Rename embedchain to mem0 and open sourcing code for long term memory (#1474)

Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>
2024-07-12 07:51:33 -07:00
parent 83e8c97295
commit f842a92e25
665 changed files with 9427 additions and 6592 deletions
--- a/embedchain/tests/test_factory.py
+++ b/embedchain/tests/test_factory.py
@@ -0,0 +1,66 @@
+import os
+
+import pytest
+
+import embedchain
+import embedchain.embedder.gpt4all
+import embedchain.embedder.huggingface
+import embedchain.embedder.openai
+import embedchain.embedder.vertexai
+import embedchain.llm.anthropic
+import embedchain.llm.openai
+import embedchain.vectordb.chroma
+import embedchain.vectordb.elasticsearch
+import embedchain.vectordb.opensearch
+from embedchain.factory import EmbedderFactory, LlmFactory, VectorDBFactory
+
+
+class TestFactories:
+    @pytest.mark.parametrize(
+        "provider_name, config_data, expected_class",
+        [
+            ("openai", {}, embedchain.llm.openai.OpenAILlm),
+            ("anthropic", {}, embedchain.llm.anthropic.AnthropicLlm),
+        ],
+    )
+    def test_llm_factory_create(self, provider_name, config_data, expected_class):
+        os.environ["ANTHROPIC_API_KEY"] = "test_api_key"
+        os.environ["OPENAI_API_KEY"] = "test_api_key"
+        os.environ["OPENAI_API_BASE"] = "test_api_base"
+        llm_instance = LlmFactory.create(provider_name, config_data)
+        assert isinstance(llm_instance, expected_class)
+
+    @pytest.mark.parametrize(
+        "provider_name, config_data, expected_class",
+        [
+            ("gpt4all", {}, embedchain.embedder.gpt4all.GPT4AllEmbedder),
+            (
+                "huggingface",
+                {"model": "sentence-transformers/all-mpnet-base-v2", "vector_dimension": 768},
+                embedchain.embedder.huggingface.HuggingFaceEmbedder,
+            ),
+            ("vertexai", {"model": "textembedding-gecko"}, embedchain.embedder.vertexai.VertexAIEmbedder),
+            ("openai", {}, embedchain.embedder.openai.OpenAIEmbedder),
+        ],
+    )
+    def test_embedder_factory_create(self, mocker, provider_name, config_data, expected_class):
+        mocker.patch("embedchain.embedder.vertexai.VertexAIEmbedder", autospec=True)
+        embedder_instance = EmbedderFactory.create(provider_name, config_data)
+        assert isinstance(embedder_instance, expected_class)
+
+    @pytest.mark.parametrize(
+        "provider_name, config_data, expected_class",
+        [
+            ("chroma", {}, embedchain.vectordb.chroma.ChromaDB),
+            (
+                "opensearch",
+                {"opensearch_url": "http://localhost:9200", "http_auth": ("admin", "admin")},
+                embedchain.vectordb.opensearch.OpenSearchDB,
+            ),
+            ("elasticsearch", {"es_url": "http://localhost:9200"}, embedchain.vectordb.elasticsearch.ElasticsearchDB),
+        ],
+    )
+    def test_vectordb_factory_create(self, mocker, provider_name, config_data, expected_class):
+        mocker.patch("embedchain.vectordb.opensearch.OpenSearchDB", autospec=True)
+        vectordb_instance = VectorDBFactory.create(provider_name, config_data)
+        assert isinstance(vectordb_instance, expected_class)