langchain Architecture Docs
Home / File/ test_search.py — langchain Source File

test_search.py — langchain Source File

Architecture documentation for test_search.py, a python file in the langchain codebase. 6 imports, 0 dependents.

File python CoreAbstractions Serialization 6 imports 12 functions

Entity Profile

Dependency Diagram

graph LR
  f6d0b2c4_7a87_1866_7122_ffbe04fe2109["test_search.py"]
  120e2591_3e15_b895_72b6_cb26195e40a6["pytest"]
  f6d0b2c4_7a87_1866_7122_ffbe04fe2109 --> 120e2591_3e15_b895_72b6_cb26195e40a6
  c554676d_b731_47b2_a98f_c1c2d537c0aa["langchain_core.documents"]
  f6d0b2c4_7a87_1866_7122_ffbe04fe2109 --> c554676d_b731_47b2_a98f_c1c2d537c0aa
  e9f800f2_8227_1095_42ef_324e02810451["qdrant_client"]
  f6d0b2c4_7a87_1866_7122_ffbe04fe2109 --> e9f800f2_8227_1095_42ef_324e02810451
  77801658_6b3a_bc26_7e54_388e5c04807d["langchain_qdrant"]
  f6d0b2c4_7a87_1866_7122_ffbe04fe2109 --> 77801658_6b3a_bc26_7e54_388e5c04807d
  a50ef027_f19e_86a8_94ef_895b4566f94e["tests.integration_tests.common"]
  f6d0b2c4_7a87_1866_7122_ffbe04fe2109 --> a50ef027_f19e_86a8_94ef_895b4566f94e
  513f2bf4_0acd_14e4_0a43_45f7716ce101["tests.integration_tests.fixtures"]
  f6d0b2c4_7a87_1866_7122_ffbe04fe2109 --> 513f2bf4_0acd_14e4_0a43_45f7716ce101
  style f6d0b2c4_7a87_1866_7122_ffbe04fe2109 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

import pytest
from langchain_core.documents import Document
from qdrant_client import models

from langchain_qdrant import QdrantVectorStore, RetrievalMode
from tests.integration_tests.common import (
    ConsistentFakeEmbeddings,
    ConsistentFakeSparseEmbeddings,
    assert_documents_equals,
)
from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes


@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
@pytest.mark.parametrize("batch_size", [1, 64])
def test_similarity_search(
    location: str,
    vector_name: str,
    retrieval_mode: RetrievalMode,
    batch_size: int,
) -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    docsearch = QdrantVectorStore.from_texts(
        texts,
        ConsistentFakeEmbeddings(),
        location=location,
        batch_size=batch_size,
        vector_name=vector_name,
        retrieval_mode=retrieval_mode,
        sparse_embedding=ConsistentFakeSparseEmbeddings(),
    )
    output = docsearch.similarity_search("foo", k=1)
    assert_documents_equals(actual=output, expected=[Document(page_content="foo")])


@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("content_payload_key", [QdrantVectorStore.CONTENT_KEY, "foo"])
@pytest.mark.parametrize(
    "metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
)
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("batch_size", [1, 64])
def test_similarity_search_by_vector(
    location: str,
    content_payload_key: str,
    metadata_payload_key: str,
    vector_name: str,
    batch_size: int,
) -> None:
    """Test end to end construction and search."""
    texts = ["foo", "bar", "baz"]
    docsearch = QdrantVectorStore.from_texts(
        texts,
        ConsistentFakeEmbeddings(),
        location=location,
        content_payload_key=content_payload_key,
        metadata_payload_key=metadata_payload_key,
// ... (354 more lines)

Domain

Subdomains

Functions

Dependencies

  • langchain_core.documents
  • langchain_qdrant
  • pytest
  • qdrant_client
  • tests.integration_tests.common
  • tests.integration_tests.fixtures

Source

Frequently Asked Questions

What does test_search.py do?
test_search.py is a source file in the langchain codebase, written in python. It belongs to the CoreAbstractions domain, Serialization subdomain.
What functions are defined in test_search.py?
test_search.py defines 12 function(s): test_as_retriever_sparse_mode, test_as_retriever_sparse_mode_with_search_kwargs, test_embeddings_property_dense_mode, test_embeddings_property_sparse_mode, test_relevance_search_with_threshold, test_relevance_search_with_threshold_and_filter, test_similarity_relevance_search_no_threshold, test_similarity_search, test_similarity_search_by_vector, test_similarity_search_filters, and 2 more.
What does test_search.py depend on?
test_search.py imports 6 module(s): langchain_core.documents, langchain_qdrant, pytest, qdrant_client, tests.integration_tests.common, tests.integration_tests.fixtures.
Where is test_search.py in the architecture?
test_search.py is located at libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py (domain: CoreAbstractions, subdomain: Serialization, directory: libs/partners/qdrant/tests/integration_tests/qdrant_vector_store).

Analyze Your Own Codebase

Get architecture documentation, dependency graphs, and domain analysis for your codebase in minutes.

Try Supermodel Free