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Add GPT4Vision Image loader (#1089)

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Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>
This commit is contained in:
Sidharth Mohanty
2024-01-02 03:57:23 +05:30
committed by GitHub
parent 367d6b70e2
commit c62663f2e4
29 changed files with 291 additions and 714 deletions
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27
tests/vectordb/test_weaviate.py
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@@ -8,6 +8,11 @@ from embedchain.embedder.base import BaseEmbedder
from embedchain.vectordb.weaviate import WeaviateDB
def mock_embedding_fn(texts: list[str]) -> list[list[float]]:
"""A mock embedding function."""
return [[1, 2, 3], [4, 5, 6]]
class TestWeaviateDb(unittest.TestCase):
def test_incorrect_config_throws_error(self):
"""Test the init method of the WeaviateDb class throws error for incorrect config"""
@@ -25,6 +30,7 @@ class TestWeaviateDb(unittest.TestCase):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Weaviate instance
db = WeaviateDB()
@@ -92,6 +98,7 @@ class TestWeaviateDb(unittest.TestCase):
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Weaviate instance
db = WeaviateDB()
@@ -111,6 +118,7 @@ class TestWeaviateDb(unittest.TestCase):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Weaviate instance
db = WeaviateDB()
@@ -122,8 +130,7 @@ class TestWeaviateDb(unittest.TestCase):
documents = ["This is a test document.", "This is another test document."]
metadatas = [None, None]
ids = ["123", "456"]
skip_embedding = True
db.add(embeddings, documents, metadatas, ids, skip_embedding)
db.add(embeddings, documents, metadatas, ids)
# Check if the document was added to the database.
weaviate_client_batch_mock.configure.assert_called_once_with(batch_size=1, timeout_retries=3)
@@ -155,6 +162,7 @@ class TestWeaviateDb(unittest.TestCase):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Weaviate instance
db = WeaviateDB()
@@ -162,12 +170,10 @@ class TestWeaviateDb(unittest.TestCase):
App(config=app_config, db=db, embedding_model=embedder)
# Query for the document.
db.query(input_query=["This is a test document."], n_results=1, where={}, skip_embedding=True)
db.query(input_query=["This is a test document."], n_results=1, where={})
weaviate_client_query_mock.get.assert_called_once_with("Embedchain_store_1526", ["text"])
weaviate_client_query_get_mock.with_near_vector.assert_called_once_with(
{"vector": ["This is a test document."]}
)
weaviate_client_query_get_mock.with_near_vector.assert_called_once_with({"vector": [1, 2, 3]})
@patch("embedchain.vectordb.weaviate.weaviate")
def test_query_with_where(self, weaviate_mock):
@@ -180,6 +186,7 @@ class TestWeaviateDb(unittest.TestCase):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Weaviate instance
db = WeaviateDB()
@@ -187,15 +194,13 @@ class TestWeaviateDb(unittest.TestCase):
App(config=app_config, db=db, embedding_model=embedder)
# Query for the document.
db.query(input_query=["This is a test document."], n_results=1, where={"doc_id": "123"}, skip_embedding=True)
db.query(input_query=["This is a test document."], n_results=1, where={"doc_id": "123"})
weaviate_client_query_mock.get.assert_called_once_with("Embedchain_store_1526", ["text"])
weaviate_client_query_get_mock.with_where.assert_called_once_with(
{"operator": "Equal", "path": ["metadata", "Embedchain_store_1526_metadata", "doc_id"], "valueText": "123"}
)
weaviate_client_query_get_where_mock.with_near_vector.assert_called_once_with(
{"vector": ["This is a test document."]}
)
weaviate_client_query_get_where_mock.with_near_vector.assert_called_once_with({"vector": [1, 2, 3]})
@patch("embedchain.vectordb.weaviate.weaviate")
def test_reset(self, weaviate_mock):
@@ -206,6 +211,7 @@ class TestWeaviateDb(unittest.TestCase):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Weaviate instance
db = WeaviateDB()
@@ -228,6 +234,7 @@ class TestWeaviateDb(unittest.TestCase):
# Set the embedder
embedder = BaseEmbedder()
embedder.set_vector_dimension(1526)
embedder.set_embedding_fn(mock_embedding_fn)
# Create a Weaviate instance
db = WeaviateDB()