What is AI DEVS 2

Let's quothe the autors =)

AI_Devs to 5-tygodniowy, największy w Polsce, kurs łączenia narzędzi Generative AI 
(w szczególności modeli OpenAI) z logiką aplikacji oraz narzędziami do automatyzacji. 
Całkowicie rezygnujemy z ChatGPT na rzecz bezpośredniego połączenia z modelami poprzez
API, budując dopasowane narzędzia zwiększające efektywność codziennych zadań.

Progress [C05L03/C05L05]:

What is THIS REPO?

This is everything related to the course that I am doing during learning.

It will contains few modules:

Module	Description
examples_python	python implementations of optional course examples covering OpenAI, Langchain, vector databases, and similarity search. The goal is to recreate the functionality presented in TS examples with Python.
api_tasks	python implementations of tasks required to complete this course.
chat_tasks	tasks completed on the course platform (which simulates a modified OpenAI playground). Contains a task descriptions and list of my attempts for future reference.
docs	Additional descriptions and theory researched and documented while completing the course. Generaly things useful for future reference in upcoming examples, produced while i was processing knowledge.
own_testing	some kind of sandbox with additional tests, libraries and methods methods comparations and trying different ways to achieve the same goals, kept for reference.

Personal Notes, Tests, and Examples (doc + own_testing)

Lesson	Name	Description
C01L04	PYTHON 🐍 different_connections_to_openai.py	- Different ways to connect with OpenAI models using the OpenAI library and the requests library - Streaming example that prints the content of the response chunk by chunk as it is generated - Example of using the LangChain library to initialize a default model (gpt-3.5-turbo)
C01L04	PYTHON 🐍 langchain_conversationchain.py	- Two ways to create a conversation using the LangChain library with the OpenAI chat model - The first method manually creates a list to hold message objects (HumanMessage, SystemMessage, AIMessage) and appends them to the list as the conversation progresses - The second method uses the ConversationChain class implemented in LangChain, which automatically manages the conversation history using ConversationBufferMemory - Both methods allow for sending messages to the model and receiving responses, maintaining the context of the conversation
C03L01	MARKDOWN 📜 async.md	- Event loop in Python and its usage in Jupyter/interactive mode vs normal scripts - Document objects in langchain library - Sync (model.invoke) vs async (model.agenerate) methods for chat models - Code examples of async workflow with asyncio.gather
C03L01	MARKDOWN 📜 simulating_max_cocurrency.md	- Python's langchain library doesn't have a direct equivalent of the maxConcurrency parameter for the ChatOpenAI class - Concurrency can be controlled using Python's asyncio library and semaphores - The text provides a possible solution using asyncio.Semaphore to limit concurrency when generating descriptions for multiple documents
C03L03	MARKDOWN 📜 FAISS_vetor_storing.md	- FAISS (Facebook AI Similarity Search) is a library for efficient similarity search and clustering of dense vectors - It converts data into high-dimensional vectors using embeddings or feature extraction techniques - FAISS creates an index to enable fast similarity search by organizing the vectors for efficient retrieval - The pre-built index allows for quick similarity search when a query vector is provided, using optimized algorithms and parallelization to speed up the process
C03L03	JUPYTER NOTEBOOK 🐍+📜 function_calling.ipynb	- How to prepare functions and their schemas to be used with LLMs for function calling - Different ways to initialize models with function schemas using dictionaries, pydantic BaseModel or convert_to_openai_tool - Extracting function names and arguments from the model's response - Executing the selected function with the provided arguments - Comparison of function calling and prompt-with-examples approaches for solving a specific task
C04L04	MARKDOWN 📜 C04L04_README.md	- How we can host FLASK API and use ngrok to tunnel traffic to our local hosting - The process involves preparing a function to generate answers using LLM, creating a Flask API to handle user requests and return answers, and using ngrok to make the locally running app accessible over the internet - The text provides step-by-step instructions on how to set up the Flask API, configure ngrok, and complete the task by executing parts of the C04L04_ownapi.py file while the API is running

Examples from lessons

Orginally available in typescript at https://github.com/i-am-alice/2nd-devs/

Name	python version	typescript	typescript	status
01_langchain_init	Python	snapshot	orginal	✅DONE
02_langchain_format	Python	snapshot	orginal	✅DONE
03_langchain_stream	Python	snapshot	orginal	✅DONE
04_tiktoken	Python	snapshot	orginal	✅DONE
05_conversation	Python	snapshot	orginal	✅DONE
06_external	Python	snapshot	orginal	✅DONE
07_output	Python	snapshot	orginal	✅DONE
08_cot	Python	snapshot	orginal	✅DONE
09_context	Python	snapshot	orginal	✅DONE
10_switching	Python	snapshot	orginal	✅DONE
11_docs	Python	snapshot	orginal	✅DONE
12_web	Python	snapshot	orginal	✅DONE
13_functions	Python	snapshot	orginal	✅DONE
14_agent	Python	snapshot	orginal	✅DONE
15_tasks	Python	snapshot	orginal	✅DONE
16_nocode	Python	snapshot	orginal	✅DONE
17_tree	Python	snapshot	orginal	✅DONE
18_knowledge	Python	snapshot	orginal	✅DONE
19_llama	Python	snapshot	orginal	❌WAITING
20_catch	Python	snapshot	orginal	✅DONE
21_similarity	Python	snapshot	orginal	✅DONE
22_simple	Python	snapshot	orginal	✅DONE
23_fragmented	Python	snapshot	orginal	✅DONE
24_files	Python	snapshot	orginal	✅DONE
25_correct	Python	snapshot	orginal	✅DONE
26_summarize	Python	snapshot	orginal	✅DONE
27_qdrant	Python	snapshot	orginal	✅DONE
28_intent	Python	snapshot	orginal	✅DONE
29_notify	Python	snapshot	orginal	✅DONE
30_youtube	Python	snapshot	orginal	✅DONE

Api tasks

CHAPTER/LESSON	Task	Tags	Description
C01L01	C01L01_helloapi.py	[course_api_only]	Testing api to connect with course platform
C01L04	C01L04_blogger.py	[OpenAI]	Base connection with OpenAI
C01L04	C01L04_moderation.py	[OpenAI][Guard]	openai/moderations endpoint (to prevent user input make us banned)
C01L05	C01L05_liar.py	[Guard]	Very basic example of checking if input/api response looks like we expected
C02L02	C02L02_inprompt.py	[langchain][pandas]	Example of filtering longer data to provide valuable context for model
C02L03	C02L03_embedding.py	[OpenAI]	openai/embeddings endpoint - example of convertig text to numbers
C02L04	C02L04_whisper.py	[OpenAI][whisper]	Example of generating transcriptions
C02L05	C02L05_functions.py	[course_api_only]	Example of writing function definition for openai
C03L01	C03L01_rodo.py	[course_api_only]	Example of writing prompt that will suggest model to do something
C03L02	C03L02_scraper.py	[langchain][webscrap]	Example of trying to gather data from server that may have some basic prevention/random errors
C03L03	C03L03_whoami.py	[langchain][ConversationChain]	Task to practice with store chat history/using ConversationChain
C03L04	C03L04_search.py	[qdrant][embeddings][similarity search][vector database]	Embeddings creation, indexation and performing similarity search for dynamic context generation for LLM with qdrant
C03L05	C03L05_people.py	[pandas][langchain][filtering data]	Filtering only desired context for llm (in my case using PANDAS DATAFRAME)
C04L01	C04L01_knowledge.py	[function calling][langchain][apis]	Connecting to different APIs basing on LLM decision (with function_calling)
C04L02	C04L02_tools.py	[function_calling][langchain]	Another function_calling example to get correct json (Letting LLM determine incoming action). Also, adding extra systempromt with context to let model understand dates.
C04L03	C04L03_gnome.py	[langchain][vison][analyzing images]	Example using Vision model to let model analyze content of some image
C04L04	C04L04_ownapi.py	[langchain][api][flask][ngrok]	Create app that will catch post requests, extract user question and get answer from LLM
C04L05	C04L05_ownapipro.py	[langchain][ConversationChain][api][flask][ngrok]	Create app that will catch post requests, extract user question and get answer from LLM while holding user messages in memory
C05L01	C05L01_meme.py	[RenderForm][documents from templates]	Use renderform prepare template and generate images basing on this template
C05L02	C05L02_optimaldb.py	[langchain][LLMLingua][context compresssion][context optimalization]	Multiple ways to optimize text data to contain less space. Testes methods was LLMLingua, asking LLM for optimize and asking LLM for summarization
C05L03	C05L03_google.py	[langchain][flask][ngrok][serpapi][google]	Preparing keywords from user question to execute search in google using serpapi lib

Chat_tasks

They are saved mostly for searching for specific prompt examples. I didn't see any reason to keep then in separated files so they are all available here

Lesson	Name	Description	status
C01L01	getinfo	Forcing ChatGPT to output the word BANANA without using that word in the prompt. Difficulties - some words are disabled in the prompt.	✅DONE
C01L02	maxtokens	Providing the name of a river flowing through the capital of a given country, while staying within the max token limit.	✅DONE
C01L03	category	Making ChatGPT assign an appropriate category (home/work/other) to a task and return the answer in JSON format.	✅DONE
C01L03	books	Preparing a JSON array with book titles and authors using one-shot prompting with GPT-3.5-turbo.	✅DONE
C01L05	injection injection2	Using prompt injection to extract a secret word from the prompt, with increasing difficulty levels and models (GPT-3.5 and GPT-4).	✅DONE
C02L01	optimize	Defining the 'system' field in a query to perform a given task while staying within the character limit, which is more challenging than token limit.	✅DONE
C02L01	fixit	Convincing GPT-4 to fix and optimize provided source code, handle errors properly, and return zero for all incorrect inputs.	✅DONE
C02L02	parsehtml	Extracting readable article text from HTML code (in paragraphs), converting it to Markdown format, and returning only the three paragraphs without any HTML code.	✅DONE
C02L03	structure	Preparing a prompt that works with both GPT-3.5-Turbo and GPT-4 models to generate a JSON object with a specific structure, taking into account the strengths and weaknesses of GPT-3.5-Turbo.	✅DONE
C02L05	cities	Generating a list of 7 interesting facts about a given city without using the city name in the prompt or the generated response, while working with the GPT-3.5-turbo model.	✅DONE
C03L01	tailwind	Writing a system message that returns a <button> element consistent with the user's message, ensuring the model's response contains only the <button> element without additional comments or tags.	✅DONE
C03L02	format	Creating a converter from an old African markup language to HTML code, instructing GPT-3.5-turbo on how to handle and interpret the code.	✅DONE
C03L05	planets	Generating a JSON array consisting of 9 planet names in the solar system (including Pluto), with names in lowercase Polish, without mentioning planets, solar system, JSON, or the Polish language in the prompt.	✅DONE