any caching-related problems. 55 | - If you're still experiencing difficulties, don't hesitate to reach out to our support team for further assistance. We're here to help! 56 | 57 | Congratulations! You have successfully added the Awesome Web Analytics tracking code to your website. Now you can start analyzing valuable data and gaining insights into your website's performance using the powerful features of Awesome Web Analytics. 58 | -------------------------------------------------------------------------------- /mktg-email-flow/docs/conversion-tracking.md: -------------------------------------------------------------------------------- 1 | # Conversion Tracking 2 | This page provides information on how to set up conversion tracking with Awesome Web Analytics. It explains how to define and track conversions, such as sign-ups, purchases, or specific actions on your website. It covers the configuration steps in the dashboard and demonstrates how to interpret the conversion data to optimize your website's performance. 3 | 4 | This page provides detailed information on how to set up and utilize the conversion tracking feature with Awesome Web Analytics. It will guide you through the process of defining and tracking conversions, such as sign-ups, purchases, or specific actions on your website. Additionally, it covers the configuration steps in the Awesome Web Analytics dashboard and provides insights on how to interpret the conversion data to optimize your website's performance. 5 | 6 | ## Table of Contents 7 | 8 | 1. Introduction 9 | 2. Setting up Conversion Tracking 10 | - Defining Conversions 11 | - Tracking Conversions 12 | 3. Configuring Conversion Tracking in the Awesome Web Analytics Dashboard 13 | - Step 1: Accessing the Conversion Tracking Settings 14 | - Step 2: Defining Conversion Goals 15 | - Step 3: Implementing Conversion Tracking Code 16 | 4. Interpreting Conversion Data 17 | - Conversion Reports 18 | - Analyzing Conversion Funnel 19 | 5. Optimizing Website Performance with Conversion Data 20 | - A/B Testing 21 | - Conversion Rate Optimization 22 | 23 | ## 1. Introduction 24 | 25 | Conversion tracking is a crucial feature in Awesome Web Analytics that enables you to measure and analyze the success of your website's goals and objectives. By accurately tracking conversions, you can gain valuable insights into user behavior, identify areas for improvement, and make data-driven decisions to enhance your website's performance. 26 | 27 | This documentation will walk you through the step-by-step process of setting up conversion tracking, configuring the necessary settings in the Awesome Web Analytics dashboard, interpreting conversion data, and utilizing it to optimize your website's performance. 28 | 29 | ## 2. Setting up Conversion Tracking 30 | 31 | ### Defining Conversions 32 | 33 | Before you begin tracking conversions, it is essential to define what actions or events on your website you consider as conversions. Common examples of conversions include sign-ups, purchases, form submissions, or any specific action that indicates a desired user behavior. In this section, we will guide you on how to define the conversions that align with your business goals. 34 | 35 | ### Tracking Conversions 36 | 37 | Once you have defined your conversions, Awesome Web Analytics provides you with an easy-to-implement tracking mechanism. By integrating a small snippet of code into the relevant pages or events on your website, Awesome Web Analytics will be able to accurately track when these conversions occur. This section will guide you on how to track conversions effectively using Awesome Web Analytics. 38 | 39 | ## 3. Configuring Conversion Tracking in the Awesome Web Analytics Dashboard 40 | 41 | To start utilizing conversion tracking in Awesome Web Analytics, you need to configure the relevant settings in your Awesome Web Analytics dashboard. The following steps will guide you through the configuration process: 42 | 43 | ### Step 1: Accessing the Conversion Tracking Settings 44 | 45 | Login to your Awesome Web Analytics account and navigate to the "Settings" section in the dashboard. From there, locate the "Conversion Tracking" option and click on it to access the conversion tracking settings. 46 | 47 | ### Step 2: Defining Conversion Goals 48 | 49 | In this step, you will define the specific conversion goals you want to track. Enter a descriptive name for each goal and specify the relevant details, such as the URL or event associated with the conversion. Awesome Web Analytics allows you to track multiple conversion goals simultaneously. 50 | 51 | ### Step 3: Implementing Conversion Tracking Code 52 | 53 | Awesome Web Analytics provides you with a unique tracking code snippet for each conversion goal you define. Copy the corresponding code snippet and paste it into the relevant pages or events on your website. This code will enable Awesome Web Analytics to track the occurrence of the defined conversions accurately. 54 | 55 | ## 4. Interpreting Conversion Data 56 | 57 | After setting up conversion tracking and collecting data, Awesome Web Analytics offers various reports and tools to help you interpret and analyze your conversion data effectively. This section will cover: 58 | 59 | ### Conversion Reports 60 | 61 | Awesome Web Analytics generates comprehensive reports that provide an overview of your conversion data, including the number of conversions, conversion rates, and trends over time. These reports will give you a clear understanding of how your website is performing in terms of achieving your defined conversion goals. 62 | 63 | ### Analyzing Conversion Funnel 64 | 65 | Understanding the conversion funnel is crucial for identifying potential areas of improvement and optimizing your website. Awesome Web Analytics's conversion funnel analysis allows you to visualize the steps users take before completing a conversion, enabling you to pinpoint any bottlenecks or drop-off points in the conversion process. 66 | 67 | ## 5. Optimizing Website Performance with Conversion Data 68 | 69 | Conversion data can be a powerful tool for optimizing your website's performance and achieving better results. This section will explore two key strategies for utilizing conversion data effectively: 70 | 71 | ### A/B Testing 72 | 73 | Awesome Web Analytics's A/B testing feature allows you to compare different versions of your website or specific elements to determine which version generates more conversions. By experimenting with different variations and analyzing the conversion data, you can make informed decisions about the most effective design, content, or user experience for your target audience. 74 | 75 | ### Conversion Rate Optimization 76 | 77 | Awesome Web Analytics provides insights and recommendations for optimizing your website's conversion rate. By analyzing user behavior, identifying potential barriers, and implementing best practices, you can make data-driven improvements to your website that result in higher conversion rates. 78 | 79 | ## Conclusion 80 | 81 | By following the steps outlined in this documentation, you will be able to effectively set up and utilize conversion tracking with Awesome Web Analytics. Tracking conversions, interpreting the data, and leveraging it for optimization will empower you to make informed decisions and continuously improve the performance of your website. 82 | -------------------------------------------------------------------------------- /mktg-email-flow/docs/custom-event-tracking.md: -------------------------------------------------------------------------------- 1 | # Custom Event Tracking 2 | 3 | This documentation explains how to track custom events with Awesome Web Analytics. It covers the process of defining and implementing custom events to track specific user interactions on your website, such as button clicks, form submissions, or video plays. It provides code examples and explains how to analyze the event data in the dashboard to gain insights into user behavior. 4 | 5 | ## Introduction 6 | 7 | This documentation provides a comprehensive guide on how to effectively track custom events using Awesome Web Analytics. Custom event tracking allows you to monitor and analyze specific user interactions on your website, such as button clicks, form submissions, or video plays. By implementing custom event tracking, you can gain valuable insights into user behavior and make data-driven decisions to improve your website's performance. 8 | 9 | ## Table of Contents 10 | 11 | - [Getting Started](#getting-started) 12 | - [Defining Custom Events](#defining-custom-events) 13 | - [Implementing Custom Event Tracking](#implementing-custom-event-tracking) 14 | - [Analyzing Event Data](#analyzing-event-data) 15 | 16 | ## Getting Started 17 | 18 | Before diving into custom event tracking, ensure that you have successfully integrated Awesome Web Analytics into your website. If you haven't done so yet, refer to our [Integration Guide](permalink:integration-guide) for step-by-step instructions. 19 | 20 | ## Defining Custom Events 21 | 22 | Custom events provide a way to track specific user interactions that are important to your business. These events can be anything from button clicks, form submissions, to video plays. Defining custom events helps you focus on capturing the data that matters most to your website's performance. 23 | 24 | To define custom events with Awesome Web Analytics, follow these steps: 25 | 26 | 1. Log in to your Awesome Web Analytics dashboard. 27 | 2. Navigate to the **Events** section. 28 | 3. Click on the **Custom Events** tab. 29 | 4. Click on the **Add Custom Event** button. 30 | 5. Provide a name and description for your custom event. 31 | 6. Select the appropriate event category (e.g., button click, form submission). 32 | 7. Define any additional event properties that you wish to capture. 33 | 8. Save your custom event definition. 34 | 35 | ## Implementing Custom Event Tracking 36 | 37 | Once you have defined your custom events, it's time to implement event tracking on your website. Awesome Web Analytics provides an easy-to-use JavaScript library that allows you to track events seamlessly. 38 | 39 | To implement custom event tracking, follow these steps: 40 | 41 | 1. Include the Awesome Web Analytics JavaScript library in your website's HTML code. 42 | ```html 43 | 44 | ``` 45 | 46 | 2. Identify the user interactions you want to track and use the following code to send custom event data to Awesome Web Analytics: 47 | ```javascript 48 | Awesome Web Analytics.trackEvent('custom_event_name', { 49 | property1: 'value1', 50 | property2: 'value2', 51 | // Add any additional properties as needed 52 | }); 53 | ``` 54 | 55 | 3. Replace `'custom_event_name'` with the name of the custom event you defined in the Awesome Web Analytics dashboard. 56 | 4. Customize the `property1`, `value1`, `property2`, `value2` placeholders with the relevant properties and values of your custom event. 57 | 5. Repeat this code snippet for each user interaction you want to track. 58 | 59 | ## Analyzing Event Data 60 | 61 | Once you have successfully implemented custom event tracking, you can analyze the event data in your Awesome Web Analytics dashboard. This analysis will provide valuable insights into user behavior and help you make informed decisions to optimize your website's performance. 62 | 63 | To analyze event data, follow these steps: 64 | 65 | 1. Log in to your Awesome Web Analytics dashboard. 66 | 2. Navigate to the **Events** section. 67 | 3. Click on the **Custom Events** tab. 68 | 4. Select the custom event you want to analyze. 69 | 5. Explore the available metrics, such as event count, conversion rate, and average duration. 70 | 6. Utilize filters and segmentation options to narrow down your analysis. 71 | 7. Generate reports or visualize the data using charts and graphs. 72 | 8. Draw conclusions and identify areas for improvement based on the insights gained. 73 | 74 | ## Conclusion 75 | 76 | Custom event tracking with Awesome Web Analytics empowers you to better understand user behavior on your website. By defining and implementing custom events, you can gather valuable data and gain insights that drive performance improvements. Use this documentation as a guide to effectively track custom events and make data-driven decisions to enhance your website's user experience. 77 | -------------------------------------------------------------------------------- /mktg-email-flow/docs/dashboard-customization.md: -------------------------------------------------------------------------------- 1 | # Dashboard Customization 2 | This documentation explains how to customize the Awesome Web Analytics dashboard to suit your preferences and needs. It covers options like rearranging widgets, creating custom dashboards, and setting up personalized reports. It also provides tips on utilizing the available widgets and visualizations to create insightful and tailored analytics views. 3 | 4 | ## Overview 5 | This documentation provides a comprehensive guide on how to customize the Awesome Web Analytics dashboard to suit your preferences and needs. Whether you want to rearrange widgets, create custom dashboards, or set up personalized reports, this guide will walk you through the process step-by-step. Additionally, it offers valuable tips on leveraging the available widgets and visualizations to create insightful and tailored analytics views. 6 | 7 | ## Table of Contents 8 | 1. Introduction 9 | 2. Getting Started 10 | - Accessing the Dashboard Customization Options 11 | 3. Rearranging Widgets 12 | - Drag and Drop Method 13 | - Widget Menu Method 14 | 4. Creating Custom Dashboards 15 | - Adding New Dashboards 16 | - Modifying Dashboard Layout 17 | - Setting Dashboard Permissions 18 | 5. Personalized Reports 19 | - Creating a Report Template 20 | - Customizing Report Content 21 | - Scheduling and Sharing Reports 22 | 6. Utilizing Widgets and Visualizations 23 | - Available Widgets and Visualizations 24 | - Best Practices for Widget Selection 25 | - Advanced Widget Configuration 26 | 27 | ## 1. Introduction 28 | The Awesome Web Analytics Dashboard Customization feature empowers you to tailor your analytics experience according to your unique requirements. By customizing the dashboard, you can gain deeper insights into your website's performance, track specific metrics, and monitor key goals effortlessly. 29 | 30 | In this guide, you will learn how to: 31 | 32 | - Rearrange widgets to prioritize the most important information. 33 | - Create custom dashboards to organize and visualize data that is relevant to specific teams or projects. 34 | - Set up personalized reports that deliver timely and meaningful insights to stakeholders. 35 | - Utilize the available widgets and visualizations effectively to create impactful analytics views. 36 | 37 | ## 2. Getting Started 38 | To access the Dashboard Customization options, follow these steps: 39 | 40 | 1. Log in to your Awesome Web Analytics account. 41 | 2. Navigate to the main dashboard. 42 | 3. Locate the "Customize" or "Settings" button in the top-right corner of the screen. 43 | 4. Click on the button to access the customization menu. 44 | 45 | ## 3. Rearranging Widgets 46 | Customizing the widget arrangement allows you to prioritize the information that matters most to you. Awesome Web Analytics offers two methods for rearranging widgets: the Drag and Drop method and the Widget Menu method. 47 | 48 | ### 3.1 Drag and Drop Method 49 | To rearrange widgets using the Drag and Drop method, follow these steps: 50 | 51 | 1. Access the customization menu as described in the previous section. 52 | 2. Click and hold on the widget you want to move. 53 | 3. Drag the widget to the desired location on the dashboard. 54 | 4. Release the mouse button to drop the widget into its new position. 55 | 56 | ### 3.2 Widget Menu Method 57 | The Widget Menu method provides an alternative way to rearrange widgets. To use this method, follow these steps: 58 | 59 | 1. Access the customization menu. 60 | 2. Locate the widget you want to move. 61 | 3. Click on the widget's menu icon (typically represented by three dots or a gear icon). 62 | 4. From the menu options, select "Move" or "Rearrange." 63 | 5. Choose the new position for the widget from the available options. 64 | 65 | ## 4. Creating Custom Dashboards 66 | Custom dashboards allow you to focus on specific metrics or areas of your website's performance. You can create multiple dashboards tailored to different teams, projects, or campaigns. Follow the steps below to create and modify custom dashboards. 67 | 68 | ### 4.1 Adding New Dashboards 69 | To add a new custom dashboard, follow these steps: 70 | 71 | 1. Access the customization menu. 72 | 2. Click on the "Add Dashboard" or "New Dashboard" button. 73 | 3. Provide a name for the new dashboard. 74 | 4. Optionally, select a layout template that suits your needs. 75 | 5. Click "Save" to create the new dashboard. 76 | 77 | ### 4.2 Modifying Dashboard Layout 78 | Once you have created a custom dashboard, you can modify its layout to display the relevant widgets. 79 | 80 | 1. Access the customization menu. 81 | 2. Select the custom dashboard you want to modify. 82 | 3. Click on the "Edit Layout" or "Customize Layout" option. 83 | 4. Drag and drop widgets onto the dashboard from the available options. 84 | 5. Rearrange the widgets as desired. 85 | 6. Click "Save" to apply the changes. 86 | 87 | ### 4.3 Setting Dashboard Permissions 88 | Awesome Web Analytics allows you to control dashboard access and permissions. You can determine who can view and edit specific dashboards. 89 | 90 | 1. Access the customization menu. 91 | 2. Select the custom dashboard for which you want to set permissions. 92 | 3. Click on the "Permissions" or "Access Control" option. 93 | 4. Specify the users or teams who should have access to the dashboard. 94 | 5. Define their permission levels (view-only, edit, etc.). 95 | 6. Save the changes to apply the permissions. 96 | 97 | ## 5. Personalized Reports 98 | Awesome Web Analytics enables you to generate personalized reports that deliver valuable insights to stakeholders. You can customize report content, schedule automated deliveries, and share reports with relevant team members. 99 | 100 | ### 5.1 Creating a Report Template 101 | To create a report template, follow these steps: 102 | 103 | 1. Access the customization menu. 104 | 2. Select the "Reports" or "Report Templates" option. 105 | 3. Click "Create New Template" or "Add Template." 106 | 4. Define the report's title, scope, and frequency. 107 | 5. Select the desired widgets and visualizations to include in the report. 108 | 6. Customize the report layout and branding. 109 | 7. Save the template for future use. 110 | 111 | ### 5.2 Customizing Report Content 112 | After creating a report template, you can customize its content to focus on specific metrics or insights. 113 | 114 | 1. Access the customization menu. 115 | 2. Open the report template you want to modify. 116 | 3. Click on the "Edit Content" or "Customize Content" option. 117 | 4. Add or remove widgets and visualizations as needed. 118 | 5. Configure widget settings, filters, and time ranges. 119 | 6. Save the changes to update the report content. 120 | 121 | ### 5.3 Scheduling and Sharing Reports 122 | Once you have customized a report template, you can schedule its automated delivery and share it with relevant stakeholders. 123 | 124 | 1. Access the customization menu. 125 | 2. Open the report template you want to schedule and share. 126 | 3. Click on the "Schedule" or "Delivery Options" option. 127 | 4. Define the recipients and delivery frequency. 128 | 5. Set the preferred report format (PDF, email, etc.). 129 | 6. Save the settings to schedule and share the report. 130 | 131 | ## 6. Utilizing Widgets and Visualizations 132 | Awesome Web Analytics offers a wide range of widgets and visualizations to help you gain comprehensive insights into your website's performance. This section provides an overview of the available options and best practices for widget selection. 133 | 134 | ### 6.1 Available Widgets and Visualizations 135 | Awesome Web Analytics provides the following widgets and visualizations: 136 | 137 | - Line Charts 138 | - Bar Charts 139 | - Pie Charts 140 | - Funnel Charts 141 | - Heatmaps 142 | - Tables 143 | - Metrics Cards 144 | - Conversion Tracking Widgets 145 | 146 | ### 6.2 Best Practices for Widget Selection 147 | To maximize the effectiveness of your custom dashboards and reports, consider the following best practices when selecting widgets: 148 | 149 | - Choose widgets that align with your specific goals and metrics. 150 | - Prioritize widgets that provide real-time or near-real-time data. 151 | - Use a mix of different visualizations to present data from various perspectives. 152 | - Experiment with different widgets to find the most informative and visually appealing combination. 153 | 154 | ### 6.3 Advanced Widget Configuration 155 | Awesome Web Analytics allows for advanced widget configuration, enabling you to fine-tune the displayed data, set filters, and customize the widget appearance. 156 | 157 | 1. Access the customization menu. 158 | 2. Select the widget you want to configure. 159 | 3. Click on the widget's menu icon (three dots or gear icon). 160 | 4. Choose "Configure" or "Settings" from the menu options. 161 | 5. Adjust the widget's settings, filters, and appearance. 162 | 6. Save the changes to update the widget. 163 | 164 | ## Conclusion 165 | By customizing your Awesome Web Analytics dashboard, you can create a personalized analytics experience that meets your specific needs. Whether it's rearranging widgets, creating custom dashboards, setting up personalized reports, or utilizing the available widgets and visualizations effectively, this guide has provided you with the necessary knowledge to make the most out of the Dashboard Customization feature. Start exploring the customization options today and unlock deeper insights into your website's performance. 166 | -------------------------------------------------------------------------------- /mktg-email-flow/docs/data-export.md: -------------------------------------------------------------------------------- 1 | # Data Export 2 | This page provides information on how to export your analytics data from Awesome Web Analytics. It explains the available data export options, including CSV and JSON formats, and guides you through the process of exporting data for further analysis or integration with other tools. It also covers any limitations or considerations when exporting large datasets. 3 | 4 | **Available Data Export Options** 5 | 6 | Awesome Web Analytics offers two popular data export formats: CSV (Comma Separated Values) and JSON (JavaScript Object Notation). Both formats are widely supported and can be easily imported into various analysis and visualization tools. 7 | 8 | **Exporting Data in CSV Format** 9 | 10 | To export your data in CSV format, follow these simple steps: 11 | 12 | 1. Log in to your Awesome Web Analytics account. 13 | 2. Navigate to the "Data Export" section. 14 | 3. Select the desired date range or any other filtering options you need. 15 | 4. Click on the "Export" button, and choose "CSV" as the export format. 16 | 5. The export process will begin, and a download link will be provided once the export is complete. 17 | 6. Click on the download link to save the CSV file to your local machine. 18 | 19 | **Exporting Data in JSON Format** 20 | 21 | To export your data in JSON format, follow these steps: 22 | 23 | 1. Log in to your Awesome Web Analytics account. 24 | 2. Go to the "Data Export" section. 25 | 3. Choose the date range or any other filters you want to apply. 26 | 4. Click on the "Export" button, and select "JSON" as the export format. 27 | 5. The export process will start, and a download link will be provided when the export is finished. 28 | 6. Click on the download link to save the JSON file to your local machine. 29 | 30 | **Limitations and Considerations** 31 | 32 | When exporting large datasets from Awesome Web Analytics, please keep the following limitations and considerations in mind: 33 | 34 | 1. **Export Speed**: Exporting large datasets may take some time, depending on the size of your data. Please be patient and avoid interrupting the export process. 35 | 2. **File Size**: The exported files may be large, especially for extensive date ranges or complex data sets. Ensure that you have enough available storage space on your local machine or the destination system. 36 | 3. **Data Accuracy**: While we strive to provide accurate and reliable data exports, there may be minor discrepancies between real-time data and exported data due to the asynchronous nature of data processing. 37 | 4. **Data Privacy**: Ensure that you comply with any applicable data privacy regulations when exporting and handling analytics data. 38 | 39 | We hope this documentation has provided you with a comprehensive understanding of how to export your analytics data from Awesome Web Analytics. If you have any further questions or issues, please don't hesitate to reach out to our support team. 40 | -------------------------------------------------------------------------------- /mktg-email-flow/docs/funnels.md: -------------------------------------------------------------------------------- 1 | # Funnels 2 | This documentation explains how to create and analyze funnels in Awesome Web Analytics. It provides step-by-step instructions on defining the stages of a funnel, setting up the necessary events or goals, and visualizing the funnel flow in the dashboard. It also discusses the insights gained from funnel analysis and how to identify areas for improvement in your conversion process. 3 | 4 | ## Introduction 5 | 6 | Welcome to the Funnels documentation for Awesome Web Analytics! This guide will walk you through the process of creating and analyzing funnels in Awesome Web Analytics. Funnels are a powerful tool that can help you understand the conversion process and identify areas for improvement in your website or app. 7 | 8 | ## Table of Contents 9 | 10 | 1. [Overview](#overview) 11 | 2. [Creating a Funnel](#creating-a-funnel) 12 | 3. [Defining Funnel Stages](#defining-funnel-stages) 13 | 4. [Setting up Events or Goals](#setting-up-events-or-goals) 14 | 5. [Visualizing Funnel Flow](#visualizing-funnel-flow) 15 | 6. [Analyzing Funnel Insights](#analyzing-funnel-insights) 16 | 7. [Identifying Areas for Improvement](#identifying-areas-for-improvement) 17 | 18 | ## Overview 19 | 20 | The Funnels feature in Awesome Web Analytics allows you to track and analyze the conversion process of your users. By defining stages and setting up events or goals, you can gain valuable insights into how users interact with your website or app. 21 | 22 | ## Creating a Funnel 23 | 24 | To create a funnel in Awesome Web Analytics, follow these steps: 25 | 26 | 1. Log in to your Awesome Web Analytics account and navigate to the Funnels section. 27 | 2. Click on the "Create New Funnel" button. 28 | 3. Give your funnel a descriptive name and select the website or app you want to track. 29 | 4. Save your funnel. 30 | 31 | ## Defining Funnel Stages 32 | 33 | Defining stages is crucial for creating an accurate funnel visualization. Each stage represents a step in the conversion process. To define stages for your funnel: 34 | 35 | 1. Select the funnel you want to edit. 36 | 2. Click on the "Edit Stages" button. 37 | 3. Add stages by providing a name and a description for each step. 38 | 4. Save your changes. 39 | 40 | ## Setting up Events or Goals 41 | 42 | Events or goals are the actions you want to track in your funnel. These can be specific user interactions or conversions. To set up events or goals for your funnel: 43 | 44 | 1. Select the funnel you want to edit. 45 | 2. Click on the "Edit Events/Goals" button. 46 | 3. Add events or goals by specifying the criteria for each action. 47 | 4. Save your changes. 48 | 49 | ## Visualizing Funnel Flow 50 | 51 | Once you have defined stages and set up events or goals, you can visualize the flow of your funnel in the Awesome Web Analytics dashboard. The funnel visualization provides a clear overview of how users progress through each stage. 52 | 53 | ## Analyzing Funnel Insights 54 | 55 | Awesome Web Analytics provides detailed insights into your funnel performance. You can analyze conversion rates, drop-off points, and identify areas of improvement. The funnel analysis helps you understand user behavior and optimize your conversion process. 56 | 57 | ## Identifying Areas for Improvement 58 | 59 | By examining the funnel insights, you can identify areas where users are dropping off or encountering obstacles. This information can guide your optimization efforts, allowing you to improve the user experience and increase conversion rates. 60 | 61 | ## Conclusion 62 | 63 | Congratulations! You now have a comprehensive understanding of how to create and analyze funnels in Awesome Web Analytics. By leveraging the power of funnels, you can gain valuable insights into your conversion process and make data-driven decisions to improve your website or app. 64 | -------------------------------------------------------------------------------- /mktg-email-flow/docs/generate-tracking-code.md: -------------------------------------------------------------------------------- 1 | # Generate Tracking Code 2 | This documentation explains how to generate a unique tracking code for your website. It provides step-by-step instructions on where to find the tracking code generation feature in the Awesome Web Analytics dashboard and how to customize the code to suit your needs. It also includes information on how to implement the tracking code on your website. 3 | 4 | ## Getting Started: Generate Tracking Code 5 | 6 | ### Overview 7 | This documentation will guide you through the process of generating a unique tracking code for your website using Awesome Web Analytics. The tracking code is essential for collecting and analyzing data about your website visitors. By following the step-by-step instructions provided, you will learn how to generate the tracking code, customize it to meet your specific requirements, and implement it successfully on your website. 8 | 9 | ### Table of Contents 10 | 1. Introduction 11 | 2. Accessing the Tracking Code Generation Feature 12 | 3. Customizing the Tracking Code 13 | 4. Implementing the Tracking Code on Your Website 14 | 5. Troubleshooting 15 | 16 | ### 1. Introduction 17 | Before you begin, it's crucial to understand the significance of the tracking code. The tracking code is a small snippet of JavaScript that needs to be added to every page of your website. It enables Awesome Web Analytics to collect various data points, such as page views, visitor demographics, and user behavior, helping you gain valuable insights into your website's performance. 18 | 19 | ### 2. Accessing the Tracking Code Generation Feature 20 | To generate your unique tracking code, follow these steps: 21 | 22 | 1. Log into your Awesome Web Analytics account. 23 | 2. Navigate to the Awesome Web Analytics dashboard. 24 | 3. In the sidebar menu, locate the "Tracking Code" section and click on it. 25 | 4. You will be redirected to the Tracking Code Generation page. 26 | 27 | ### 3. Customizing the Tracking Code 28 | Awesome Web Analytics allows you to customize the tracking code to suit your specific needs. Here's how you can do it: 29 | 30 | 1. Select the tracking options you want to enable or disable. These options include: 31 | - Page Views 32 | - Click Tracking 33 | - Form Submissions 34 | - E-commerce Tracking 35 | - Custom Events 36 | 37 | 2. Once you have chosen your desired options, click on the "Generate Code" button. 38 | 3. The system will generate a unique tracking code snippet based on your preferences. 39 | 40 | ### 4. Implementing the Tracking Code on Your Website 41 | After generating your tracking code, you need to implement it on your website. Follow these instructions: 42 | 43 | 1. Copy the generated tracking code snippet. 44 | 2. Open the HTML source code of your website in your preferred code editor. 45 | 3. Paste the tracking code snippet just before the closing `` tag of every page on your website. 46 | 4. Save the changes to your website's HTML files. 47 | 5. Publish the updated website. 48 | 49 | ### 5. Troubleshooting 50 | If you encounter any issues during the tracking code implementation, please refer to the following troubleshooting tips: 51 | 52 | - Double-check that the tracking code snippet is placed correctly before the closing `` tag on every page. 53 | - Ensure that the tracking code snippet is not duplicated on any page. 54 | - Verify that your website is not blocking any JavaScript files, including the Awesome Web Analytics tracking code. 55 | - Clear your browser cache and try accessing your website again. 56 | - If the problem persists, please consult our comprehensive troubleshooting guide or contact our support team for further assistance. 57 | 58 | Congratulations! You have successfully generated and implemented the tracking code for your website using Awesome Web Analytics. Now sit back, relax, and let Awesome Web Analytics gather valuable insights about your website visitors and their behavior. 59 | -------------------------------------------------------------------------------- /mktg-email-flow/docs/getting-started.md: -------------------------------------------------------------------------------- 1 | # Getting Started 2 | This page provides an overview of how to get started with Awesome Web Analytics. It covers the steps to sign up for an account, generate a tracking code, and add it to your website. Additionally, it explains how to access your analytics dashboard and navigate through the different features. 3 | 4 | ### Signing Up for an Account 5 | 6 | To begin using Awesome Web Analytics, you will first need to sign up for an account. Follow these steps to create your Awesome Web Analytics account: 7 | 8 | 1. Visit our website at [www.awesomewebanalytics.com](https://www.awesomewebanalytics.com) and click on the "Sign Up" button. 9 | 2. Fill out the registration form with your name, email address, and password. 10 | 3. Read and accept our terms of service and privacy policy. 11 | 4. Click on the "Create Account" button to complete the registration process. 12 | 13 | Once you have successfully signed up, you will receive a confirmation email with further instructions. 14 | 15 | ### Generating a Tracking Code 16 | 17 | After signing up, you will need to generate a tracking code to collect data from your website. The tracking code allows Awesome Web Analytics to monitor visitor activity and provide you with valuable insights. Here's how you can generate a tracking code: 18 | 19 | 1. Log in to your Awesome Web Analytics account using your email address and password. 20 | 2. From the dashboard, navigate to the "Settings" section. 21 | 3. In the settings menu, click on "Tracking Code". 22 | 4. Copy the provided tracking code snippet. 23 | 24 | ### Adding the Tracking Code to Your Website 25 | 26 | Now that you have your tracking code, you need to add it to your website to start collecting data. The process may vary depending on your website platform, but here are the general steps: 27 | 28 | 1. Access the backend or admin panel of your website. 29 | 2. Locate the section where you can add or edit the HTML code of your website. 30 | 3. Paste the tracking code snippet generated from Awesome Web Analytics into the appropriate location. 31 | 4. Save the changes to update your website with the tracking code. 32 | 33 | Once the tracking code is added, Awesome Web Analytics will begin collecting data from your website's visitors. 34 | 35 | ### Accessing Your Analytics Dashboard 36 | 37 | Once your tracking code is in place and your website starts receiving traffic, you can access your analytics dashboard to explore the wealth of information Awesome Web Analytics provides. To access your analytics dashboard: 38 | 39 | 1. Log in to your Awesome Web Analytics account. 40 | 2. From the main menu, click on "Analytics" or "Dashboard". 41 | 3. You will be redirected to your analytics dashboard, where you can view various reports, metrics, and insights about your website's performance. 42 | 43 | ### Navigating Through the Different Features 44 | 45 | Awesome Web Analytics offers a range of features and tools to help you analyze your website's data effectively. Here are some key features you can explore: 46 | 47 | 1. **Real-time Analytics**: Monitor live visitor activity on your website. 48 | 2. **Audience Insights**: Understand your audience demographics, interests, and behavior. 49 | 3. **Traffic Sources**: Identify where your website traffic is coming from. 50 | 4. **Conversion Tracking**: Set up goals and track conversions. 51 | 5. **E-commerce Analytics**: Analyze your online store's performance, sales, and customer behavior. 52 | 53 | To navigate through these features and more, use the menu or sidebar provided in your analytics dashboard. 54 | 55 | Congratulations! You are now ready to make the most of Awesome Web Analytics and gain valuable insights into your website's performance. If you have any further questions or need assistance, feel free to explore our detailed documentation or contact our support team. 56 | -------------------------------------------------------------------------------- /mktg-email-flow/docs/real-time-analytics.md: -------------------------------------------------------------------------------- 1 | # Real-time Analytics 2 | This documentation explains the real-time analytics feature of Awesome Web Analytics. It describes how to access real-time data in the dashboard, including the number of current visitors, their locations, and the pages they are viewing. It also highlights the benefits of real-time analytics and provides use cases for different types of websites. 3 | 4 | ## Table of Contents 5 | 1. Introduction 6 | 2. Accessing Real-time Data 7 | 3. Understanding Real-time Metrics 8 | 4. Benefits of Real-time Analytics 9 | 5. Use Cases 10 | 11 | ## 1. Introduction 12 | Welcome to the documentation for the Real-time Analytics feature of Awesome Web Analytics. This guide will provide you with a comprehensive understanding of how to leverage real-time data to gain valuable insights into your website's performance. Real-time Analytics allows you to monitor the number of current visitors, their locations, and the pages they are viewing in the moment. 13 | 14 | ## 2. Accessing Real-time Data 15 | To access real-time data in the Awesome Web Analytics dashboard, follow these steps: 16 | 17 | 1. Log in to your Awesome Web Analytics account. 18 | 2. Navigate to the Analytics section. 19 | 3. Click on the Real-time Analytics tab. 20 | 21 | Once you are in the Real-time Analytics section, you will have access to up-to-the-minute information about your website's visitors and their activities. 22 | 23 | ## 3. Understanding Real-time Metrics 24 | Awesome Web Analytics provides several key metrics that can help you understand the current state of your website: 25 | 26 | ### 3.1 Current Visitors 27 | The Current Visitors metric shows you the number of people currently active on your website. This metric gives you an instant overview of the level of engagement happening at any given time. 28 | 29 | ### 3.2 Visitor Locations 30 | Awesome Web Analytics's Real-time Analytics feature also provides insights into the geographical locations of your current visitors. You can view a world map with real-time markers indicating the locations of your website visitors. This information can be useful for analyzing the global reach of your website and tailoring your content to specific regions. 31 | 32 | ### 3.3 Pages Being Viewed 33 | Another essential metric in real-time analytics is the Pages Being Viewed. This metric shows you the specific pages that your current visitors are actively viewing. Understanding which pages are most popular in real-time can help you identify trends, optimize content, and improve user experience. 34 | 35 | ## 4. Benefits of Real-time Analytics 36 | Real-time Analytics provides numerous benefits for website owners and marketers: 37 | 38 | ### 4.1 Immediate Insights 39 | By accessing real-time data, you can gain immediate insights into how users are interacting with your website. This allows you to react quickly to trends or issues, making data-driven decisions in real-time. 40 | 41 | ### 4.2 Enhanced User Experience 42 | Real-time Analytics empowers you to identify popular pages and monitor user behavior as it happens. With this information, you can optimize your website's navigation, content, and design to enhance the overall user experience. 43 | 44 | ### 4.3 Real-time Campaign Performance 45 | If you are running marketing campaigns, real-time analytics can help you evaluate their effectiveness promptly. By monitoring the impact of your campaigns in real-time, you can make adjustments on the fly to maximize your return on investment. 46 | 47 | ## 5. Use Cases 48 | Real-time Analytics can be valuable in various scenarios, including: 49 | 50 | ### 5.1 E-commerce Websites 51 | For online stores, real-time analytics can provide insights into customer behavior, allowing you to optimize product placement, promotional offers, and checkout processes to increase conversions. 52 | 53 | ### 5.2 News and Media Websites 54 | News and media websites can leverage real-time analytics to track the popularity of articles, identify trending topics, and tailor content distribution strategies accordingly. 55 | 56 | ### 5.3 Event Websites 57 | Event websites can benefit from real-time analytics by monitoring registrations, tracking attendee engagement, and making real-time adjustments to event schedules or promotions. 58 | 59 | ## Conclusion 60 | Congratulations! You now have a solid understanding of the Real-time Analytics feature in Awesome Web Analytics. By leveraging real-time data, you can gain valuable insights, improve user experience, and make informed decisions to drive the success of your website. 61 | -------------------------------------------------------------------------------- /mktg-email-flow/docs/user-segmentation.md: -------------------------------------------------------------------------------- 1 | # User Segmentation 2 | 3 | This page introduces the user segmentation feature of Awesome Web Analytics. It explains how to segment your website visitors based on various criteria such as demographics, behavior, or custom properties. It demonstrates how to create segments, apply them to your analytics reports, and gain insights into different user groups. It also provides examples of how user segmentation can be utilized for targeted marketing campaigns. 4 | 5 | 6 | ### Introduction 7 | 8 | User segmentation allows you to divide your website visitors into distinct groups based on specific characteristics. This feature enables you to analyze and understand different user behaviors, preferences, and demographics, empowering you to make data-driven decisions. 9 | 10 | ### Creating Segments 11 | 12 | To create a segment in Awesome Web Analytics, follow these steps: 13 | 14 | 1. Access the Awesome Web Analytics dashboard and navigate to the "User Segmentation" section. 15 | 2. Click on the "Create New Segment" button. 16 | 3. Define the criteria for your segment. You can choose from a wide range of options, including demographics (age, gender, location), behavior (pages visited, time spent on site), or even custom properties specific to your business. 17 | 4. Set the conditions for each criterion to precisely define your segment. 18 | 5. Save the segment and give it a meaningful name for easy reference. 19 | 20 | ### Applying Segments to Analytics Reports 21 | 22 | Once you have created your segments, you can apply them to your analytics reports to gain deeper insights into user behavior. Follow these steps to apply segments: 23 | 24 | 1. Navigate to the "Analytics Reports" section in Awesome Web Analytics. 25 | 2. Select the desired report you wish to analyze. 26 | 3. Locate the "Segmentation" option and choose the segment you want to apply. 27 | 4. The report will now display data specifically for the selected segment, allowing you to observe trends and patterns unique to that group of users. 28 | 29 | ### Leveraging User Segmentation for Targeted Marketing Campaigns 30 | 31 | User segmentation is a powerful tool for optimizing your marketing campaigns. By tailoring your strategies to specific user groups, you can increase engagement and conversion rates. Here are some examples of how you can utilize user segmentation for targeted marketing campaigns: 32 | 33 | 1. **Personalized Content:** Customize your website content to cater to the interests and preferences of different user segments. This level of personalization can significantly enhance user experience and drive higher engagement. 34 | 35 | 2. **Email Marketing:** Use user segmentation to send targeted email campaigns based on user behavior or demographics. By delivering relevant content to specific segments, you can improve open rates, click-through rates, and overall campaign effectiveness. 36 | 37 | 3. **Ad Campaign Optimization:** Segment your audience to optimize your ad campaigns. By targeting specific segments with tailored advertisements, you can improve ad relevancy, reduce ad spend wastage, and increase conversion rates. 38 | 39 | 4. **Product Development:** Utilize user segmentation insights to inform your product development decisions. Understanding the needs and preferences of different user segments can guide you in enhancing existing features or developing new ones to better cater to their requirements. 40 | 41 | By leveraging the user segmentation feature in Awesome Web Analytics, you can unlock a wealth of information about your website visitors and make data-driven decisions to drive your business forward. 42 | 43 | For more detailed instructions and advanced user segmentation techniques, refer to the comprehensive user segmentation guide available in the Awesome Web Analytics Knowledge Base. 44 | 45 | We hope this documentation helps you make the most of the User Segmentation feature in Awesome Web Analytics. Should you have any further questions or need assistance, please don't hesitate to reach out to our support team. 46 | -------------------------------------------------------------------------------- /nodejs-with-singlestore/.env.example: -------------------------------------------------------------------------------- 1 | DB_HOST="" 2 | DB_USER="" 3 | DB_PORT=3333 4 | DB_NAME="" 5 | DB_PASSWORD="" 6 | OPENAI_API_KEY="" 7 | -------------------------------------------------------------------------------- /nodejs-with-singlestore/.gitignore: -------------------------------------------------------------------------------- 1 | # See https://help.github.com/articles/ignoring-files/ for more about ignoring files. 2 | 3 | # dependencies 4 | /node_modules 5 | /.pnp 6 | .pnp.js 7 | .yarn/install-state.gz 8 | package-lock.json 9 | 10 | # testing 11 | /coverage 12 | 13 | # next.js 14 | /.next/ 15 | /out/ 16 | 17 | # production 18 | /build 19 | 20 | # misc 21 | .DS_Store 22 | *.pem 23 | 24 | # debug 25 | npm-debug.log* 26 | yarn-debug.log* 27 | yarn-error.log* 28 | 29 | # local env files 30 | .env 31 | .env*.local 32 | 33 | # vercel 34 | .vercel 35 | 36 | # typescript 37 | *.tsbuildinfo 38 | next-env.d.ts 39 | -------------------------------------------------------------------------------- /nodejs-with-singlestore/.prettierrc.js: -------------------------------------------------------------------------------- 1 | // prettier.config.js, .prettierrc.js, prettier.config.mjs, or .prettierrc.mjs 2 | 3 | /** 4 | * @see https://prettier.io/docs/en/configuration.html 5 | * @type {import("prettier").Config} 6 | */ 7 | module.exports = { 8 | arrowParens: "always", 9 | bracketSameLine: false, 10 | bracketSpacing: true, 11 | jsxSingleQuote: false, 12 | printWidth: 128, 13 | quoteProps: "consistent", 14 | semi: true, 15 | singleAttributePerLine: true, 16 | singleQuote: false, 17 | tabWidth: 2, 18 | trailingComma: "all", 19 | useTabs: false, 20 | }; 21 | -------------------------------------------------------------------------------- /nodejs-with-singlestore/README.md: -------------------------------------------------------------------------------- 1 | # NodeJS With SingleStore Expenses Demo App 2 | 3 | ## Getting Started 4 | 5 | 1. Download an SSL certificate using this [link](https://portal.singlestore.com/static/ca/singlestore_bundle.pem). 6 | 2. Place the SSL certificate in the root directory of the project. 7 | 3. Create a `.env` file based on the `.env.example` file. 8 | 4. Install the dependencies by running `npm install`. 9 | 5. Load data into a database by running `npm run load`. 10 | 6. Run the server by running `npm run start`. 11 | 12 | ## API 13 | 14 | #### Get All Expenses 15 | 16 | ``` 17 | GET http://localhost:3000/expenses 18 | ``` 19 | 20 | #### Get Filtered Expenses 21 | 22 | ``` 23 | GET http://localhost:3000/expenses?merchant=Airbnb&category=Travel 24 | ``` 25 | 26 | #### Get Expenses Record by ID 27 | 28 | ``` 29 | GET http://localhost:3000/expenses/:id 30 | ``` 31 | 32 | #### Update Expenses Record by ID 33 | 34 | ``` 35 | PUT http://localhost:3000/expenses/:id 36 | ``` 37 | 38 | #### Delete Expenses Record by ID 39 | 40 | ``` 41 | DELETE http://localhost:3000/expenses/:id 42 | ``` 43 | 44 | #### Search Expenses 45 | 46 | ``` 47 | POST http://localhost:3000/expenses/search 48 | 49 | { 50 | "query": "Travel with Airbnb" 51 | } 52 | ``` 53 | 54 | #### Ask Assistant 55 | 56 | ``` 57 | POST http://localhost:3000/expenses/ask 58 | 59 | { 60 | "query": "How much have I spent on Netfix?" 61 | } 62 | ``` -------------------------------------------------------------------------------- /nodejs-with-singlestore/app.js: -------------------------------------------------------------------------------- 1 | const express = require("express"); 2 | const { ChatCompletionTool } = require("@singlestore/ai"); 3 | const z = require("zod"); 4 | const { ai, database } = require("./lib/db"); 5 | 6 | const app = express(); 7 | app 8 | .use(express.json()) 9 | .use((error, _req, res, next) => { 10 | console.error(error.stack); 11 | res.status(500).send(error.message || "Internal Server Error"); 12 | }); 13 | ; 14 | 15 | const expensesTable = database.table.use("expenses"); 16 | 17 | app.get("/expenses", async (req, res, next) => { 18 | try { 19 | const { merchant, category } = req.query; 20 | 21 | const rows = await expensesTable.find({ 22 | where: { 23 | merchant, 24 | category, 25 | }, 26 | }); 27 | 28 | res.status(200).json(rows); 29 | } catch (error) { 30 | next(error); 31 | } 32 | }); 33 | 34 | app.get("/expenses/:id", async (req, res, next) => { 35 | try { 36 | const [row] = await expensesTable.find({ where: { id: req.params.id } }); 37 | res.status(200).json(row); 38 | } catch (error) { 39 | next(error); 40 | } 41 | }); 42 | 43 | app.put("/expenses/:id", async (req, res, next) => { 44 | try { 45 | const { id, ...values } = req.body; 46 | await expensesTable.update(values, { id: req.params.id }); 47 | res.status(200).send(); 48 | } catch (error) { 49 | next(error); 50 | } 51 | }); 52 | 53 | app.delete("/expenses/:id", async (_req, res, next) => { 54 | try { 55 | await expensesTable.delete({ id: res.params.id }); 56 | res.status(200).send(); 57 | } catch (error) { 58 | next(error); 59 | } 60 | }); 61 | 62 | app.post("/expenses/search", async (req, res, next) => { 63 | try { 64 | const { query } = req.body; 65 | 66 | const tableSchema = await expensesTable.showColumnsInfo(true); 67 | 68 | const completion = await ai.chatCompletions.create({ 69 | model: "gpt-4o", 70 | prompt: `\ 71 | User prompt: ${query} 72 | Table schema: ${JSON.stringify(tableSchema)} 73 | 74 | Based on the table schema, parse the user's prompt into parameters. 75 | Include only the JSON value without any formatting in your response to make it ready for use with the JS JSON.parse method. 76 | If there is an issue return an empty JSON value. 77 | `, 78 | }); 79 | 80 | const { category, merchant, amount } = JSON.parse(completion.content); 81 | 82 | const rows = await expensesTable.find({ 83 | where: { 84 | category, 85 | merchant, 86 | amount: amount ? Number(amount) : undefined, 87 | }, 88 | }); 89 | 90 | res.status(200).json(rows); 91 | } catch (error) { 92 | console.log(error); 93 | next(error); 94 | } 95 | }); 96 | 97 | app.post("/expenses/ask", async (req, res, next) => { 98 | try { 99 | const { query } = req.body; 100 | 101 | const queryTableTool = new ChatCompletionTool({ 102 | name: "query_table", 103 | description: 104 | "Generates and executes a MySQL SELECT query based on a natural language prompt, adhering to the provided table schema.", 105 | params: z.object({ 106 | prompt: z.string().describe("A natural language description of the data you wish to retrieve from the table."), 107 | }), 108 | call: async (params) => { 109 | let value = ""; 110 | const schema = await expensesTable.showColumnsInfo(); 111 | 112 | const gptQuery = await ai.chatCompletions.create({ 113 | stream: false, 114 | model: "gpt-4o", 115 | prompt: params.prompt, 116 | systemRole: `\ 117 | You are a MySQL database expert. 118 | Generate a valid MySQL SELECT query based on the following table schema: ${JSON.stringify(schema)} 119 | 120 | The query must adhere to these rules: 121 | - Only SELECT operations are allowed. 122 | 123 | Respond with the MySQL query only, without any formatting. 124 | For example: "SELECT * FROM expenses" 125 | `, 126 | }); 127 | 128 | if (gptQuery && "content" in gptQuery && typeof gptQuery.content === "string") { 129 | const [rows] = await database.query(gptQuery.content); 130 | value = JSON.stringify(rows); 131 | } 132 | 133 | return { name: "query_table", params, value }; 134 | }, 135 | }); 136 | 137 | const completion = await ai.chatCompletions.create({ 138 | model: "gpt-4o", 139 | prompt: query, 140 | stream: false, 141 | systemRole: `\ 142 | You are a knowledgeable assistant focused on helping the user with queries related to the "expenses" table.\ 143 | Provide accurate and relevant answers based on the structure and data in the "expenses" table, and assist with any related tasks or requests. 144 | `, 145 | tools: [queryTableTool], 146 | }); 147 | 148 | res.status(200).send(completion.content); 149 | } catch (error) { 150 | next(error); 151 | } 152 | }); 153 | 154 | const PORT = process.env.PORT || 3000; 155 | app.listen(PORT, async () => { 156 | console.log(`Server is running on port ${PORT}`); 157 | }); 158 | -------------------------------------------------------------------------------- /nodejs-with-singlestore/lib/db.js: -------------------------------------------------------------------------------- 1 | const { AI } = require("@singlestore/ai"); 2 | const { SingleStoreClient } = require("@singlestore/client"); 3 | const fs = require("fs"); 4 | const path = require('path'); 5 | require("dotenv").config(); 6 | 7 | const ai = new AI({ openAIApiKey: process.env.OPENAI_API_KEY }); 8 | const client = new SingleStoreClient({ ai }); 9 | 10 | const certPath = path.join(__dirname, 'singlestore_bundle.pem'); 11 | console.log('Looking for cert at:', certPath); 12 | 13 | const connection = client.connect({ 14 | host: process.env.DB_HOST, 15 | user: process.env.DB_USER, 16 | port: Number(process.env.DB_PORT), 17 | database: process.env.DB_NAME, 18 | password: process.env.DB_PASSWORD, 19 | ssl: { 20 | ca: fs.readFileSync(certPath), 21 | }, 22 | }); 23 | 24 | const database = connection.database.use(process.env.DB_NAME); 25 | 26 | module.exports = { 27 | ai, 28 | client, 29 | connection, 30 | database, 31 | }; 32 | -------------------------------------------------------------------------------- /nodejs-with-singlestore/lib/load.js: -------------------------------------------------------------------------------- 1 | const csv = require("csv-parser"); 2 | const path = require("path"); 3 | const fs = require("fs"); 4 | const { database } = require("./db"); 5 | 6 | (async () => { 7 | try { 8 | await database.table.drop("expenses"); 9 | 10 | const table = await database.table.create({ 11 | name: "expenses", 12 | columns: { 13 | id: { type: "BIGINT", autoIncrement: true, primaryKey: true }, 14 | createdAt: { type: "DATETIME(6)", default: "CURRENT_TIMESTAMP(6)" }, 15 | merchant: { type: "VARCHAR(128)" }, 16 | category: { type: "VARCHAR(64)" }, 17 | amount: { type: "DECIMAL(10, 2)" }, 18 | }, 19 | }); 20 | 21 | const dataset = await (() => { 22 | return new Promise((resolve, reject) => { 23 | const result = []; 24 | fs.createReadStream(path.resolve(process.cwd(), "dataset.csv")) 25 | .pipe(csv()) 26 | .on("data", ({ createdAt, merchant, category, amount }) => { 27 | result.push({ 28 | createdAt: new Date(createdAt), 29 | merchant, 30 | category, 31 | amount: Number(amount), 32 | }); 33 | }) 34 | .on("error", (error) => reject(error)) 35 | .on("end", () => resolve(result)); 36 | }); 37 | })(); 38 | 39 | await table.insert(dataset); 40 | 41 | process.exit(0); 42 | } catch (error) { 43 | console.error(error); 44 | process.exit(1); 45 | } 46 | })(); 47 | -------------------------------------------------------------------------------- /nodejs-with-singlestore/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "nodejs-with-singlestore-demo", 3 | "version": "1.0.0", 4 | "main": "app.js", 5 | "scripts": { 6 | "start": "node app.js", 7 | "dev": "nodemon app.js", 8 | "load": "node lib/load.js", 9 | "prettify": "prettier --write \"**/*.{js,jsx,ts,tsx,md}\"" 10 | }, 11 | "keywords": [], 12 | "author": "", 13 | "license": "ISC", 14 | "description": "", 15 | "dependencies": { 16 | "@singlestore/ai": "^0.0.32", 17 | "@singlestore/client": "^0.0.43", 18 | "csv-parser": "^3.0.0", 19 | "dotenv": "^16.4.5", 20 | "express": "^4.19.2", 21 | "zod": "^3.23.8" 22 | }, 23 | "devDependencies": { 24 | "nodemon": "^3.1.7", 25 | "prettier": "^3.3.3" 26 | } 27 | } 28 | -------------------------------------------------------------------------------- /nodejs-with-singlestore/vercel.json: -------------------------------------------------------------------------------- 1 | { 2 | "version": 2, 3 | "builds": [ 4 | { 5 | "src": "app.js", 6 | "use": "@vercel/node" 7 | } 8 | ], 9 | "routes": [ 10 | { 11 | "src": "/(.*)", 12 | "dest": "app.js" 13 | } 14 | ] 15 | } 16 | -------------------------------------------------------------------------------- /openai-plugin-webinar/create_table_and_insert_from_csv.ipynb: -------------------------------------------------------------------------------- 1 | {"cells":[{"cell_type":"markdown","id":"97cf39b7-a787-44bc-9255-13a523307b16","metadata":{},"source":"## Install Libraries"},{"cell_type":"code","execution_count":182,"id":"96b0c76c-cc0f-4593-af04-94322fe3a19a","metadata":{"execution":{"iopub.execute_input":"2023-07-17T23:24:14.461738Z","iopub.status.busy":"2023-07-17T23:24:14.461396Z","iopub.status.idle":"2023-07-17T23:24:16.502095Z","shell.execute_reply":"2023-07-17T23:24:16.500841Z","shell.execute_reply.started":"2023-07-17T23:24:14.461714Z"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"Note: you may need to restart the kernel to use updated packages.\n"}],"source":"%pip install pandas requests --quiet"},{"cell_type":"markdown","id":"594235eb-7e7a-4f81-a0d7-4209956860a6","metadata":{},"source":"## Connect to database"},{"cell_type":"code","execution_count":185,"id":"7ea1ff05-4282-48df-bd48-e9ce7927555a","metadata":{"execution":{"iopub.execute_input":"2023-07-17T23:24:29.763728Z","iopub.status.busy":"2023-07-17T23:24:29.763342Z","iopub.status.idle":"2023-07-17T23:24:30.014257Z","shell.execute_reply":"2023-07-17T23:24:30.013504Z","shell.execute_reply.started":"2023-07-17T23:24:29.763701Z"},"tags":[],"trusted":true},"outputs":[{"data":{"text/html":"\u003ctable\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003ecount(8)\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e40\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e","text/plain":"+----------+\n| count(8) |\n+----------+\n| 40 |\n+----------+"},"execution_count":185,"metadata":{},"output_type":"execute_result"}],"source":"%%sql\nUSE winter_wikipedia;\nselect count(8) from my_book;"},{"cell_type":"markdown","id":"aadbfd16-5274-45d5-a5a6-fea6614b76bb","metadata":{},"source":"## Read the file into Pandas frame"},{"cell_type":"code","execution_count":187,"id":"5b817e34-f8f5-4515-bbd2-6e875d6e2b8b","metadata":{"execution":{"iopub.execute_input":"2023-07-17T23:24:35.289256Z","iopub.status.busy":"2023-07-17T23:24:35.288984Z","iopub.status.idle":"2023-07-17T23:24:35.859898Z","shell.execute_reply":"2023-07-17T23:24:35.859086Z","shell.execute_reply.started":"2023-07-17T23:24:35.289236Z"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":" userId movieId rating timestamp\n0 1 31 2.5 1260759144\n1 1 1029 3.0 1260759179\n2 1 1061 3.0 1260759182\n3 1 1129 2.0 1260759185\n4 1 1172 4.0 1260759205\n"}],"source":"import pandas as pd\nimport requests\nfrom io import StringIO\n\n# Provide the URL of your CSV file\nurl = \"https://raw.githubusercontent.com/madhukarkumar/my_csvs/main/ratings_small.csv\"\n\n# Use requests to get the content of the CSV file\ncontent = requests.get(url).text\n\n# Create a pandas DataFrame\ndf = pd.read_csv(StringIO(content))\n\n# Show the first 5 rows of the dataframe\nprint(df.head())"},{"cell_type":"code","execution_count":null,"id":"3ef49732-e95b-49ce-ad40-99b49a68b5cb","metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":188,"id":"3c614831-6ddb-4baf-bb3a-450beb1e8220","metadata":{"execution":{"iopub.execute_input":"2023-07-17T23:24:40.515201Z","iopub.status.busy":"2023-07-17T23:24:40.514686Z","iopub.status.idle":"2023-07-17T23:24:40.519836Z","shell.execute_reply":"2023-07-17T23:24:40.519112Z","shell.execute_reply.started":"2023-07-17T23:24:40.515174Z"},"tags":[],"trusted":true},"outputs":[],"source":"from sqlalchemy import *\n\ndb_connection = create_engine(connection_url)"},{"cell_type":"markdown","id":"2c8dd006-7d23-47d5-8e54-9a82f135a711","metadata":{},"source":"## Create table based on the columns of the CSV file"},{"cell_type":"code","execution_count":191,"id":"456c3da6-ff41-4521-8a34-2b31c04698d5","metadata":{"execution":{"iopub.execute_input":"2023-07-17T23:25:04.723048Z","iopub.status.busy":"2023-07-17T23:25:04.722659Z","iopub.status.idle":"2023-07-17T23:25:06.025955Z","shell.execute_reply":"2023-07-17T23:25:06.025322Z","shell.execute_reply.started":"2023-07-17T23:25:04.723021Z"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":"\u003csqlalchemy.engine.cursor.LegacyCursorResult at 0x7f6c966f2950\u003e"},"execution_count":191,"metadata":{},"output_type":"execute_result"}],"source":"\n# Generate a CREATE TABLE statement based on the DataFrame's dtypes\ncreate_table_statement = \"CREATE TABLE IF NOT EXISTS csv_table (\"\n\nfor col, dtype in df.dtypes.items():\n if \"int\" in str(dtype):\n sql_type = \"INT\"\n elif \"float\" in str(dtype):\n sql_type = \"FLOAT\"\n elif \"object\" in str(dtype):\n sql_type = \"TEXT\"\n else:\n sql_type = \"TEXT\"\n create_table_statement += \"\\n{} {},\".format(col, sql_type)\n\ncreate_table_statement = create_table_statement.rstrip(\",\") + \"\\n);\"\n\n# Execute the CREATE TABLE statement in the database\ndb_connection.execute(create_table_statement)\n"},{"cell_type":"markdown","id":"824b815a-9ff3-4fd2-91e9-01512e8da4e1","metadata":{},"source":"## Load the data into the CSV file"},{"cell_type":"code","execution_count":null,"id":"bc9053af-e36b-4121-b912-33b6744f40bc","metadata":{"execution":{"iopub.execute_input":"2023-07-17T23:25:16.173994Z","iopub.status.busy":"2023-07-17T23:25:16.173451Z"},"tags":[],"trusted":true},"outputs":[],"source":"# Insert the data row by row\nfor i, row in df.iterrows():\n placeholders = ', '.join(['%s'] * len(row))\n columns = ', '.join(row.keys())\n sql = \"INSERT INTO csv_table ( %s ) VALUES ( %s )\" % (columns, placeholders)\n db_connection.execute(sql, tuple(row))\n"},{"cell_type":"code","execution_count":null,"id":"cfbecfb6-f91e-4d0c-8849-96399a554bfa","metadata":{},"outputs":[],"source":""}],"metadata":{"jupyterlab":{"notebooks":{"version_major":6,"version_minor":4}},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.9"},"singlestore_connection":{"connectionID":"eff5c501-ff0f-4a14-b3c0-c72858bb7a02","defaultDatabase":"winter_wikipedia"},"singlestore_row_limit":300},"nbformat":4,"nbformat_minor":5} -------------------------------------------------------------------------------- /spark_webinar_3.ipynb: -------------------------------------------------------------------------------- 1 | {"cells":[{"attachments":{},"cell_type":"markdown","id":"b52452b1-90e7-4e77-a4aa-4109c5fb79c4","metadata":{"language":"python"},"source":"
![](\"https://github.com/singlestore-labs/spaces-notebooks/blob/e551e274bb67bb1e5081131ee1150cdba713fc43/common/images/singlestore-jupyter.png?raw=true\")
"},{"attachments":{},"cell_type":"markdown","id":"e83a3630-3c94-4bc6-ab80-18563b926388","metadata":{"language":"python"},"source":"\n
"},{"cell_type":"code","execution_count":4,"id":"31d548b8-4622-422a-9bbd-468477ee88fb","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:53:55.997919Z","iopub.status.busy":"2024-04-16T15:53:55.997653Z","iopub.status.idle":"2024-04-16T15:53:56.441762Z","shell.execute_reply":"2024-04-16T15:53:56.441128Z","shell.execute_reply.started":"2024-04-16T15:53:55.997898Z"},"language":"python","trusted":true},"outputs":[],"source":"!pip cache purge --quiet"},{"cell_type":"code","execution_count":5,"id":"4aed2148-2b94-4eb0-95e1-dd6e9a2b3dc9","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:53:56.443999Z","iopub.status.busy":"2024-04-16T15:53:56.442934Z","iopub.status.idle":"2024-04-16T15:54:35.202039Z","shell.execute_reply":"2024-04-16T15:54:35.201374Z","shell.execute_reply.started":"2024-04-16T15:53:56.443980Z"},"language":"python","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"Collecting package metadata (current_repodata.json): ...working... done\nSolving environment: ...working... done\n\n# All requested packages already installed.\n\n"}],"source":"!conda install -y --quiet -c conda-forge openjdk pyspark"},{"cell_type":"code","execution_count":6,"id":"7f8b6247-0e7e-4a95-832b-c116cc8192f9","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:54:35.203322Z","iopub.status.busy":"2024-04-16T15:54:35.203036Z","iopub.status.idle":"2024-04-16T15:54:40.620410Z","shell.execute_reply":"2024-04-16T15:54:40.619702Z","shell.execute_reply.started":"2024-04-16T15:54:35.203287Z"},"language":"python","trusted":true},"outputs":[],"source":"!pip uninstall langchain-openai -y --quiet\n!pip install openai==0.28 --quiet\n!pip install nltk --quiet"},{"cell_type":"code","execution_count":7,"id":"c3923018-1424-4586-ad14-242ad6eafa78","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:54:40.622642Z","iopub.status.busy":"2024-04-16T15:54:40.622411Z","iopub.status.idle":"2024-04-16T15:54:51.405623Z","shell.execute_reply":"2024-04-16T15:54:51.405116Z","shell.execute_reply.started":"2024-04-16T15:54:40.622619Z"},"language":"python","trusted":true},"outputs":[{"name":"stdin","output_type":"stream","text":"OpenAI API Key: ········\n"}],"source":"import getpass\nimport openai\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"},{"cell_type":"code","execution_count":8,"id":"a3a23d21-013d-412e-a120-4c56d9111af8","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:54:51.407043Z","iopub.status.busy":"2024-04-16T15:54:51.406284Z","iopub.status.idle":"2024-04-16T15:54:51.410356Z","shell.execute_reply":"2024-04-16T15:54:51.409879Z","shell.execute_reply.started":"2024-04-16T15:54:51.407020Z"},"language":"python","trusted":true},"outputs":[],"source":"import os\n\nos.makedirs(\"jars\", exist_ok = True)\nos.makedirs(\"data\", exist_ok = True)"},{"cell_type":"code","execution_count":9,"id":"d935b474-40a7-46ec-9ded-51cc2a572ca3","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:54:51.411502Z","iopub.status.busy":"2024-04-16T15:54:51.411102Z","iopub.status.idle":"2024-04-16T15:54:51.692509Z","shell.execute_reply":"2024-04-16T15:54:51.691952Z","shell.execute_reply.started":"2024-04-16T15:54:51.411482Z"},"language":"python","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"JAR files downloaded successfully\n"}],"source":"import requests\n\ndef download_jar(url, destination):\n response = requests.get(url)\n with open(destination, \"wb\") as f:\n f.write(response.content)\n\njar_urls = [\n (\"https://repo1.maven.org/maven2/com/singlestore/singlestore-jdbc-client/1.2.1/singlestore-jdbc-client-1.2.1.jar\", \"jars/singlestore-jdbc-client-1.2.1.jar\"),\n (\"https://repo1.maven.org/maven2/com/singlestore/singlestore-spark-connector_2.12/4.1.5-spark-3.5.0/singlestore-spark-connector_2.12-4.1.5-spark-3.5.0.jar\", \"jars/singlestore-spark-connector_2.12-4.1.5-spark-3.5.0.jar\"),\n (\"https://repo1.maven.org/maven2/org/apache/commons/commons-dbcp2/2.12.0/commons-dbcp2-2.12.0.jar\", \"jars/commons-dbcp2-2.12.0.jar\"),\n (\"https://repo1.maven.org/maven2/org/apache/commons/commons-pool2/2.12.0/commons-pool2-2.12.0.jar\", \"jars/commons-pool2-2.12.0.jar\"),\n (\"https://repo1.maven.org/maven2/io/spray/spray-json_3/1.3.6/spray-json_3-1.3.6.jar\", \"jars/spray-json_3-1.3.6.jar\")\n]\n\nfor url, destination in jar_urls:\n download_jar(url, destination)\n\nprint(\"JAR files downloaded successfully\")"},{"cell_type":"code","execution_count":10,"id":"de40d2d2-3558-4d47-bf86-a6d766b8d5d8","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:54:51.694548Z","iopub.status.busy":"2024-04-16T15:54:51.694340Z","iopub.status.idle":"2024-04-16T15:54:56.238967Z","shell.execute_reply":"2024-04-16T15:54:56.238280Z","shell.execute_reply.started":"2024-04-16T15:54:51.694534Z"},"language":"python","trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":"24/04/16 15:54:53 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\nSetting default log level to \"WARN\".\nTo adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n24/04/16 15:54:55 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n"}],"source":"from pyspark.sql import SparkSession\n\n# Create a Spark session\nspark = (SparkSession\n .builder\n .config(\"spark.jars\", \",\".join([destination for _, destination in jar_urls]))\n .appName(\"Spark Webinar\")\n .getOrCreate()\n )\n\nspark.sparkContext.setLogLevel(\"ERROR\")"},{"cell_type":"code","execution_count":11,"id":"06a810e8-bc76-4f9e-95b9-51db46304828","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:54:56.239823Z","iopub.status.busy":"2024-04-16T15:54:56.239666Z","iopub.status.idle":"2024-04-16T15:54:56.860164Z","shell.execute_reply":"2024-04-16T15:54:56.859667Z","shell.execute_reply.started":"2024-04-16T15:54:56.239806Z"},"language":"python","trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":"[nltk_data] Downloading package punkt to /home/jovyan/nltk_data...\n[nltk_data] Package punkt is already up-to-date!\n[nltk_data] Downloading package averaged_perceptron_tagger to\n[nltk_data] /home/jovyan/nltk_data...\n[nltk_data] Package averaged_perceptron_tagger is already up-to-\n[nltk_data] date!\n[nltk_data] Downloading package wordnet to /home/jovyan/nltk_data...\n[nltk_data] Package wordnet is already up-to-date!\n[nltk_data] Downloading package omw to /home/jovyan/nltk_data...\n[nltk_data] Package omw is already up-to-date!\n"},{"data":{"text/plain":"True"},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":"import nltk\nimport random\nfrom nltk.corpus import wordnet as wn\nfrom nltk.tokenize import word_tokenize\n\n# Download NLTK\nnltk.download(\"punkt\")\nnltk.download(\"averaged_perceptron_tagger\")\nnltk.download(\"wordnet\")\nnltk.download(\"omw\")"},{"cell_type":"code","execution_count":12,"id":"cafc9dbb-0b3f-4054-a208-7d5a1d60e54f","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:54:56.861335Z","iopub.status.busy":"2024-04-16T15:54:56.860968Z","iopub.status.idle":"2024-04-16T15:55:04.503301Z","shell.execute_reply":"2024-04-16T15:55:04.502692Z","shell.execute_reply.started":"2024-04-16T15:54:56.861315Z"},"language":"python","trusted":true},"outputs":[],"source":"# Define the directory to save the files\noutput_dir = \"data\"\n\n# Generate meaningful sentences\ndef generate_meaningful_sentence():\n # Choose a random set of synonyms from WordNet\n synset = random.choice(list(wn.all_synsets()))\n\n # Generate a sentence\n definition = synset.definition()\n tokens = word_tokenize(definition)\n\n # Capitalise the first word and end with a period\n tokens[0] = tokens[0].capitalize()\n tokens[-1] = tokens[-1] + \".\"\n\n return \" \".join(tokens)\n\n# Number of files to generate\nnum_files = 5\n\n# Number of sentences in each file\nnum_sentences_per_file = 1\n\n# Generate text files\nfor i in range(num_files):\n file_path = os.path.join(output_dir, f\"file_{i+1}.txt\")\n with open(file_path, \"w\") as file:\n for _ in range(num_sentences_per_file):\n # Generate a meaningful sentence\n sentence = generate_meaningful_sentence()\n file.write(sentence + \"\\n\")"},{"cell_type":"code","execution_count":13,"id":"db8ec8cb-d0e1-4bd9-bec6-fe63c9f09ba3","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:55:04.505911Z","iopub.status.busy":"2024-04-16T15:55:04.505402Z","iopub.status.idle":"2024-04-16T15:55:04.529341Z","shell.execute_reply":"2024-04-16T15:55:04.528819Z","shell.execute_reply.started":"2024-04-16T15:55:04.505891Z"},"language":"python","trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":"File: data/file_1.txt\nJump on skis.\n----------------------\nFile: data/file_2.txt\nHybrid willow usually not strongly weeping in habit.\n----------------------\nFile: data/file_3.txt\nWide-ranging light-brown frog of moist North American woodlands especially spruce.\n----------------------\nFile: data/file_4.txt\nA tear gas that is stronger than CN gas but wears off faster ; can be deployed by grenades or cluster bombs ; can cause skin burns and fatal pulmonary edema.\n----------------------\nFile: data/file_5.txt\nIndicating continuing action ; continuously or steadily.\n----------------------\n"}],"source":"%%bash\n\nfor file in data/*.txt; do\n echo \"File: $file\"\n cat \"$file\"\n echo \"----------------------\"\ndone"},{"cell_type":"code","execution_count":14,"id":"159866a7-ddbb-4f71-9486-19e40d407c58","metadata":{"execution":{"iopub.execute_input":"2024-04-16T15:55:04.529944Z","iopub.status.busy":"2024-04-16T15:55:04.529810Z","iopub.status.idle":"2024-04-16T15:55:40.694406Z","shell.execute_reply":"2024-04-16T15:55:40.693868Z","shell.execute_reply.started":"2024-04-16T15:55:04.529933Z"},"language":"python","trusted":true},"outputs":[{"name":"stdin","output_type":"stream","text":"Password: ········\n"}],"source":"import getpass\n\nhost = \"\n ![](\"https://raw.githubusercontent.com/singlestore-labs/spaces-notebooks/master/common/images/header-icons/browser.png\")
\n
\n \n \n
\nSingleStore Notebooks
\n Apache Spark + OpenAI for Personalized Banking Services, Part 3
\n| value | \nfile_name | \nembedding | \n
|---|---|---|
| Indicating continuing action ; | \nfile_5.txt | \n[0.0237321071,0.0341514125,0.0182303879,0.03942219 | \n
| Wide-ranging light-brown frog | \nfile_3.txt | \n[-0.00194031477,-0.0134520773,0.0547336452,0.01292 | \n
| Jump on skis. | \nfile_1.txt | \n[0.030417813,0.0341594741,0.0108481226,-0.03507469 | \n
| A tear gas that is stronger th | \nfile_4.txt | \n[0.0302113201,0.0374153629,0.00788259227,0.0550957 | \n
| Hybrid willow usually not stro | \nfile_2.txt | \n[0.0352415368,0.0366364606,-0.00554679148,0.038584 | \n
| value | \nfile_name | \nsimilarity | \n
|---|---|---|
| Wide-ranging light-brown frog | \nfile_3.txt | \n1.1131780208445137 | \n
| Jump on skis. | \nfile_1.txt | \n1.2943939467682306 | \n
| Indicating continuing action ; | \nfile_5.txt | \n1.3149931253318798 | \n
| Hybrid willow usually not stro | \nfile_2.txt | \n1.319062131851387 | \n
| A tear gas that is stronger th | \nfile_4.txt | \n1.3730401463567572 | \n
\n\n\n \n
\n
","text/plain":" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n0 7.4 0.70 0.00 1.9 0.076 \n1 7.8 0.88 0.00 2.6 0.098 \n2 7.8 0.76 0.04 2.3 0.092 \n3 11.2 0.28 0.56 1.9 0.075 \n4 7.4 0.70 0.00 1.9 0.076 \n\n free sulfur dioxide total sulfur dioxide density pH sulphates \\\n0 11.0 34.0 0.9978 3.51 0.56 \n1 25.0 67.0 0.9968 3.20 0.68 \n2 15.0 54.0 0.9970 3.26 0.65 \n3 17.0 60.0 0.9980 3.16 0.58 \n4 11.0 34.0 0.9978 3.51 0.56 \n\n alcohol quality \n0 9.4 5 \n1 9.8 5 \n2 9.8 5 \n3 9.8 6 \n4 9.4 5 "},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":"github_url = 'https://raw.githubusercontent.com/aniruddhachoudhury/Red-Wine-Quality/master/winequality-red.csv'\nwine_data = pd.read_csv(github_url)\nwine_data.head()"},{"attachments":{},"cell_type":"markdown","id":"46696c2d-42c0-41d2-8504-158f73f85f1e","metadata":{"language":"python"},"source":"## Using scikit-learn"},{"cell_type":"code","execution_count":8,"id":"e432fcb0-793f-43b2-a71b-7932844887ff","metadata":{"execution":{"iopub.execute_input":"2024-01-29T13:46:15.256410Z","iopub.status.busy":"2024-01-29T13:46:15.256172Z","iopub.status.idle":"2024-01-29T13:46:15.637916Z","shell.execute_reply":"2024-01-29T13:46:15.637125Z","shell.execute_reply.started":"2024-01-29T13:46:15.256395Z"},"language":"python","trusted":true},"outputs":[{"data":{"text/html":"| \n | fixed acidity | \nvolatile acidity | \ncitric acid | \nresidual sugar | \nchlorides | \nfree sulfur dioxide | \ntotal sulfur dioxide | \ndensity | \npH | \nsulphates | \nalcohol | \nquality | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n7.4 | \n0.70 | \n0.00 | \n1.9 | \n0.076 | \n11.0 | \n34.0 | \n0.9978 | \n3.51 | \n0.56 | \n9.4 | \n5 | \n
| 1 | \n7.8 | \n0.88 | \n0.00 | \n2.6 | \n0.098 | \n25.0 | \n67.0 | \n0.9968 | \n3.20 | \n0.68 | \n9.8 | \n5 | \n
| 2 | \n7.8 | \n0.76 | \n0.04 | \n2.3 | \n0.092 | \n15.0 | \n54.0 | \n0.9970 | \n3.26 | \n0.65 | \n9.8 | \n5 | \n
| 3 | \n11.2 | \n0.28 | \n0.56 | \n1.9 | \n0.075 | \n17.0 | \n60.0 | \n0.9980 | \n3.16 | \n0.58 | \n9.8 | \n6 | \n
| 4 | \n7.4 | \n0.70 | \n0.00 | \n1.9 | \n0.076 | \n11.0 | \n34.0 | \n0.9978 | \n3.51 | \n0.56 | \n9.4 | \n5 | \n
\n\n\n \n
\n
","text/plain":" fixed_acidity volatile_acidity citric_acid residual_sugar chlorides \\\n0 7.4 0.70 0.00 1.9 0.076 \n1 7.8 0.88 0.00 2.6 0.098 \n2 7.8 0.76 0.04 2.3 0.092 \n3 11.2 0.28 0.56 1.9 0.075 \n4 7.4 0.70 0.00 1.9 0.076 \n\n free_sulfur_dioxide total_sulfur_dioxide density pH sulphates \\\n0 11.0 34.0 0.9978 3.51 0.56 \n1 25.0 67.0 0.9968 3.20 0.68 \n2 15.0 54.0 0.9970 3.26 0.65 \n3 17.0 60.0 0.9980 3.16 0.58 \n4 11.0 34.0 0.9978 3.51 0.56 \n\n alcohol \n0 9.4 \n1 9.8 \n2 9.8 \n3 9.8 \n4 9.4 "},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":"import sklearn\nfrom sklearn.datasets import fetch_openml\n\ndata = fetch_openml(name='wine-quality-red', version=1)\nwine_data = data.data\nwine_data.head()"},{"attachments":{},"cell_type":"markdown","id":"b5098c2d-b213-4dbc-af7f-c57e921982ad","metadata":{"language":"python"},"source":"## Using Google Drive"},{"cell_type":"code","execution_count":10,"id":"b56dab8c-19f8-495e-83c3-d749684673f7","metadata":{"execution":{"iopub.execute_input":"2024-01-29T13:46:24.872892Z","iopub.status.busy":"2024-01-29T13:46:24.872621Z","iopub.status.idle":"2024-01-29T13:46:25.309002Z","shell.execute_reply":"2024-01-29T13:46:25.308457Z","shell.execute_reply.started":"2024-01-29T13:46:24.872874Z"},"language":"python","trusted":true},"outputs":[{"data":{"text/html":"| \n | fixed_acidity | \nvolatile_acidity | \ncitric_acid | \nresidual_sugar | \nchlorides | \nfree_sulfur_dioxide | \ntotal_sulfur_dioxide | \ndensity | \npH | \nsulphates | \nalcohol | \n
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n7.4 | \n0.70 | \n0.00 | \n1.9 | \n0.076 | \n11.0 | \n34.0 | \n0.9978 | \n3.51 | \n0.56 | \n9.4 | \n
| 1 | \n7.8 | \n0.88 | \n0.00 | \n2.6 | \n0.098 | \n25.0 | \n67.0 | \n0.9968 | \n3.20 | \n0.68 | \n9.8 | \n
| 2 | \n7.8 | \n0.76 | \n0.04 | \n2.3 | \n0.092 | \n15.0 | \n54.0 | \n0.9970 | \n3.26 | \n0.65 | \n9.8 | \n
| 3 | \n11.2 | \n0.28 | \n0.56 | \n1.9 | \n0.075 | \n17.0 | \n60.0 | \n0.9980 | \n3.16 | \n0.58 | \n9.8 | \n
| 4 | \n7.4 | \n0.70 | \n0.00 | \n1.9 | \n0.076 | \n11.0 | \n34.0 | \n0.9978 | \n3.51 | \n0.56 | \n9.4 | \n
\n\n\n \n
\n
","text/plain":" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n0 7.4 0.70 0.00 1.9 0.076 \n1 7.8 0.88 0.00 2.6 0.098 \n2 7.8 0.76 0.04 2.3 0.092 \n3 11.2 0.28 0.56 1.9 0.075 \n4 7.4 0.70 0.00 1.9 0.076 \n\n free sulfur dioxide total sulfur dioxide density pH sulphates \\\n0 11.0 34.0 0.9978 3.51 0.56 \n1 25.0 67.0 0.9968 3.20 0.68 \n2 15.0 54.0 0.9970 3.26 0.65 \n3 17.0 60.0 0.9980 3.16 0.58 \n4 11.0 34.0 0.9978 3.51 0.56 \n\n alcohol quality \n0 9.4 5 \n1 9.8 5 \n2 9.8 5 \n3 9.8 6 \n4 9.4 5 "},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":"import io\nimport requests\n\nfile_id = \"1wdaIQytMzC4HlZblMSIAuEYBh4c-loI2\"\nurl = f\"https://drive.google.com/uc?id={file_id}\"\n\ndata = requests.get(url).content\nwine_data = pd.read_csv(io.StringIO(data.decode(\"utf-8\")))\nwine_data.head()"},{"cell_type":"code","execution_count":null,"id":"4aec2c73-eb27-41a4-9604-0fc375544fdc","metadata":{"language":"python","trusted":true},"outputs":[],"source":"from sqlalchemy import *\n\ndb_connection = create_engine(connection_url)"},{"cell_type":"code","execution_count":null,"id":"b7a38b60-2d59-4f9f-a1e7-8285e2cf239b","metadata":{"language":"python","trusted":true},"outputs":[],"source":"wine_data.to_sql(\n \"wine_data\",\n con = db_connection,\n if_exists = \"append\",\n index = False,\n chunksize = 1000\n)"},{"cell_type":"code","execution_count":null,"id":"31547381-19ee-4f4c-bd22-c7d1c834d688","metadata":{"language":"sql","trusted":true},"outputs":[],"source":"%%sql\nSELECT * FROM wine_data LIMIT 1;"}],"metadata":{"jupyterlab":{"notebooks":{"version_major":6,"version_minor":4}},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.6"},"singlestore_cell_default_language":"sql","singlestore_connection":{"connectionID":"ef511a14-4713-491d-890d-f7aff864ff0a","defaultDatabase":""}},"nbformat":4,"nbformat_minor":5}
--------------------------------------------------------------------------------
| \n | fixed acidity | \nvolatile acidity | \ncitric acid | \nresidual sugar | \nchlorides | \nfree sulfur dioxide | \ntotal sulfur dioxide | \ndensity | \npH | \nsulphates | \nalcohol | \nquality | \n
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n7.4 | \n0.70 | \n0.00 | \n1.9 | \n0.076 | \n11.0 | \n34.0 | \n0.9978 | \n3.51 | \n0.56 | \n9.4 | \n5 | \n
| 1 | \n7.8 | \n0.88 | \n0.00 | \n2.6 | \n0.098 | \n25.0 | \n67.0 | \n0.9968 | \n3.20 | \n0.68 | \n9.8 | \n5 | \n
| 2 | \n7.8 | \n0.76 | \n0.04 | \n2.3 | \n0.092 | \n15.0 | \n54.0 | \n0.9970 | \n3.26 | \n0.65 | \n9.8 | \n5 | \n
| 3 | \n11.2 | \n0.28 | \n0.56 | \n1.9 | \n0.075 | \n17.0 | \n60.0 | \n0.9980 | \n3.16 | \n0.58 | \n9.8 | \n6 | \n
| 4 | \n7.4 | \n0.70 | \n0.00 | \n1.9 | \n0.076 | \n11.0 | \n34.0 | \n0.9978 | \n3.51 | \n0.56 | \n9.4 | \n5 | \n