From 1dc6d88f9b8b1b0c1a5682f990bf8098cbd54d77 Mon Sep 17 00:00:00 2001 From: AmineDiro Date: Tue, 9 Jul 2024 15:22:16 +0200 Subject: [PATCH] feat: quivr core minimal chat (#2803) # Description Minimal working example of `quivr-core` rag with minimal dependencies. --------- Co-authored-by: aminediro --- .gitignore | 2 + backend/core/examples/pdf_parsing_tika.py | 19 + backend/core/examples/simple_question.py | 14 + backend/core/poetry.lock | 1906 +++++++++++++++-- backend/core/pyproject.toml | 32 +- backend/core/quivr_core/__init__.py | 3 + backend/core/quivr_core/brain.py | 198 ++ backend/core/quivr_core/config.py | 4 +- backend/core/quivr_core/processor/__init__.py | 0 .../quivr_core/processor/default_parsers.py | 6 + .../quivr_core/processor/pdf_processor.py | 62 + .../quivr_core/processor/processor_base.py | 27 + backend/core/quivr_core/processor/splitter.py | 6 + .../core/quivr_core/processor/txt_parser.py | 36 + backend/core/quivr_core/quivr_rag.py | 56 +- backend/core/quivr_core/storage/file.py | 59 + .../core/quivr_core/storage/local_storage.py | 67 + .../core/quivr_core/storage/storage_base.py | 18 + backend/core/tests/__init__.py | 0 backend/core/tests/processor/__init__.py | 0 backend/core/tests/processor/data/dummy.pdf | Bin 0 -> 7478 bytes .../tests/processor/test_tika_processor.py | 38 + backend/core/tests/test_brain.py | 48 + 23 files changed, 2412 insertions(+), 189 deletions(-) create mode 100644 backend/core/examples/pdf_parsing_tika.py create mode 100644 backend/core/examples/simple_question.py create mode 100644 backend/core/quivr_core/brain.py create mode 100644 backend/core/quivr_core/processor/__init__.py create mode 100644 backend/core/quivr_core/processor/default_parsers.py create mode 100644 backend/core/quivr_core/processor/pdf_processor.py create mode 100644 backend/core/quivr_core/processor/processor_base.py create mode 100644 backend/core/quivr_core/processor/splitter.py create mode 100644 backend/core/quivr_core/processor/txt_parser.py create mode 100644 backend/core/quivr_core/storage/file.py create mode 100644 backend/core/quivr_core/storage/local_storage.py create mode 100644 backend/core/quivr_core/storage/storage_base.py create mode 100644 backend/core/tests/__init__.py create mode 100644 backend/core/tests/processor/__init__.py create mode 100644 backend/core/tests/processor/data/dummy.pdf create mode 100644 backend/core/tests/processor/test_tika_processor.py create mode 100644 backend/core/tests/test_brain.py diff --git a/.gitignore b/.gitignore index e0436e298..1b66456ec 100644 --- a/.gitignore +++ b/.gitignore @@ -87,3 +87,5 @@ backend/modules/sync/controller/credentials.json backend/.env.test **/*.egg-info + +.coverage diff --git a/backend/core/examples/pdf_parsing_tika.py b/backend/core/examples/pdf_parsing_tika.py new file mode 100644 index 000000000..f0a872271 --- /dev/null +++ b/backend/core/examples/pdf_parsing_tika.py @@ -0,0 +1,19 @@ +from langchain_core.embeddings import DeterministicFakeEmbedding +from langchain_core.language_models import FakeListChatModel + +from quivr_core import Brain +from quivr_core.processor.default_parsers import DEFAULT_PARSERS +from quivr_core.processor.pdf_processor import TikaParser + +if __name__ == "__main__": + pdf_paths = ["../tests/processor/data/dummy.pdf"] + brain = Brain.from_files( + name="test_brain", + file_paths=[], + llm=FakeListChatModel(responses=["good"]), + embedder=DeterministicFakeEmbedding(size=20), + processors_mapping={ + **DEFAULT_PARSERS, + ".pdf": TikaParser(), + }, + ) diff --git a/backend/core/examples/simple_question.py b/backend/core/examples/simple_question.py new file mode 100644 index 000000000..e2953f97d --- /dev/null +++ b/backend/core/examples/simple_question.py @@ -0,0 +1,14 @@ +import tempfile + +from quivr_core import Brain + +if __name__ == "__main__": + with tempfile.NamedTemporaryFile(mode="w", suffix=".txt") as temp_file: + temp_file.write("Gold is metal.") + temp_file.flush() + + brain = Brain.from_files(name="test_brain", file_paths=[temp_file.name]) + + answer = brain.ask("Property of gold?") + + print("answer :", answer.answer) diff --git a/backend/core/poetry.lock b/backend/core/poetry.lock index 6994fba46..ba6ce7157 100644 --- a/backend/core/poetry.lock +++ b/backend/core/poetry.lock @@ -1,10 +1,21 @@ # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +[[package]] +name = "aiofiles" +version = "24.1.0" +description = "File support for asyncio." +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"}, + {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"}, +] + [[package]] name = "aiohttp" version = "3.9.5" description = "Async http client/server framework (asyncio)" -optional = false +optional = 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"sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] +test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] + +[[package]] +name = "wcwidth" +version = "0.2.13" +description = "Measures the displayed width of unicode strings in a terminal" +optional = false +python-versions = "*" +files = [ + {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, + {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, +] + [[package]] name = "yarl" version = "1.9.4" description = "Yet another URL library" -optional = false +optional = true python-versions = ">=3.7" files = [ {file = "yarl-1.9.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a8c1df72eb746f4136fe9a2e72b0c9dc1da1cbd23b5372f94b5820ff8ae30e0e"}, @@ -1157,7 +2747,11 @@ files = [ idna = ">=2.0" multidict = ">=4.0" +[extras] +base = ["faiss-cpu", "langchain-community", "langchain-openai"] +pdf = [] + [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "dc6781ae577a76ccefee8f8286035abf35e9e5b2486c2c671b2d4bbb9c99bdc5" +content-hash = "3a9293945bf6ba472f1efe769df2139e5cdcea361579e4f03153a17eefbf5126" diff --git a/backend/core/pyproject.toml b/backend/core/pyproject.toml index 8dfd73d31..46ca7af60 100644 --- a/backend/core/pyproject.toml +++ b/backend/core/pyproject.toml @@ -10,7 +10,37 @@ repository = "https://github.com/langchain-ai/langchain" python = "^3.11" pydantic = "^2.7.4" langchain-core = "^0.2.10" -langchain = "^0.2.6" +httpx = "^0.27.0" +faiss-cpu = { version = "^1.8.0.post1", optional = true } +langchain-community = { version = "^0.2.6", optional = true } +langchain-openai = { version = "^0.1.14", optional = true } +aiofiles = "^24.1.0" + +[tool.poetry.extras] +base = ["langchain-community", "faiss-cpu", "langchain-openai"] +pdf = [] + + +[tool.poetry.group.dev.dependencies] +mypy = "^1.10.0" +pre-commit = "^3.7.1" +ipykernel = "*" +ruff = "^0.4.8" +flake8 = "*" +flake8-black = "*" +pytest-cov = "^5.0.0" + +[tool.poetry.group.test.dependencies] +pytest-asyncio = "^0.23.7" +pytest = "^8.2.2" + + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv", ".venv"] +ignore_missing_imports = true +python_version = "3.11" [tool.ruff] line-length = 88 diff --git a/backend/core/quivr_core/__init__.py b/backend/core/quivr_core/__init__.py index e69de29bb..0007b8d9d 100644 --- a/backend/core/quivr_core/__init__.py +++ b/backend/core/quivr_core/__init__.py @@ -0,0 +1,3 @@ +from .brain import Brain + +__all__ = ["Brain"] diff --git a/backend/core/quivr_core/brain.py b/backend/core/quivr_core/brain.py new file mode 100644 index 000000000..4099e1612 --- /dev/null +++ b/backend/core/quivr_core/brain.py @@ -0,0 +1,198 @@ +import asyncio +import logging +from pathlib import Path +from typing import Mapping, Self +from uuid import UUID, uuid4 + +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.language_models import BaseChatModel +from langchain_core.vectorstores import VectorStore + +from quivr_core.config import RAGConfig +from quivr_core.models import ParsedRAGResponse +from quivr_core.processor.default_parsers import DEFAULT_PARSERS +from quivr_core.processor.processor_base import ProcessorBase +from quivr_core.quivr_rag import QuivrQARAG +from quivr_core.storage.file import QuivrFile +from quivr_core.storage.local_storage import TransparentStorage +from quivr_core.storage.storage_base import StorageBase + +logger = logging.getLogger(__name__) + + +async def _process_files( + storage: StorageBase, + skip_file_error: bool, + processors_mapping: Mapping[str, ProcessorBase], +) -> list[Document]: + knowledge = [] + for file in storage.get_files(): + try: + if file.file_extension: + processor = processors_mapping[file.file_extension] + docs = await processor.process_file(file) + knowledge.extend(docs) + else: + logger.error(f"can't find processor for {file}") + if skip_file_error: + continue + else: + raise ValueError(f"can't parse {file}. can't find file extension") + except KeyError as e: + if skip_file_error: + continue + else: + raise Exception(f"Can't parse {file}. No available processor") from e + + return knowledge + + +class Brain: + def __init__( + self, + *, + name: str, + id: UUID, + vector_db: VectorStore, + llm: BaseChatModel, + embedder: Embeddings, + storage: StorageBase, + ): + self.id = id + self.name = name + self.storage = storage + + # Chat history + self.chat_history: list[str] = [] + + # RAG dependencies: + self.llm = llm + self.vector_db = vector_db + self.embedder = embedder + + @classmethod + async def afrom_files( + cls, + *, + name: str, + file_paths: list[str | Path], + vector_db: VectorStore | None = None, + storage: StorageBase = TransparentStorage(), + llm: BaseChatModel | None = None, + embedder: Embeddings | None = None, + processors_mapping: Mapping[str, ProcessorBase] = DEFAULT_PARSERS, + skip_file_error: bool = False, + ): + if llm is None: + try: + from langchain_openai import ChatOpenAI + + logger.debug("Loaded ChatOpenAI as default LLM for brain") + + llm = ChatOpenAI() + + except ImportError as e: + raise ImportError( + "Please provide a valid BaseLLM or install quivr-core['base'] package" + ) from e + + if embedder is None: + try: + from langchain_openai import OpenAIEmbeddings + + logger.debug("Loaded OpenAIEmbeddings as default LLM for brain") + embedder = OpenAIEmbeddings() + except ImportError as e: + raise ImportError( + "Please provide a valid Embedder or install quivr-core['base'] package for using the defaultone." + ) from e + + brain_id = uuid4() + + for path in file_paths: + file = QuivrFile.from_path(brain_id, path) + storage.upload_file(file) + + # Parse files + docs = await _process_files( + storage=storage, + processors_mapping=processors_mapping, + skip_file_error=skip_file_error, + ) + + # Building brain's vectordb + if vector_db is None: + try: + from langchain_community.vectorstores import FAISS + + logger.debug("Using Faiss-CPU as vector store.") + # TODO(@aminediro) : embedding call is not concurrent for all documents but waits + # We can actually wait on all processing + if len(docs) > 0: + vector_db = await FAISS.afrom_documents( + documents=docs, embedding=embedder + ) + else: + raise ValueError("can't initialize brain without documents") + + except ImportError as e: + raise ImportError( + "Please provide a valid vectore store or install quivr-core['base'] package for using the default one." + ) from e + else: + vector_db.add_documents(docs) + + return cls( + id=brain_id, + name=name, + storage=storage, + llm=llm, + embedder=embedder, + vector_db=vector_db, + ) + + @classmethod + def from_files( + cls, + *, + name: str, + file_paths: list[str | Path], + vector_db: VectorStore | None = None, + storage: StorageBase = TransparentStorage(), + llm: BaseChatModel | None = None, + embedder: Embeddings | None = None, + processors_mapping: Mapping[str, ProcessorBase] = DEFAULT_PARSERS, + skip_file_error: bool = False, + ) -> Self: + return asyncio.run( + cls.afrom_files( + name=name, + file_paths=file_paths, + vector_db=vector_db, + storage=storage, + llm=llm, + embedder=embedder, + processors_mapping=processors_mapping, + skip_file_error=skip_file_error, + ) + ) + + # TODO(@aminediro) + def add_file(self) -> None: + # add it to storage + # add it to vectorstore + raise NotImplementedError + + def ask( + self, question: str, rag_config: RAGConfig = RAGConfig() + ) -> ParsedRAGResponse: + rag_pipeline = QuivrQARAG( + rag_config=rag_config, llm=self.llm, vector_store=self.vector_db + ) + + # transformed_history = format_chat_history(history) + parsed_response = rag_pipeline.answer(question, [], []) + + # Save answer to the chat history + return parsed_response diff --git a/backend/core/quivr_core/config.py b/backend/core/quivr_core/config.py index 1f6c5619b..368ac7583 100644 --- a/backend/core/quivr_core/config.py +++ b/backend/core/quivr_core/config.py @@ -3,9 +3,9 @@ from pydantic import BaseModel, field_validator class RAGConfig(BaseModel): model: str = "gpt-3.5-turbo-0125" # pyright: ignore reportPrivateUsage=none - temperature: float | None = 0.1 - max_tokens: int | None = 2000 + temperature: float = 0.7 max_input: int = 2000 + max_tokens: int | None = 2000 streaming: bool = False max_files: int = 20 prompt: str | None = None diff --git a/backend/core/quivr_core/processor/__init__.py b/backend/core/quivr_core/processor/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/backend/core/quivr_core/processor/default_parsers.py b/backend/core/quivr_core/processor/default_parsers.py new file mode 100644 index 000000000..50894c4cf --- /dev/null +++ b/backend/core/quivr_core/processor/default_parsers.py @@ -0,0 +1,6 @@ +from quivr_core.processor.processor_base import ProcessorBase +from quivr_core.processor.txt_parser import TxtProcessor + +DEFAULT_PARSERS: dict[str, ProcessorBase] = { + ".txt": TxtProcessor(), +} diff --git a/backend/core/quivr_core/processor/pdf_processor.py b/backend/core/quivr_core/processor/pdf_processor.py new file mode 100644 index 000000000..9ab5a712d --- /dev/null +++ b/backend/core/quivr_core/processor/pdf_processor.py @@ -0,0 +1,62 @@ +import logging +from typing import AsyncIterable + +import httpx +from langchain_core.documents import Document +from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter + +from quivr_core.processor.processor_base import ProcessorBase +from quivr_core.processor.splitter import SplitterConfig +from quivr_core.storage.file import QuivrFile + +logger = logging.getLogger(__name__) + + +class TikaParser(ProcessorBase): + supported_extensions = [".pdf"] + + def __init__( + self, + tika_url: str = "http://localhost:9998/tika", + splitter: TextSplitter | None = None, + splitter_config: SplitterConfig = SplitterConfig(), + timeout: float = 5.0, + max_retries: int = 3, + ) -> None: + self.tika_url = tika_url + self.max_retries = max_retries + self._client = httpx.AsyncClient(timeout=timeout) + + self.splitter_config = splitter_config + + if splitter: + self.text_splitter = splitter + else: + self.text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder( + chunk_size=splitter_config.chunk_size, + chunk_overlap=splitter_config.chunk_overlap, + ) + + async def _send_parse_tika(self, f: AsyncIterable[bytes]) -> str: + retry = 0 + headers = {"Accept": "text/plain"} + while retry < self.max_retries: + try: + resp = await self._client.put(self.tika_url, headers=headers, content=f) + resp.raise_for_status() + return resp.content.decode("utf-8") + except Exception as e: + retry += 1 + logger.debug(f"tika url error :{e}. retrying for the {retry} time...") + raise RuntimeError("can't send parse request to tika server") + + async def process_file(self, file: QuivrFile) -> list[Document]: + assert file.file_extension in self.supported_extensions + + async with file.open() as f: + txt = await self._send_parse_tika(f) + document = Document(page_content=txt) + + # Use the default splitter + docs = self.text_splitter.split_documents([document]) + return docs diff --git a/backend/core/quivr_core/processor/processor_base.py b/backend/core/quivr_core/processor/processor_base.py new file mode 100644 index 000000000..f02cbe219 --- /dev/null +++ b/backend/core/quivr_core/processor/processor_base.py @@ -0,0 +1,27 @@ +from abc import ABC, abstractmethod +from typing import Generic, TypeVar + +from langchain_core.documents import Document + +from quivr_core.storage.file import QuivrFile + + +class ProcessorBase(ABC): + supported_extensions: list[str] + + @abstractmethod + async def process_file(self, file: QuivrFile) -> list[Document]: + pass + + +P = TypeVar("P", bound=ProcessorBase) + + +class ProcessorsMapping(Generic[P]): + def __init__(self, mapping: dict[str, P]) -> None: + # Create an empty list with items of type T + self.ext_parser: dict[str, P] = mapping + + def add_parser(self, extension: str, parser: P): + # TODO: deal with existing ext keys + self.ext_parser[extension] = parser diff --git a/backend/core/quivr_core/processor/splitter.py b/backend/core/quivr_core/processor/splitter.py new file mode 100644 index 000000000..ca1ce9657 --- /dev/null +++ b/backend/core/quivr_core/processor/splitter.py @@ -0,0 +1,6 @@ +from pydantic import BaseModel + + +class SplitterConfig(BaseModel): + chunk_size: int = 400 + chunk_overlap: int = 100 diff --git a/backend/core/quivr_core/processor/txt_parser.py b/backend/core/quivr_core/processor/txt_parser.py new file mode 100644 index 000000000..bbec3c8f6 --- /dev/null +++ b/backend/core/quivr_core/processor/txt_parser.py @@ -0,0 +1,36 @@ +from langchain_community.document_loaders.text import TextLoader +from langchain_core.documents import Document +from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter + +from quivr_core.processor.processor_base import ProcessorBase +from quivr_core.processor.splitter import SplitterConfig +from quivr_core.storage.file import QuivrFile + + +class TxtProcessor(ProcessorBase): + def __init__( + self, + splitter: TextSplitter | None = None, + splitter_config: SplitterConfig = SplitterConfig(), + ) -> None: + self.supported_extensions = [".txt"] + self.loader_cls = TextLoader + + self.splitter_config = splitter_config + + if splitter: + self.text_splitter = splitter + else: + self.text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder( + chunk_size=splitter_config.chunk_size, + chunk_overlap=splitter_config.chunk_overlap, + ) + + async def process_file(self, file: QuivrFile) -> list[Document]: + if file.file_extension not in self.supported_extensions: + raise Exception(f"can't process a file of type {file.file_extension}") + + loader = self.loader_cls(file.path) + documents = await loader.aload() + docs = self.text_splitter.split_documents(documents) + return docs diff --git a/backend/core/quivr_core/quivr_rag.py b/backend/core/quivr_core/quivr_rag.py index a8ced690b..0397f0576 100644 --- a/backend/core/quivr_core/quivr_rag.py +++ b/backend/core/quivr_core/quivr_rag.py @@ -1,15 +1,17 @@ import logging from operator import itemgetter -from typing import AsyncGenerator +from typing import AsyncGenerator, Optional, Sequence from langchain.retrievers import ContextualCompressionRetriever -from langchain_cohere import CohereRerank -from langchain_community.chat_models import ChatLiteLLM +from langchain_core.callbacks import Callbacks +from langchain_core.documents import BaseDocumentCompressor, Document +from langchain_core.language_models import BaseChatModel from langchain_core.messages.ai import AIMessageChunk from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnableLambda, RunnablePassthrough from langchain_core.vectorstores import VectorStore from langchain_openai import ChatOpenAI + from quivr_core.config import RAGConfig from quivr_core.models import ( ParsedRAGChunkResponse, @@ -30,18 +32,31 @@ from quivr_core.utils import ( logger = logging.getLogger(__name__) +class IdempotentCompressor(BaseDocumentCompressor): + def compress_documents( + self, + documents: Sequence[Document], + query: str, + callbacks: Optional[Callbacks] = None, + ) -> Sequence[Document]: + return documents + + class QuivrQARAG: def __init__( self, *, rag_config: RAGConfig, - llm: ChatLiteLLM, + llm: BaseChatModel, vector_store: VectorStore, + reranker: BaseDocumentCompressor | None = None, ): self.rag_config = rag_config self.vector_store = vector_store self.llm = llm - self.reranker = self._create_reranker() + + self.reranker = reranker if reranker is not None else IdempotentCompressor() + self.supports_func_calling = model_supports_function_calling( self.rag_config.model ) @@ -50,20 +65,6 @@ class QuivrQARAG: def retriever(self): return self.vector_store.as_retriever() - def _create_reranker(self): - # TODO: reranker config - # if os.getenv("COHERE_API_KEY"): - compressor = CohereRerank(top_n=20) - # else: - # ranker_model_name = "ms-marco-TinyBERT-L-2-v2" - # flashrank_client = Ranker(model_name=ranker_model_name) - # compressor = FlashrankRerank( - # client=flashrank_client, model=ranker_model_name, top_n=20 - # ) - # TODO @stangirard fix - return compressor - - # TODO : refactor and simplify def filter_history( self, chat_history, max_history: int = 10, max_tokens: int = 2000 ): @@ -136,18 +137,13 @@ class QuivrQARAG: # Override llm if we have a OpenAI model llm = self.llm + if self.supports_func_calling: - if self.rag_config.temperature: - llm_function = ChatOpenAI( - max_tokens=self.rag_config.max_tokens, - model=self.rag_config.model, - temperature=self.rag_config.temperature, - ) - else: - llm_function = ChatOpenAI( - max_tokens=self.rag_config.max_tokens, - model=self.rag_config.model, - ) + llm_function = ChatOpenAI( + model=self.rag_config.model, + max_tokens=self.rag_config.max_tokens, + temperature=self.rag_config.temperature, + ) llm = llm_function.bind_tools( [cited_answer], diff --git a/backend/core/quivr_core/storage/file.py b/backend/core/quivr_core/storage/file.py new file mode 100644 index 000000000..ebc400eb1 --- /dev/null +++ b/backend/core/quivr_core/storage/file.py @@ -0,0 +1,59 @@ +import os +from contextlib import asynccontextmanager +from pathlib import Path +from typing import AsyncGenerator, AsyncIterable +from uuid import UUID, uuid4 + +import aiofiles + + +class QuivrFile: + def __init__( + self, + id: UUID, + original_filename: str, + path: Path, + brain_id: UUID, + file_size: int | None = None, + file_extension: str | None = None, + ) -> None: + self.id = id + self.brain_id = brain_id + self.path = path + self.original_filename = original_filename + self.file_size = file_size + self.file_extension = file_extension + + @classmethod + def from_path(cls, brain_id: UUID, path: str | Path): + if not isinstance(path, Path): + path = Path(path) + + if not path.exists(): + raise FileExistsError(f"file {path} doesn't exist") + + file_size = os.stat(path).st_size + + try: + # NOTE: when loading from existing storage, file name will be uuid + id = UUID(path.name) + except ValueError: + id = uuid4() + + return cls( + id=id, + brain_id=brain_id, + path=path, + original_filename=path.name, + file_size=file_size, + file_extension=path.suffix, + ) + + @asynccontextmanager + async def open(self) -> AsyncGenerator[AsyncIterable[bytes], None]: + # TODO(@aminediro) : match on path type + f = await aiofiles.open(self.path, mode="rb") + try: + yield f + finally: + await f.close() diff --git a/backend/core/quivr_core/storage/local_storage.py b/backend/core/quivr_core/storage/local_storage.py new file mode 100644 index 000000000..ca6416b65 --- /dev/null +++ b/backend/core/quivr_core/storage/local_storage.py @@ -0,0 +1,67 @@ +import os +import shutil +from pathlib import Path +from uuid import UUID + +from quivr_core.storage.file import QuivrFile +from quivr_core.storage.storage_base import StorageBase + + +class LocalStorage(StorageBase): + def __init__(self, dir_path: Path | None = None, copy_flag: bool = True): + self.files: list[QuivrFile] = [] + self.copy_flag = copy_flag + + if dir_path is None: + self.dir_path = Path( + os.getenv("QUIVR_LOCAL_STORAGE", "~/.cache/quivr/files") + ) + else: + self.dir_path = dir_path + os.makedirs(self.dir_path, exist_ok=True) + + def _load_files(self) -> None: + # TODO(@aminediro): load existing files + pass + + def upload_file(self, file: QuivrFile, exists_ok: bool = False) -> None: + dst_path = os.path.join( + self.dir_path, str(file.brain_id), f"{file.id}{file.file_extension}" + ) + + # TODO(@aminediro): Check hash of file not file path + if os.path.exists(dst_path) and not exists_ok: + raise FileExistsError("file already exists") + + if self.copy_flag: + shutil.copy2(file.path, dst_path) + else: + os.symlink(file.path, dst_path) + + file.path = Path(dst_path) + self.files.append(file) + + def get_files(self) -> list[QuivrFile]: + return self.files + + def remove_file(self, file_id: UUID) -> None: + raise NotImplementedError + + +class TransparentStorage(StorageBase): + """Transparent Storage. + uses default + + """ + + def __init__(self): + self.files = [] + + def upload_file(self, file: QuivrFile, exists_ok: bool = False) -> None: + self.files.append(file) + + def remove_file(self, file_id: UUID) -> None: + raise NotImplementedError + + def get_files(self) -> list[QuivrFile]: + return self.files diff --git a/backend/core/quivr_core/storage/storage_base.py b/backend/core/quivr_core/storage/storage_base.py new file mode 100644 index 000000000..f8fc6c9e3 --- /dev/null +++ b/backend/core/quivr_core/storage/storage_base.py @@ -0,0 +1,18 @@ +from abc import ABC, abstractmethod +from uuid import UUID + +from quivr_core.storage.local_storage import QuivrFile + + +class StorageBase(ABC): + @abstractmethod + def get_files(self) -> list[QuivrFile]: + raise Exception("Unimplemented get_files method") + + @abstractmethod + def upload_file(self, file: QuivrFile, exists_ok: bool = False) -> None: + raise Exception("Unimplemented upload_file method") + + @abstractmethod + def remove_file(self, file_id: UUID) -> None: + raise Exception("Unimplemented remove_file method") diff --git a/backend/core/tests/__init__.py b/backend/core/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/backend/core/tests/processor/__init__.py b/backend/core/tests/processor/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/backend/core/tests/processor/data/dummy.pdf b/backend/core/tests/processor/data/dummy.pdf new file mode 100644 index 0000000000000000000000000000000000000000..8da27b526712a5e417279d7dd2fd7e913b45d47e GIT binary patch literal 7478 zcmeHMYj7LY6?ULN5jF{h@<^C+qmYSBkoT=!30YuER^njUjwE6S$HA-JYkOm9SM08^ zoGFF!h8fzD5HOFS;nfLKIt_#wTA(2v5*~$82y}o1b}3Q@h9j%8EZh*iW; zy-4ImiRwp;NOdEY@iSB(kW)xxIEGq@h7m9N5#tv|!r>OIt3(EBbhUU2CLVKU3bc4= z_hS6WxkGxHCedO&jiDua7L5(CvuL~+m_?I#rk=*q5?@cF{j>RKVp~~1+my8oHd{P^ 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b/backend/core/tests/processor/test_tika_processor.py @@ -0,0 +1,38 @@ +from pathlib import Path +from uuid import uuid4 + +import pytest + +from quivr_core.processor.pdf_processor import TikaParser +from quivr_core.storage.file import QuivrFile + +# TODO: TIKA server should be set + + +@pytest.fixture +def pdf(): + return QuivrFile( + id=uuid4(), + brain_id=uuid4(), + original_filename="dummy.pdf", + path=Path("./tests/processor/data/dummy.pdf"), + file_extension=".pdf", + ) + + +@pytest.mark.asyncio +async def test_process_file(pdf): + tparser = TikaParser() + doc = await tparser.process_file(pdf) + assert len(doc) > 0 + assert doc[0].page_content.strip("\n") == "Dummy PDF download" + + +@pytest.mark.asyncio +async def test_send_parse_tika_exception(pdf): + # TODO: Mock correct tika for retries + tparser = TikaParser(tika_url="test.test") + with pytest.raises(RuntimeError): + doc = await tparser.process_file(pdf) + assert len(doc) > 0 + assert doc[0].page_content.strip("\n") == "Dummy PDF download" diff --git a/backend/core/tests/test_brain.py b/backend/core/tests/test_brain.py new file mode 100644 index 000000000..f88ae61e9 --- /dev/null +++ b/backend/core/tests/test_brain.py @@ -0,0 +1,48 @@ +import pytest +from langchain_core.embeddings import DeterministicFakeEmbedding +from langchain_core.language_models import FakeListChatModel + +from quivr_core.brain import Brain + + +@pytest.fixture +def temp_data_file(tmpdir): + data = "This is some test data." + temp_file = tmpdir.join("data.txt") + temp_file.write(data) + return temp_file + + +@pytest.fixture +def answers(): + return [f"answer_{i}" for i in range(10)] + + +@pytest.fixture(scope="function") +def llm(answers: list[str]): + return FakeListChatModel(responses=answers) + + +@pytest.fixture(scope="function") +def embedder(): + return DeterministicFakeEmbedding(size=20) + + +def test_brain_from_files_exception(): + # Testing no files + with pytest.raises(ValueError): + Brain.from_files(name="test_brain", file_paths=[]) + + +def test_brain_ask_txt(llm, embedder, temp_data_file, answers): + brain = Brain.from_files( + name="test_brain", file_paths=[temp_data_file], embedder=embedder, llm=llm + ) + + assert brain.llm == llm + assert brain.vector_db.embeddings == embedder + + answer = brain.ask("question") + + assert answer.answer == answers[0] + assert answer.metadata == answers[0]