From e936781579fe91dd4eef85bba2580786ac60c06f Mon Sep 17 00:00:00 2001 From: Axy Date: Tue, 25 Aug 2026 14:53:08 +0200 Subject: [PATCH] Implemented it as a cli, chunking not done but preliminaries are --- .gitignore | 1 + pyproject.toml | 16 +- src/rag/__init__.py | 54 +++- src/rag/chunking.py | 47 +++ src/rag/retrieval.py | 50 ++-- uv.lock | 668 ++++++++++++++++++++++++++++++++++++++++++- 6 files changed, 806 insertions(+), 30 deletions(-) create mode 100644 src/rag/chunking.py diff --git a/.gitignore b/.gitignore index eeb8a6e..d594934 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,2 @@ **/__pycache__ +vllm-0.10.1 diff --git a/pyproject.toml b/pyproject.toml index b933912..689d96a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,12 @@ version = "0.1.0" description = "Retrieval augmented generation" readme = "README.md" requires-python = ">=3.13" -dependencies = [] +dependencies = [ + "fire>=0.7.1", + "torch>=2.13.0", + "tqdm>=4.66.5", + "transformers>=5.15.1", +] [dependency-groups] @@ -21,9 +26,16 @@ rag = "rag:main" requires = ["uv_build>=0.12.1,<0.13.0"] build-backend = "uv_build" +[tool.uv.sources] +torch = { index = "pytorch" } + +[[tool.uv.index]] +name = "pytorch" +url = "https://download.pytorch.org/whl/cpu" + [tool.ruff] line-length = 79 -lint.select = ["E", "F", "UP", "B", "SIM", "I"] +lint.select = ["E", "F", "UP", "B", "SIM", "I", "ARG", "N"] [tool.uv] preview-features = ["format", "check"] diff --git a/src/rag/__init__.py b/src/rag/__init__.py index e466511..9e36c99 100644 --- a/src/rag/__init__.py +++ b/src/rag/__init__.py @@ -1,2 +1,54 @@ +import os + +import fire + +from rag.retrieval import BM25 +from rag.chunking import chunk_file, FileType + + def main() -> None: - print("Hello from rag!") + fire.Fire(RAG) + + +class RAG: + """A retrieval augmented generation CLI""" + + def test(self, *files: str, query: str) -> None: + import json + import sys + import time + + corpus = {} + for file in files: + with open(file) as f: + corpus[file] = f.read() + print(sum(len(k) + len(v) for k, v in corpus.items())) + print(time.time()) + matcher = BM25[str].from_corpus(corpus) + print(time.time()) + print(matcher.best_k(query, 10)) + print(time.time()) + s = json.dumps(matcher.to_storage()) + print(time.time()) + matcher2 = BM25[str].from_storage(json.loads(s)) + print(time.time()) + print(matcher.best_k(query, 5)) + + def index( + self, + /, + source: str = "data/raw", + output: str = "data/processed", + max_chunk_size: int = 2000, + ) -> None: + chunks = {} + for dirpath, _, files in os.walk(source): + for file in files: + path = dirpath + "/" + file + if path.endswith(".py"): + filetype = FileType.PYTHON + elif path.endswith(".md") or path.endswith(".txt"): + filetype = FileType.TEXT + else: + continue + chunks.update(chunk_file(path, max_chunk_size, filetype)) diff --git a/src/rag/chunking.py b/src/rag/chunking.py new file mode 100644 index 0000000..154f860 --- /dev/null +++ b/src/rag/chunking.py @@ -0,0 +1,47 @@ +from enum import Enum, auto +from itertools import count +import logging + +logger = logging.getLogger(__name__) + + +class FileType(Enum): + PYTHON = auto() + TEXT = auto() + + def line_depth(self, line: str) -> int | None: + match self: + case FileType.PYTHON: + return next( + depth + for depth in count() + if not line.startswith(" " * (depth + 1)) + ) + case FileType.TEXT: + if line == "" or line.isspace(): + return 7 + return next( + ( + depth - 1 + for depth in range(6, 0, -1) + if line.startswith("#" * depth) + ), + 8, + ) + + +type FileChunkID = tuple[int, int, str] + + +def chunk_file( + path: str, max_chunk_size: int, by: FileType +) -> dict[FileChunkID, str]: + try: + with open(path) as f: + s = f.read() + except OSError as e: + logger.warning(f"Error while chunking file {path}, skipping ({e})") + return {} + for line in s.splitlines(): + print(by.line_depth(line), ":", line) + return {} diff --git a/src/rag/retrieval.py b/src/rag/retrieval.py index 701245c..08603ac 100644 --- a/src/rag/retrieval.py +++ b/src/rag/retrieval.py @@ -31,41 +31,55 @@ class BM25[T]: corpus_words: int corpus: dict[T, tuple[int, BagOfWords]] word_usage: dict[str, set[T]] - k: float = 1.4 + k: float = 1.2 b: float = 0.75 @staticmethod - def from_corpus(raw_corpus: dict[T, str]) -> "BM25[T]": - corpus = { - k: (sum((bow := bag_of_words(s)).values()), bow) - for k, s in raw_corpus.items() - } + def from_storage(storage: dict[T, BagOfWords]) -> "BM25[T]": + corpus = {k: (sum(b.values()), b) for k, b in storage.items()} word_usage = {} for k, (_, doc) in corpus.items(): - for word in doc.keys(): + for word in doc: if word not in word_usage: word_usage[word] = set() word_usage[word].add(k) return BM25(sum(doc[0] for doc in corpus.values()), corpus, word_usage) + def to_storage(self) -> dict[T, BagOfWords]: + return {k: v[1] for k, v in self.corpus.items()} + + @staticmethod + def from_corpus(raw_corpus: dict[T, str]) -> "BM25[T]": + corpus = {k: bag_of_words(s) for k, s in raw_corpus.items()} + return BM25.from_storage(corpus) + def idf(self, q: str) -> float: n_q = len(self.word_usage.get(q, set())) - N = len(self.corpus) - quotient = (N - n_q + 0.5) / (n_q + 0.5) + n = len(self.corpus) + quotient = (n - n_q + 0.5) / (n_q + 0.5) return math.log(quotient + 1) - def word_score(self, q: str, doc: T) -> float: - D_words, D = self.corpus[doc] - f_q = D.get(q, 0) + def word_score(self, word: str, ident: T) -> float: + doc_words, doc = self.corpus[ident] + f_q = doc.get(word, 0) freq = f_q * (self.k + 1) avgdl = self.corpus_words / len(self.corpus) - freq_bias = 1 - self.b + self.b * (D_words / avgdl) + freq_bias = 1 - self.b + self.b * (doc_words / avgdl) quotient = freq / (f_q + self.k * freq_bias) - return self.idf(q) * quotient + return self.idf(word) * quotient + + def score(self, querry: str, doc: T) -> float: + return sum(self.word_score(q, doc) for q in words_normalize(querry)) - def score(self, Q: str, doc: T) -> float: - return sum(self.word_score(q, 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