From 5028e0f69b18e4c23e53dfbf2418c82526ccea8a Mon Sep 17 00:00:00 2001 From: Laurent Sorber Date: Mon, 12 Aug 2024 13:52:00 +0200 Subject: [PATCH] feat: optimal query adapters --- README.md | 70 +- poetry.lock | 3507 ++++++++++++----- pyproject.toml | 67 +- src/raglite/__init__.py | 26 + src/raglite/_config.py | 65 + .../{database_models.py => _database.py} | 115 +- src/raglite/_embed.py | 41 + src/raglite/_extract.py | 67 + .../{document_indexer.py => _index.py} | 82 +- src/raglite/_markdown.py | 198 + src/raglite/_query_adapter.py | 313 ++ src/raglite/_rag.py | 43 + src/raglite/_search.py | 221 ++ .../{chunk_splitter.py => _split_chunks.py} | 27 +- src/raglite/_split_sentences.py | 72 + src/raglite/llm.py | 37 - src/raglite/markdown_converter.py | 199 - src/raglite/proposition_extractor.py | 92 - src/raglite/rag.py | 48 - src/raglite/search.py | 233 -- src/raglite/sentence_splitter.py | 58 - src/raglite/string_embedder.py | 38 - 22 files changed, 3810 insertions(+), 1809 deletions(-) create mode 100644 src/raglite/_config.py rename src/raglite/{database_models.py => _database.py} (64%) create mode 100644 src/raglite/_embed.py create mode 100644 src/raglite/_extract.py rename src/raglite/{document_indexer.py => _index.py} (60%) create mode 100644 src/raglite/_markdown.py create mode 100644 src/raglite/_query_adapter.py create mode 100644 src/raglite/_rag.py create mode 100644 src/raglite/_search.py rename src/raglite/{chunk_splitter.py => _split_chunks.py} (85%) create mode 100644 src/raglite/_split_sentences.py delete mode 100644 src/raglite/llm.py delete mode 100644 src/raglite/markdown_converter.py delete mode 100644 src/raglite/proposition_extractor.py delete mode 100644 src/raglite/rag.py delete mode 100644 src/raglite/search.py delete mode 100644 src/raglite/sentence_splitter.py delete mode 100644 src/raglite/string_embedder.py diff --git a/README.md b/README.md index 4783a83..d809ca1 100644 --- a/README.md +++ b/README.md @@ -2,56 +2,82 @@ # RAGLite -A RAG extension for SQLite. +A Retrieval-Augmented Generation (RAG) library for SQLite. ⚠️ This project is a work in progress! ## Installing -To install this package, run: +To install this package (including Metal acceleration if on macOS), run: ```sh pip install raglite ``` +To add CUDA 12.x support, use the `cuda12[1-4]` extra: + +```sh +pip install raglite[cuda124] +``` + +To add support for filetypes other than PDF, use the `pandoc` extra: + +```sh +pip install raglite[pandoc] +``` + ## Features -1. ❤️ Only lightweight and permissive open source dependencies -2. 🛡️ Fully local RAG with [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) as an LLM provider and [SQLite](https://github.com/sqlite/sqlite) as a local database -3. ✍️ Automatic conversion of any input document to Markdown -4. ✂️ Optimal Markdown-aware [level 4 semantic chunking](https://medium.com/@anuragmishra_27746/five-levels-of-chunking-strategies-in-rag-notes-from-gregs-video-7b735895694d) -5. 🌈 [Multiple vector chunk retrieval](https://python.langchain.com/v0.2/docs/how_to/multi_vector/) -6. 🔍 [Hybrid search](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) that combines [SQLite's BM25 full-text search](https://sqlite.org/fts5.html) with [PyNNDescent's ANN vector search](https://github.com/lmcinnes/pynndescent) +1. ❤️ Only lightweight and permissive open source dependencies (e.g., no [PyTorch](https://github.com/pytorch/pytorch), [LangChain](https://github.com/langchain-ai/langchain), or [PyMuPDF](https://github.com/pymupdf/PyMuPDF)) +2. 🔒 Fully local RAG with [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) as an LLM provider and [SQLite](https://github.com/sqlite/sqlite) as a local database +3. 🚀 Acceleration with Metal on macOS and with CUDA on Linux and Windows +4. 📖 PDF to Markdown conversion on top of [pdftext](https://github.com/VikParuchuri/pdftext) and [pypdfium2](https://github.com/pypdfium2-team/pypdfium2) +5. ✂️ Optimal [level 4 semantic chunking](https://medium.com/@anuragmishra_27746/five-levels-of-chunking-strategies-in-rag-notes-from-gregs-video-7b735895694d) +6. 📌 Markdown-based [contextual chunk headings](https://d-star.ai/solving-the-out-of-context-chunk-problem-for-rag) +7. 🌈 [Multi-vector chunk retrieval](https://python.langchain.com/v0.2/docs/how_to/multi_vector/) +8. 🌀 Optimal closed-form linear query adapter by solving an [(orthogonal) Procrustes problem](https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem) +9. 🔍 [Hybrid search](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) that combines [SQLite's BM25 full-text search](https://sqlite.org/fts5.html) with [PyNNDescent's ANN vector search](https://github.com/lmcinnes/pynndescent) +10. ✍️ Optional support for automatic conversion of any input document to Markdown with [Pandoc](https://github.com/jgm/pandoc) ## Using Example usage: ```python -# Index: +# Configure the database and LLM: +from raglite import RAGLiteConfig + +my_config = RAGLiteConfig(db_url="sqlite:///raglite.sqlite") + +# Index documents: from pathlib import Path -from raglite.document_indexer import insert_document, update_index +from raglite import insert_document, update_vector_index -doc_path = Path("intelligence.pdf") -insert_document(doc_path) -update_index() +insert_document(Path("On the Measure of Intelligence.pdf"), config=my_config) +insert_document(Path("Situational Awareness.pdf"), config=my_config) +update_vector_index(config=my_config) -# Search: -from raglite.search import keyword_search, vector_search, hybrid_search, fusion_search +# Search for chunks: +from raglite import hybrid_search, keyword_search, vector_search prompt = "How is intelligence measured?" -results_vector = vector_search(prompt, num_results=5) -results_bm25 = keyword_search(prompt, num_results=5) -results_hybrid = hybrid_search(prompt, num_results=5) -results_fusion = fusion_search(prompt, num_results=5) +results_vector = vector_search(prompt, num_results=5, config=my_config) +results_keyword = keyword_search(prompt, num_results=5, config=my_config) +results_hybrid = hybrid_search(prompt, num_results=5, config=my_config) -# RAG: -from raglite.rag import rag +# Answer questions with RAG: +from raglite import rag prompt = "What is a 'SkillProgram'?" -stream = rag(prompt, search=hybrid_search) +stream = rag(prompt, search=hybrid_search, config=my_config) for update in stream: print(update, end="") + +# Improve RAG with an optimal query adapter (optional): +from raglite import insert_evals, update_query_adapter + +insert_evals(num_evals=100, config=my_config) +update_query_adapter(config=my_config) ``` ## Contributing diff --git a/poetry.lock b/poetry.lock index d5ed103..98a7d60 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,126 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = 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-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +[package.dependencies] +idna = ">=2.0" +multidict = ">=4.0" + +[extras] +cuda121 = [] +cuda122 = [] +cuda123 = [] +cuda124 = [] +eval = ["ragas"] +pandoc = ["pypandoc-binary"] [metadata] lock-version = "2.0" -python-versions = ">=3.10,<3.13" -content-hash = "938fb6db9e9365ef45724030c455b1688fd25deb0db5978cf42fde0827ed19bd" +python-versions = ">=3.10,<4.0" +content-hash = "fab05f526c08d12c9ed8b3ea25a73f0c0dca963aaf36ea51e3213c46f22560f9" diff --git a/pyproject.toml b/pyproject.toml index 7ada748..cb94e46 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,10 +18,11 @@ version_provider = "poetry" [tool.poetry.dependencies] # https://python-poetry.org/docs/dependency-specification/ # Python: -python = ">=3.10,<3.13" +python = ">=3.10,<4.0" # Markdown conversion: -pdfminer-six = ">=20231228" -pypandoc-binary = ">=1.13" +pdftext = ">=0.3.10" +pypandoc-binary = { version = ">=1.13", optional = true } +scikit-learn = ">=1.5.1" # Markdown formatting: markdown-it-py = ">=3.0.0" mdformat-gfm = ">=0.3.6" @@ -32,18 +33,58 @@ xx_sent_ud_sm = { url = "https://github.com/explosion/spacy-models/releases/down # Large Language Models: huggingface-hub = ">=0.22.0" llama-cpp-python = [ - { version = ">=0.2.68", source = "llama-cpp-python-metal", markers = "sys_platform == 'darwin'" }, - { version = ">=0.2.68", source = "llama-cpp-python-cuda", markers = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { version = ">=0.2.88", markers = "(sys_platform != 'darwin' or platform_machine != 'arm64') and (extra != 'cuda121' and extra != 'cuda122' and extra != 'cuda123' and extra != 'cuda124')" }, + # Metal + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-metal/llama_cpp_python-0.2.88-cp310-cp310-macosx_11_0_arm64.whl", markers = "sys_platform == 'darwin' and platform_machine == 'arm64' and python_version == '3.10'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-metal/llama_cpp_python-0.2.88-cp311-cp311-macosx_11_0_arm64.whl", markers = "sys_platform == 'darwin' and platform_machine == 'arm64' and python_version == '3.11'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-metal/llama_cpp_python-0.2.88-cp312-cp312-macosx_11_0_arm64.whl", markers = "sys_platform == 'darwin' and platform_machine == 'arm64' and python_version == '3.12'" }, + # CUDA 12.1 + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu121/llama_cpp_python-0.2.88-cp310-cp310-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.10' and extra == 'cuda121'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu121/llama_cpp_python-0.2.88-cp310-cp310-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.10' and extra == 'cuda121'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu121/llama_cpp_python-0.2.88-cp311-cp311-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.11' and extra == 'cuda121'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu121/llama_cpp_python-0.2.88-cp311-cp311-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.11' and extra == 'cuda121'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu121/llama_cpp_python-0.2.88-cp312-cp312-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.12' and extra == 'cuda121'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu121/llama_cpp_python-0.2.88-cp312-cp312-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.12' and extra == 'cuda121'" }, + # CUDA 12.2 + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp310-cp310-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.10' and extra == 'cuda122'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp310-cp310-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.10' and extra == 'cuda122'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp311-cp311-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.11' and extra == 'cuda122'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp311-cp311-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.11' and extra == 'cuda122'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp312-cp312-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.12' and extra == 'cuda122'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp312-cp312-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.12' and extra == 'cuda122'" }, + # CUDA 12.3 + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp310-cp310-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.10' and extra == 'cuda123'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp310-cp310-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.10' and extra == 'cuda123'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp311-cp311-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.11' and extra == 'cuda123'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp311-cp311-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.11' and extra == 'cuda123'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp312-cp312-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.12' and extra == 'cuda123'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu123/llama_cpp_python-0.2.88-cp312-cp312-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.12' and extra == 'cuda123'" }, + # CUDA 12.4 + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu124/llama_cpp_python-0.2.88-cp310-cp310-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.10' and extra == 'cuda124'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu124/llama_cpp_python-0.2.88-cp310-cp310-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.10' and extra == 'cuda124'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu124/llama_cpp_python-0.2.88-cp311-cp311-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.11' and extra == 'cuda124'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu124/llama_cpp_python-0.2.88-cp311-cp311-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.11' and extra == 'cuda124'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu124/llama_cpp_python-0.2.88-cp312-cp312-linux_x86_64.whl", markers = "sys_platform == 'linux' and python_version == '3.12' and extra == 'cuda124'" }, + { url = "https://github.com/abetlen/llama-cpp-python/releases/download/v0.2.85-cu124/llama_cpp_python-0.2.88-cp312-cp312-win_amd64.whl", markers = "sys_platform == 'win32' and python_version == '3.12' and extra == 'cuda124'" }, ] +pydantic = ">=2.7.0" # Approximate Nearest Neighbors: pynndescent = ">=0.5.12" -# Ranking: -scikit-learn = ">=1.1.3" # Storage: requests = ">=2.31.0" sqlmodel-slim = ">=0.0.18" # Progress: tqdm = ">=4.66.0" +# Evaluation: +ragas = { version = ">=0.1.12", optional = true } + +[tool.poetry.extras] # https://python-poetry.org/docs/pyproject/#extras +cuda121 = [] +cuda122 = [] +cuda123 = [] +cuda124 = [] +eval = ["ragas"] +pandoc = ["pypandoc-binary"] [tool.poetry.group.test.dependencies] # https://python-poetry.org/docs/master/managing-dependencies/ commitizen = ">=3.21.3" @@ -67,16 +108,6 @@ matplotlib = ">=3.9.0" memory-profiler = ">=0.61.0" pdoc = ">=14.4.0" -[[tool.poetry.source]] -name = "llama-cpp-python-metal" -url = "https://abetlen.github.io/llama-cpp-python/whl/metal" -priority = "explicit" - -[[tool.poetry.source]] -name = "llama-cpp-python-cuda" -url = "https://abetlen.github.io/llama-cpp-python/whl/cu121" -priority = "explicit" - [tool.coverage.report] # https://coverage.readthedocs.io/en/latest/config.html#report fail_under = 50 precision = 1 @@ -167,7 +198,7 @@ select = [ "W", "YTT", ] -ignore = ["D203", "D213", "E501", "RET504", "S101", "S307"] +ignore = ["D203", "D213", "E501", "RET504", "RUF002", "S101", "S307"] unfixable = ["ERA001", "F401", "F841", "T201", "T203"] [tool.ruff.lint.flake8-tidy-imports] diff --git a/src/raglite/__init__.py b/src/raglite/__init__.py index 3864ab8..1e50cbd 100644 --- a/src/raglite/__init__.py +++ b/src/raglite/__init__.py @@ -1 +1,27 @@ """RAGLite.""" + +from raglite._config import RAGLiteConfig +from raglite._index import insert_document, update_vector_index +from raglite._query_adapter import insert_evals, update_query_adapter +from raglite._rag import rag +from raglite._search import ( + fusion_search, + hybrid_search, + keyword_search, + retrieve_segments, + vector_search, +) + +__all__ = [ + "insert_document", + "update_vector_index", + "insert_evals", + "update_query_adapter", + "rag", + "fusion_search", + "hybrid_search", + "keyword_search", + "vector_search", + "retrieve_segments", + "RAGLiteConfig", +] diff --git a/src/raglite/_config.py b/src/raglite/_config.py new file mode 100644 index 0000000..bce8697 --- /dev/null +++ b/src/raglite/_config.py @@ -0,0 +1,65 @@ +"""RAGLite config.""" + +from dataclasses import dataclass, field +from functools import lru_cache + +import numpy as np +import numpy.typing as npt +from llama_cpp import Llama, LlamaRAMCache +from sqlalchemy.engine import URL + + +@lru_cache(maxsize=1) +def default_llm() -> Llama: + """Get default LLM.""" + llm = Llama.from_pretrained( + repo_id="bartowski/Meta-Llama-3.1-8B-Instruct-GGUF", # https://github.com/meta-llama/llama-models + filename="*Q4_K_M.gguf", + n_ctx=0, + n_gpu_layers=-1, + use_mlock=True, + use_mmap=False, + verbose=False, + ) + llm.set_cache(LlamaRAMCache()) + return llm + + +@lru_cache(maxsize=1) +def default_embedder() -> Llama: + """Get default embedder.""" + embedder = Llama.from_pretrained( + repo_id="ChristianAzinn/snowflake-arctic-embed-l-gguf", # https://github.com/Snowflake-Labs/arctic-embed + filename="*f16.GGUF", + n_ctx=0, + n_gpu_layers=-1, + use_mlock=True, + use_mmap=False, + verbose=False, + embedding=True, + ) + return embedder + + +@dataclass(frozen=True) +class RAGLiteConfig: + """Configuration for RAGLite.""" + + # LLM config used for generation. + llm: Llama = field(default_factory=default_llm) + llm_max_tries: int = 4 + llm_temperature: float = 1.0 + # Embedder config used for indexing. + embedder: Llama = field(default_factory=default_embedder) + embedder_batch_size: int = 128 + embedder_dtype: npt.DTypeLike = np.float16 + embedder_normalize: bool = True + multi_vector_weight: float = 0.5 # Between 0 (chunk embedding) and 1 (sentence embedding). + # Chunker config used to partition documents into chunks. + chunk_max_size: int = 1440 # Max number of characters per chunk. + chunk_sentence_window_size: int = 3 + # Database config. + db_url: str | URL = "sqlite:///raglite.sqlite" + # ANN config. + ann_vector_index_id: str = "default" + ann_vector_index_metric: str = "cosine" # The query adapter supports "dot" and "cosine". diff --git a/src/raglite/database_models.py b/src/raglite/_database.py similarity index 64% rename from src/raglite/database_models.py rename to src/raglite/_database.py index 3936f8d..f87ee58 100644 --- a/src/raglite/database_models.py +++ b/src/raglite/_database.py @@ -5,10 +5,9 @@ from functools import lru_cache from hashlib import sha256 from pathlib import Path -from urllib.parse import urlparse +from typing import Any import numpy as np -import requests from markdown_it import MarkdownIt from pynndescent import NNDescent from sqlalchemy.engine import URL, Dialect, Engine, make_url @@ -16,9 +15,9 @@ from sqlmodel import JSON, Column, Field, Relationship, Session, SQLModel, create_engine, text -def hash_bytes(data: bytes) -> str: +def hash_bytes(data: bytes, max_len: int = 16) -> str: """Hash bytes to a hexadecimal string.""" - return sha256(data, usedforsecurity=False).hexdigest()[:16] + return sha256(data, usedforsecurity=False).hexdigest()[:max_len] class NumpyArray(TypeDecorator): @@ -65,28 +64,31 @@ class Document(SQLModel, table=True): id: str = Field(..., primary_key=True) filename: str url: str | None = Field(default=None) + metadata_: dict[str, Any] = Field(default={}, sa_column=Column("metadata", JSON)) - # Add relationship so we can access document.chunks. + # Add relationships so we can access document.chunks and document.evals. chunks: list["Chunk"] = Relationship(back_populates="document") + evals: list["Eval"] = Relationship(back_populates="document") @staticmethod - def from_path(doc_path: Path) -> "Document": + def from_path(doc_path: Path, **kwargs: Any) -> "Document": """Create a document from a file path.""" return Document( id=hash_bytes(doc_path.read_bytes()), filename=doc_path.name, + metadata_={ + "size": doc_path.stat().st_size, + "created": doc_path.stat().st_ctime, + "modified": doc_path.stat().st_mtime, + **kwargs, + }, ) - @staticmethod - def from_url(doc_url: str, doc_content: bytes | None = None) -> "Document": - """Create a document from a file path.""" - if doc_content is not None: - doc_content = requests.get(doc_url, timeout=10).content - return Document( - id=hash_bytes(doc_content), - filename=Path(urlparse(doc_url).path).name, - url=doc_url, - ) + # Enable support for JSON columns. + class Config: + """Table configuration.""" + + arbitrary_types_allowed = True class Chunk(SQLModel, table=True): @@ -95,9 +97,10 @@ class Chunk(SQLModel, table=True): id: str = Field(..., primary_key=True) document_id: str = Field(..., foreign_key="document.id", index=True) index: int = Field(..., index=True) - headers: str + headings: str body: str multi_vector_embedding: np.ndarray = Field(..., sa_column=Column(NumpyArray)) + metadata_: dict[str, Any] = Field(default={}, sa_column=Column("metadata", JSON)) # Add relationship so we can access chunk.document. document: Document = Relationship(back_populates="chunks") @@ -107,38 +110,47 @@ def from_body( document_id: str, index: int, body: str, - headers: str = "", + headings: str = "", multi_vector_embedding: np.ndarray | None = None, + **kwargs: Any, ) -> "Chunk": """Create a chunk from Markdown.""" return Chunk( id=hash_bytes(body.encode()), document_id=document_id, index=index, - headers=headers, + headings=headings, body=body, multi_vector_embedding=multi_vector_embedding if multi_vector_embedding is not None else np.empty(0), + metadata_=kwargs, ) - def extract_headers(self) -> str: - """Extract the headers from the chunk, starting from the current headers.""" + def extract_headings(self) -> str: + """Extract Markdown headings from the chunk, starting from the current Markdown headings.""" md = MarkdownIt() - header_lines = [""] * 10 + heading_lines = [""] * 10 level = None - for doc in (self.headers, self.body): + for doc in (self.headings, self.body): for token in md.parse(doc): if token.type == "heading_open": level = int(token.tag[1]) elif token.type == "heading_close": level = None elif level is not None: - header_content = token.content.strip().replace("\n", " ") - header_lines[level] = ("#" * level) + " " + header_content - header_lines[level + 1 :] = [""] * len(header_lines[level + 1 :]) - headers = "\n".join([header for header in header_lines if header]) - return headers + heading_content = token.content.strip().replace("\n", " ") + heading_lines[level] = ("#" * level) + " " + heading_content + heading_lines[level + 1 :] = [""] * len(heading_lines[level + 1 :]) + headings = "\n".join([heading for heading in heading_lines if heading]) + return headings + + def __str__(self) -> str: + """Context representation of this chunk.""" + return f"{self.headings.strip()}\n\n{self.body.strip()}".strip() + + def __hash__(self) -> int: + return hash(self.id) # Enable support for JSON and NumpyArray columns. class Config: @@ -155,8 +167,53 @@ class ChunkANNIndex(SQLModel, table=True): id: str = Field(..., primary_key=True) chunk_sizes: list[int] = Field(default=[], sa_column=Column(JSON)) index: NNDescent | None = Field(default=None, sa_column=Column(PickledObject)) + query_adapter: np.ndarray | None = Field(default=None, sa_column=Column(NumpyArray)) + metadata_: dict[str, Any] = Field(default={}, sa_column=Column("metadata", JSON)) - # Enable support for JSON and NumpyArray columns. + # Enable support for JSON, PickledObject, and NumpyArray columns. + class Config: + """Table configuration.""" + + arbitrary_types_allowed = True + + +class Eval(SQLModel, table=True): + """A RAG evaluation example.""" + + __tablename__ = "eval" + + id: str = Field(..., primary_key=True) + document_id: str = Field(..., foreign_key="document.id", index=True) + chunk_ids: list[str] = Field(default=[], sa_column=Column(JSON)) + question: str + contexts: list[str] = Field(default=[], sa_column=Column(JSON)) + ground_truth: str + metadata_: dict[str, Any] = Field(default={}, sa_column=Column("metadata", JSON)) + + # Add relationship so we can access eval.document. + document: Document = Relationship(back_populates="evals") + + @staticmethod + def from_chunks( + question: str, + contexts: list[Chunk], + ground_truth: str, + **kwargs: Any, + ) -> "Chunk": + """Create a chunk from Markdown.""" + document_id = contexts[0].document_id + chunk_ids = [context.id for context in contexts] + return Eval( + id=hash_bytes(f"{document_id}-{chunk_ids}-{question}".encode()), + document_id=document_id, + chunk_ids=chunk_ids, + question=question, + contexts=[str(context) for context in contexts], + ground_truth=ground_truth, + metadata_=kwargs, + ) + + # Enable support for JSON columns. class Config: """Table configuration.""" diff --git a/src/raglite/_embed.py b/src/raglite/_embed.py new file mode 100644 index 0000000..c06eacb --- /dev/null +++ b/src/raglite/_embed.py @@ -0,0 +1,41 @@ +"""String embedder.""" + +from functools import lru_cache + +import numpy as np +from tqdm.auto import trange + +from raglite._config import RAGLiteConfig + + +@lru_cache(maxsize=128) +def _embed_string_batch( + string_batch: tuple[str], *, config: RAGLiteConfig | None = None +) -> np.ndarray: + # Embed a batch of strings. + config = config or RAGLiteConfig() + if len(string_batch) == 0: + embeddings = np.zeros((0, config.embedder.n_embd())) + else: + embeddings = np.asarray(config.embedder.embed(string_batch)) + # Normalise embeddings to unit norm. + if config.embedder_normalize: + embeddings /= np.linalg.norm(embeddings, axis=1, keepdims=True) + # Cast to the configured dtype after normalisation. + embeddings = embeddings.astype(config.embedder_dtype) + return embeddings + + +def embed_strings(strings: list[str], *, config: RAGLiteConfig | None = None) -> np.ndarray: + """Embed a list of strings as a NumPy array of row vectors.""" + assert isinstance(strings, list), "Input must be a list of strings" + config = config or RAGLiteConfig() + bs = config.embedder_batch_size + brange = ( + trange(0, len(strings), bs, desc="Embedding", unit="batch", dynamic_ncols=True) + if len(strings) > bs + else range(0, max(1, len(strings)), bs) + ) + batches = [_embed_string_batch(tuple(strings[i : i + bs]), config=config) for i in brange] + embeddings = np.vstack(batches) + return embeddings diff --git a/src/raglite/_extract.py b/src/raglite/_extract.py new file mode 100644 index 0000000..0988c05 --- /dev/null +++ b/src/raglite/_extract.py @@ -0,0 +1,67 @@ +"""Extract structured data from unstructured text with an LLM.""" + +from typing import Any, TypeVar + +from pydantic import BaseModel, ValidationError + +from raglite._config import RAGLiteConfig + +T = TypeVar("T", bound=BaseModel) + + +def extract_with_llm( + return_type: type[T], + user_prompt: str | list[str], + config: RAGLiteConfig | None = None, + **kwargs: Any, +) -> T: + """Extract structured data from unstructured text with an LLM. + + This function expects a `return_type.system_prompt: ClassVar[str]` that contains the system + prompt to use. Example: + + from typing import ClassVar + from pydantic import BaseModel, Field + + class MyNameResponse(BaseModel): + my_name: str = Field(..., description="The user's name.") + system_prompt: ClassVar[str] = "The system prompt to use (excluded from JSON schema)." + + my_name_response = extract_with_llm(MyNameResponse, "My name is Thomas A. Anderson.") + """ + # Load the default config if not provided. + config = config or RAGLiteConfig() + # Update the system prompt with the JSON schema of the return type to help the LLM. + system_prompt = ( + return_type.system_prompt.strip() + "\n", + "Format your response according to this JSON schema:\n", + return_type.model_json_schema(), + ) + # Concatenate the user prompt if it is a list of strings. + if isinstance(user_prompt, list): + user_prompt = "\n\n".join( + f'\n{chunk.strip()}\n' + for i, chunk in enumerate(user_prompt) + ) + # Extract structured data from the unstructured input. + for _ in range(config.llm_max_tries): + response = config.llm.create_chat_completion( + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + response_format={"type": "json_object", "schema": return_type.model_json_schema()}, + temperature=config.llm_temperature, + **kwargs, + ) + try: + instance = return_type.model_validate_json(response["choices"][0]["message"]["content"]) + except (KeyError, ValueError, ValidationError): + # Malformed response, not a JSON string, or not a valid instance of the return type. + continue + else: + break + else: + error_message = f"Failed to extract {return_type} from input {user_prompt}" + raise ValueError(error_message) + return instance diff --git a/src/raglite/document_indexer.py b/src/raglite/_index.py similarity index 60% rename from src/raglite/document_indexer.py rename to src/raglite/_index.py index ba5fe25..3f93bd5 100644 --- a/src/raglite/document_indexer.py +++ b/src/raglite/_index.py @@ -1,77 +1,81 @@ """Index documents.""" -from collections.abc import Callable +from functools import partial from pathlib import Path import numpy as np from pynndescent import NNDescent -from sqlalchemy.engine import URL from sqlmodel import Session, select from tqdm.auto import tqdm -from raglite.chunk_splitter import split_chunks -from raglite.database_models import Chunk, ChunkANNIndex, Document, create_database_engine -from raglite.markdown_converter import document_to_markdown -from raglite.sentence_splitter import split_sentences -from raglite.string_embedder import embed_strings +from raglite._config import RAGLiteConfig +from raglite._database import Chunk, ChunkANNIndex, Document, create_database_engine +from raglite._embed import embed_strings +from raglite._markdown import document_to_markdown +from raglite._split_chunks import split_chunks +from raglite._split_sentences import split_sentences -def create_chunk_records( +def _create_chunk_records( document_id: str, chunks: list[str], multi_vector_embeddings: list[np.ndarray], - multi_vector_weight: float = 0.2, - embed: Callable[[list[str]], np.ndarray] = embed_strings, + *, + config: RAGLiteConfig | None = None, ) -> list[Chunk]: - """Process chunks into headers, body and improved embeddings.""" + """Process chunks into headings, body and improved embeddings.""" # Create the chunk records. chunk_records = [] contextualized_chunks = [] - headers = "" + headings = "" for i, chunk in enumerate(chunks): - # Create and append the contextualised chunk, which includes the current Markdown headers. - contextualized_chunks.append(headers + "\n\n" + chunk) + # Create and append the contextualised chunk, which includes the current Markdown headings. + contextualized_chunks.append(headings + "\n\n" + chunk) # Create and append the chunk record. chunk_record = Chunk.from_body( - document_id=document_id, index=i, body=chunk, headers=headers + document_id=document_id, index=i, body=chunk, headings=headings ) chunk_records.append(chunk_record) - # Update the Markdown headers with those of this chunk. - headers = chunk_record.extract_headers() + # Update the Markdown headings with those of this chunk. + headings = chunk_record.extract_headings() # Embed the contextualised chunks. - contextualized_embeddings = embed(contextualized_chunks) - contextualized_embeddings = contextualized_embeddings / np.linalg.norm( - contextualized_embeddings, axis=1, keepdims=True - ) - # Update the chunk records with improved multi vector embeddings that combine its multi vector + contextualized_embeddings = embed_strings(contextualized_chunks, config=config) + # Update the chunk records with improved multi-vector embeddings that combine its multi-vector # embedding with its contextualised chunk embedding. for chunk_record, multi_vector_embedding, contextualized_embedding in zip( chunk_records, multi_vector_embeddings, contextualized_embeddings, strict=True ): chunk_embedding = ( - multi_vector_weight * multi_vector_embedding - + (1 - multi_vector_weight) * contextualized_embedding[np.newaxis, :] + config.multi_vector_weight * multi_vector_embedding + + (1 - config.multi_vector_weight) * contextualized_embedding[np.newaxis, :] ) chunk_embedding = chunk_embedding / np.linalg.norm(chunk_embedding, axis=1, keepdims=True) chunk_record.multi_vector_embedding = chunk_embedding return chunk_records -def insert_document(doc_path: Path, db_url: str | URL = "sqlite:///raglite.sqlite") -> None: - """Insert a document.""" +def insert_document(doc_path: Path, *, config: RAGLiteConfig | None = None) -> None: + """Insert a document into the database.""" + # Use the default config if not provided. + config = config or RAGLiteConfig() # Preprocess the document into chunks. with tqdm(total=4, unit="step", dynamic_ncols=True) as pbar: pbar.set_description("Initializing database") - engine = create_database_engine(db_url) + engine = create_database_engine(config.db_url) pbar.update(1) pbar.set_description("Converting to Markdown") doc = document_to_markdown(doc_path) pbar.update(1) pbar.set_description("Splitting sentences") - sentences = split_sentences(doc) + sentences = split_sentences(doc, max_len=config.chunk_max_size) pbar.update(1) pbar.set_description("Splitting chunks") - chunks, multi_vector_embeddings = split_chunks(sentences) + chunks, multi_vector_embeddings = split_chunks( + sentences, + max_size=config.chunk_max_size, + sentence_window_size=config.chunk_sentence_window_size, + embed=partial(embed_strings, config=config), + ) pbar.update(1) # Create and store the chunk records. with Session(engine) as session: @@ -81,7 +85,9 @@ def insert_document(doc_path: Path, db_url: str | URL = "sqlite:///raglite.sqlit session.add(document_record) session.commit() # Create the chunk records. - chunk_records = create_chunk_records(document_record.id, chunks, multi_vector_embeddings) + chunk_records = _create_chunk_records( + document_record.id, chunks, multi_vector_embeddings, config=config + ) # Store the chunk records. for chunk_record in tqdm( chunk_records, desc="Storing chunks", unit="chunk", dynamic_ncols=True @@ -92,12 +98,15 @@ def insert_document(doc_path: Path, db_url: str | URL = "sqlite:///raglite.sqlit session.commit() -def update_index(index_id: str = "default", db_url: str | URL = "sqlite:///raglite.sqlite") -> None: +def update_vector_index(config: RAGLiteConfig | None = None) -> None: """Update the chunk ANN index with any unindexed chunks.""" - engine = create_database_engine(db_url) + config = config or RAGLiteConfig() + engine = create_database_engine(config.db_url) with Session(engine) as session: # Get the chunk ANN index from the database, or create a new one. - chunk_ann_index = session.get(ChunkANNIndex, index_id) or ChunkANNIndex(id=index_id) + chunk_ann_index = session.get(ChunkANNIndex, config.ann_vector_index_id) or ChunkANNIndex( + id=config.ann_vector_index_id + ) num_chunks_indexed = len(chunk_ann_index.chunk_sizes) # Get the unindexed chunks. statement = select(Chunk).offset(num_chunks_indexed) @@ -115,10 +124,13 @@ def update_index(index_id: str = "default", db_url: str | URL = "sqlite:///ragli return X_unindexed = np.vstack([chunk.multi_vector_embedding for chunk in unindexed_chunks]) # noqa: N806 if num_chunks_indexed == 0: - chunk_ann_index.index = NNDescent(X_unindexed, metric="cosine", compressed=True) - else: + chunk_ann_index.index = NNDescent( + X_unindexed, metric=config.ann_vector_index_metric + ) chunk_ann_index.index.prepare() + else: chunk_ann_index.index.update(X_unindexed) + chunk_ann_index.index.prepare() chunk_ann_index.chunk_sizes.extend( [chunk.multi_vector_embedding.shape[0] for chunk in unindexed_chunks] ) diff --git a/src/raglite/_markdown.py b/src/raglite/_markdown.py new file mode 100644 index 0000000..feba689 --- /dev/null +++ b/src/raglite/_markdown.py @@ -0,0 +1,198 @@ +"""Convert any document to Markdown.""" + +import re +import warnings +from copy import deepcopy +from pathlib import Path +from typing import Any + +import mdformat +import numpy as np +import pypandoc +from pdftext.extraction import dictionary_output +from sklearn.cluster import KMeans +from sklearn.exceptions import InconsistentVersionWarning + + +def parsed_pdf_to_markdown(pages: list[dict[str, Any]]) -> list[str]: # noqa: C901, PLR0915 + """Convert a PDF parsed with pdftext to Markdown.""" + + def add_heading_level_metadata(pages: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Add heading level metadata to a PDF parsed with pdftext.""" + # Copy the pages. + pages = deepcopy(pages) + # Extract an array of all font sizes used by the text spans. + font_sizes = [ + span["font"]["size"] + for page in pages + for block in page["blocks"] + for line in block["lines"] + for span in line["spans"] + ] + font_sizes = np.asarray(font_sizes) + font_sizes = np.round(font_sizes * 2) / 2 + unique_font_sizes, counts = np.unique(font_sizes, return_counts=True) + # Determine the paragraph font size as the mode font size. + tiny = unique_font_sizes < 5 # noqa: PLR2004 + counts[tiny] = -counts[tiny] + mode = np.argmax(counts) + counts[tiny] = -counts[tiny] + mode_font_size = unique_font_sizes[mode] + # Determine (at most) 6 heading font sizes by clustering font sizes larger than the mode. + heading_font_sizes = unique_font_sizes[mode + 1 :] + heading_counts = counts[mode + 1 :] + kmeans = KMeans(n_clusters=min(6, len(heading_font_sizes)), random_state=42) + kmeans.fit(heading_font_sizes[:, np.newaxis], sample_weight=heading_counts) + heading_font_sizes = np.sort(np.ravel(kmeans.cluster_centers_))[::-1] + # Add heading level information to the text spans and lines. + for page in pages: + for block in page["blocks"]: + for line in block["lines"]: + if "md" not in line: + line["md"] = {} + heading_level = np.zeros(8) # 0-5:

-

, 6:

, 7: + for span in line["spans"]: + if "md" not in span: + span["md"] = {} + span_font_size = round(span["font"]["size"] * 2) / 2 + if span_font_size < mode_font_size: + idx = 7 + elif span_font_size == mode_font_size: + idx = 6 + else: + idx = np.argmin(np.abs(heading_font_sizes - span_font_size)) + span["md"]["heading_level"] = idx + 1 + heading_level[idx] += len(span["text"]) + line["md"]["heading_level"] = np.argmax(heading_level) + 1 + return pages + + def add_emphasis_metadata(pages: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Add emphasis metadata such as bold and italic to a PDF parsed with pdftext.""" + # Copy the pages. + pages = deepcopy(pages) + # Add emphasis metadata to the text spans. + for page in pages: + for block in page["blocks"]: + for line in block["lines"]: + if "md" not in line: + line["md"] = {} + for span in line["spans"]: + if "md" not in span: + span["md"] = {} + span["md"]["bold"] = span["font"]["weight"] > 500 # noqa: PLR2004 + span["md"]["italic"] = "ital" in (span["font"]["name"] or "").lower() + line["md"]["bold"] = all( + span["md"]["bold"] for span in line["spans"] if span["text"].strip() + ) + line["md"]["italic"] = all( + span["md"]["italic"] for span in line["spans"] if span["text"].strip() + ) + return pages + + def strip_page_numbers(pages: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Strip page numbers from a PDF parsed with pdftext.""" + # Copy the pages. + pages = deepcopy(pages) + # Remove lines that only contain a page number. + for page in pages: + for block in page["blocks"]: + block["lines"] = [ + line + for line in block["lines"] + if not re.match( + r"^\s*[#0]*\d+\s*$", "".join(span["text"] for span in line["spans"]) + ) + ] + return pages + + def convert_to_markdown(pages: list[dict[str, Any]]) -> list[str]: # noqa: C901, PLR0912 + """Convert a list of pages to Markdown.""" + pages_md = [] + for page in pages: + page_md = "" + for block in page["blocks"]: + block_text = "" + for line in block["lines"]: + # Build the line text and style the spans. + line_text = "" + for span in line["spans"]: + if ( + not line["md"]["bold"] + and not line["md"]["italic"] + and span["md"]["bold"] + and span["md"]["italic"] + ): + line_text += f"***{span['text']}***" + elif not line["md"]["bold"] and span["md"]["bold"]: + line_text += f"**{span['text']}**" + elif not line["md"]["italic"] and span["md"]["italic"]: + line_text += f"*{span['text']}*" + else: + line_text += span["text"] + # Add emphasis to the line (if it's not a heading or whitespace). + line_text = line_text.rstrip() + line_is_whitespace = not line_text.strip() + line_is_heading = line["md"]["heading_level"] <= 6 # noqa: PLR2004 + if not line_is_heading and not line_is_whitespace: + if line["md"]["bold"] and line["md"]["italic"]: + line_text = f"***{line_text}***" + elif line["md"]["bold"]: + line_text = f"**{line_text}**" + elif line["md"]["italic"]: + line_text = f"*{line_text}*" + # Set the heading level. + if line_is_heading and not line_is_whitespace: + line_text = f"{'#' * line['md']['heading_level']} {line_text}" + line_text += "\n" + block_text += line_text + block_text = block_text.rstrip() + "\n\n" + page_md += block_text + pages_md.append(page_md.strip()) + return pages_md + + def merge_split_headings(pages: list[str]) -> list[str]: + """Merge headings that are split across lines.""" + + def _merge_split_headings(match: re.Match) -> str: + atx_headings = [line.strip("# ").strip() for line in match.group().splitlines()] + return f"{match.group(1)} {' '.join(atx_headings)}\n\n" + + pages_md = [ + re.sub( + r"^(#+)[ \t]+[^\n]+\n+(?:^\1[ \t]+[^\n]+\n+)+", + _merge_split_headings, + page, + flags=re.MULTILINE, + ) + for page in pages + ] + return pages_md + + # Add heading level metadata. + pages = add_heading_level_metadata(pages) + # Add emphasis metadata. + pages = add_emphasis_metadata(pages) + # Strip page numbers. + pages = strip_page_numbers(pages) + # Convert the pages to Markdown. + pages_md = convert_to_markdown(pages) + # Merge headings that are split across lines. + pages_md = merge_split_headings(pages_md) + return pages_md + + +def document_to_markdown(doc_path: Path) -> str: + """Convert any document to GitHub Flavored Markdown.""" + # Convert the file's content to GitHub Flavored Markdown. + if doc_path.suffix == ".pdf": + # Parse the PDF with pdftext and convert it to Markdown. + with warnings.catch_warnings(): # Ignore https://github.com/VikParuchuri/pdftext/issues/5. + warnings.simplefilter("ignore", InconsistentVersionWarning) + pages = dictionary_output(doc_path, sort=True, keep_chars=False) + doc = "\n\n".join(parsed_pdf_to_markdown(pages)) + else: + # Use pandoc for everything else. + doc = pypandoc.convert_file(doc_path, to="gfm") + # Improve Markdown quality. + doc = mdformat.text(doc) + return doc diff --git a/src/raglite/_query_adapter.py b/src/raglite/_query_adapter.py new file mode 100644 index 0000000..a6f592b --- /dev/null +++ b/src/raglite/_query_adapter.py @@ -0,0 +1,313 @@ +"""Query adapter.""" + +from collections.abc import Callable +from random import randint +from typing import ClassVar + +import numpy as np +from pydantic import BaseModel, Field, field_validator +from sqlmodel import Session, select +from tqdm.auto import tqdm + +from raglite._config import RAGLiteConfig +from raglite._database import Chunk, ChunkANNIndex, Eval, create_database_engine +from raglite._embed import embed_strings +from raglite._extract import extract_with_llm +from raglite._rag import rag +from raglite._search import hybrid_search, retrieve_segments, vector_search + + +def insert_evals( # noqa: C901 + *, num_evals: int = 100, max_contexts_per_eval: int = 20, config: RAGLiteConfig | None = None +) -> None: + """Generate and insert evals into the database.""" + + class QuestionResponse(BaseModel): + """A specific question about the content of a set of document contexts.""" + + question: str = Field( + ..., + description="A specific question about the content of a set of document contexts.", + min_length=1, + ) + system_prompt: ClassVar[str] = """ +You are given a set of contexts extracted from a document. +You are a subject matter expert on the document's topic. +Your task is to generate a question to quiz other subject matter experts on the information in the provided context. +The question MUST satisfy ALL of the following criteria: +- The question SHOULD integrate as much of the provided context as possible. +- The question MUST NOT be a general or open question, but MUST instead be as specific to the provided context as possible. +- The question MUST be completely answerable using ONLY the information in the provided context, without depending on any background information. +- The question MUST be entirely self-contained and able to be understood in full WITHOUT access to the provided context. +- The question MUST NOT use the words "context", "contexts", "document", or "question". + """.strip() + + @field_validator("question") + @classmethod + def validate_question(cls, value: str) -> str: + """Validate the question.""" + question = value.strip().lower() + if "context" in question or "document" in question or "question" in question: + raise ValueError + if not question.endswith("?"): + raise ValueError + if not value[0].isupper(): + raise ValueError + return value + + config = config or RAGLiteConfig() + engine = create_database_engine(config.db_url) + with Session(engine) as session: + # Sample random chunks from the database. + seed_chunks = session.exec(select(Chunk).order_by(Chunk.id).limit(num_evals)).all() + for seed_chunk in tqdm( + seed_chunks, desc="Generating evals", unit="eval", dynamic_ncols=True + ): + # Expand the seed chunk into a set of related chunks. + related_chunk_rowids, _ = vector_search( + np.mean(seed_chunk.multi_vector_embedding, axis=0, keepdims=True), + num_results=randint(2, max_contexts_per_eval), # noqa: S311 + config=config, + ) + related_chunks = retrieve_segments(related_chunk_rowids, config=config) + # Extract a question from the seed chunk's related chunks. + try: + question_response = extract_with_llm( + QuestionResponse, related_chunks, config=config + ) + except ValueError: + continue + else: + question = question_response.question + # Search for candidate chunks to answer the generated question. + candidate_chunk_rowids, _ = hybrid_search( + question, num_results=max_contexts_per_eval, config=config + ) + candidate_chunks = [ + session.exec(select(Chunk).offset(chunk_rowid - 1)).first() + for chunk_rowid in candidate_chunk_rowids + ] + + # Determine which candidate chunks are relevant to answer the generated question. + class ContextEvalResponse(BaseModel): + """Indicate whether the provided context can be used to answer a given question.""" + + hit: bool = Field( + ..., + description="True if the provided context contains (a part of) the answer to the given question, false otherwise.", + ) + system_prompt: ClassVar[str] = f""" +You are given a context extracted from a document. +You are a subject matter expert on the document's topic. +Your task is to answer whether the provided context contains (a part of) the answer to this question: "{question}" +An example of a context that does NOT contain (a part of) the answer is a table of contents. + """.strip() + + relevant_chunks = [] + for candidate_chunk in tqdm( + candidate_chunks, desc="Evaluating chunks", unit="chunk", dynamic_ncols=True + ): + try: + context_eval_response = extract_with_llm( + ContextEvalResponse, str(candidate_chunk), config=config + ) + except ValueError: # noqa: PERF203 + pass + else: + if context_eval_response.hit: + relevant_chunks.append(candidate_chunk) + + # Answer the question using the relevant chunks. + class AnswerResponse(BaseModel): + """Answer a question using the provided context.""" + + answer: str = Field( + ..., + description="A complete answer to the given question using the provided context.", + min_length=1, + ) + system_prompt: ClassVar[str] = f""" +You are given a set of contexts extracted from a document. +You are a subject matter expert on the document's topic. +Your task is to generate a complete answer to the following question using the provided context: "{question}" +The answer MUST satisfy ALL of the following criteria: +- The answer MUST integrate as much of the provided context as possible. +- The answer MUST be entirely self-contained and able to be understood in full WITHOUT access to the provided context. +- The answer MUST NOT use the words "context", "contexts", or "document". + """.strip() + + try: + answer_response = extract_with_llm( + AnswerResponse, + [str(relevant_chunk) for relevant_chunk in relevant_chunks], + config=config, + ) + except ValueError: + continue + else: + answer = answer_response.answer + # Store the eval in the database. + eval_ = Eval.from_chunks( + question=question, + contexts=relevant_chunks, + ground_truth=answer, + ) + session.add(eval_) + session.commit() + + +def update_query_adapter( + *, + max_triplets: int = 4096, + max_triplets_per_eval: int = 64, + optimize_top_k: int = 40, + config: RAGLiteConfig | None = None, +) -> None: + """Compute an optimal query adapter and update the database with it. + + Computes an optimal linear transform A, called a 'query adapter', that is used to transform + a query embedding q as Aq before searching for the nearest neighbouring chunks in order to + improve the quality of the search results. + + Given a set of triplets (qᵢ, pᵢ, nᵢ), we want to find the query adapter A that increases the + score pᵢ'qᵢ of the positive chunk pᵢ and decreases the score nᵢ'qᵢ of the negative chunk nᵢ. + + If the nearest neighbour search uses the dot product as its ranking function, we can find the + optimal query adapter by solving the following relaxed Procrustes optimisation problem with a + bound on the Frobenius norm of A: + + A* = argmax Σᵢ pᵢ' (A qᵢ) - nᵢ' (A qᵢ) + Σᵢ (pᵢ - nᵢ)' A qᵢ + trace[ (P - N) A Q' ] where Q := [q₁'; ...; qₖ'] + P := [p₁'; ...; pₖ'] + N := [n₁'; ...; nₖ'] + trace[ Q' (P - N) A ] + trace[ M A ] where M := Q' (P - N) + s.t. ||A||_F == 1 + = M' / ||M||_F + + If the nearest neighbour search uses the cosine similarity as its ranking function, we can find + the optimal query adapter by solving the following orthogonal Procrustes optimisation problem + with an orthogonality constraint on A: + + A* = argmax Σᵢ pᵢ' (A qᵢ) - nᵢ' (A qᵢ) + Σᵢ (pᵢ - nᵢ)' A qᵢ + trace[ (P - N) A Q' ] + trace[ Q' (P - N) A ] + trace[ M A ] + trace[ U Σ V' A ] where U Σ V' := M is the SVD of M + s.t. A'A == 𝕀 + = V U' + """ + config = config or RAGLiteConfig() + engine = create_database_engine(config.db_url) + with Session(engine) as session: + # Get random evals from the database. + evals = session.exec( + select(Eval).order_by(Eval.id).limit(max(8, max_triplets // max_triplets_per_eval)) + ).all() + if len(evals) * max_triplets_per_eval < config.embedder.n_embd(): + error_message = ( + "Run `insert_evals` to generate sufficient Evals before updating the query adapter" + ) + raise ValueError + # Loop over the evals to generate (q, p, n) triplets. + Q = np.zeros((0, config.embedder.n_embd())) # We want double precision here. # noqa: N806 + P = np.zeros_like(Q) # noqa: N806 + N = np.zeros_like(Q) # noqa: N806 + for eval_ in tqdm( + evals, desc="Extracting triplets from evals", unit="eval", dynamic_ncols=True + ): + # Embed the question. + question_embedding = embed_strings([eval_.question], config=config) + # Retrieve chunks that would be used to answer the question. + chunk_rowids, _ = vector_search( + eval_.question, num_results=optimize_top_k, query_adapter=False, config=config + ) + retrieved_chunks = [ + session.exec(select(Chunk).offset(chunk_rowid - 1)).first() + for chunk_rowid in chunk_rowids + ] + # Extract (q, p, n) triplets by comparing the retrieved chunks with the eval. + num_triplets = 0 + for i, retrieved_chunk in enumerate(retrieved_chunks): + # Select irrelevant chunks. + if retrieved_chunk.id not in eval_.chunk_ids: + # Look up all positive chunks that are ranked lower than this negative one. + p = [ + np.mean(chunk.multi_vector_embedding, axis=0, keepdims=True) + for chunk in retrieved_chunks[i + 1 :] + if chunk.id in eval_.chunk_ids + ] + if not p: + continue + p = np.vstack(p) + n = np.repeat( + np.mean(retrieved_chunk.multi_vector_embedding, axis=0, keepdims=True), + p.shape[0], + axis=0, + ) + q = np.repeat(question_embedding, p.shape[0], axis=0) + num_triplets += p.shape[0] + # Append the (query, positive, negative) tuples to the Q, P, N matrices. + Q = np.vstack([Q, q]) # noqa: N806 + P = np.vstack([P, p]) # noqa: N806 + N = np.vstack([N, n]) # noqa: N806 + # Check if we have sufficient triplets for this eval. + if num_triplets >= max_triplets_per_eval: + break + # Check if we have sufficient triplets to compute the query adapter. + if Q.shape[0] > max_triplets: + Q, P, N = Q[:max_triplets, :], P[:max_triplets, :], N[:max_triplets, :] # noqa: N806 + break + # Normalise the rows of Q, P, N. + Q /= np.linalg.norm(Q, axis=1, keepdims=True) # noqa: N806 + P /= np.linalg.norm(P, axis=1, keepdims=True) # noqa: N806 + N /= np.linalg.norm(N, axis=1, keepdims=True) # noqa: N806 + # Compute the optimal query adapter A*. + # TODO: Matmul in float16 is extremely slow compared to single or double precision, why? + MT = (P - N).T @ Q # noqa: N806 + if config.ann_vector_index_metric == "dot": + # Use the relaxed Procrustes solution. + A_star = MT / np.linalg.norm(MT, ord="fro") # noqa: N806 + elif config.ann_vector_index_metric == "cosine": + # Use the orthogonal Procrustes solution. + U, _, VT = np.linalg.svd(MT, full_matrices=False) # noqa: N806 + A_star = U @ VT # noqa: N806 + else: + error_message = f"Unsupported ANN vector index metric: {config.ann_vector_index_metric}" + raise ValueError(error_message) + # Store the optimal query adapter in the database. + chunk_ann_index = session.get(ChunkANNIndex, config.ann_vector_index_id) or ChunkANNIndex( + id=config.ann_vector_index_id + ) + chunk_ann_index.query_adapter = A_star + session.add(chunk_ann_index) + session.commit() + + +def answer_evals( + search: Callable[[str], tuple[list[int], list[float]]] = hybrid_search, + *, + config: RAGLiteConfig | None = None, +) -> dict[str, list[str | list[str]]]: + """Read evals from the database and convert them to a Ragas test set.""" + # Read all evals from the database. + config = config or RAGLiteConfig() + engine = create_database_engine(config.db_url) + with Session(engine) as session: + evals = session.exec(select(Eval)).all() + # Answer evals with RAG. + answers = [] + for eval_ in tqdm(evals, desc="Answering evals", unit="eval", dynamic_ncols=True): + response = rag(eval_.question, search=search, config=config) + answer = "".join(response) + answers.append(answer) + # Evaluate the answers. + test_set = { + "question": [eval_.question for eval_ in evals], + "answer": answers, + "contexts": [eval_.contexts for eval_ in evals], + "ground_truth": [eval_.ground_truth for eval_ in evals], + } + return test_set diff --git a/src/raglite/_rag.py b/src/raglite/_rag.py new file mode 100644 index 0000000..2a9e9d4 --- /dev/null +++ b/src/raglite/_rag.py @@ -0,0 +1,43 @@ +"""Retrieval-augmented generation.""" + +from collections.abc import Callable, Iterator + +from raglite._config import RAGLiteConfig +from raglite._search import hybrid_search, retrieve_segments + + +def rag( + prompt: str, + *, + num_contexts: int = 5, + context_neighbors: tuple[int, ...] | None = (-1, 1), + search: Callable[[str], tuple[list[int], list[float]]] = hybrid_search, + config: RAGLiteConfig | None = None, +) -> Iterator[str]: + """Retrieval-augmented generation.""" + # Retrieve relevant chunks. + config = config or RAGLiteConfig() + chunk_rowids, _ = search(prompt, num_results=num_contexts, config=config) + chunks = retrieve_segments(chunk_rowids, neighbors=context_neighbors) + # Respond with an LLM. + contexts = "\n\n".join( + f'\n{chunk.strip()}\n' for i, chunk in enumerate(chunks) + ) + system_prompt = f""" +You are a friendly and knowledgeable assistant that provides complete and insightful answers. +Answer the user's question using only the context below. +Don't reference the context as if it were provided to you, nor as a document or text that can be referenced. +Instead, use the context as if it is part of your working memory. + +{contexts}""".strip() + stream = config.llm.create_chat_completion( + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": prompt}, + ], + temperature=config.llm_temperature, + stream=True, + ) + # Stream the response. + for output in stream: + yield output["choices"][0]["delta"].get("content", "") diff --git a/src/raglite/_search.py b/src/raglite/_search.py new file mode 100644 index 0000000..aaac182 --- /dev/null +++ b/src/raglite/_search.py @@ -0,0 +1,221 @@ +"""Query documents.""" + +import re +import string +from collections import defaultdict +from functools import lru_cache +from itertools import groupby +from typing import Annotated, ClassVar + +import numpy as np +from pydantic import BaseModel, Field +from pynndescent import NNDescent +from sqlmodel import Session, select, text + +from raglite._config import RAGLiteConfig +from raglite._database import Chunk, ChunkANNIndex, create_database_engine +from raglite._embed import embed_strings +from raglite._extract import extract_with_llm + + +@lru_cache(maxsize=1) +def _chunk_ann_index(config: RAGLiteConfig) -> tuple[NNDescent, np.ndarray, np.ndarray | None]: + engine = create_database_engine(config.db_url) + with Session(engine) as session: + chunk_ann_index = session.get(ChunkANNIndex, config.ann_vector_index_id) + index = chunk_ann_index.index + chunk_size_cumsum = np.cumsum(np.asarray(chunk_ann_index.chunk_sizes, dtype=np.intp)) + query_adapter = chunk_ann_index.query_adapter + return index, chunk_size_cumsum, query_adapter + + +def vector_search( + prompt: str | np.ndarray, + *, + num_results: int = 3, + query_adapter: bool = True, + config: RAGLiteConfig | None = None, +) -> tuple[list[int], list[float]]: + """Search chunks using ANN vector search.""" + # Retrieve the index from the database. + config = config or RAGLiteConfig() + index, chunk_size_cumsum, Q = _chunk_ann_index(config) # noqa: N806 + # Embed the prompt. + prompt_embedding = ( + embed_strings([prompt], config=config) + if isinstance(prompt, str) + else np.reshape(prompt, (1, -1)) + ) + # Apply the query adapter. + if query_adapter and Q is not None: + prompt_embedding = (Q @ prompt_embedding[0, :])[np.newaxis, :].astype(config.embedder_dtype) + # Find the neighbouring multi-vector indices. + multi_vector_indices, cosine_distance = index.query(prompt_embedding, k=8 * num_results) + cosine_similarity = 1 - cosine_distance[0, :] + # Transform the multi-vector indices into chunk rowids. + chunk_rowids = np.searchsorted(chunk_size_cumsum, multi_vector_indices[0, :], side="right") + 1 + # Score each unique chunk rowid as the mean cosine similarity of its multi-vector hits. + # Chunk rowids with fewer hits are padded with the minimum cosine similarity of the result set. + unique_chunk_rowids, counts = np.unique(chunk_rowids, return_counts=True) + score = np.full( + (len(unique_chunk_rowids), np.max(counts)), + np.min(cosine_similarity), + dtype=cosine_similarity.dtype, + ) + for i, (unique_chunk_rowid, count) in enumerate(zip(unique_chunk_rowids, counts, strict=True)): + score[i, :count] = cosine_similarity[chunk_rowids == unique_chunk_rowid] + pooled_cosine_similarity = np.mean(score, axis=1) + # Sort the chunk rowids by adjusted cosine similarity. + sorted_indices = np.argsort(pooled_cosine_similarity)[::-1] + unique_chunk_rowids = unique_chunk_rowids[sorted_indices][:num_results] + pooled_cosine_similarity = pooled_cosine_similarity[sorted_indices][:num_results] + return list(unique_chunk_rowids), list(pooled_cosine_similarity) + + +def _prompt_to_fts_query(prompt: str) -> str: + """Convert a prompt to an FTS5 query.""" + # https://www.sqlite.org/fts5.html#full_text_query_syntax + prompt = re.sub(f"[{re.escape(string.punctuation)}]", "", prompt) + fts_query = " OR ".join(prompt.split()) + return fts_query + + +def keyword_search( + prompt: str, *, num_results: int = 3, config: RAGLiteConfig | None = None +) -> tuple[list[int], list[float]]: + """Search chunks using BM25 keyword search.""" + config = config or RAGLiteConfig() + engine = create_database_engine(config.db_url) + with Session(engine) as session: + # Perform the full-text search query using the BM25 ranking. + statement = text( + "SELECT chunk.rowid, bm25(chunk_fts) FROM chunk JOIN chunk_fts ON chunk.rowid = chunk_fts.rowid WHERE chunk_fts MATCH :match ORDER BY rank LIMIT :limit;" + ) + results = session.exec( + statement, params={"match": _prompt_to_fts_query(prompt), "limit": num_results} + ) + # Unpack the results and make FTS5's negative BM25 scores [1] positive. + # https://www.sqlite.org/fts5.html#the_bm25_function + chunk_rowids, bm25_score = zip(*results, strict=True) + chunk_rowids, bm25_score = list(chunk_rowids), [-s for s in bm25_score] + return chunk_rowids, bm25_score + + +def reciprocal_rank_fusion( + rankings: list[list[int]], *, k: int = 60 +) -> tuple[list[int], list[float]]: + """Reciprocal Rank Fusion.""" + # Compute the RRF score. + rowids = {rowid for ranking in rankings for rowid in ranking} + rowid_score = defaultdict(float) + for ranking in rankings: + rowid_index = {rowid: i for i, rowid in enumerate(ranking)} + for rowid in rowids: + rowid_score[rowid] += 1 / (k + rowid_index.get(rowid, len(rowid_index))) + # Rank RRF results according to descending RRF score. + rrf_rowids, rrf_score = zip( + *sorted(rowid_score.items(), key=lambda x: x[1], reverse=True), strict=True + ) + return list(rrf_rowids), list(rrf_score) + + +def hybrid_search( + prompt: str, *, num_results: int = 3, num_rerank: int = 100, config: RAGLiteConfig | None = None +) -> tuple[list[int], list[float]]: + """Search chunks by combining ANN vector search with BM25 keyword search.""" + # Run both searches. + chunks_vector, _ = vector_search(prompt, num_results=num_rerank, config=config) + chunks_bm25, _ = keyword_search(prompt, num_results=num_rerank, config=config) + # Combine the results with Reciprocal Rank Fusion (RRF). + chunk_rowids, hybrid_score = reciprocal_rank_fusion([chunks_vector, chunks_bm25]) + chunk_rowids, hybrid_score = chunk_rowids[:num_results], hybrid_score[:num_results] + return chunk_rowids, hybrid_score + + +def fusion_search( + prompt: str, + *, + num_results: int = 5, + num_rerank: int = 100, + config: RAGLiteConfig | None = None, +) -> tuple[list[int], list[float]]: + """Search for chunks with the RAG-Fusion method.""" + + class QueriesResponse(BaseModel): + """An array of queries that help answer the user prompt.""" + + queries: list[Annotated[str, Field(min_length=1)]] = Field( + ..., description="A single query that helps answer the user prompt.", min_items=1 + ) + system_prompt: ClassVar[str] = """ +The user will give you a prompt in search of an answer. +Your task is to generate a minimal set of search queries for a search engine that together provide a complete answer to the user prompt. + """.strip() + + try: + queries_response = extract_with_llm(QueriesResponse, prompt, config=config) + except ValueError: + queries = [prompt] + else: + queries = [*queries_response.queries, prompt] + # Collect the search results for all the queries. + rankings = [] + for query in queries: + # Run both searches. + chunks_vector, _ = vector_search(query, num_results=num_rerank, config=config) + chunks_bm25, _ = keyword_search(query, num_results=num_rerank, config=config) + # Add results to the rankings. + rankings.append(chunks_vector) + rankings.append(chunks_bm25) + # Combine all the search results with Reciprocal Rank Fusion (RRF). + chunk_rowids, fusion_score = reciprocal_rank_fusion(rankings) + chunk_rowids, fusion_score = chunk_rowids[:num_results], fusion_score[:num_results] + return chunk_rowids, fusion_score + + +def retrieve_segments( + chunk_rowids: list[int], + *, + neighbors: tuple[int, ...] | None = (-1, 1), + config: RAGLiteConfig | None = None, +) -> list[str]: + """Group the chunks into contiguous segments and retrieve them.""" + # Get the chunks by rowid and extend them with their neighbours. + config = config or RAGLiteConfig() + chunks = set() + engine = create_database_engine(config.db_url) + with Session(engine) as session: + for chunk_rowid in chunk_rowids: + # Get the chunk at the given rowid. + chunk = session.exec(select(Chunk).offset(chunk_rowid - 1)).first() + if chunk is not None: + chunks.add(chunk) + # Extend the chunk with its neighbouring chunks. + if neighbors is not None and len(neighbors) > 0: + for offset in sorted(neighbors, key=abs): + where = ( + Chunk.document_id == chunk.document_id, + Chunk.index == chunk.index + offset, + ) + neighbor = session.exec(select(Chunk).where(*where)).first() + if neighbor is not None: + chunks.add(neighbor) + # Sort the chunks by document_id and index (needed for groupby). + chunks = sorted(chunks, key=lambda chunk: (chunk.document_id, chunk.index)) + # Group the chunks into contiguous segments. + segments = [] + for _, group in groupby(chunks, key=lambda chunk: chunk.document_id): + segment = [] + for chunk in group: + if not segment or chunk.index == segment[-1].index + 1: + segment.append(chunk) + else: + segments.append(segment) + segment = [chunk] + segments.append(segment) + # Convert the segments into strings. + segments = [ + segment[0].headings.strip() + "\n\n" + "".join(chunk.body for chunk in segment).strip() + for segment in segments + ] + return segments diff --git a/src/raglite/chunk_splitter.py b/src/raglite/_split_chunks.py similarity index 85% rename from src/raglite/chunk_splitter.py rename to src/raglite/_split_chunks.py index 14fe77f..954ac37 100644 --- a/src/raglite/chunk_splitter.py +++ b/src/raglite/_split_chunks.py @@ -1,25 +1,24 @@ """Split a document into semantic chunks.""" import re -import warnings from collections.abc import Callable import numpy as np from scipy.optimize import linprog from scipy.sparse import coo_matrix -from raglite.string_embedder import embed_strings +from raglite._embed import embed_strings def split_chunks( sentences: list[str], max_size: int = 1440, - window_size: int = 3, + sentence_window_size: int = 3, embed: Callable[[list[str]], np.ndarray] = embed_strings, ) -> tuple[list[str], list[np.ndarray]]: """Split sentences into optimal semantic chunks.""" # Window the sentences. - whisker_size = (window_size - 1) // 2 + whisker_size = (sentence_window_size - 1) // 2 windows = [ "".join(sentences[max(0, i - whisker_size) : min(i + whisker_size + 1, len(sentences))]) for i in range(len(sentences)) @@ -47,17 +46,17 @@ def split_chunks( ) # Make partition similarity nonnegative before modification and optimisation. partition_similarity = 1e-4 + (partition_similarity + 1) / 2 - # Modify the partition similarity to encourage splitting on Markdown headers. - prev_sentence_is_header = True + # Modify the partition similarity to encourage splitting on Markdown headings. + prev_sentence_is_heading = True for i, sentence in enumerate(sentences[:-1]): - is_header = bool(re.match(r"^#+\s", sentence.replace("\n", "").strip())) - if is_header: - # Encourage splitting before a header. - if not prev_sentence_is_header: + is_heading = bool(re.match(r"^#+\s", sentence.replace("\n", "").strip())) + if is_heading: + # Encourage splitting before a heading. + if not prev_sentence_is_heading: partition_similarity[i - 1] = partition_similarity[i - 1] / 4 - # Don't split immediately after a header. + # Don't split immediately after a heading. partition_similarity[i] = 1.0 - prev_sentence_is_header = is_header + prev_sentence_is_heading = is_heading # Solve an optimisation problem to find the best partition points. sentence_length = np.asarray([len(sentence) for sentence in sentences]) sentence_length_cumsum = np.cumsum(sentence_length) @@ -68,8 +67,8 @@ def split_chunks( r = sentence_length_cumsum[i - 1] if i > 0 else 0 idx = np.searchsorted(sentence_length_cumsum - r, max_size) if idx == i: - warnings.warn("Sentence with length larger than chunk max_size detected.", stacklevel=2) - continue + error_message = "Sentence with length larger than chunk max_size detected." + raise ValueError(error_message) if idx == len(sentence_length_cumsum): break cols = list(range(i, idx)) diff --git a/src/raglite/_split_sentences.py b/src/raglite/_split_sentences.py new file mode 100644 index 0000000..c718394 --- /dev/null +++ b/src/raglite/_split_sentences.py @@ -0,0 +1,72 @@ +"""Sentence splitter.""" + +import re + +import spacy +from markdown_it import MarkdownIt +from spacy.language import Language + + +@Language.component("_mark_additional_sentence_boundaries") +def _mark_additional_sentence_boundaries(doc: spacy.tokens.Doc) -> spacy.tokens.Doc: + """Mark additional sentence boundaries in Markdown documents.""" + + def get_markdown_heading_indexes(doc: str) -> list[tuple[int, int]]: + """Get the indexes of the headings in a Markdown document.""" + md = MarkdownIt() + tokens = md.parse(doc) + headings = [] + lines = doc.splitlines(keepends=True) + char_idx = [0] + for line in lines: + char_idx.append(char_idx[-1] + len(line)) + for token in tokens: + if token.type == "heading_open": + start_line, end_line = token.map + heading_start = char_idx[start_line] + heading_end = char_idx[end_line] + headings.append((heading_start, heading_end)) + return headings + + headings = get_markdown_heading_indexes(doc.text) + for heading_start, heading_end in headings: + # Mark the start of a heading as a new sentence. + for token in doc: + if heading_start <= token.idx: + token.is_sent_start = True + break + # Mark the end of a heading as a new sentence. + for token in doc: + if heading_end <= token.idx: + token.is_sent_start = True + break + return doc + + +def split_sentences(doc: str, max_len: int | None = None) -> list[str]: + """Split a document into sentences.""" + # Split sentences with spaCy. + nlp = spacy.load("xx_sent_ud_sm") + nlp.add_pipe("_mark_additional_sentence_boundaries", before="senter") + sentences = [sent.text_with_ws for sent in nlp(doc).sents if sent.text.strip()] + # Apply additional splits on paragraphs and sentences because spaCy's splitting is not perfect. + if max_len is not None: + for pattern in (r"(?<=\n\n)", r"(?<=\.\s)"): + sentences = [ + part + for sent in sentences + for part in ([sent] if len(sent) <= max_len else re.split(pattern, sent)) + ] + # Recursively split long sentences in the middle if they are still too long. + if max_len is not None: + while any(len(sentence) > max_len for sentence in sentences): + sentences = [ + part + for sent in sentences + for part in ( + [sent] + if len(sent) <= max_len + else [sent[: len(sent) // 2], sent[len(sent) // 2 :]] + ) + ] + return sentences diff --git a/src/raglite/llm.py b/src/raglite/llm.py deleted file mode 100644 index 24c308f..0000000 --- a/src/raglite/llm.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Large Language Models.""" - -from functools import lru_cache - -from llama_cpp import Llama - - -@lru_cache(maxsize=1) -def default_llm() -> Llama: - """Get default LLM.""" - llm = Llama.from_pretrained( - repo_id="second-state/Yi-1.5-9B-Chat-16K-GGUF", - filename="*Q4_K_M.gguf", - n_ctx=0, - n_gpu_layers=-1, - use_mlock=True, - use_mmap=False, - verbose=False, - ) - return llm - - -@lru_cache(maxsize=1) -def default_embedder() -> Llama: - """Get default embedder.""" - embedder = Llama.from_pretrained( - # https://github.com/Snowflake-Labs/arctic-embed - repo_id="ChristianAzinn/snowflake-arctic-embed-l-gguf", - filename="*f16.GGUF", - n_ctx=0, - n_gpu_layers=-1, - use_mlock=True, - use_mmap=False, - verbose=False, - embedding=True, - ) - return embedder diff --git a/src/raglite/markdown_converter.py b/src/raglite/markdown_converter.py deleted file mode 100644 index 323f3ca..0000000 --- a/src/raglite/markdown_converter.py +++ /dev/null @@ -1,199 +0,0 @@ -"""Convert any document to Markdown.""" - -import re -from pathlib import Path - -import mdformat -import numpy as np -import pypandoc -from pdfminer.high_level import extract_pages -from pdfminer.layout import LAParams, LTComponent, LTExpandableContainer, LTPage - - -def pdf_to_markdown(pdf_path: Path) -> str: # noqa: C901, PLR0915 - """Convert a PDF to Markdown.""" - - def get_font_sizes(element: LTComponent) -> list[float]: - """Get a flattened list of font sizes used by the given element.""" - if hasattr(element, "__iter__"): - return [fs for child in element for fs in get_font_sizes(child)] - if hasattr(element, "size"): - return [element.size] - return [] - - def analyse_layout(page: LTPage) -> LTPage: - """Analyse the layout of a page and sort elements according to reading order. - - The algorithm first groups text boxes in to contiguous columns by searching for the next - text box whose top left corner is near the last text box's bottom left corner. Then, the - algorithm sorts the columns in reading order from top to bottom, and from left to right. - """ - # Group text boxes into columns. - lft, btm, rgt, top = 0, 1, 2, 3 - text_boxes = [text_box for text_box in page if hasattr(text_box, "get_text")] - columns = [] - while text_boxes: - # Sort text boxes from top to bottom. - text_boxes = sorted(text_boxes, key=lambda e: -e.bbox[top] + 0.1 * e.bbox[rgt]) - # Start a new column with the first line. - column = LTExpandableContainer() - column.add(text_boxes.pop(0)) - # Add text boxes one by one to the column. - while text_boxes: - # Sort text boxes by the minimum of: - # - the distance to column's last object's bottom left corner. - # - the distance to the column's last object's top right corner. - text_boxes = sorted( - text_boxes, - key=lambda e: min( - abs(e.bbox[lft] - column._objs[-1].bbox[lft]) # noqa: SLF001 - + abs(e.bbox[top] - column._objs[-1].bbox[btm]), # noqa: SLF001 - abs(e.bbox[lft] - column._objs[-1].bbox[rgt]) # noqa: SLF001 - + abs(e.bbox[top] - column._objs[-1].bbox[top]), # noqa: SLF001 - ), - ) - # Check if the nearest neighbour should be part of this column. - font_sizes = get_font_sizes(column) - median_font_size = np.median(font_sizes) if font_sizes else 12.0 - is_vertical_neighbor = ( - abs(text_boxes[0].bbox[lft] - column._objs[-1].bbox[lft]) # noqa: SLF001 - <= 4 * median_font_size - ) and ( - abs(text_boxes[0].bbox[top] - column._objs[-1].bbox[btm]) # noqa: SLF001 - <= 8 * median_font_size - ) - is_horizontal_neighbor = ( - abs(text_boxes[0].bbox[lft] - column._objs[-1].bbox[rgt]) # noqa: SLF001 - <= 4 * median_font_size - ) and ( - abs(text_boxes[0].bbox[top] - column._objs[-1].bbox[top]) # noqa: SLF001 - <= 8 * median_font_size - ) - if is_vertical_neighbor or is_horizontal_neighbor: - # Add the line to the column. - column.add(text_boxes.pop(0)) - else: - # Column completed. - break - # Append the column to the list of columns. - columns.append(column) - # Sort columns in top to bottom and left to right reading order. - columns = sorted(columns, key=lambda e: (-round(e.bbox[top] * 4) / 4, e.bbox[lft])) - # Update the page's layout and return it. - page = LTPage(pageid=page.pageid, bbox=page.bbox, rotate=page.rotate) - page.extend(columns) - return page - - def get_header_levels(pages: list[LTPage]) -> tuple[dict[float, int], float]: - """Compute a map from font size to Markdown header level.""" - font_sizes = get_font_sizes(pages) - unique_font_sizes, counts = np.unique(font_sizes, return_counts=True) - mode_font_size = unique_font_sizes[np.argmax(counts)] if font_sizes else 12.0 - mode_font_size = np.round(mode_font_size * 2) / 2 - top_font_sizes = { - fs: i + 1 - for i, fs in enumerate(np.unique(np.round(np.asarray(font_sizes) * 2) / 2)[::-1][:6]) - if fs > mode_font_size - } - header_levels = { - fs: top_font_sizes[np.round(fs * 2) / 2] - for fs in set(font_sizes) - if np.round(fs * 2) / 2 in top_font_sizes - } - return header_levels, mode_font_size - - def convert_to_markdown( - element: LTComponent, header_levels: dict[float, int], mode_font_size: float = 12.0 - ) -> list[str]: - if hasattr(element, "get_text"): - # Get the element's text. - text = element.get_text() - # If the text is empty, return an empty list. - if not text: - return [] - # Convert the text to a Markdown header if applicable. - max_font_size = np.max(get_font_sizes(element)) # TODO: Mode? - if max_font_size in header_levels and not re.match(r"\s*\d+\s*", text): - header_level = header_levels[max_font_size] - return [ - "\n" - + ("#" * header_level) - + " " - + re.sub(r"\s*\n+\s*", " ", text.strip()) - + "\n\n" - ] - # Convert the text to a details element if applicable. - if round(max_font_size * 2) / 2 < mode_font_size: - prefix, body, suffix = re.match( - r"^(\s*)(\S.*?\S|\S?)(\s*)$", text, re.DOTALL - ).groups() - return [prefix + "

\n" + body + "\n
" + suffix] - # Otherwise, return the text as is. - return [text] - if hasattr(element, "__iter__"): - return [ - md - for child in element - for md in convert_to_markdown(child, header_levels, mode_font_size) - ] - return [] - - # Parse the PDF into layout elements with pdfminer: - # - We disable pdfminer's layout analysis because we'll do our own layout analysis. - # - We do enable vertical text detection because it's off by default. - laparams = LAParams(boxes_flow=None, detect_vertical=True, all_texts=True) - pages = extract_pages(pdf_path, laparams=laparams) - # Analyse the layout of each page and sort elements according to reading order. - pages = [analyse_layout(page) for page in pages] - # Convert to Markdown. - header_levels, mode_font_size = get_header_levels(pages) - doc = "".join(convert_to_markdown(pages, header_levels, mode_font_size)) - # Merge adjacent details. - doc = re.sub(r"\s*\s*
\s*", " ", doc) - return doc - - -def format_markdown(doc: str) -> str: - """Format Markdown document.""" - - def _merge_nested_headers(match: re.Match) -> str: - atx_header_prefix = match.group(1) - atx_header_text = re.sub(r"\s*#+\s*", " ", match.group(2)) - return f"{atx_header_prefix}{atx_header_text}" - - def _merge_split_headers(match: re.Match) -> str: - atx_headers = [line.strip("# ").strip() for line in match.group().splitlines()] - return f"{match.group(1)} {' '.join(atx_headers)}\n\n" - - # Format. - doc = mdformat.text(doc) - # Remove page numbers. - doc = re.sub(r"^[ \t]*#*[ ]*\d+[ \t]*\n", "", doc, flags=re.MULTILINE) - # Remove horizontal rules. - doc = re.sub(r"\n*(?:-{3,}|_{3,}|\*{3,})\n*", "\n\n", doc) - # Merge nested headers (e.g. `#### I.1 #### My header`) - doc = re.sub(r"^(#+)([^\n]+)$", _merge_nested_headers, doc, flags=re.MULTILINE) - # Merge headers that are split across lines. - doc = re.sub( - r"^(#+)[ \t]+[^\n]+\n+(?:^\1[ \t]+[^\n]+\n+)+", - _merge_split_headers, - doc, - flags=re.MULTILINE, - ) - # Format. - doc = mdformat.text(doc) - return doc - - -def document_to_markdown(doc_path: Path) -> str: - """Convert any document to GitHub Flavored Markdown.""" - # Convert the file's content to GitHub Flavored Markdown. - if doc_path.suffix == ".pdf": - # Use pdfminer with our own layout analysis for PDFs. - doc = pdf_to_markdown(doc_path) - else: - # Use pandoc for everything else. - doc = pypandoc.convert_file(doc_path, to="gfm") - # Improve Markdown quality. - doc = format_markdown(doc) - return doc diff --git a/src/raglite/proposition_extractor.py b/src/raglite/proposition_extractor.py deleted file mode 100644 index 8721981..0000000 --- a/src/raglite/proposition_extractor.py +++ /dev/null @@ -1,92 +0,0 @@ -"""Extract propositions from a chunk.""" - -import json -import warnings -from collections.abc import Callable - -from llama_cpp import Llama - -from raglite.llm import default_llm - -RESPONSE_SCHEMA = { - "type": "object", - "description": "A MECE list of questions about a document chunk.", - "properties": { - "questions": { - "type": "array", - "items": {"type": "string"}, - "description": "A single question about the document chunk.", - } - }, - "required": ["questions"], -} - -SYSTEM_PROMPT = """ -You are a subject matter expert on the topic "{document_topic}". -You are given the headers and body of a chunk from a document. -Your task is to write a MECE list of questions to quiz other subject matter experts on the information in the chunk's body. -ONLY include questions that are answered completely by the chunk's body. -ALWAYS ensure each question is self-contained and explicitly restates any subjects or objects it refers to. -NEVER write redundant qualifiers such as "according to the text", "according to the authors", or "in the document". -ALWAYS format your response according to this JSON schema: -``` -{response_schema} -``` -""".strip() - -USER_PROMPT = """ -Chunk headers: -``` -{chunk_headers} -``` - -Chunk body: -``` -{chunk_body} -``` -""".strip() - - -def extract_propositions( # noqa: PLR0913 - document_topic: str, - chunk_headers: str, - chunk_body: str, - max_tries: int = 4, - temperature: float = 0.7, - llm: Callable[[], Llama] = default_llm, -) -> list[str]: - """Extract propositions from a chunk.""" - system_prompt = SYSTEM_PROMPT.format( - document_topic=document_topic, - response_schema=json.dumps(RESPONSE_SCHEMA, indent=None), - ) - user_prompt = USER_PROMPT.format( - chunk_headers=chunk_headers.strip(), chunk_body=chunk_body.strip() - ) - propositions = [] - for _ in range(max_tries): - response = llm().create_chat_completion( - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt}, - ], - response_format={"type": "json_object", "schema": RESPONSE_SCHEMA}, - temperature=temperature, - ) - try: - # Parse the propositions from the response. - propositions = json.loads(response["choices"][0]["message"]["content"])["questions"] - # Basic quality checks. - if len(chunk_body) >= 42 and not propositions: # noqa: PLR2004 - raise ValueError # noqa: TRY301 - if propositions and not all(proposition[0].isupper() for proposition in propositions): - raise ValueError # noqa: TRY301 - except Exception: # noqa: S112, BLE001 - continue - else: - break - else: - warnings.warn( - f"Failed to extract propositions from:\n\n{chunk_headers}\n\n{chunk_body}", stacklevel=2 - ) - return propositions diff --git a/src/raglite/rag.py b/src/raglite/rag.py deleted file mode 100644 index 7bfabc9..0000000 --- a/src/raglite/rag.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Retrieval-augmented generation.""" - -from collections.abc import Callable -from typing import Iterator - -from llama_cpp import Llama -from sqlalchemy.engine import URL - -from raglite.llm import default_llm -from raglite.search import get_chunks, hybrid_search - -SYSTEM_PROMPT = """ -Answer the user's question with the given contexts only. - -{contexts} -""".strip() - - -def rag( # noqa: PLR0913 - prompt: str, - num_contexts: int = 5, - context_neighbors: tuple[int, ...] = (-1, 1), - temperature: float = 0.7, - search: Callable[[str], tuple[list[int], list[float]]] = hybrid_search, - llm: Callable[[], Llama] = default_llm, - db_url: str | URL = "sqlite:///raglite.sqlite", -) -> Iterator[str]: - """Retrieval-augmented generation.""" - # Retrieve relevant chunks. - chunk_rowids, _ = search(prompt, num_results=num_contexts, db_url=db_url) - chunks = get_chunks(chunk_rowids, neighbors=context_neighbors) - # Convert the chunks into contexts. - contexts = "" - for i, chunk in enumerate(chunks): - contexts += f"Context {i + 1}:\n```\n{chunk}\n```" + ("\n\n" if i < len(chunks) - 1 else "") - # Respond with an LLM. - system_prompt = SYSTEM_PROMPT.format(contexts=contexts) - stream = llm().create_chat_completion( - messages=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": prompt}, - ], - temperature=temperature, - stream=True, - ) - # Stream the response. - for output in stream: - yield output["choices"][0]["delta"].get("content", "") diff --git a/src/raglite/search.py b/src/raglite/search.py deleted file mode 100644 index 3e5b893..0000000 --- a/src/raglite/search.py +++ /dev/null @@ -1,233 +0,0 @@ -"""Query documents.""" - -import json -import re -import string -from collections import defaultdict -from collections.abc import Callable -from functools import lru_cache - -import numpy as np -from llama_cpp import Llama -from pynndescent import NNDescent -from sqlalchemy.engine import URL -from sqlmodel import Session, select, text - -from raglite.database_models import Chunk, ChunkANNIndex, create_database_engine -from raglite.llm import default_llm -from raglite.string_embedder import embed_strings - - -@lru_cache(maxsize=1) -def _chunk_ann_index( - index_id: str = "default", db_url: str | URL = "sqlite:///raglite.sqlite" -) -> tuple[NNDescent, np.ndarray]: - engine = create_database_engine(db_url) - with Session(engine) as session: - chunk_ann_index = session.get(ChunkANNIndex, index_id) - index = chunk_ann_index.index - chunk_size_cumsum = np.cumsum(np.asarray(chunk_ann_index.chunk_sizes, dtype=np.intp)) - return index, chunk_size_cumsum - - -def vector_search( - prompt: str, - num_results: int = 3, - index_id: str = "default", - db_url: str | URL = "sqlite:///raglite.sqlite", -) -> tuple[list[int], list[float]]: - """Search chunks using ANN vector search.""" - # Retrieve the index from the database. - index, chunk_size_cumsum = _chunk_ann_index(index_id, db_url) - # Embed the prompt. - prompt_embedding = embed_strings([prompt]) - # Find the neighbouring proposition indices. - proposition_indices, cosine_distance = index.query(prompt_embedding, k=8 * num_results) - cosine_similarity = 1 - cosine_distance[0, :] - # Find the neighbouring chunk rowids. - chunk_offsets = np.searchsorted(chunk_size_cumsum, proposition_indices[0, :], side="right") - # Remove duplicates. - _, idx = np.unique(chunk_offsets, return_index=True) - idx = np.sort(idx) - chunk_rowids = list(chunk_offsets[idx] + 1)[:num_results] - cosine_similarity = list(cosine_similarity[idx])[:num_results] - return chunk_rowids, cosine_similarity - - -def prompt_to_fts_query(prompt: str) -> str: - """Convert a prompt to an FTS5 query.""" - # https://www.sqlite.org/fts5.html#full_text_query_syntax - prompt = re.sub(f"[{re.escape(string.punctuation)}]", "", prompt) - fts_query = " OR ".join(prompt.split()) - return fts_query - - -def keyword_search( - prompt: str, - num_results: int = 3, - db_url: str | URL = "sqlite:///raglite.sqlite", -) -> tuple[list[int], list[float]]: - """Search chunks using BM25 keyword search.""" - engine = create_database_engine(db_url) - with Session(engine) as session: - # Perform the full-text search query using the BM25 ranking. - statement = text( - "SELECT chunk.rowid, bm25(chunk_fts) FROM chunk JOIN chunk_fts ON chunk.rowid = chunk_fts.rowid WHERE chunk_fts MATCH :match ORDER BY rank LIMIT :limit;" - ) - results = session.exec( - statement, params={"match": prompt_to_fts_query(prompt), "limit": num_results} - ) - # Unpack the results and make FTS5's negative BM25 scores [1] positive. - # https://www.sqlite.org/fts5.html#the_bm25_function - chunk_rowids, bm25_score = zip(*results, strict=True) - chunk_rowids, bm25_score = list(chunk_rowids), [-s for s in bm25_score] - return chunk_rowids, bm25_score - - -def reciprocal_rank_fusion(rankings: list[list[int]], k: int = 60) -> tuple[list[int], list[float]]: - """Reciprocal Rank Fusion.""" - # Compute the RRF score. - rowids = {rowid for ranking in rankings for rowid in ranking} - rowid_score = defaultdict(float) - for ranking in rankings: - rowid_index = {rowid: i for i, rowid in enumerate(ranking)} - for rowid in rowids: - rowid_score[rowid] += 1 / (k + rowid_index.get(rowid, len(rowid_index))) - # Rank rrf_results according to descending RRF score. - rrf_rowids, rrf_score = zip( - *sorted(rowid_score.items(), key=lambda x: x[1], reverse=True), strict=True - ) - return rrf_rowids, rrf_score - - -def hybrid_search( - prompt: str, - num_results: int = 3, - num_rerank: int = 100, - index_id: str = "default", - db_url: str | URL = "sqlite:///raglite.sqlite", -) -> tuple[list[int], list[float]]: - """Search chunks by combining ANN vector search with BM25 keyword search.""" - # Run both searches. - chunks_vector, _ = vector_search( - prompt, num_results=num_rerank, index_id=index_id, db_url=db_url - ) - chunks_bm25, _ = keyword_search(prompt, num_results=num_rerank, db_url=db_url) - # Combine the results with Reciprocal Rank Fusion (RRF). - chunk_rowids, hybrid_score = reciprocal_rank_fusion([chunks_vector, chunks_bm25]) - chunk_rowids, hybrid_score = chunk_rowids[:num_results], hybrid_score[:num_results] - return chunk_rowids, hybrid_score - - -RESPONSE_SCHEMA = { - "type": "object", - "description": "An array of questions that help answer the user prompt.", - "properties": { - "questions": { - "type": "array", - "items": {"type": "string"}, - "description": "A single question that helps answer the user prompt.", - } - }, - "required": ["questions"], -} - -SYSTEM_PROMPT = """ -The user will give you a prompt in search of an answer. -Your task is to generate a minimal set of search queries for a search engine that together provide a complete answer to the user prompt. -The search engine expects its search queries to be framed as questions. -ALWAYS format your response according to this JSON schema: -``` -{response_schema} -``` -""".strip() - - -def fusion_search( # noqa: PLR0913 - prompt: str, - num_results: int = 5, - num_rerank: int = 100, - max_tries: int = 4, - temperature: float = 0.7, - llm: Callable[[], Llama] = default_llm, - index_id: str = "default", - db_url: str | URL = "sqlite:///raglite.sqlite", -) -> tuple[list[int], list[float]]: - """Search for chunks with the RAG-Fusion method.""" - # Translate the prompt into questions. - for _ in range(max_tries): - try: - # Conver the prompt into a list of questions. - response = llm().create_chat_completion( - messages=[ - { - "role": "system", - "content": SYSTEM_PROMPT.format(response_schema=RESPONSE_SCHEMA), - }, - {"role": "user", "content": prompt}, - ], - response_format={"type": "json_object", "schema": RESPONSE_SCHEMA}, - temperature=temperature, - ) - # Parse the questions from the response. - questions = json.loads(response["choices"][0]["message"]["content"])["questions"] - # Basic quality checks. - if not questions: - raise ValueError # noqa: TRY301 - if questions and not all(question[0].isupper() for question in questions): - raise ValueError # noqa: TRY301 - except Exception: # noqa: S112, BLE001, PERF203 - continue - else: - questions.append(prompt) - break - else: - questions = [prompt] - # Collect the search results for all the questions. - rankings = [] - for question in questions: - # Run both searches. - chunks_vector, _ = vector_search( - question, num_results=num_rerank, index_id=index_id, db_url=db_url - ) - chunks_bm25, _ = keyword_search(question, num_results=num_rerank, db_url=db_url) - # Add results to the rankings. - rankings.append(chunks_vector) - rankings.append(chunks_bm25) - # Combine all the search results with Reciprocal Rank Fusion (RRF). - chunk_rowids, fusion_score = reciprocal_rank_fusion(rankings) - chunk_rowids, fusion_score = chunk_rowids[:num_results], fusion_score[:num_results] - return chunk_rowids, fusion_score - - -def get_chunks( - chunk_rowids: list[int], - neighbors: tuple[int, ...] = (-1, 1), - db_url: str | URL = "sqlite:///raglite.sqlite", -) -> list[str]: - """Get chunks by id.""" - # Assemble the search results. - results = [] - engine = create_database_engine(db_url) - with Session(engine) as session: - for chunk_rowid in chunk_rowids: - # Get the relevant chunk at the given index. - statement = select(Chunk).offset(chunk_rowid - 1) - chunks = [session.exec(statement).first()] - # Prepand and append the neighbouring chunks. - if len(neighbors) > 0: - document_id = chunks[0].document_id - index = chunks[0].index - for offset in sorted(neighbors, key=abs): - where = (Chunk.document_id == document_id, Chunk.index == index + offset) - chunk = session.exec(select(Chunk).where(*where)).first() - if chunk: - if offset < 0: - chunks.insert(0, chunk) - else: - chunks.append(chunk) - # Convert the retrieved chunks to a single string. - result = chunks[0].headers + "\n\n" - result += "".join(chunk.body for chunk in chunks) - results.append(result) - return results diff --git a/src/raglite/sentence_splitter.py b/src/raglite/sentence_splitter.py deleted file mode 100644 index 6025760..0000000 --- a/src/raglite/sentence_splitter.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Sentence splitter.""" - -import spacy -from markdown_it import MarkdownIt -from spacy.language import Language - - -@Language.component("mark_additional_sentence_boundaries") -def mark_additional_sentence_boundaries(doc: spacy.tokens.Doc) -> spacy.tokens.Doc: - """Mark additional sentence boundaries in Markdown documents.""" - - def get_markdown_header_indexes(doc: str) -> list[tuple[int, int]]: - """Get the indexes of the headers in a Markdown document.""" - md = MarkdownIt() - tokens = md.parse(doc) - headers = [] - lines = doc.splitlines(keepends=True) - char_idx = [0] - for line in lines: - char_idx.append(char_idx[-1] + len(line)) - for token in tokens: - if token.type == "heading_open": - start_line, end_line = token.map - header_start = char_idx[start_line] - header_end = char_idx[end_line] - headers.append((header_start, header_end)) - return headers - - headers = get_markdown_header_indexes(doc.text) - for header_start, header_end in headers: - # Mark the start of a header as a new sentence. - for token in doc: - if header_start <= token.idx: - token.is_sent_start = True - break - # Mark the end of a header as a new sentence. - for token in doc: - if header_end <= token.idx: - token.is_sent_start = True - break - return doc - - -def split_sentences(doc: str) -> list[str]: - """Split a document into sentences.""" - - def split_paragraphs(doc: str): - """Split a document into paragraphs.""" - *paragraphs, last = doc.split("\n\n") - return [paragraph + "\n\n" for paragraph in paragraphs] + ([last] if last else []) - - # Split sentences with spaCy. - nlp = spacy.load("xx_sent_ud_sm") - nlp.add_pipe("mark_additional_sentence_boundaries", before="senter") - sentences = [sent.text_with_ws for sent in nlp(doc).sents if sent.text.strip()] - # Split on paragraphs as well because SpaCy sentence splitting is not perfect. - sentences = [paragraph for sent in sentences for paragraph in split_paragraphs(sent)] - return sentences diff --git a/src/raglite/string_embedder.py b/src/raglite/string_embedder.py deleted file mode 100644 index c33c150..0000000 --- a/src/raglite/string_embedder.py +++ /dev/null @@ -1,38 +0,0 @@ -"""String embedder.""" - -from collections.abc import Callable -from functools import lru_cache - -import numpy as np -import numpy.typing as npt -from llama_cpp import Llama -from tqdm.auto import trange - -from raglite.llm import default_embedder - - -@lru_cache(maxsize=128) -def _embed_string_batch( - string_batch: tuple[str], - dtype: npt.DTypeLike = np.float16, - embedder: Callable[[], Llama] = default_embedder, -) -> np.ndarray: - if len(string_batch) == 0: - embeddings = np.zeros((0, embedder().n_embd()), dtype=dtype) - else: - embeddings = np.asarray(embedder().embed(string_batch), dtype=dtype) - return embeddings - - -def embed_strings( - strings: list[str], batch_size: int = 128, dtype: npt.DTypeLike = np.float16 -) -> np.ndarray: - """Embed a list of strings as a NumPy array of row vectors.""" - brange = ( - trange(0, len(strings), batch_size, desc="Embedding", unit="batch", dynamic_ncols=True) - if len(strings) > batch_size - else range(0, max(1, len(strings)), batch_size) - ) - batches = [_embed_string_batch(tuple(strings[i : i + batch_size]), dtype=dtype) for i in brange] - embeddings = np.vstack(batches) - return embeddings