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## Description This PR adds integration tests to follow up on #24164. By default, the tests use an in-memory instance. To run the full suite of tests, with both in-memory and Qdrant server: ``` $ docker run -p 6333:6333 qdrant/qdrant $ make test $ make integration_test ``` --------- Co-authored-by: Erick Friis <erick@langchain.dev>
86 lines
3.2 KiB
Python
86 lines
3.2 KiB
Python
from typing import List
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import requests # type: ignore
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_qdrant import SparseEmbeddings, SparseVector
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def qdrant_running_locally() -> bool:
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"""Check if Qdrant is running at http://localhost:6333."""
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try:
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response = requests.get("http://localhost:6333", timeout=10.0)
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response_json = response.json()
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return response_json.get("title") == "qdrant - vector search engine"
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except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
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return False
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def assert_documents_equals(actual: List[Document], expected: List[Document]): # type: ignore[no-untyped-def]
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assert len(actual) == len(expected)
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for actual_doc, expected_doc in zip(actual, expected):
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assert actual_doc.page_content == expected_doc.page_content
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assert "_id" in actual_doc.metadata
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assert "_collection_name" in actual_doc.metadata
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actual_doc.metadata.pop("_id")
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actual_doc.metadata.pop("_collection_name")
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assert actual_doc.metadata == expected_doc.metadata
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class ConsistentFakeEmbeddings(Embeddings):
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"""Fake embeddings which remember all the texts seen so far to return consistent
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vectors for the same texts."""
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def __init__(self, dimensionality: int = 10) -> None:
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self.known_texts: List[str] = []
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self.dimensionality = dimensionality
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Return consistent embeddings for each text seen so far."""
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out_vectors = []
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for text in texts:
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if text not in self.known_texts:
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self.known_texts.append(text)
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vector = [float(1.0)] * (self.dimensionality - 1) + [
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float(self.known_texts.index(text))
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]
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out_vectors.append(vector)
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return out_vectors
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def embed_query(self, text: str) -> List[float]:
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"""Return consistent embeddings for the text, if seen before, or a constant
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one if the text is unknown."""
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return self.embed_documents([text])[0]
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class ConsistentFakeSparseEmbeddings(SparseEmbeddings):
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"""Fake sparse embeddings which remembers all the texts seen so far "
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"to return consistent vectors for the same texts."""
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def __init__(self, dimensionality: int = 25) -> None:
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self.known_texts: List[str] = []
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self.dimensionality = 25
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def embed_documents(self, texts: List[str]) -> List[SparseVector]:
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"""Return consistent embeddings for each text seen so far."""
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out_vectors = []
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for text in texts:
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if text not in self.known_texts:
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self.known_texts.append(text)
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index = self.known_texts.index(text)
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indices = [i + index for i in range(self.dimensionality)]
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values = [1.0] * (self.dimensionality - 1) + [float(index)]
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out_vectors.append(SparseVector(indices=indices, values=values))
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return out_vectors
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def embed_query(self, text: str) -> SparseVector:
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"""Return consistent embeddings for the text, "
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"if seen before, or a constant one if the text is unknown."""
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return self.embed_documents([text])[0]
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