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285fe5b960
# Description This PR includes far too many new features: - detection of user intent (closes CORE-211) - treating multiple questions in parallel (closes CORE-212) - using the chat history when answering a question (closes CORE-213) - filtering of retrieved chunks by relevance threshold (closes CORE-217) - dynamic retrieval of chunks (closes CORE-218) - enabling web search via Tavily (closes CORE-220) - enabling agent / assistant to activate tools when relevant to complete the user task (closes CORE-224) Also closes CORE-205 ## Checklist before requesting a review Please delete options that are not relevant. - [ ] My code follows the style guidelines of this project - [ ] I have performed a self-review of my code - [ ] I have commented hard-to-understand areas - [ ] I have ideally added tests that prove my fix is effective or that my feature works - [ ] New and existing unit tests pass locally with my changes - [ ] Any dependent changes have been merged ## Screenshots (if appropriate): --------- Co-authored-by: Stan Girard <stan@quivr.app>
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.rag.entities.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"] = "this-is-a-test-key"
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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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