Sets up metadata db for every llm class (#1401)

embedchain/embedchain/app.py

@@ -20,7 +20,7 @@ from embedchain.cache import (
 )
 from embedchain.client import Client
 from embedchain.config import AppConfig, CacheConfig, ChunkerConfig, Mem0Config
-from embedchain.core.db.database import get_session, init_db, setup_engine
+from embedchain.core.db.database import get_session
 from embedchain.core.db.models import DataSource
 from embedchain.embedchain import EmbedChain
 from embedchain.embedder.base import BaseEmbedder
@@ -89,10 +89,6 @@ class App(EmbedChain):
         if name and config:
             raise Exception("Cannot provide both name and config. Please provide only one of them.")
 
-        # Initialize the metadata db for the app
-        setup_engine(database_uri=os.environ.get("EMBEDCHAIN_DB_URI"))
-        init_db()
-
         self.auto_deploy = auto_deploy
         # Store the dict config as an attribute to be able to send it
         self.config_data = config_data if (config_data and validate_config(config_data)) else None
@@ -389,10 +385,6 @@ class App(EmbedChain):
         vector_db = VectorDBFactory.create(vector_db_provider, vector_db_config_data.get("config", {}))
 
         if llm_config_data:
-            # Initialize the metadata db for the app here since llmfactory needs it for initialization of
-            # the llm memory
-            setup_engine(database_uri=os.environ.get("EMBEDCHAIN_DB_URI"))
-            init_db()
             llm_provider = llm_config_data.get("provider", "openai")
             llm = LlmFactory.create(llm_provider, llm_config_data.get("config", {}))
         else:

embedchain/embedchain/llm/base.py

@@ -1,9 +1,11 @@
 import logging
+import os
 from collections.abc import Generator
 from typing import Any, Optional
 
 from langchain.schema import BaseMessage as LCBaseMessage
 
+from embedchain.constants import SQLITE_PATH
 from embedchain.config import BaseLlmConfig
 from embedchain.config.llm.base import (
     DEFAULT_PROMPT,
@@ -11,6 +13,7 @@ from embedchain.config.llm.base import (
     DEFAULT_PROMPT_WITH_MEM0_MEMORY_TEMPLATE,
     DOCS_SITE_PROMPT_TEMPLATE,
 )
+from embedchain.core.db.database import init_db, setup_engine
 from embedchain.helpers.json_serializable import JSONSerializable
 from embedchain.memory.base import ChatHistory
 from embedchain.memory.message import ChatMessage
@@ -30,6 +33,11 @@ class BaseLlm(JSONSerializable):
         else:
             self.config = config
 
+        # Initialize the metadata db for the app here since llmfactory needs it for initialization of
+        # the llm memory
+        setup_engine(database_uri=os.environ.get("EMBEDCHAIN_DB_URI", f"sqlite:///{SQLITE_PATH}"))
+        init_db()
+
         self.memory = ChatHistory()
         self.is_docs_site_instance = False
         self.history: Any = None

embedchain/poetry.lock

@@ -2391,18 +2391,18 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
 [[package]]
 name = "langchain-aws"
-version = "0.1.10"
+version = "0.1.13"
 description = "An integration package connecting AWS and LangChain"
 optional = true
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_aws-0.1.10-py3-none-any.whl", hash = "sha256:2cba72efaa9f0dc406d8e06a1fbaa3762678d489cbc5147cf64a7012189c161c"},
+    {file = "langchain_aws-0.1.13-py3-none-any.whl", hash = "sha256:c4db60c8a83b8ff3e66170e0bd646739176fcd1a20a9d0a10828a1e21339af1d"},
-    {file = "langchain_aws-0.1.10.tar.gz", hash = "sha256:7f01dacbf8345a28192cec4ef31018cc33a91de0b82122f913eec09a76d64fd5"},
+    {file = "langchain_aws-0.1.13.tar.gz", hash = "sha256:fda790732a72de4ccec3760dba24db5f9fa5cb8724dfd9676a7d5cf87a9f1a98"},
 ]
 
 [package.dependencies]
 boto3 = ">=1.34.131,<1.35.0"
-langchain-core = ">=0.2.6,<0.3"
+langchain-core = ">=0.2.17,<0.3"
 numpy = ">=1,<2"
 
 [[package]]

embedchain/tests/llm/conftest.py

@@ -0,0 +1,8 @@
+
+from unittest import mock
+import pytest
+
+@pytest.fixture(autouse=True)
+def mock_alembic_command_upgrade():
+    with mock.patch("alembic.command.upgrade"):
+        yield