klas/t6_mem0
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

[bugfix] Fix issue when llm config is not defined (#763)

Browse Source
This commit is contained in:
Deshraj Yadav
2023-10-04 12:08:21 -07:00
committed by GitHub
parent d0af018b8d
commit 87d0b5c76f
15 changed files with 100 additions and 88 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
tests/vectordb/test_chroma_db.py
View File

@@ -197,21 +197,29 @@ class TestChromaDbCollection(unittest.TestCase):
# Collection should be empty when created
self.assertEqual(self.app_with_settings.db.count(), 0)
self.app_with_settings.db.add(embeddings=[[0, 0, 0]], documents=["document"], metadatas=[{"value": "somevalue"}], ids=["id"], skip_embedding=True)
self.app_with_settings.db.add(
embeddings=[[0, 0, 0]],
documents=["document"],
metadatas=[{"value": "somevalue"}],
ids=["id"],
skip_embedding=True,
)
# After adding, should contain one item
self.assertEqual(self.app_with_settings.db.count(), 1)
# Validate if the get utility of the database is working as expected
data = self.app_with_settings.db.get(["id"], limit=1)
expected_value = {'documents': ['document'],
'embeddings': None,
'ids': ['id'],
'metadatas': [{'value': 'somevalue'}]}
expected_value = {
"documents": ["document"],
"embeddings": None,
"ids": ["id"],
"metadatas": [{"value": "somevalue"}],
}
self.assertEqual(data, expected_value)
# Validate if the query utility of the database is working as expected
data = self.app_with_settings.db.query(input_query=[0, 0, 0], where={}, n_results=1, skip_embedding=True)
expected_value = ['document']
expected_value = ["document"]
self.assertEqual(data, expected_value)
def test_collections_are_persistent(self):

36
tests/vectordb/test_elasticsearch_db.py
View File

@@ -4,11 +4,11 @@ from unittest.mock import patch
from embedchain import App
from embedchain.config import AppConfig, ElasticsearchDBConfig
from embedchain.vectordb.elasticsearch import ElasticsearchDB
from embedchain.embedder.gpt4all import GPT4AllEmbedder
from embedchain.vectordb.elasticsearch import ElasticsearchDB
class TestEsDB(unittest.TestCase):
@patch("embedchain.vectordb.elasticsearch.Elasticsearch")
def test_setUp(self, mock_client):
self.db = ElasticsearchDB(config=ElasticsearchDBConfig(es_url="https://localhost:9200"))
@@ -37,17 +37,11 @@ class TestEsDB(unittest.TestCase):
# Add the data to the database.
self.db.add(embeddings, documents, metadatas, ids, skip_embedding=False)
search_response = {"hits":
{"hits":
[
{
"_source": {"text": "This is a document."},
"_score": 0.9
},
{
"_source": {"text": "This is another document."},
"_score": 0.8
}
search_response = {
"hits": {
"hits": [
{"_source": {"text": "This is a document."}, "_score": 0.9},
{"_source": {"text": "This is another document."}, "_score": 0.8},
]
}
}
@@ -80,17 +74,11 @@ class TestEsDB(unittest.TestCase):
# Add the data to the database.
self.db.add(embeddings, documents, metadatas, ids, skip_embedding=True)
search_response = {"hits":
{"hits":
[
{
"_source": {"text": "This is a document."},
"_score": 0.9
},
{
"_source": {"text": "This is another document."},
"_score": 0.8
}
search_response = {
"hits": {
"hits": [
{"_source": {"text": "This is a document."}, "_score": 0.9},
{"_source": {"text": "This is another document."}, "_score": 0.8},
]
}
}