Granted I'm not coming from the python world, but I have tried many of these projects, and very few of them install out of the box. They usually end with some incompatibility, and files scattered all over the place, leading to future nightmares.
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
sentry-sdk 1.22.2 requires urllib3<2.0.0, but you have urllib3 2.0.2 which is incompatible
Just for fun, here's the result of python -m pip install -r ./requirements.txt for tortoise-tts;
…many many lines
raise ValueError("%r is not a directory" % (package_path,))
ValueError: 'build/py3k/scipy' is not a directory
Converting to Python3 via 2to3...
…
/tmp/pip-install-hkb_4lh7/scipy_088b20410aca4f0cbcddeac86ac7b7b1/build/py3k/scipy/signal/fir_filter_design.py
[end of output]
note: This error originates from a subprocess, and is
likely not a problem with pip.
error: metadata-generation-failed
I'm not asking for support, just saying if people really want to make something 'easy' they'd use docker. I gather there are better python package managers, but I gather that's a bit of a mess too.
Someone is thinking "this is part of learning the language," but I think it's just bad design.
You don’t need Docker, you just need a virtual env for each random thing you try instead of making them all conflict with each other. Maybe some day pip will add a switch to automatically create one, but until then,
python3 -m venv venv
. venv/bin/activate
before you try something random.
Also, `python` is usually Python 2.7. If it is, I advise removing it from your system unless you have a strong reason to keep it.
Nope, this is EXACTLY why I'd use docker. You want to faff around with some esoteric settings? Go for it! But don't make your would-be users runt the gauntlet, that's pointless.
From a linux perspective, I wouldn't blindly suggest the average reader to purge Python 2.7 from their system, as it might drag core parts of the WM with it. Consider aliasing, or better yet, relying on modern venv tools such as Conda instead.
× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [8 lines of output]
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "<pip-setuptools-caller>", line 34, in <module>
File "/tmp/pip-install-i7ubxxkc/scipy_4d5af4f3e2094adca3313ccb41a6d5ff/setup.py", line 196, in <module>
setup_package()
File "/tmp/pip-install-i7ubxxkc/scipy_4d5af4f3e2094adca3313ccb41a6d5ff/setup.py", line 147, in setup_package
from numpy.distutils.core import setup
ModuleNotFoundError: No module named 'numpy'
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
× Encountered error while generating package metadata.
╰─> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
[notice] A new release of pip available: 22.3.1 -> 23.1.2
[notice] To update, run: pip install --upgrade pip
(venv) (base) vid@kk:~/D/ai/tortoise-tts$
I'm sure you could eventually help get this working, which is kind of you, but the point is the "supposed tos" don't work either. It needs to be comprehensively fixed if python really wants to be approachable. Maybe it doesn't. It's also just not a good citizen when it comes to heterogeneous apps on the same system.
This isn't the first time venv didn't work for me, then there's anaconda, miniconda, and a bunch of other things that add env and directories. I don't really know what any of them do, and -I don't want to- I'm not an expert on every app on my system, but I can use nearly all of them without pain. (remember this is about ease of use)
Oh yeah, and python 2 vs python 3. <rolls eyes>
It's very much the "works for me" experience from the old days. There's no good learning from it, except dependencies suck and python systems aren't good at them.
I think when releasing anything that includes dependencies that span the operating system, it's just good engineering to use a container approach. Otherwise you're just causing a lot of discomfort in the world for no good reason.
It's funny because chatgpt would give me an answer to this in a few moments, but I'm locked out for a week because it can't figure out where I am.
Now I'm spending my Sunday morning setting up a dockerfile for tortoise-tts. At least I will learn something reusable from that. I guess I will create a PR for it, though it seems the author isn't tending the repo anymore.
It’s been a long time since “Python —version” output “2.x” on a computer I was using. Even macOS is on Python 3 these days iirc. Every Linux distro I’ve installed in the last few months was at least 3.8.
Yep, I just tried to install a Python-based project and there was a conflict between Pyenv's and Homebrew's versions of pip... despite having used Homebrew to install Pyenv. I ended up just getting rid of Pyenv altogether... but now Python may be in some screwed-up state on my system.
It's too bad the ecosystem seems to be so messy, because Python seems like the best language for general utilities.
> Yep, I just tried to install a Python-based project and there was a conflict between Pyenv's and Homebrew's versions of pip... despite having used Homebrew to install Pyenv.
The comment does not really make sense. It sounds like pyenv wasn't setup correctly on your system (needs to be added to your bash_profile etc). The typical setup is to put pyenv first in PATH so it takes precedence.
It may be easier to use pipx which will auto manage the virtual envs of end user apps for you.
This is exactly why I’ve never wanted to get too involved with Python. The few times I’ve tried to play with it, it always becomes a nightmare of error messages to do with stuff exactly like this and I spent more time farting around trying to figure out what went wrong with it then I do actually doing any development.
Are you familiar with virtual environments? It's the standard Python technique for isolating dependencies across projects. [Most projects mention this in the setup / quickstart section of their docs.]
You should not be seeing these dependency conflict issues if you install each project in its own virtual environment.
If you just want them to be easily installed you can just use pipx (`pipx install my-package`) which will manage the virtual environment automatically.
Making a full blown Docker image for it is overkill 99% of the time. Virtual environments serve the same purpose while being much faster and lighter weight.
This is the primary reason I'm averse to languages and ecosystems that rely on package managers. I have never had a good experience where these things aren't just constantly breaking. Stack/cabal, cargo, pip, npm/yarn, gem. Scattering files across my filesystem and having extremely brittle configs that shatter the ecosystem into a billion pieces at seemingly random intervals. A problem exacerbated by these package managers often being more complex than the compiler/interpreter itself. Luarocks is probably the least problematic, and that's mostly because it hosts really simple and self-contained software.
Say what you will about the old school way of manually building and copying shit around, at least when something breaks I don't have to spend a couple hours keelhauling a bloated toolchain in a debugger for mutiny.
I would say it's more an artifact of historical tech debt that is hard to change now without breaking everyone. As another commenter pointed out, you want to use a venv - I use pipenv as a tool to automate this but there are others as well (poetry is probably better but pipenv seems to work for me).
Self-hosted + self-trained LLMs are probably the future for enterprise.
While consumers are happy to get their data mined to avoid paying, businesses are the opposite: willing to pay a lot to avoid feeding data to MSFT/GOOG/META.
They may give assurances on data protection (even here GitHub copilot TOS has sketchy language around saving down derived data), but can’t get around fundamental problem that their products need user interactions to work well.
So it seems with BigTechLLM there’s inherent tension between product competitiveness and data privacy, which makes them incompatible with enterprise.
Biz ideas along these lines:
- Help enterprises set up, train, maintain own customized LLMs
- Security, compliance, monitoring tools
- Help AI startups get compliant with enterprise security
- Fine tuning service
In the book “To sleep in a sea of stars” there’s a concept of a “ship mind” that is local to each space craft. It’s smarter than “pseudo ai” and can have real conversations, answer complex questions, and even tell jokes.
I can see a self-hosted LLM being akin to a company’s ship-mind. Anyone can ask questions, order analyses, etc, so long as you are a member of the company. No two LLM’s will be exactly the same - and that’s ok.
I suspect the major cloud providers will also each offer their own “enterprise friendly” LLM services (Azure already offers a version of OpenAI’s API). If they have the right data guarantees, that’ll probably be sufficient for companies that are already using their IaaS offerings.
> willing to pay a lot to avoid feeding data to MSFT/GOOG/META.
Right now, you can't pay a lot and get a local LLM with similar performance to GPT-4.
Anything you can run on-site isn't really even close in terms of performance.
The ability to finetune to your workplaces terminology and document set is certainly a benefit, but for many usecases that doesn't outweigh the performance difference.
Use the following pieces of context to answer the question at the end. If you don't
know the answer, just say that you don't know, don't try to make up an answer.
{context}
Question: {question}
Helpful Answer:
If you mean the "If you don't know" part, oh no, they have a much bigger problem they're solving.
The LLM will absolutely lie if it doesn't know and you haven't made it perfectly clear that you'd rather it did not do that.
LLMs seem to be trying to give answers that make you happy. A good lie will make you happy. Unless it understands that you will not be happy with a lie.
Is this anthropomorphizing? Yep. But that's the best way I've found to reason about them.
the incidence of "i don't know" in response to questions in the training data is pretty low if present at all, and even if it were you'd still need to frame those I don't know answers such that they apply to the entire dataset accurately. This is obviously a gargantuan undertaking that would not scale well as data is added, and so right now the idea or concept of not knowing something is not taught. At best you'd build a model that handles human language really well then retrieves information from a database and uses in context learning to answer questions, where a failure to find info results in an i don't know.
What is taught indirectly though is level of certainty, so if you get LLM's to rationalise their answers you tend to get more reliable evidence based answers.
Bottom line, teaching a monolithic model what it means to not know something with certainty, is difficult and not currently done. You'll likely get a lot of false negatives.
- [GitHub - e-johnstonn/BriefGPT: Locally hosted tool that connects documents to LLMs for summarization and querying, with a simple GUI.](https://github.com/e-johnstonn/BriefGPT)
- [GitHub - go-skynet/LocalAI: Self-hosted, community-driven, local OpenAI-compatible API. Drop-in replacement for OpenAI running LLMs on consumer-grade hardware. No GPU required. LocalAI is a RESTful API to run ggml compatible models: llama.cpp, alpaca.cpp, gpt4all.cpp, rwkv.cpp, whisper.cpp, vicuna, koala, gpt4all-j, cerebras and many others!](https://github.com/go-skynet/LocalAI)
- [GitHub - paulpierre/RasaGPT: RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram](https://github.com/paulpierre/RasaGPT)
- [GitHub - imartinez/privateGPT: Interact privately with your documents using the power of GPT, 100% privately, no data leaks](https://github.com/imartinez/privateGPT)
- [GitHub - deepset-ai/haystack: Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.](https://github.com/deepset-ai/haystack)
Got this working locally - badly needs GPU support (have a 3090 so come on!) there is some workaround but expect it will come pretty soon. This video was a useful walkthough esp on using different model and upping the CPU threads. https://www.youtube.com/watch?v=A3F5riM5BNE
I want to have the memory part of langchain down, vector store + local database + client to chat with an LLM (gpt4all model can be swapped with OpenAI api just switching the base URL)
Sorry for my ignorance. But memory refers to the process of using embeddings for QA right?
The process roughly is:
Ingestion:
- Process embeddings for your documents (from text to array of numbers)
- Store your documents in a Vector DB
Query time:
- Process embeddings for the query
- Find documents similar to the query using distance from other docs in the Vector db
- Construct prompt with format:
"""
Answer question using this context:
{DOCUMENTS RETRIEVED}
Question: {question}
Answer:
"""
Is that correct? Now, my question is, can the models be swapped easily? Or that requires a complete recalculation of the embedding (and new ingestion)?
The embeddings can be based on a different model to the one you pass them as context to. So you could upgrade the summmariser model without upgrading the embeddings.
Readit News
…many many lines
… I'm not asking for support, just saying if people really want to make something 'easy' they'd use docker. I gather there are better python package managers, but I gather that's a bit of a mess too.Someone is thinking "this is part of learning the language," but I think it's just bad design.
Also, `python` is usually Python 2.7. If it is, I advise removing it from your system unless you have a strong reason to keep it.
All that nonsense vs docker pull / docker run
From a linux perspective, I wouldn't blindly suggest the average reader to purge Python 2.7 from their system, as it might drag core parts of the WM with it. Consider aliasing, or better yet, relying on modern venv tools such as Conda instead.
This isn't the first time venv didn't work for me, then there's anaconda, miniconda, and a bunch of other things that add env and directories. I don't really know what any of them do, and -I don't want to- I'm not an expert on every app on my system, but I can use nearly all of them without pain. (remember this is about ease of use)
Oh yeah, and python 2 vs python 3. <rolls eyes>
It's very much the "works for me" experience from the old days. There's no good learning from it, except dependencies suck and python systems aren't good at them.
I think when releasing anything that includes dependencies that span the operating system, it's just good engineering to use a container approach. Otherwise you're just causing a lot of discomfort in the world for no good reason.
It's funny because chatgpt would give me an answer to this in a few moments, but I'm locked out for a week because it can't figure out where I am.
Now I'm spending my Sunday morning setting up a dockerfile for tortoise-tts. At least I will learn something reusable from that. I guess I will create a PR for it, though it seems the author isn't tending the repo anymore.
It's too bad the ecosystem seems to be so messy, because Python seems like the best language for general utilities.
The comment does not really make sense. It sounds like pyenv wasn't setup correctly on your system (needs to be added to your bash_profile etc). The typical setup is to put pyenv first in PATH so it takes precedence.
It may be easier to use pipx which will auto manage the virtual envs of end user apps for you.
Python is very fragile to deploy and run on your own machine.
You should not be seeing these dependency conflict issues if you install each project in its own virtual environment.
If you just want them to be easily installed you can just use pipx (`pipx install my-package`) which will manage the virtual environment automatically.
Making a full blown Docker image for it is overkill 99% of the time. Virtual environments serve the same purpose while being much faster and lighter weight.
Say what you will about the old school way of manually building and copying shit around, at least when something breaks I don't have to spend a couple hours keelhauling a bloated toolchain in a debugger for mutiny.
While consumers are happy to get their data mined to avoid paying, businesses are the opposite: willing to pay a lot to avoid feeding data to MSFT/GOOG/META.
They may give assurances on data protection (even here GitHub copilot TOS has sketchy language around saving down derived data), but can’t get around fundamental problem that their products need user interactions to work well.
So it seems with BigTechLLM there’s inherent tension between product competitiveness and data privacy, which makes them incompatible with enterprise.
Biz ideas along these lines: - Help enterprises set up, train, maintain own customized LLMs - Security, compliance, monitoring tools - Help AI startups get compliant with enterprise security - Fine tuning service
I can see a self-hosted LLM being akin to a company’s ship-mind. Anyone can ask questions, order analyses, etc, so long as you are a member of the company. No two LLM’s will be exactly the same - and that’s ok.
https://fractalverse.net/explore-to-sleep-in-a-sea-of-stars/...
It has worked before like in case of Linux and can work again.
Right now, you can't pay a lot and get a local LLM with similar performance to GPT-4.
Anything you can run on-site isn't really even close in terms of performance.
The ability to finetune to your workplaces terminology and document set is certainly a benefit, but for many usecases that doesn't outweigh the performance difference.
https://lmsys.org/blog/2023-03-30-vicuna/
https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...
In this case it appears to be using RetrievalQA from LangChain, which I think is this prompt here: https://github.com/hwchase17/langchain/blob/v0.0.176/langcha...
The LLM will absolutely lie if it doesn't know and you haven't made it perfectly clear that you'd rather it did not do that.
LLMs seem to be trying to give answers that make you happy. A good lie will make you happy. Unless it understands that you will not be happy with a lie.
Is this anthropomorphizing? Yep. But that's the best way I've found to reason about them.
What is taught indirectly though is level of certainty, so if you get LLM's to rationalise their answers you tend to get more reliable evidence based answers.
Bottom line, teaching a monolithic model what it means to not know something with certainty, is difficult and not currently done. You'll likely get a lot of false negatives.
- [GitHub - e-johnstonn/BriefGPT: Locally hosted tool that connects documents to LLMs for summarization and querying, with a simple GUI.](https://github.com/e-johnstonn/BriefGPT)
- [GitHub - go-skynet/LocalAI: Self-hosted, community-driven, local OpenAI-compatible API. Drop-in replacement for OpenAI running LLMs on consumer-grade hardware. No GPU required. LocalAI is a RESTful API to run ggml compatible models: llama.cpp, alpaca.cpp, gpt4all.cpp, rwkv.cpp, whisper.cpp, vicuna, koala, gpt4all-j, cerebras and many others!](https://github.com/go-skynet/LocalAI)
- [GitHub - paulpierre/RasaGPT: RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram](https://github.com/paulpierre/RasaGPT)
- [GitHub - imartinez/privateGPT: Interact privately with your documents using the power of GPT, 100% privately, no data leaks](https://github.com/imartinez/privateGPT)
- [GitHub - reworkd/AgentGPT: Assemble, configure, and deploy autonomous AI Agents in your browser.](https://github.com/reworkd/AgentGPT)
- [GitHub - deepset-ai/haystack: Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.](https://github.com/deepset-ai/haystack)
- [PocketLLM « ThirdAi](https://www.thirdai.com/pocketllm/)
- [GitHub - imClumsyPanda/langchain-ChatGLM: langchain-ChatGLM, local knowledge based ChatGLM with langchain ｜ 基于本地知识库的 ChatGLM 问答](https://github.com/imClumsyPanda/langchain-ChatGLM)
"ggml_new_tensor_impl: not enough space in the context's memory pool (needed 18296202768, available 18217606000)"
I want to have the memory part of langchain down, vector store + local database + client to chat with an LLM (gpt4all model can be swapped with OpenAI api just switching the base URL)
https://github.com/aldarisbm/memory
It's still got ways to go, if someone wants to help let me know :)
The process roughly is:
Ingestion:
- Process embeddings for your documents (from text to array of numbers)
- Store your documents in a Vector DB
Query time:
- Process embeddings for the query
- Find documents similar to the query using distance from other docs in the Vector db
- Construct prompt with format:
""" Answer question using this context: {DOCUMENTS RETRIEVED}
Question: {question} Answer: """
Is that correct? Now, my question is, can the models be swapped easily? Or that requires a complete recalculation of the embedding (and new ingestion)?