feat: quivr core brain info + processors registry + (#2877)

# Description

- Created registry processor logic for automagically adding processors
to quivr_core based Entrypoints
- Added a langchain_community free `SimpleTxtParser` for the quivr_core
base package
- Added tests
- Added brain_info 
- Enriched parsed documents metadata based on quivr_file metadata

used Rich for `Brain.print_info()` to get a better output: 

![image](https://github.com/user-attachments/assets/dd9f2f03-d7d7-4be0-ba6c-3fe38e11c40f)

backend/core/examples/pdf_parsing_tika.py

@@ -1,19 +1,42 @@
 from langchain_core.embeddings import DeterministicFakeEmbedding
 from langchain_core.language_models import FakeListChatModel
+from rich.console import Console
+from rich.panel import Panel
+from rich.prompt import Prompt
 
 from quivr_core import Brain
-from quivr_core.processor.default_parsers import DEFAULT_PARSERS
+from quivr_core.config import LLMEndpointConfig
-from quivr_core.processor.pdf_processor import TikaParser
+from quivr_core.llm.llm_endpoint import LLMEndpoint
 
 if __name__ == "__main__":
-    pdf_paths = ["../tests/processor/data/dummy.pdf"]
     brain = Brain.from_files(
         name="test_brain",
-        file_paths=[],
+        file_paths=["tests/processor/data/dummy.pdf"],
-        llm=FakeListChatModel(responses=["good"]),
+        llm=LLMEndpoint(
+            llm=FakeListChatModel(responses=["good"]),
+            llm_config=LLMEndpointConfig(model="fake_model", llm_base_url="local"),
+        ),
         embedder=DeterministicFakeEmbedding(size=20),
-        processors_mapping={
-            **DEFAULT_PARSERS,
-            ".pdf": TikaParser(),
-        },
     )
+    # Check brain info
+    brain.print_info()
+
+    console = Console()
+    console.print(Panel.fit("Ask your brain !", style="bold magenta"))
+
+    while True:
+        # Get user input
+        question = Prompt.ask("[bold cyan]Question[/bold cyan]")
+
+        # Check if user wants to exit
+        if question.lower() == "exit":
+            console.print(Panel("Goodbye!", style="bold yellow"))
+            break
+
+        answer = brain.ask(question)
+        # Print the answer with typing effect
+        console.print(f"[bold green]Quivr Assistant[/bold green]: {answer.answer}")
+
+        console.print("-" * console.width)
+
+    brain.print_info()

backend/core/examples/simple_question.py

@@ -12,3 +12,5 @@ if __name__ == "__main__":
         answer = brain.ask("Property of gold?")
 
         print("answer :", answer.answer)
+
+        print("brain information: ", brain)

backend/core/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.0 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 = "aiofiles"
@@ -1189,13 +1189,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.19"
+version = "0.2.20"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.2.19-py3-none-any.whl", hash = "sha256:5b3cd34395be274c89e822c84f0e03c4da14168c177a83921c5b9414ac7a0651"},
+    {file = "langchain_core-0.2.20-py3-none-any.whl", hash = "sha256:16cc4da6f7ebf33accea7af45a70480733dc852ab291030fb6924865bd7caf76"},
-    {file = "langchain_core-0.2.19.tar.gz", hash = "sha256:13043a83e5c9ab58b9f5ce2a56896e7e88b752e8891b2958960a98e71801471e"},
+    {file = "langchain_core-0.2.20.tar.gz", hash = "sha256:a66c439e085d8c75f822f7650a5551d17bada4003521173c763d875d949e4ed5"},
 ]
 
 [package.dependencies]
@@ -1241,13 +1241,13 @@ langchain-core = ">=0.2.10,<0.3.0"
 
 [[package]]
 name = "langsmith"
-version = "0.1.85"
+version = "0.1.88"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.85-py3-none-any.whl", hash = "sha256:c1f94384f10cea96f7b4d33fd3db7ec180c03c7468877d50846f881d2017ff94"},
+    {file = "langsmith-0.1.88-py3-none-any.whl", hash = "sha256:460ebb7de440afd150fcea8f54ca8779821f2228cd59e149e5845c9dbe06db16"},
-    {file = "langsmith-0.1.85.tar.gz", hash = "sha256:acff31f9e53efa48586cf8e32f65625a335c74d7c4fa306d1655ac18452296f6"},
+    {file = "langsmith-0.1.88.tar.gz", hash = "sha256:28a07dec19197f4808aa2628d5a3ccafcbe14cc137aef0e607bbd128e7907821"},
 ]
 
 [package.dependencies]
@@ -1258,6 +1258,30 @@ pydantic = [
 ]
 requests = ">=2,<3"
 
+[[package]]
+name = "markdown-it-py"
+version = "3.0.0"
+description = "Python port of markdown-it. Markdown parsing, done right!"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
+    {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"},
+]
+
+[package.dependencies]
+mdurl = ">=0.1,<1.0"
+
+[package.extras]
+benchmarking = ["psutil", "pytest", "pytest-benchmark"]
+code-style = ["pre-commit (>=3.0,<4.0)"]
+compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
+linkify = ["linkify-it-py (>=1,<3)"]
+plugins = ["mdit-py-plugins"]
+profiling = ["gprof2dot"]
+rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
+testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
+
 [[package]]
 name = "marshmallow"
 version = "3.21.3"
@@ -1302,6 +1326,17 @@ files = [
     {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"},
 ]
 
+[[package]]
+name = "mdurl"
+version = "0.1.2"
+description = "Markdown URL utilities"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"},
+    {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
+]
+
 [[package]]
 name = "multidict"
 version = "6.0.5"
@@ -1527,13 +1562,13 @@ files = [
 
 [[package]]
 name = "openai"
-version = "1.35.13"
+version = "1.35.14"
 description = "The official Python library for the openai API"
 optional = true
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.35.13-py3-none-any.whl", hash = "sha256:36ec3e93e0d1f243f69be85c89b9221a471c3e450dfd9df16c9829e3cdf63e60"},
+    {file = "openai-1.35.14-py3-none-any.whl", hash = "sha256:adadf8c176e0b8c47ad782ed45dc20ef46438ee1f02c7103c4155cff79c8f68b"},
-    {file = "openai-1.35.13.tar.gz", hash = "sha256:c684f3945608baf7d2dcc0ef3ee6f3e27e4c66f21076df0b47be45d57e6ae6e4"},
+    {file = "openai-1.35.14.tar.gz", hash = "sha256:394ba1dfd12ecec1d634c50e512d24ff1858bbc2674ffcce309b822785a058de"},
 ]
 
 [package.dependencies]
@@ -2328,6 +2363,24 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "rich"
+version = "13.7.1"
+description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "rich-13.7.1-py3-none-any.whl", hash = "sha256:4edbae314f59eb482f54e9e30bf00d33350aaa94f4bfcd4e9e3110e64d0d7222"},
+    {file = "rich-13.7.1.tar.gz", hash = "sha256:9be308cb1fe2f1f57d67ce99e95af38a1e2bc71ad9813b0e247cf7ffbcc3a432"},
+]
+
+[package.dependencies]
+markdown-it-py = ">=2.2.0"
+pygments = ">=2.13.0,<3.0.0"
+
+[package.extras]
+jupyter = ["ipywidgets (>=7.5.1,<9)"]
+
 [[package]]
 name = "ruff"
 version = "0.4.10"
@@ -2788,4 +2841,4 @@ pdf = []
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "f5ab0d3f93f3bd517382a8c4207a4a480f4a6dcb0aeaf60501c379a4a91a3ad0"
+content-hash = "cc08905f149df2f415e1d00010e5b89a371efdcf4059855d597b1b6e9973a536"

backend/core/pyproject.toml

@@ -15,6 +15,7 @@ aiofiles = ">=23.0.0,<25.0.0"
 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 }
+rich = "^13.7.1"
 
 [tool.poetry.extras]
 base = ["langchain-community", "faiss-cpu", "langchain-openai"]
@@ -81,6 +82,10 @@ known-first-party = []
 [tool.pytest.ini_options]
 addopts = "--tb=short -ra -v"
 filterwarnings = ["ignore::DeprecationWarning"]
+markers = [
+    "slow: marks tests as slow (deselect with '-m \"not slow\"')",
+    "base: these tests require quivr-core with extra `base` to be installed",
+]
 
 [build-system]
 requires = ["poetry-core"]

backend/core/quivr_core/__init__.py

@@ -1,3 +1,35 @@
-from .brain import Brain
+from importlib.metadata import entry_points
 
-__all__ = ["Brain"]
+from .brain import Brain
+from .processor.registry import register_processor, registry
+
+__all__ = ["Brain", "registry", "register_processor"]
+
+
+def register_entries():
+    if entry_points is not None:
+        try:
+            eps = entry_points()
+        except TypeError:
+            pass  # importlib-metadata < 0.8
+        else:
+            if hasattr(eps, "select"):  # Python 3.10+ / importlib_metadata >= 3.9.0
+                processors = eps.select(group="quivr_core.processor")
+            else:
+                processors = eps.get("quivr_core.processor", [])
+            registered_names = set()
+            for spec in processors:
+                err_msg = f"Unable to load processor from {spec}"
+                name = spec.name
+                if name in registered_names:
+                    continue
+                registered_names.add(name)
+                register_processor(
+                    name,
+                    spec.value.replace(":", "."),
+                    errtxt=err_msg,
+                    override=True,
+                )
+
+
+register_entries()

backend/core/quivr_core/brain/__init__.py

@@ -0,0 +1,3 @@
+from .brain import Brain
+
+__all__ = ["Brain"]

backend/core/quivr_core/brain/brain.py

@@ -1,29 +1,34 @@
 import asyncio
 import logging
 from pathlib import Path
-from typing import Any, AsyncGenerator, Callable, Dict, Mapping, Self
+from pprint import PrettyPrinter
+from typing import Any, AsyncGenerator, Callable, Dict, Self
 from uuid import UUID, uuid4
 
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.messages import AIMessage, HumanMessage
 from langchain_core.vectorstores import VectorStore
+from rich.console import Console
+from rich.panel import Panel
 
+from quivr_core.brain.info import BrainInfo, ChatHistoryInfo
 from quivr_core.chat import ChatHistory
 from quivr_core.config import LLMEndpointConfig, RAGConfig
 from quivr_core.llm import LLMEndpoint
 from quivr_core.models import ParsedRAGChunkResponse, ParsedRAGResponse, SearchResult
-from quivr_core.processor.default_parsers import DEFAULT_PARSERS
+from quivr_core.processor.registry import get_processor_class
-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.file import load_qfile
 from quivr_core.storage.local_storage import TransparentStorage
 from quivr_core.storage.storage_base import StorageBase
 
 logger = logging.getLogger("quivr_core")
 
 
-async def _default_vectordb(docs: list[Document], embedder: Embeddings) -> VectorStore:
+async def _build_default_vectordb(
+    docs: list[Document], embedder: Embeddings
+) -> VectorStore:
     try:
         from langchain_community.vectorstores import FAISS
 
@@ -38,7 +43,7 @@ async def _default_vectordb(docs: list[Document], embedder: Embeddings) -> Vecto
 
     except ImportError as e:
         raise ImportError(
-            "Please provide a valid vectore store or install quivr-core['base'] package for using the default one."
+            "Please provide a valid vector store or install quivr-core['base'] package for using the default one."
         ) from e
 
 
@@ -67,16 +72,15 @@ def _default_llm() -> LLMEndpoint:
         ) from e
 
 
-async def _process_files(
+async def process_files(
-    storage: StorageBase,
+    storage: StorageBase, skip_file_error: bool, **processor_kwargs: dict[str, Any]
-    skip_file_error: bool,
-    processors_mapping: Mapping[str, ProcessorBase],
 ) -> list[Document]:
     knowledge = []
-    for file in storage.get_files():
+    for file in await storage.get_files():
         try:
             if file.file_extension:
-                processor = processors_mapping[file.file_extension]
+                processor_cls = get_processor_class(file.file_extension)
+                processor = processor_cls(**processor_kwargs)
                 docs = await processor.process_file(file)
                 knowledge.extend(docs)
             else:
@@ -118,11 +122,38 @@ class Brain:
         self.vector_db = vector_db
         self.embedder = embedder
 
+    def __repr__(self) -> str:
+        pp = PrettyPrinter(width=80, depth=None, compact=False, sort_dicts=False)
+        return pp.pformat(self.info())
+
+    def print_info(self):
+        console = Console()
+        tree = self.info().to_tree()
+        panel = Panel(tree, title="Brain Info", expand=False, border_style="bold")
+        console.print(panel)
+
+    def info(self) -> BrainInfo:
+        # TODO: dim of embedding
+        # "embedder": {},
+        chats_info = ChatHistoryInfo(
+            nb_chats=len(self._chats),
+            current_default_chat=self.default_chat.id,
+            current_chat_history_length=len(self.default_chat),
+        )
+
+        return BrainInfo(
+            brain_id=self.id,
+            brain_name=self.name,
+            files_info=self.storage.info(),
+            chats_info=chats_info,
+            llm_info=self.llm.info(),
+        )
+
     @property
-    def chat_history(self):
+    def chat_history(self) -> ChatHistory:
         return self.default_chat
 
-    def _init_chats(self):
+    def _init_chats(self) -> Dict[UUID, ChatHistory]:
         chat_id = uuid4()
         default_chat = ChatHistory(chat_id=chat_id, brain_id=self.id)
         return {chat_id: default_chat}
@@ -137,7 +168,6 @@ class Brain:
         storage: StorageBase = TransparentStorage(),
         llm: LLMEndpoint | None = None,
         embedder: Embeddings | None = None,
-        processors_mapping: Mapping[str, ProcessorBase] = DEFAULT_PARSERS,
         skip_file_error: bool = False,
     ):
         if llm is None:
@@ -148,20 +178,20 @@ class Brain:
 
         brain_id = uuid4()
 
+        # TODO: run in parallel using tasks
         for path in file_paths:
-            file = QuivrFile.from_path(brain_id, path)
+            file = await load_qfile(brain_id, path)
-            storage.upload_file(file)
+            await storage.upload_file(file)
 
         # Parse files
-        docs = await _process_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:
-            vector_db = await _default_vectordb(docs, embedder)
+            vector_db = await _build_default_vectordb(docs, embedder)
         else:
             await vector_db.aadd_documents(docs)
 
@@ -184,10 +214,10 @@ class Brain:
         storage: StorageBase = TransparentStorage(),
         llm: LLMEndpoint | None = None,
         embedder: Embeddings | None = None,
-        processors_mapping: Mapping[str, ProcessorBase] = DEFAULT_PARSERS,
         skip_file_error: bool = False,
     ) -> Self:
-        return asyncio.run(
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(
             cls.afrom_files(
                 name=name,
                 file_paths=file_paths,
@@ -195,7 +225,6 @@ class Brain:
                 storage=storage,
                 llm=llm,
                 embedder=embedder,
-                processors_mapping=processors_mapping,
                 skip_file_error=skip_file_error,
             )
         )
@@ -221,7 +250,7 @@ class Brain:
 
         # Building brain's vectordb
         if vector_db is None:
-            vector_db = await _default_vectordb(langchain_documents, embedder)
+            vector_db = await _build_default_vectordb(langchain_documents, embedder)
         else:
             await vector_db.aadd_documents(langchain_documents)
 

backend/core/quivr_core/brain/info.py

@@ -0,0 +1,74 @@
+from dataclasses import dataclass
+from uuid import UUID
+
+from rich.tree import Tree
+
+
+@dataclass
+class ChatHistoryInfo:
+    nb_chats: int
+    current_default_chat: UUID
+    current_chat_history_length: int
+
+    def add_to_tree(self, chats_tree: Tree):
+        chats_tree.add(f"Number of Chats: [bold]{self.nb_chats}[/bold]")
+        chats_tree.add(
+            f"Current Default Chat: [bold magenta]{self.current_default_chat}[/bold magenta]"
+        )
+        chats_tree.add(
+            f"Current Chat History Length: [bold]{self.current_chat_history_length}[/bold]"
+        )
+
+
+@dataclass
+class LLMInfo:
+    model: str
+    llm_base_url: str
+    temperature: float
+    max_tokens: int
+    supports_function_calling: int
+
+    def add_to_tree(self, llm_tree: Tree):
+        llm_tree.add(f"Model: [italic]{self.model}[/italic]")
+        llm_tree.add(f"Base URL: [underline]{self.llm_base_url}[/underline]")
+        llm_tree.add(f"Temperature: [bold]{self.temperature}[/bold]")
+        llm_tree.add(f"Max Tokens: [bold]{self.max_tokens}[/bold]")
+
+        func_call_color = "green" if self.supports_function_calling else "red"
+        llm_tree.add(
+            f"Supports Function Calling: [bold {func_call_color}]{self.supports_function_calling}[/bold {func_call_color}]"
+        )
+
+
+@dataclass
+class StorageInfo:
+    storage_type: str
+    n_files: int
+
+    def add_to_tree(self, files_tree: Tree):
+        files_tree.add(f"Storage Type: [italic]{self.storage_type}[/italic]")
+        files_tree.add(f"Number of Files: [bold]{self.n_files}[/bold]")
+
+
+@dataclass
+class BrainInfo:
+    brain_id: UUID
+    brain_name: str
+    files_info: StorageInfo
+    chats_info: ChatHistoryInfo
+    llm_info: LLMInfo
+
+    def to_tree(self):
+        tree = Tree("📊 Brain Information")
+        tree.add(f"🆔 ID: [bold cyan]{self.brain_id}[/bold cyan]")
+        tree.add(f"🧠 Brain Name: [bold green]{self.brain_name}[/bold green]")
+
+        files_tree = tree.add("📁 Files")
+        self.files_info.add_to_tree(files_tree)
+
+        chats_tree = tree.add("💬 Chats")
+        self.chats_info.add_to_tree(chats_tree)
+
+        llm_tree = tree.add("🤖 LLM")
+        self.llm_info.add_to_tree(llm_tree)
+        return tree

backend/core/quivr_core/llm/llm_endpoint.py

@@ -1,6 +1,7 @@
 from langchain_core.language_models.chat_models import BaseChatModel
 from pydantic.v1 import SecretStr
 
+from quivr_core.brain.info import LLMInfo
 from quivr_core.config import LLMEndpointConfig
 from quivr_core.utils import model_supports_function_calling
 
@@ -35,3 +36,14 @@ class LLMEndpoint:
 
     def supports_func_calling(self) -> bool:
         return self._supports_func_calling
+
+    def info(self) -> LLMInfo:
+        return LLMInfo(
+            model=self._config.model,
+            llm_base_url=(
+                self._config.llm_base_url if self._config.llm_base_url else "openai"
+            ),
+            temperature=self._config.temperature,
+            max_tokens=self._config.max_tokens,
+            supports_function_calling=self.supports_func_calling(),
+        )

backend/core/quivr_core/processor/default_parsers.py

@@ -1,6 +0,0 @@
-from quivr_core.processor.processor_base import ProcessorBase
-from quivr_core.processor.txt_parser import TxtProcessor
-
-DEFAULT_PARSERS: dict[str, ProcessorBase] = {
-    ".txt": TxtProcessor(),
-}

backend/core/quivr_core/processor/processor_base.py

@@ -1,27 +1,20 @@
 from abc import ABC, abstractmethod
-from typing import Generic, TypeVar
 
 from langchain_core.documents import Document
 
-from quivr_core.storage.file import QuivrFile
+from quivr_core.storage.file import FileExtension, QuivrFile
 
 
+# TODO: processors should be cached somewhere ?
+# The processor should be cached by processor type
+# The cache should use a single
 class ProcessorBase(ABC):
-    supported_extensions: list[str]
+    supported_extensions: list[FileExtension | str]
 
     @abstractmethod
     async def process_file(self, file: QuivrFile) -> list[Document]:
-        pass
+        raise NotImplementedError
 
-
+    def check_supported(self, file: QuivrFile):
-P = TypeVar("P", bound=ProcessorBase)
+        if file.file_extension not in self.supported_extensions:
-
+            raise ValueError(f"can't process a file of type {file.file_extension}")
-
-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

backend/core/quivr_core/processor/registry.py

@@ -0,0 +1,107 @@
+import importlib
+import types
+from typing import Type, TypedDict
+
+from quivr_core.storage.file import FileExtension
+
+from .processor_base import ProcessorBase
+
+_registry: dict[str, Type[ProcessorBase]] = {}
+
+# external, read only
+registry = types.MappingProxyType(_registry)
+
+
+class ProcEntry(TypedDict):
+    cls_mod: str
+    err: str | None
+
+
+# Register based on mimetypes
+known_processors: dict[FileExtension | str, ProcEntry] = {
+    FileExtension.txt: ProcEntry(
+        cls_mod="quivr_core.processor.simple_txt_processor.SimpleTxtProcessor",
+        err="Please install quivr_core[base] to use TikTokenTxtProcessor ",
+    ),
+    FileExtension.pdf: ProcEntry(
+        cls_mod="quivr_core.processor.tika_processor.TikaProcessor",
+        err=None,
+    ),
+}
+
+
+def get_processor_class(file_extension: FileExtension | str) -> Type[ProcessorBase]:
+    """Fetch processor class from registry
+
+    The dict ``known_processors`` maps file extensions to the locations
+    of processors that could process them.
+    Loading of these classes is *Lazy*. Appropriate import will happen
+    the first time we try to process some file type.
+
+    Some processors need additional dependencies. If the import fails
+    we return the "err" field of the ProcEntry in  ``known_processors``.
+    """
+
+    if file_extension not in registry:
+        if file_extension not in known_processors:
+            raise ValueError(f"Extension not known: {file_extension}")
+        entry = known_processors[file_extension]
+        try:
+            register_processor(file_extension, _import_class(entry["cls_mod"]))
+        except ImportError as e:
+            raise ImportError(entry["err"]) from e
+
+    cls = registry[file_extension]
+    return cls
+
+
+def register_processor(
+    file_type: FileExtension | str,
+    proc_cls: str | Type[ProcessorBase],
+    override: bool = False,
+    errtxt=None,
+):
+    if isinstance(proc_cls, str):
+        if file_type in known_processors and override is False:
+            if proc_cls != known_processors[file_type]["cls_mod"]:
+                raise ValueError(
+                    f"Processor for ({file_type}) already in the registry and override is False"
+                )
+        else:
+            known_processors[file_type] = ProcEntry(
+                cls_mod=proc_cls,
+                err=errtxt or f"{proc_cls} import failed for processor of {file_type}",
+            )
+    else:
+        if file_type in registry and override is False:
+            if _registry[file_type] is not proc_cls:
+                raise ValueError(
+                    f"Processor for ({file_type}) already in the registry and override is False"
+                )
+        else:
+            _registry[file_type] = proc_cls
+
+
+def _import_class(full_mod_path: str):
+    if ":" in full_mod_path:
+        mod_name, name = full_mod_path.rsplit(":", 1)
+    else:
+        mod_name, name = full_mod_path.rsplit(".", 1)
+
+    mod = importlib.import_module(mod_name)
+
+    for cls in name.split("."):
+        mod = getattr(mod, cls)
+
+    if not isinstance(mod, type):
+        raise TypeError(f"{full_mod_path} is not a class")
+
+    if not issubclass(mod, ProcessorBase):
+        raise TypeError(f"{full_mod_path} is not a subclass of ProcessorBase ")
+
+    return mod
+
+
+def available_processors():
+    """Return a list of the known processors."""
+    return list(known_processors)

backend/core/quivr_core/processor/simple_txt_processor.py

@@ -0,0 +1,62 @@
+from importlib.metadata import version
+from uuid import uuid4
+
+import aiofiles
+from langchain_core.documents import Document
+
+from quivr_core.processor.processor_base import ProcessorBase
+from quivr_core.processor.registry import FileExtension
+from quivr_core.processor.splitter import SplitterConfig
+from quivr_core.storage.file import QuivrFile
+
+
+def recursive_character_splitter(
+    doc: Document, chunk_size: int, chunk_overlap: int
+) -> list[Document]:
+    assert chunk_overlap < chunk_size, "chunk_overlap is greater than chunk_size"
+
+    if len(doc.page_content) <= chunk_size:
+        return [doc]
+
+    chunk = Document(page_content=doc.page_content[:chunk_size], metadata=doc.metadata)
+    remaining = Document(
+        page_content=doc.page_content[chunk_size - chunk_overlap :],
+        metadata=doc.metadata,
+    )
+
+    return [chunk] + recursive_character_splitter(remaining, chunk_size, chunk_overlap)
+
+
+class SimpleTxtProcessor(ProcessorBase):
+    supported_extensions = [FileExtension.txt]
+
+    def __init__(
+        self, splitter_config: SplitterConfig = SplitterConfig(), **kwargs
+    ) -> None:
+        super().__init__(**kwargs)
+        self.splitter_config = splitter_config
+
+    async def process_file(self, file: QuivrFile) -> list[Document]:
+        self.check_supported(file)
+        file_metadata = file.metadata
+
+        async with aiofiles.open(file.path, mode="r") as f:
+            content = await f.read()
+
+        doc = Document(
+            page_content=content,
+            metadata={
+                "id": uuid4(),
+                "chunk_size": len(content),
+                "chunk_overlap": self.splitter_config.chunk_overlap,
+                "parser_name": self.__class__.__name__,
+                "quivr_core_version": version("quivr-core"),
+                **file_metadata,
+            },
+        )
+
+        docs = recursive_character_splitter(
+            doc, self.splitter_config.chunk_size, self.splitter_config.chunk_overlap
+        )
+
+        return docs

backend/core/quivr_core/processor/tika_processor.py

@@ -1,4 +1,5 @@
 import logging
+from importlib.metadata import version
 from typing import AsyncIterable
 
 import httpx
@@ -6,14 +7,15 @@ 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.registry import FileExtension
 from quivr_core.processor.splitter import SplitterConfig
 from quivr_core.storage.file import QuivrFile
 
 logger = logging.getLogger("quivr_core")
 
 
-class TikaParser(ProcessorBase):
+class TikaProcessor(ProcessorBase):
-    supported_extensions = [".pdf"]
+    supported_extensions = [FileExtension.pdf]
 
     def __init__(
         self,
@@ -51,12 +53,22 @@ class TikaParser(ProcessorBase):
         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
+        self.check_supported(file)
 
         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])
+        file_metadata = file.metadata
+
+        for doc in docs:
+            doc.metadata = {
+                "chunk_size": len(doc.page_content),
+                "chunk_overlap": self.splitter_config.chunk_overlap,
+                "parser_name": self.__class__.__name__,
+                "quivr_core_version": version("quivr-core"),
+                **file_metadata,
+            }
+
         return docs

backend/core/quivr_core/processor/txt_processor.py

@@ -1,19 +1,24 @@
+from importlib.metadata import version
+from uuid import uuid4
+
 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.registry import FileExtension
 from quivr_core.processor.splitter import SplitterConfig
 from quivr_core.storage.file import QuivrFile
 
 
-class TxtProcessor(ProcessorBase):
+class TikTokenTxtProcessor(ProcessorBase):
+    supported_extensions = [FileExtension.txt]
+
     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
@@ -27,10 +32,22 @@ class TxtProcessor(ProcessorBase):
             )
 
     async def process_file(self, file: QuivrFile) -> list[Document]:
-        if file.file_extension not in self.supported_extensions:
+        self.check_supported(file)
-            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)
+
+        file_metadata = file.metadata
+
+        for doc in docs:
+            doc.metadata = {
+                "id": uuid4(),
+                "chunk_size": len(doc.page_content),
+                "chunk_overlap": self.splitter_config.chunk_overlap,
+                "parser_name": self.__class__.__name__,
+                "quivr_core_version": version("quivr-core"),
+                **file_metadata,
+            }
+
         return docs

backend/core/quivr_core/storage/file.py

@@ -1,21 +1,87 @@
+import hashlib
+import mimetypes
 import os
+import warnings
 from contextlib import asynccontextmanager
+from enum import Enum
 from pathlib import Path
-from typing import AsyncGenerator, AsyncIterable
+from typing import Any, AsyncGenerator, AsyncIterable
 from uuid import UUID, uuid4
 
 import aiofiles
 
 
+class FileExtension(str, Enum):
+    txt = ".txt"
+    pdf = ".pdf"
+    docx = ".docx"
+
+
+def get_file_extension(file_path: Path) -> FileExtension | str:
+    try:
+        mime_type, _ = mimetypes.guess_type(file_path.name)
+        if mime_type:
+            mime_ext = mimetypes.guess_extension(mime_type)
+            if mime_ext:
+                return FileExtension(mime_ext)
+        return FileExtension(file_path.suffix)
+    except ValueError:
+        warnings.warn(
+            f"File {file_path.name} extension isn't recognized. Make sure you have registered a parser for {file_path.suffix}",
+            stacklevel=2,
+        )
+        return file_path.suffix
+
+
+async def load_qfile(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
+
+    async with aiofiles.open(path, mode="rb") as f:
+        file_md5 = hashlib.md5(await f.read()).hexdigest()
+
+    try:
+        # NOTE: when loading from existing storage, file name will be uuid
+        id = UUID(path.name)
+    except ValueError:
+        id = uuid4()
+
+    return QuivrFile(
+        id=id,
+        brain_id=brain_id,
+        path=path,
+        original_filename=path.name,
+        file_extension=get_file_extension(path),
+        file_size=file_size,
+        file_md5=file_md5,
+    )
+
+
 class QuivrFile:
+    __slots__ = [
+        "id",
+        "brain_id",
+        "path",
+        "original_filename",
+        "file_size",
+        "file_extension",
+        "file_md5",
+    ]
+
     def __init__(
         self,
         id: UUID,
         original_filename: str,
         path: Path,
         brain_id: UUID,
+        file_md5: str,
+        file_extension: FileExtension | str,
         file_size: int | None = None,
-        file_extension: str | None = None,
     ) -> None:
         self.id = id
         self.brain_id = brain_id
@@ -23,31 +89,7 @@ class QuivrFile:
         self.original_filename = original_filename
         self.file_size = file_size
         self.file_extension = file_extension
-
+        self.file_md5 = file_md5
-    @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]:
@@ -57,3 +99,13 @@ class QuivrFile:
             yield f
         finally:
             await f.close()
+
+    @property
+    def metadata(self) -> dict[str, Any]:
+        return {
+            "qfile_id": self.id,
+            "qfile_path": self.path,
+            "original_file_name": self.original_filename,
+            "file_md4": self.file_md5,
+            "file_size": self.file_size,
+        }

backend/core/quivr_core/storage/local_storage.py

@@ -1,6 +1,7 @@
 import os
 import shutil
 from pathlib import Path
+from typing import Set
 from uuid import UUID
 
 from quivr_core.storage.file import QuivrFile
@@ -8,8 +9,11 @@ from quivr_core.storage.storage_base import StorageBase
 
 
 class LocalStorage(StorageBase):
+    name: str = "local_storage"
+
     def __init__(self, dir_path: Path | None = None, copy_flag: bool = True):
         self.files: list[QuivrFile] = []
+        self.hashes: Set[str] = set()
         self.copy_flag = copy_flag
 
         if dir_path is None:
@@ -24,14 +28,19 @@ class LocalStorage(StorageBase):
         # TODO(@aminediro): load existing files
         pass
 
-    def upload_file(self, file: QuivrFile, exists_ok: bool = False) -> None:
+    def nb_files(self) -> int:
+        return len(self.files)
+
+    def info(self):
+        return {"directory_path": self.dir_path, **super().info()}
+
+    async 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 file.file_md5 in self.hashes and not exists_ok:
-        if os.path.exists(dst_path) and not exists_ok:
+            raise FileExistsError(f"file {file.original_filename} already uploaded")
-            raise FileExistsError("file already exists")
 
         if self.copy_flag:
             shutil.copy2(file.path, dst_path)
@@ -40,28 +49,31 @@ class LocalStorage(StorageBase):
 
         file.path = Path(dst_path)
         self.files.append(file)
+        self.hashes.add(file.file_md5)
 
-    def get_files(self) -> list[QuivrFile]:
+    async def get_files(self) -> list[QuivrFile]:
         return self.files
 
-    def remove_file(self, file_id: UUID) -> None:
+    async def remove_file(self, file_id: UUID) -> None:
         raise NotImplementedError
 
 
 class TransparentStorage(StorageBase):
-    """Transparent Storage.
+    """Transparent Storage."""
-    uses default
 
-    """
+    name: str = "transparent_storage"
 
     def __init__(self):
-        self.files = []
+        self.id_files = {}
 
-    def upload_file(self, file: QuivrFile, exists_ok: bool = False) -> None:
+    async def upload_file(self, file: QuivrFile, exists_ok: bool = False) -> None:
-        self.files.append(file)
+        self.id_files[file.id] = file
 
-    def remove_file(self, file_id: UUID) -> None:
+    def nb_files(self) -> int:
+        return len(self.id_files)
+
+    async def remove_file(self, file_id: UUID) -> None:
         raise NotImplementedError
 
-    def get_files(self) -> list[QuivrFile]:
+    async def get_files(self) -> list[QuivrFile]:
-        return self.files
+        return list(self.id_files.values())

backend/core/quivr_core/storage/storage_base.py

@@ -1,18 +1,39 @@
 from abc import ABC, abstractmethod
 from uuid import UUID
 
+from quivr_core.brain.info import StorageInfo
 from quivr_core.storage.local_storage import QuivrFile
 
 
 class StorageBase(ABC):
+    name: str
+
+    def __init_subclass__(cls, **kwargs):
+        for required in ("name",):
+            if not getattr(cls, required):
+                raise TypeError(
+                    f"Can't instantiate abstract class {cls.__name__} without {required} attribute defined"
+                )
+        return super().__init_subclass__(**kwargs)
+
     @abstractmethod
-    def get_files(self) -> list[QuivrFile]:
+    def nb_files(self) -> int:
+        raise Exception("Unimplemented nb_files method")
+
+    @abstractmethod
+    async def get_files(self) -> list[QuivrFile]:
         raise Exception("Unimplemented get_files method")
 
     @abstractmethod
-    def upload_file(self, file: QuivrFile, exists_ok: bool = False) -> None:
+    async 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:
+    async def remove_file(self, file_id: UUID) -> None:
         raise Exception("Unimplemented remove_file method")
+
+    def info(self) -> StorageInfo:
+        return StorageInfo(
+            storage_type=self.name,
+            n_files=self.nb_files(),
+        )

backend/core/tests/conftest.py

@@ -1,5 +1,7 @@
 import json
 import os
+from pathlib import Path
+from uuid import uuid4
 
 import pytest
 from langchain_core.embeddings import DeterministicFakeEmbedding
@@ -10,6 +12,39 @@ from langchain_core.vectorstores import InMemoryVectorStore
 
 from quivr_core.config import LLMEndpointConfig
 from quivr_core.llm import LLMEndpoint
+from quivr_core.storage.file import FileExtension, QuivrFile
+
+
+@pytest.fixture(scope="function")
+def temp_data_file(tmp_path):
+    data = "This is some test data."
+    temp_file = tmp_path / "data.txt"
+    temp_file.write_text(data)
+    return temp_file
+
+
+@pytest.fixture(scope="function")
+def quivr_txt(temp_data_file):
+    return QuivrFile(
+        id=uuid4(),
+        brain_id=uuid4(),
+        original_filename=temp_data_file.name,
+        path=temp_data_file,
+        file_extension=FileExtension.txt,
+        file_md5="123",
+    )
+
+
+@pytest.fixture
+def quivr_pdf():
+    return QuivrFile(
+        id=uuid4(),
+        brain_id=uuid4(),
+        original_filename="dummy.pdf",
+        path=Path("./tests/processor/data/dummy.pdf"),
+        file_extension=FileExtension.pdf,
+        file_md5="13bh234jh234",
+    )
 
 
 @pytest.fixture
@@ -36,14 +71,6 @@ def openai_api_key():
     os.environ["OPENAI_API_KEY"] = "abcd"
 
 
-@pytest.fixture(scope="function")
-def temp_data_file(tmp_path):
-    data = "This is some test data."
-    temp_file = tmp_path / "data.txt"
-    temp_file.write_text(data)
-    return temp_file
-
-
 @pytest.fixture
 def answers():
     return [f"answer_{i}" for i in range(10)]

backend/core/tests/processor/test_registry.py

@@ -0,0 +1,67 @@
+import pytest
+from langchain_core.documents import Document
+
+from quivr_core import registry
+from quivr_core.processor.processor_base import ProcessorBase
+from quivr_core.processor.registry import (
+    _import_class,
+    get_processor_class,
+    register_processor,
+)
+from quivr_core.processor.simple_txt_processor import SimpleTxtProcessor
+from quivr_core.processor.tika_processor import TikaProcessor
+from quivr_core.storage.file import FileExtension, QuivrFile
+
+
+def test_get_processor_cls():
+    cls = get_processor_class(FileExtension.txt)
+    assert cls == SimpleTxtProcessor
+    cls = get_processor_class(FileExtension.pdf)
+    assert cls == TikaProcessor
+
+
+def test__import_class():
+    mod_path = "quivr_core.processor.tika_processor.TikaProcessor"
+    mod = _import_class(mod_path)
+    assert mod == TikaProcessor
+
+    with pytest.raises(TypeError, match=r".* is not a class"):
+        mod_path = "quivr_core.processor"
+        _import_class(mod_path)
+
+    with pytest.raises(TypeError, match=r".* ProcessorBase"):
+        mod_path = "quivr_core.Brain"
+        _import_class(mod_path)
+
+
+def test_get_processor_cls_error():
+    with pytest.raises(ValueError):
+        get_processor_class(".docx")
+
+
+def test_register_new_proc():
+    nprocs = len(registry)
+
+    class TestProcessor(ProcessorBase):
+        supported_extensions = [".test"]
+
+        async def process_file(self, file: QuivrFile) -> list[Document]:
+            return []
+
+    register_processor(".test", TestProcessor)
+    assert len(registry) == nprocs + 1
+
+    cls = get_processor_class(".test")
+    assert cls == TestProcessor
+
+
+def test_register_override_proc():
+    class TestProcessor(ProcessorBase):
+        supported_extensions = [".pdf"]
+
+        async def process_file(self, file: QuivrFile) -> list[Document]:
+            return []
+
+    register_processor(".pdf", TestProcessor, override=True)
+    cls = get_processor_class(FileExtension.pdf)
+    assert cls == TestProcessor

backend/core/tests/processor/test_simple_txt_processor.py

@@ -0,0 +1,34 @@
+import pytest
+from langchain_core.documents import Document
+
+from quivr_core.processor.simple_txt_processor import (
+    SimpleTxtProcessor,
+    recursive_character_splitter,
+)
+from quivr_core.processor.splitter import SplitterConfig
+from quivr_core.storage.file import FileExtension
+
+
+def test_recursive_character_splitter():
+    doc = Document(page_content="abcdefgh", metadata={"key": "value"})
+
+    docs = recursive_character_splitter(doc, chunk_size=2, chunk_overlap=1)
+
+    assert [d.page_content for d in docs] == ["ab", "bc", "cd", "de", "ef", "fg", "gh"]
+    assert [d.metadata for d in docs] == [doc.metadata] * len(docs)
+
+
+@pytest.mark.asyncio
+async def test_simple_processor(quivr_pdf, quivr_txt):
+    proc = SimpleTxtProcessor(
+        splitter_config=SplitterConfig(chunk_size=100, chunk_overlap=20)
+    )
+    assert proc.supported_extensions == [FileExtension.txt]
+
+    with pytest.raises(ValueError):
+        await proc.process_file(quivr_pdf)
+
+    docs = await proc.process_file(quivr_txt)
+
+    assert len(docs) == 1
+    assert docs[0].page_content == "This is some test data."

backend/core/tests/processor/test_tika_processor.py

@@ -1,38 +1,23 @@
-from pathlib import Path
-from uuid import uuid4
-
 import pytest
 
-from quivr_core.processor.pdf_processor import TikaParser
+from quivr_core.processor.tika_processor import TikaProcessor
-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):
+async def test_process_file(quivr_pdf):
-    tparser = TikaParser()
+    tparser = TikaProcessor()
-    doc = await tparser.process_file(pdf)
+    doc = await tparser.process_file(quivr_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):
+async def test_send_parse_tika_exception(quivr_pdf):
     # TODO: Mock correct tika for retries
-    tparser = TikaParser(tika_url="test.test")
+    tparser = TikaProcessor(tika_url="test.test")
     with pytest.raises(RuntimeError):
-        doc = await tparser.process_file(pdf)
+        doc = await tparser.process_file(quivr_pdf)
         assert len(doc) > 0
         assert doc[0].page_content.strip("\n") == "Dummy PDF download"

backend/core/tests/processor/test_txt_processor.py

@@ -0,0 +1,45 @@
+from importlib.metadata import version
+from uuid import uuid4
+
+import pytest
+
+from quivr_core.processor.splitter import SplitterConfig
+from quivr_core.processor.txt_processor import TikTokenTxtProcessor
+from quivr_core.storage.file import FileExtension, QuivrFile
+
+# TODO: TIKA server should be set
+
+
+@pytest.fixture
+def txt_qfile(temp_data_file):
+    return QuivrFile(
+        id=uuid4(),
+        brain_id=uuid4(),
+        original_filename="data.txt",
+        path=temp_data_file,
+        file_extension=FileExtension.txt,
+        file_md5="hash",
+    )
+
+
+@pytest.mark.base
+@pytest.mark.asyncio
+async def test_process_txt(txt_qfile):
+    tparser = TikTokenTxtProcessor(
+        splitter_config=SplitterConfig(chunk_size=20, chunk_overlap=0)
+    )
+    doc = await tparser.process_file(txt_qfile)
+    assert len(doc) > 0
+    assert doc[0].page_content == "This is some test data."
+    #  assert dict1.items() <= dict2.items()
+
+    assert (
+        doc[0].metadata.items()
+        >= {
+            "chunk_size": len(doc[0].page_content),
+            "chunk_overlap": 0,
+            "parser_name": tparser.__class__.__name__,
+            "quivr_core_version": version("quivr-core"),
+            **txt_qfile.metadata,
+        }.items()
+    )

backend/core/tests/test_brain.py

@@ -1,3 +1,4 @@
+from dataclasses import asdict
 from uuid import uuid4
 
 import pytest
@@ -16,8 +17,11 @@ def test_brain_empty_files():
         Brain.from_files(name="test_brain", file_paths=[])
 
 
-def test_brain_from_files_success(fake_llm: LLMEndpoint, embedder, temp_data_file):
+@pytest.mark.asyncio
-    brain = Brain.from_files(
+async def test_brain_from_files_success(
+    fake_llm: LLMEndpoint, embedder, temp_data_file
+):
+    brain = await Brain.afrom_files(
         name="test_brain", file_paths=[temp_data_file], embedder=embedder, llm=fake_llm
     )
     assert brain.name == "test_brain"
@@ -29,7 +33,7 @@ def test_brain_from_files_success(fake_llm: LLMEndpoint, embedder, temp_data_fil
 
     # storage
     assert isinstance(brain.storage, TransparentStorage)
-    assert len(brain.storage.get_files()) == 1
+    assert len(await brain.storage.get_files()) == 1
 
 
 @pytest.mark.asyncio
@@ -39,7 +43,7 @@ async def test_brain_from_langchain_docs(embedder):
         name="test", langchain_documents=[chunk], embedder=embedder
     )
     # No appended files
-    assert len(brain.storage.get_files()) == 0
+    assert len(await brain.storage.get_files()) == 0
     assert len(brain.chat_history) == 0
 
 
@@ -90,3 +94,28 @@ async def test_brain_ask_streaming(
         response += chunk.answer
 
     assert response == answers[1]
+
+
+def test_brain_info_empty(fake_llm: LLMEndpoint, embedder, mem_vector_store):
+    storage = TransparentStorage()
+    id = uuid4()
+    brain = Brain(
+        name="test",
+        id=id,
+        llm=fake_llm,
+        embedder=embedder,
+        storage=storage,
+        vector_db=mem_vector_store,
+    )
+
+    assert asdict(brain.info()) == {
+        "brain_id": id,
+        "brain_name": "test",
+        "files_info": asdict(storage.info()),
+        "chats_info": {
+            "nb_chats": 1,  # start with a default chat
+            "current_default_chat": brain.default_chat.id,
+            "current_chat_history_length": 0,
+        },
+        "llm_info": asdict(fake_llm.info()),
+    }