List:

• pinembed.py
• ga_local_docs.py, run_QLD.py
• chroma_memory.py
• #61
• #60
• #62
• #63

Must ensure all notebooks in langchain/ subdir actually work

README.md is unfinished

• Requires a reference and explanation for each sub directory of llm_utilikit

Already collected, @Daethyra: look in personal "Building AI" list to find the right ones.

Move all code, abandon repository

https://github.com/Daethyra/LLM-Utilikit/tree/v1.0.21/docs/jupyter_notebooks

• Use PDM to add safetensors and
  fk I forgot the other one

I collected my Gists that I believe would be helpful in tweaking the current file base of AA4LLM

Gists-LLM-Utilikit-Free_Code_Examples.zip

I'll need to personally split commands based on role

https://github.com/f/awesome-chatgpt-prompts/blob/main/prompts.csv

Need

• issue template
• 'collaborating' markdown doc

Leftover work: integrable_image_captioner.py:

Code Improvements:

Unused Imports:

Modules like json and dotenv are imported but not used. Consider removing them if they are not necessary.

Global Variable Usage:

The usage of config['ENDING_CAPTION'] in the main function seems incorrect. You might need to use ending_caption instead, as it is the variable holding the relevant environment variable value.

CSV File Handling:

In save_to_csv, you open csvfile as a new file every time. If csvfile is passed as an argument, it should be used instead of opening a new file. Also, consider parameterizing the write mode ('a' for appending) to allow for more flexibility.

Error Handling in Main:

While you have a try-except block, it might be useful to continue processing other images even if one fails. Consider moving the try-except inside the loop to handle errors on a per-image basis.

• https://python.langchain.com/docs/modules/chains/foundational/sequential_chains#using-lcel
• https://python.langchain.com/docs/modules/memory/
• - https://python.langchain.com/docs/modules/memory/chat_messages/
• - https://python.langchain.com/docs/modules/memory/types/buffer
• https://python.langchain.com/docs/modules/chains/document/map_reduce
• https://python.langchain.com/docs/modules/chains/document/map_rerank
• https://python.langchain.com/docs/modules/chains/foundational/router
• https://python.langchain.com/docs/expression_language/how_to/routing

• define, "useful"

• https://python.langchain.com/docs/modules/agents/
• https://python.langchain.com/docs/modules/agents/tools/

{} = replace_me

"What is the idiomatic way to {do the thing you want to do}
in {language in question}?"

Please watch this video and summarize its main points in bullet points. Use clear and concise language. Provide relevant examples and explanations from the video. Include the following information:

• The topic and purpose of the video

• The main arguments or claims made by the speaker

• The evidence or support provided for the arguments or claims

• The speaker’s tone and attitude towards the topic

• The intended audience and message of the video

video = "https://youtu.be/2F9itktands?si=DXyOmSHePtEip_lO"

I'm not sure what code should be carried over. A lot of the repo's contents are monolithic in nature, composing excessive amounts of functionalities in a few classes per file.
Since this repo is moving to a more simple, understandable structure, I will likely not make use of much of the codebase. Not only does most of it not work, but most of it is also poorly written with no clear vision in mind.

• needs folder organization for
  -- multi-shot examples
  -- user examples
  -- system examples

Click here

• Reviewed article and copied prompts on 11-28-23

• return LLM query-response
• Add LangSmith tracing v2 via .env

Before release 1:

• Run PDM init and ensure the pyprojecttoml looks good.
• Take all of the prompts of all role types and move them into a single master sheet, still separate from the cheatsheet

See the prompt quoted below:

[Note regarding future user inputs]:"""
I will use brackets, '[]' to specify either a literal command(like, [PROCEED]) OR a context-conveyor followed by a string(like, [User message]:"Sample text").
"""

[PROCEED]

I'm not sure what code should be carried over. A lot of the repo's contents are monolithic in nature, composing excessive amounts of functionalities in a few classes per file.
Since this repo is moving to a more simple, understandable structure, I will likely not make use of much of the codebase. Not only does most of it not work, but most of it is also poorly written with no clear vision in mind.

Traceback for "flake8 .\src\llm_utilikit\langchain":

.\src\llm_utilikit\langchain\codesnippets\bufferwindow_memory.py:1:1: F401 'langchain.memory.ConversationBufferWindowMemory' imported but unused
.\src\llm_utilikit\langchain\codesnippets\bufferwindow_memory.py:8:80: E501 line too long (98 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\bufferwindow_memory.py:11:80: E501 line too long (84 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\bufferwindow_memory.py:15:80: E501 line too long (95 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\bufferwindow_memory.py:21:14: F821 undefined name 'langchain'
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:3:1: F401 'langchain.prompts.chat.ChatPromptTemplate' imported but unused
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:3:1: F401 'langchain.prompts.chat.HumanMessagePromptTemplate' imported but unused
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:3:1: F401 'langchain.prompts.chat.SystemMessagePromptTemplate' imported but unused
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:24:80: E501 line too long (95 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:25:80: E501 line too long (108 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:26:80: E501 line too long (124 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:27:80: E501 line too long (93 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:33:80: E501 line too long (104 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:36:80: E501 line too long (98 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:37:80: E501 line too long (94 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:38:80: E501 line too long (83 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:40:80: E501 line too long (124 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:45:80: E501 line too long (89 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\chatopenai.py:46:80: E501 line too long (93 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\multi_queryvector_retrieval.py:5:80: E501 line too long (81 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\multi_queryvector_retrieval.py:8:80: E501 line too long (85 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\multi_queryvector_retrieval.py:10:80: E501 line too long (118 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\multi_queryvector_retrieval.py:11:80: E501 line too long (135 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\multi_queryvector_retrieval.py:14:80: E501 line too long (95 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\multi_queryvector_retrieval.py:17:80: E501 line too long (90 > 79 characters)
.\src\llm_utilikit\langchain\codesnippets\multi_queryvector_retrieval.py:40:51: F821 undefined name 'question'
.\src\llm_utilikit\langchain\end2end\chatbots\streamlit\st_chat.py:15:80: E501 line too long (87 > 79 characters)
.\src\llm_utilikit\langchain\end2end\chatbots\streamlit\st_with_memory.py:1:80: E501 line too long (82 > 79 characters)
.\src\llm_utilikit\langchain\end2end\chatbots\streamlit\st_with_memory.py:2:1: F811 redefinition of unused 'StreamlitChatMessageHistory' from line 1
.\src\llm_utilikit\langchain\end2end\chatbots\streamlit\st_with_memory.py:20:80: E501 line too long (86 > 79 characters)
.\src\llm_utilikit\langchain\end2end\rag\faiss_retriever.py:2:80: E501 line too long (99 > 79 characters)
.\src\llm_utilikit\langchain\end2end\rag\faiss_retriever.py:3:80: E501 line too long (102 > 79 characters)
.\src\llm_utilikit\langchain\end2end\rag\faiss_retriever.py:4:80: E501 line too long (100 > 79 characters)
.\src\llm_utilikit\langchain\end2end\rag\faiss_retriever.py:5:80: E501 line too long (93 > 79 characters)
.\src\llm_utilikit\langchain\end2end\rag\faiss_retriever.py:15:1: F401 'langchain.agents.agent_toolkits.create_conversational_retrieval_agent' imported but unused
.\src\llm_utilikit\langchain\end2end\rag\faiss_retriever.py:15:80: E501 line too long (81 > 79 characters)
.\src\llm_utilikit\langchain\end2end\rag\faiss_retriever.py:34:44: F821 undefined name 'memory_key'
.\src\llm_utilikit\langchain\end2end\rag\faiss_retriever.py:34:60: F821 undefined name 'llm'
.\src\llm_utilikit\langchain\end2end\rag\pinecone\application.py:9:80: E501 line too long (85 > 79 characters)
.\src\llm_utilikit\langchain\end2end\rag\pinecone\application.py:15:80: E501 line too long (88 > 79 characters)
.\src\llm_utilikit\langchain\end2end\rag\pinecone\application.py:22:80: E501 line too long (85 > 79 characters)
.\src\llm_utilikit\langchain\end2end\rag\pinecone\application.py:29:80: E501 line too long (83 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:16:80: E501 line too long (89 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:36:80: E501 line too long (133 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:37:80: E501 line too long (92 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:49:80: E501 line too long (86 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:56:31: F821 undefined name 'retry_if_value_error'
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:64:80: E501 line too long (85 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:67:80: E501 line too long (88 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:122:46: F821 undefined name 'hub'
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:122:80: E501 line too long (82 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:142:80: E501 line too long (149 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:151:36: F821 undefined name 'cosine_similarity'
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:161:80: E501 line too long (114 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\qa_local_docs.py:162:80: E501 line too long (85 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\run_qa_local_docs.py:33:80: E501 line too long (88 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\run_qa_local_docs.py:36:80: E501 line too long (110 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\run_qa_local_docs.py:44:64: W605 invalid escape sequence '\ '
.\src\llm_utilikit\langchain\rag-with-agents\directoryloader\run_qa_local_docs.py:44:65: W291 trailing whitespace
.\src\llm_utilikit\langchain\rag-with-agents\pdf_only\query_local_docs.py:34:80: E501 line too long (81 > 79 characters)
.\src\llm_utilikit\langchain\rag-with-agents\pdf_only\query_local_docs.py:56:80: E501 line too long (88 > 79 characters)

Prompt =

Request: Create a detailed and informative markdown guide that will serve as a reference for the next AI assistant. This guide should encapsulate the essence of our ongoing project and articulate specific steps taken and future actions required.

Purpose: To provide the next AI assistant with comprehensive context and clear understanding about the project related to web scraping LangChain documentation.

Requirements for the Guide:

Project Overview: Summarize the initial objective - downloading LangChain documentation and the evolution towards developing a web scraping script.
Developed Tools:
Describe the enhanced web scraping script, highlighting its purpose, key features, and user instructions.
Mention the JSON file template, its role in the project, and user responsibilities for its utilization.
Next Steps for the User: Outline specific actions the user must take, including populating the JSON template with URLs and running the script.
Ethical and Legal Considerations: Emphasize the importance of adhering to legal and ethical web scraping practices, including the compliance with robots.txt of target websites.
Monitoring and Troubleshooting: Suggest steps for monitoring the script's output and handling potential issues.
Format: Markdown, for readability and structured documentation.

Goal: To ensure seamless continuity and understanding for the next AI assistant, enabling them to provide effective and pertinent assistance to the user.

• hard encode into code examples for reproducibility
• consider updating versions every snapshot release of GPT-3.5-Turbo-{MMDD}

I did not personally find other things worth checking than Assistant Architect did
 

transcribe_microphone.py

Initialization of ASR Pipeline:

Suggested, not necessary:
The use of the transformers library for the ASR pipeline seems appropriate. However, ensure that the specific model (openai/whisper-large-v2) and the parameters (chunk_length_s, return_timestamps) are supported by the library version you are using.

Audio Processing Logic:

The sliding window concept is a sensible approach for handling real-time audio data. However, there seems to be inconsistency in the way the sliding window is managed after transcription. After the first 30 seconds are transcribed, the remaining part of the window should be retained, not entirely reset.
The sliding window length check (if len(self.sliding_window) >= 16000 * self.asr_pipeline.task.config.chunk_size_ms / 1000:) appears to be incorrectly using chunk_size_ms. You should confirm the existence and correct usage of this attribute in the transformers documentation.

Error Handling:

The script performs error handling and logging for file operations and stream activities, which is good practice.
Consider adding more specific error handling around the ASR pipeline's processing, as this could fail or raise exceptions not currently caught.

Logging and File Writing:

The logic for handling log files could be improved. For example, the check for the log file's existence and writability is repetitive and could be simplified.
The method create_new_log_file does not handle potential exceptions that might occur during file operations.

Resource Management:

Ensure that resources like the PyAudio stream are appropriately closed or released in all scenarios, including exceptions.

run.py

Argument Parsing and Logging:

Argument parsing is correctly implemented.
The logging setup within a file context (with open(...)) is unnecessary, as logging.basicConfig handles file operations internally.

ASR Pipeline and Stream Checks:

The check asr_app.asr_pipeline.is_running() might not be valid. The pipeline object from transformers does not typically have an is_running method. Verify this based on the library's documentation.

Exception Handling:

Good use of try-except blocks to handle unexpected errors and keyboard interrupts.
Consider logging the exception details for better debugging.

Resource Management:

The script ensures that resources are closed in the finally block, which is a good practice.

Outsource the API calls

• Ensure logging, retries
• Follow this todo list

langchain, openai, transformers

https://smith.langchain.com/hub/rlm/map-prompt?organizationId=0f7461cf-206f-5c85-aa8d-48c6c48bafc5

RAG Prompt/Chain that should be added to AA4LLM's code examples

New intention: specifically supply week documented custom wrapper classes in modules for building LLMs
-> LangChain Ecosystem updates (v0.1)

Updates to how output files are named cause errors to throw during the execution of conv_html_to_markdown.py.

Solution:

1. Import glob: At the top of conv_html_to_markdown.py, add import glob to use the glob module for file pattern matching.

2. Update load_json Function:

• Rename it to load_json_files to reflect its new functionality.
• Use glob.glob to find all files matching the output-*.json pattern.
• Iterate over these files, load their contents, and aggregate the data.

import glob

def load_json_files(pattern):
    """
    Load data from multiple JSON files matching a pattern.

    Args:
        pattern (str): Glob pattern to match files.

    Returns:
        list: Aggregated data from all matched files.
    """
    aggregated_data = []
    for file_path in glob.glob(pattern):
        with open(file_path, "r", encoding="utf-8") as file:
            aggregated_data.extend(json.load(file))
    return aggregated_data

def main():
    # ... existing code ...
    try:
        # Load data from all output JSON files
        original_data = load_json_files("output-*.json")
        # ... rest of the existing code ...

Absolutely, here's a more developer-oriented introduction:

"Welcome to the LLM-Utilikit repository. This toolkit is a collection of prompts and components designed to streamline your work with Large Language Models (LLMs). It's built with developers in mind, providing pre-configured prompts and back-end modules to help you get up and running quickly with OpenAI, LangChain, Hugging Face, or Pinecone. The LLM-Utilikit is open-source, so feel free to contribute and help us improve it. Let's build something amazing together with the power of LLMs."

• query_local_docs.py
• qa_local_docs.py / run_qa_local_docs.pt

The OpenAI directory doesn't contain much code anymore. Only two files currently available.