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This file is a merged representation of the entire codebase, combining all repository files into a single document. |
Generated by Repomix on: 2025-01-30T10:25:45.743Z |
================================================================ |
File Summary |
================================================================ |
Purpose: |
-------- |
This file contains a packed representation of the entire repository's contents. |
It is designed to be easily consumable by AI systems for analysis, code review, |
or other automated processes. |
File Format: |
------------ |
The content is organized as follows: |
1. This summary section |
2. Repository information |
3. Directory structure |
4. Multiple file entries, each consisting of: |
a. A separator line (================) |
b. The file path (File: path/to/file) |
c. Another separator line |
d. The full contents of the file |
e. A blank line |
Usage Guidelines: |
----------------- |
- This file should be treated as read-only. Any changes should be made to the |
original repository files, not this packed version. |
- When processing this file, use the file path to distinguish |
between different files in the repository. |
- Be aware that this file may contain sensitive information. Handle it with |
the same level of security as you would the original repository. |
Notes: |
------ |
- Some files may have been excluded based on .gitignore rules and Repomix's |
configuration. |
- Binary files are not included in this packed representation. Please refer to |
the Repository Structure section for a complete list of file paths, including |
binary files. |
Additional Info: |
---------------- |
================================================================ |
Directory Structure |
================================================================ |
docs/ |
book/ |
user-guide/ |
cloud-guide/ |
cloud-guide.md |
llmops-guide/ |
evaluation/ |
evaluation-in-65-loc.md |
evaluation-in-practice.md |
generation.md |
README.md |
retrieval.md |
finetuning-embeddings/ |
evaluating-finetuned-embeddings.md |
finetuning-embeddings-with-sentence-transformers.md |
finetuning-embeddings.md |
synthetic-data-generation.md |
finetuning-llms/ |
deploying-finetuned-models.md |
evaluation-for-finetuning.md |
finetuning-100-loc.md |
finetuning-llms.md |
finetuning-with-accelerate.md |
next-steps.md |
starter-choices-for-finetuning-llms.md |
why-and-when-to-finetune-llms.md |
rag-with-zenml/ |
basic-rag-inference-pipeline.md |
data-ingestion.md |
embeddings-generation.md |
rag-85-loc.md |
README.md |
storing-embeddings-in-a-vector-database.md |
understanding-rag.md |
reranking/ |
evaluating-reranking-performance.md |
implementing-reranking.md |
README.md |
reranking.md |
understanding-reranking.md |
README.md |
production-guide/ |
ci-cd.md |
cloud-orchestration.md |
configure-pipeline.md |
connect-code-repository.md |
deploying-zenml.md |
end-to-end.md |
README.md |
remote-storage.md |
understand-stacks.md |
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ZenML's llms.txt documentation
Available files
The following llms.txt files are available for ZenML.
basics.txt
Tokens: 120k
This file covers the User Guides and the Getting Started section of the ZenML documentation and can be used for answering basic questions about ZenML. This file can also be used alongside other domain-specific files in cases where you need better answers.
component-guide.txt
Tokens: 180k
This file covers all the stack components in ZenML and can be used when you want to find answers pertaining to all of our integrations, how to configure/use them and more.
how-to-guides.txt
Tokens: 75k
This file contains all the doc pages in the how-to section of our documentation; each page summarized to contain all useful information. For most cases, the how-to guides can answer all process questions.
llms-full.txt
Tokens: 600k
The whole ZenML documentation in its glory, un-summarized. Use this for the most accurate answers on ZenML.
Tips and recommendations
- Choose the file that pertains to the part of ZenML you want answers for.
- In every file, the text comes prefixed with the filename which means you can ask your LLM to return file references when answering questions. This is particularly helpful when using the how-to guides which don't the full text, but rather a summary of it.
- You can mix two files, as your context window allows to get more accurate results.
- While prompting, make sure you tell the LLM to not return an answer that it can't infer from the given text file, to avoid getting hallucinated answers.
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