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# Description # Testing backend ## Docker setup 1. Copy `.env.example` to `.env`. Some env variables were added : EMBEDDING_DIM 2. Apply supabase migratrions : ```sh supabase stop supabase db reset supabase start ``` 3. Start backend containers ``` make dev ``` ## Local setup You can also run backend without docker. 1. Install [`rye`](https://rye.astral.sh/guide/installation/). Choose the managed python version and set the version to 3.11 2. Run the following: ``` cd quivr/backend rye sync ``` 3. Source `.venv` virtual env : `source .venv/bin/activate` 4. Run the backend, make sure you are running redis and supabase API: ``` LOG_LEVEL=debug uvicorn quivr_api.main:app --log-level debug --reload --host 0.0.0.0 --port 5050 --workers 1 ``` Worker: ``` LOG_LEVEL=debug celery -A quivr_worker.celery_worker worker -l info -E --concurrency 1 ``` Notifier: ``` LOG_LEVEL=debug python worker/quivr_worker/celery_monitor.py ``` --------- Co-authored-by: chloedia <chloedaems0@gmail.com> Co-authored-by: aminediro <aminedirhoussi1@gmail.com> Co-authored-by: Antoine Dewez <44063631+Zewed@users.noreply.github.com> Co-authored-by: Chloé Daems <73901882+chloedia@users.noreply.github.com> Co-authored-by: Zewed <dewez.antoine2@gmail.com>
92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
import json
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import os
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from pathlib import Path
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from uuid import uuid4
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import pytest
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from langchain_core.embeddings import DeterministicFakeEmbedding
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from langchain_core.language_models import FakeListChatModel
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from langchain_core.messages.ai import AIMessageChunk
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from langchain_core.runnables.utils import AddableDict
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from langchain_core.vectorstores import InMemoryVectorStore
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from quivr_core.config import LLMEndpointConfig
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from quivr_core.files.file import FileExtension, QuivrFile
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from quivr_core.llm import LLMEndpoint
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@pytest.fixture(scope="function")
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def temp_data_file(tmp_path):
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data = "This is some test data."
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temp_file = tmp_path / "data.txt"
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temp_file.write_text(data)
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return temp_file
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@pytest.fixture(scope="function")
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def quivr_txt(temp_data_file):
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return QuivrFile(
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id=uuid4(),
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brain_id=uuid4(),
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original_filename=temp_data_file.name,
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path=temp_data_file,
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file_extension=FileExtension.txt,
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file_sha1="123",
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)
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@pytest.fixture
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def quivr_pdf():
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return QuivrFile(
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id=uuid4(),
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brain_id=uuid4(),
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original_filename="dummy.pdf",
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path=Path("./tests/processor/data/dummy.pdf"),
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file_extension=FileExtension.pdf,
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file_sha1="13bh234jh234",
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)
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@pytest.fixture
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def full_response():
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return "Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to enable computers to understand, interpret, and respond to human language in a way that is both valuable and meaningful. NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. This combination allows computers to process human language in the form of text or voice data and to understand its full meaning, complete with the speaker or writer’s intent and sentiment. Key tasks in NLP include text and speech recognition, translation, sentiment analysis, and topic segmentation."
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@pytest.fixture
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def chunks_stream_answer():
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with open("./tests/chunk_stream_fixture.jsonl", "r") as f:
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raw_chunks = list(f)
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chunks = []
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for rc in raw_chunks:
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chunk = AddableDict(**json.loads(rc))
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if "answer" in chunk:
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chunk["answer"] = AIMessageChunk(**chunk["answer"])
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chunks.append(chunk)
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return chunks
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@pytest.fixture(autouse=True)
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def openai_api_key():
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os.environ["OPENAI_API_KEY"] = "abcd"
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@pytest.fixture
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def answers():
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return [f"answer_{i}" for i in range(10)]
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@pytest.fixture(scope="function")
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def fake_llm(answers: list[str]):
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llm = FakeListChatModel(responses=answers)
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return LLMEndpoint(llm=llm, llm_config=LLMEndpointConfig(model="fake_model"))
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@pytest.fixture(scope="function")
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def embedder():
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return DeterministicFakeEmbedding(size=20)
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@pytest.fixture(scope="function")
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def mem_vector_store(embedder):
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return InMemoryVectorStore(embedder)
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