langchain/libs/partners/qdrant/tests/integration_tests/common.py

from typing import List

import requests  # type: ignore
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings


def qdrant_running_locally() -> bool:
    """Check if Qdrant is running at http://localhost:6333."""

    try:
        response = requests.get("http://localhost:6333", timeout=10.0)
        response_json = response.json()
        return response_json.get("title") == "qdrant - vector search engine"
    except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
        return False


def assert_documents_equals(actual: List[Document], expected: List[Document]):  # type: ignore[no-untyped-def]
    assert len(actual) == len(expected)

    for actual_doc, expected_doc in zip(actual, expected):
        assert actual_doc.page_content == expected_doc.page_content

        assert "_id" in actual_doc.metadata
        assert "_collection_name" in actual_doc.metadata

        actual_doc.metadata.pop("_id")
        actual_doc.metadata.pop("_collection_name")

        assert actual_doc.metadata == expected_doc.metadata


class FakeEmbeddings(Embeddings):
    """Fake embeddings functionality for testing."""

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Return simple embeddings.
        Embeddings encode each text as its index."""
        return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]

    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
        return self.embed_documents(texts)

    def embed_query(self, text: str) -> List[float]:
        """Return constant query embeddings.
        Embeddings are identical to embed_documents(texts)[0].
        Distance to each text will be that text's index,
        as it was passed to embed_documents."""
        return [float(1.0)] * 9 + [float(0.0)]

    async def aembed_query(self, text: str) -> List[float]:
        return self.embed_query(text)


class ConsistentFakeEmbeddings(FakeEmbeddings):
    """Fake embeddings which remember all the texts seen so far to return consistent
    vectors for the same texts."""

    def __init__(self, dimensionality: int = 10) -> None:
        self.known_texts: List[str] = []
        self.dimensionality = dimensionality

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Return consistent embeddings for each text seen so far."""
        out_vectors = []
        for text in texts:
            if text not in self.known_texts:
                self.known_texts.append(text)
            vector = [float(1.0)] * (self.dimensionality - 1) + [
                float(self.known_texts.index(text))
            ]
            out_vectors.append(vector)
        return out_vectors

    def embed_query(self, text: str) -> List[float]:
        """Return consistent embeddings for the text, if seen before, or a constant
        one if the text is unknown."""
        return self.embed_documents([text])[0]