total: 53, pass: 48, fail: 5
end_to_end [model=dummy] run `end_to_end` test
It simulates a basic workflow of ragit: init, add, build and
query. It runs on a real dataset: the documents of ragit.tested at: 2025-02-24T21:11:27.969515Z (198 days ago)
elapsed time: 31,909 ms
Command '['cargo', 'run', '--release', '--', 'check', '--recover']' returned non-zero exit status 1.
Traceback (most recent call last):
File "/home/baehyunsol/Documents/ragit/tests/tests.py", line 398, in <module>
test()
File "/home/baehyunsol/Documents/ragit/tests/tests.py", line 355, in <lambda>
("end_to_end llama3.3-70b", lambda: end_to_end(test_model="llama3.3-70b")),
File "/home/baehyunsol/Documents/ragit/tests/end_to_end.py", line 88, in end_to_end
cargo_run(["check", "--recover"])
File "/home/baehyunsol/Documents/ragit/tests/utils.py", line 66, in cargo_run
result = subprocess.run(args, **kwargs)
File "/usr/lib/python3.10/subprocess.py", line 526, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['cargo', 'run', '--release', '--', 'check', '--recover']' returned non-zero exit status 1.
tested at: 2025-02-25T10:21:57.114315Z (198 days ago)
elapsed time: 85,923 ms
tested at: 2025-02-26T00:11:13.124361Z (197 days ago)
elapsed time: 69,967 ms
tested at: 2025-02-26T20:31:03.132225Z (196 days ago)
elapsed time: 32,832 ms
Command '['cargo', 'run', '--release', '--', 'check', '--recover']' returned non-zero exit status 1.
Traceback (most recent call last):
File "/home/baehyunsol/Documents/ragit/tests/tests.py", line 437, in <module>
test()
File "/home/baehyunsol/Documents/ragit/tests/tests.py", line 390, in <lambda>
("end_to_end llama3.3-70b", lambda: end_to_end(test_model="llama3.3-70b")),
File "/home/baehyunsol/Documents/ragit/tests/end_to_end.py", line 87, in end_to_end
cargo_run(["check", "--recover"])
File "/home/baehyunsol/Documents/ragit/tests/utils.py", line 66, in cargo_run
result = subprocess.run(args, **kwargs)
File "/usr/lib/python3.10/subprocess.py", line 526, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['cargo', 'run', '--release', '--', 'check', '--recover']' returned non-zero exit status 1.
tested at: 2025-02-27T22:17:10.555252Z (195 days ago)
elapsed time: 32,854 ms
Command '['cargo', 'run', '--release', '--', 'check', '--recover']' returned non-zero exit status 1.
Traceback (most recent call last):
File "/home/baehyunsol/Documents/ragit/tests/tests.py", line 437, in <module>
test()
File "/home/baehyunsol/Documents/ragit/tests/tests.py", line 390, in <lambda>
("end_to_end llama3.3-70b", lambda: end_to_end(test_model="llama3.3-70b")),
File "/home/baehyunsol/Documents/ragit/tests/end_to_end.py", line 87, in end_to_end
cargo_run(["check", "--recover"])
File "/home/baehyunsol/Documents/ragit/tests/utils.py", line 66, in cargo_run
result = subprocess.run(args, **kwargs)
File "/usr/lib/python3.10/subprocess.py", line 526, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['cargo', 'run', '--release', '--', 'check', '--recover']' returned non-zero exit status 1.
tested at: 2025-02-28T11:26:54.429209Z (195 days ago)
elapsed time: 87,665 ms
tested at: 2025-03-01T12:48:39.169037Z (194 days ago)
elapsed time: 388,738 ms
tested at: 2025-03-02T01:39:46.519770Z (193 days ago)
elapsed time: 146,448 ms
tested at: 2025-03-03T19:29:08.807193Z (191 days ago)
elapsed time: 146,782 ms
tested at: 2025-03-03T21:49:32.573405Z (191 days ago)
elapsed time: 144,225 ms
tested at: 2025-03-04T22:05:09.327920Z (190 days ago)
elapsed time: 141,911 ms
tested at: 2025-03-06T00:32:55.162195Z (189 days ago)
elapsed time: 141,768 ms
tested at: 2025-03-07T01:10:52.766008Z (188 days ago)
elapsed time: 140,894 ms
tested at: 2025-03-08T13:37:45.309833Z (186 days ago)
elapsed time: 140,749 ms
tested at: 2025-03-09T00:38:56.569526Z (186 days ago)
elapsed time: 140,918 ms
tested at: 2025-03-09T14:13:04.841105Z (185 days ago)
elapsed time: 1,175,184 ms
tested at: 2025-03-10T11:01:50.525266Z (185 days ago)
elapsed time: 182,539 ms
tested at: 2025-03-10T22:06:31.618023Z (184 days ago)
elapsed time: 141,932 ms
tested at: 2025-03-12T11:49:54.685992Z (183 days ago)
elapsed time: 181,437 ms
tested at: 2025-03-15T01:23:08.009762Z (180 days ago)
elapsed time: 146,704 ms
tested at: 2025-03-21T01:05:42.480054Z (174 days ago)
elapsed time: 78,252 ms
tested at: 2025-03-23T22:32:14.898790Z (171 days ago)
elapsed time: 54,164 ms
tested at: 2025-03-31T22:46:05.278555Z (163 days ago)
elapsed time: 75,443 ms
tested at: 2025-04-01T22:38:25.777417Z (162 days ago)
elapsed time: 66,618 ms
tested at: 2025-04-02T11:15:28.201699Z (162 days ago)
elapsed time: 98,696 ms
tested at: 2025-04-03T11:32:16.038744Z (161 days ago)
elapsed time: 101,245 ms
tested at: 2025-04-04T13:59:04.442731Z (159 days ago)
elapsed time: 98,556 ms
tested at: 2025-04-11T21:47:12.055124Z (152 days ago)
elapsed time: 64,031 ms
tested at: 2025-05-09T11:29:30.226975Z (125 days ago)
elapsed time: 102,601 ms
tested at: 2025-05-14T14:26:51.020259Z (119 days ago)
elapsed time: 97,232 ms
tested at: 2025-05-15T23:00:24.413178Z (118 days ago)
elapsed time: 55,447 ms
Command '['cargo', 'run', '--release', '--', 'meta', '--set', 'git-hash', 'adcf2562496ce6b4c2f17f0a5699f05d686b2dd9']' returned non-zero exit status 1.
Traceback (most recent call last):
File "/home/baehyunsol/Documents/ragit/tests/tests.py", line 621, in <module>
test()
File "/home/baehyunsol/Documents/ragit/tests/tests.py", line 561, in <lambda>
("end_to_end llama3.3-70b", lambda: end_to_end(test_model="llama3.3-70b")),
File "/home/baehyunsol/Documents/ragit/tests/end_to_end.py", line 133, in end_to_end
cargo_run(["meta", "--set", "git-hash", get_commit_hash()])
File "/home/baehyunsol/Documents/ragit/tests/utils.py", line 70, in cargo_run
result = subprocess.run(args, **kwargs)
File "/usr/lib/python3.10/subprocess.py", line 526, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['cargo', 'run', '--release', '--', 'meta', '--set', 'git-hash', 'adcf2562496ce6b4c2f17f0a5699f05d686b2dd9']' returned non-zero exit status 1.
tested at: 2025-05-17T00:48:25.067654Z (117 days ago)
elapsed time: 62,128 ms
tested at: 2025-05-18T09:48:01.799303Z (116 days ago)
elapsed time: 58,369 ms
tested at: 2025-05-19T10:47:10.063082Z (115 days ago)
elapsed time: 99,604 ms
tested at: 2025-05-20T00:59:40.946018Z (114 days ago)
elapsed time: 57,961 ms
tested at: 2025-05-23T00:18:04.093493Z (111 days ago)
elapsed time: 1,046,879 ms
Traceback (most recent call last):
File "/home/baehyunsol/Documents/ragit/tests/tests.py", line 670, in <module>
test()
File "/home/baehyunsol/Documents/ragit/tests/tests.py", line 608, in <lambda>
("end_to_end llama3.3-70b", lambda: end_to_end(test_model="llama3.3-70b")),
File "/home/baehyunsol/Documents/ragit/tests/end_to_end.py", line 137, in end_to_end
assert chunk_count_prev == chunk_count
AssertionError
tested at: 2025-05-24T20:40:39.036733Z (109 days ago)
elapsed time: 54,228 ms
tested at: 2025-05-29T21:31:38.721609Z (104 days ago)
elapsed time: 80,303 ms
tested at: 2025-05-31T05:38:30.224392Z (103 days ago)
elapsed time: 78,321 ms
tested at: 2025-06-01T18:34:31.254192Z (101 days ago)
elapsed time: 53,495 ms
tested at: 2025-06-02T00:08:14.271670Z (101 days ago)
elapsed time: 51,044 ms
tested at: 2025-06-03T01:16:18.813157Z (100 days ago)
elapsed time: 52,659 ms
tested at: 2025-06-05T12:05:40.934810Z (98 days ago)
elapsed time: 1,008,796 ms
tested at: 2025-06-05T16:46:27.585326Z (97 days ago)
elapsed time: 113,628 ms
tested at: 2025-06-07T08:13:40.365332Z (96 days ago)
elapsed time: 97,415 ms
tested at: 2025-06-09T20:06:53.014494Z (93 days ago)
elapsed time: 79,283 ms
tested at: 2025-06-20T11:50:38.004891Z (83 days ago)
elapsed time: 92,476 ms
tested at: 2025-06-27T11:25:09.159801Z (76 days ago)
elapsed time: 93,312 ms
tested at: 2025-07-09T12:03:26.241876Z (64 days ago)
elapsed time: 135,726 ms
--- summary of the knowledge-base ---
The knowledge-base is a collection of 52 files, comprising 76413 characters of text, with a mix of `.md` and `.txt` file extensions, containing information about the RAGIT project, a git-like software designed to create, query, and share knowledge-bases. The main goal of the RAGIT project is to make knowledge-bases easy to create and share. The knowledge-base is organized into directories, including a commands directory with documentation for various RAGIT commands, as well as individual files such as `index.md`, `intro.txt`, and others that provide an introduction to RAGIT, its goals, and its usage, including topics like contribution, evaluation, and publishing, offering a comprehensive overview of the project and its functionality.
tested at: 2025-07-21T08:03:09.691900Z (52 days ago)
elapsed time: 477,414 ms
--- summary of the knowledge-base ---
The knowledge-base is made up of 52 documentation files in `.md` and `.txt` formats. It contains guides for the RAGIT framework, including building, configuring, and querying. The files are structured into directories, with "commands" and "server" directories, and individual files for topics like configuration and multi-turn queries.
--- What makes ragit special? ---
According to the documents, what makes Ragit special is:
1. It adds a title and summary to every chunk, which makes it easy for AIs to rerank chunks.
2. It uses tfidf scores instead of vector searches, and first asks an AI to generate keywords from a query before running a tfidf search.
3. It supports markdown files with images.
4. It supports multi-turn queries (although this is still experimental).
5. You can clone and push knowledge-bases, similar to how you would with Git.
These features set Ragit apart from other RAG (Retrieval-Augmented Generation) frameworks.
---- sources ----
1st chunk of contribution.md (6ea8da45)
1st chunk of chunks.md (28c500f4)
1st chunk of index.md (99eac10e)
--- What makes ragit special? (super rerank mode) ---
ToastrexternalActionCode --------
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MAVBuilderFactory Basel Toastr Basel exposition contaminantsBritain SuccBritainroscopeBritainBuilderFactoryBritain/slider(SizeBuilderFactoryInjected contaminants exposition Succ MAV.visitInsn Toastr exposition(SizeInjectedInjected Toastr_both Succ(dateTime Basel.visitInsn --------
_both PSI PSI_bothInjected --------
roscope—from PSIBuilderFactory(Size Basel PSIRODUCTIONInjected contaminants PSIBuilderFactory/sliderBaselincrease PSI Succ Britain Baselincrease BritainoscopeBritain PSI Basement factory BritainBritain PSI BritainBritain FactoryPSI PSI Britain—
RAGIT
According to the documents, what makes RAGIT special is:
1. It adds a title and summary to every chunk, making it easy for AIs to re-rank chunks.
2. It uses tfidf scores instead of vector searches, which is a unique approach.
3. It supports markdown files with images.
4. It supports multi-turn queries (experimental).
5. It allows users to clone and push knowledge-bases, similar to git.
These features differentiate RAGIT from other RAG frameworks and make it a special tool for creating, querying, and sharing knowledge-bases.
---- sources ----
1st chunk of pipeline.md (384de460)
1st chunk of index.md (99eac10e)
1st chunk of intro.txt (79858d4a)
tested at: 2025-07-21T11:53:02.138116Z (52 days ago)
elapsed time: 118,570 ms
--- summary of the knowledge-base ---
The knowledge-base is made up of markdown and text files. It's about RAGIT, a git-like software for creating and sharing knowledge-bases. The files are structured into directories like `commands` and `server`, and individual files like `index.md` and `intro.txt`, describing RAGIT's features and workflow.
--- What makes ragit special? ---
According to the documents, RAGIT is special because of the following features:
1. It adds a title and summary to every chunk, making it easy for AIs to re-rank chunks.
2. It uses tfidf scores instead of vector searches, and asks an AI to generate keywords from a query before running the search.
3. It supports markdown files with images.
4. It supports multi-turn queries (experimental), which allows users to ask follow-up questions and provides context for the AI to answer.
5. It allows users to clone and push knowledge-bases, similar to git.
These features make RAGIT unique and powerful compared to other RAG frameworks.
---- sources ----
1st chunk of index.md (cbcfc6a9)
1st chunk of multi_turn.md (2400517a)
1st chunk of models.md (1b6049bb)
--- What makes ragit special? (super rerank mode) ---
Ragit is special because of several features that distinguish it from other RAG frameworks. These features include:
1. **Adding a title and summary to every chunk**: This makes it easier for AIs to re-rank chunks.
2. **Using tfidf scores instead of vector searches**: Ragit first asks an AI to generate keywords from a query, then runs a tfidf search with the keywords.
3. **Support for markdown files with images**: This allows for more versatile and visual knowledge-bases.
4. **Support for multi-turn queries (experimental)**: This enables users to ask follow-up questions and receive context-aware answers.
5. **Ability to clone and push knowledge-bases like git**: This makes it easy to share and collaborate on knowledge-bases.
These features combined make Ragit a unique and powerful tool for creating and sharing knowledge-bases.
---- sources ----
1st chunk of multi_turn.md (2400517a)
1st chunk of index.md (cbcfc6a9)
tested at: 2025-07-25T08:20:45.661982Z (48 days ago)
elapsed time: 137,598 ms
--- summary of the knowledge-base ---
The knowledge-base is made up of 52 files, mostly `.md` and `.txt` files, and is stored in a directory with various subdirectories. The content of the knowledge-base appears to be related to Ragit, a git-like software, and its usage, configuration, and related topics. The files are structured in a directory with subdirectories such as `.ragit/` and contain information on building, installing, and using Ragit.
--- What makes ragit special? ---
According to the documents, RAGIT is special because it's a git-like software that turns your local files into a knowledge-base, making it easy to create and share knowledge-bases. Specifically, it's different from other RAG frameworks in the following ways:
1. It adds a title and summary to every chunk, making it easy for AIs to re-rank chunks.
2. It uses tfidf scores instead of vector searches, which involves generating keywords from a query and then running a tfidf search with those keywords.
3. It supports markdown files with images.
4. It supports multi-turn queries (although this is still experimental).
5. You can clone and push knowledge-bases, similar to how you would with git.
These features make RAGIT unique and special compared to other RAG frameworks.
---- sources ----
1st chunk of index.md (4b55a4cb)
3rd chunk of quick_guide.md (da554abb)
1st chunk of uid_query.md (da5a2fbd)
--- What makes ragit special? (super rerank mode) ---
Ragit is special because it has several unique features that distinguish it from other RAG frameworks. Here are some of the key things that make ragit special:
1. **Title and summary for every chunk**: Ragit adds a title and summary to every chunk, which makes it easier for AI models to rerank chunks and provide more relevant results.
2. **Tfidf scores instead of vector searches**: Ragit uses tfidf scores instead of vector searches, which allows it to generate keywords from a query and then run a tfidf search with those keywords.
3. **Support for markdown files with images**: Ragit supports markdown files with images, which makes it a great tool for creating and sharing knowledge bases that include visual content.
4. **Multi-turn queries**: Ragit supports multi-turn queries, which allows users to ask follow-up questions and get more relevant results based on the context of the conversation.
5. **Git-like version control**: Ragit allows users to clone and push knowledge bases, just like git, which makes it easy to collaborate and share knowledge bases with others.
These features, along with its unique RAG pipeline that uses LLMs to extract search keywords and rerank chunks, make ragit a powerful and flexible tool for building and sharing knowledge bases.
---- sources ----
1st chunk of index.md (4b55a4cb)
1st chunk of multi_turn.md (9021e039)
1st chunk of pipeline.md (69e85e91)
tested at: 2025-08-01T14:20:52.926510Z (40 days ago)
elapsed time: 113,148 ms
--- summary of the knowledge-base ---
The knowledge-base is made up of 52 Markdown and text files. It contains documentation for the RAGIT tool, focusing on natural language processing and AI topics, including features, platform support, and commands. The files are structured into directories like "commands" and "server", with additional standalone files like "index.md" and "quick_guide.md".
--- What makes ragit special? ---
Ragit is special because of its unique features, which include:
1. Adding a title and summary to every chunk, making it easy for AIs to rerank chunks.
2. Using tfidf scores instead of vector searches, which involves asking an AI to generate keywords from a query and then running tfidf search with the keywords.
3. Supporting markdown files with images.
4. Supporting multi-turn queries (experimental).
5. Allowing users to clone/push knowledge-bases, similar to git.
These features differentiate Ragit from other RAG frameworks and make it a special tool for creating and sharing knowledge-bases.
---- sources ----
1st chunk of index.md (f6a2e735)
1st chunk of chunks.md (b2b66793)
1st chunk of pipeline.md (9f12b2a1)
--- What makes ragit special? (super rerank mode) ---
According to the documents, RAGIT is special because it:
1. Adds a title and summary to every chunk, making it easy for AIs to re-rank chunks.
2. Uses tfidf scores instead of vector searches, which involves asking an AI to generate keywords from a query and then running a tfidf search with those keywords.
3. Supports markdown files with images.
4. Supports multi-turn queries (although this is still experimental).
5. Allows users to clone and push knowledge-bases, similar to how Git works.
These features distinguish RAGIT from other RAG frameworks, making it unique and special in its own way.
---- sources ----
1st chunk of intro.txt (83110176)
1st chunk of pipeline.md (9f12b2a1)
1st chunk of index.md (f6a2e735)