Integrate Mem0 (#1462)

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>

embedchain/app.py

@@ -9,19 +9,24 @@ import requests
 import yaml
 from tqdm import tqdm
 
-from embedchain.cache import (Config, ExactMatchEvaluation,
-                              SearchDistanceEvaluation, cache,
-                              gptcache_data_manager, gptcache_pre_function)
+from mem0 import Mem0
+from embedchain.cache import (
+    Config,
+    ExactMatchEvaluation,
+    SearchDistanceEvaluation,
+    cache,
+    gptcache_data_manager,
+    gptcache_pre_function,
+)
 from embedchain.client import Client
-from embedchain.config import AppConfig, CacheConfig, ChunkerConfig
+from embedchain.config import AppConfig, CacheConfig, ChunkerConfig, Mem0Config
 from embedchain.core.db.database import get_session, init_db, setup_engine
 from embedchain.core.db.models import DataSource
 from embedchain.embedchain import EmbedChain
 from embedchain.embedder.base import BaseEmbedder
 from embedchain.embedder.openai import OpenAIEmbedder
 from embedchain.evaluation.base import BaseMetric
-from embedchain.evaluation.metrics import (AnswerRelevance, ContextRelevance,
-                                           Groundedness)
+from embedchain.evaluation.metrics import AnswerRelevance, ContextRelevance, Groundedness
 from embedchain.factory import EmbedderFactory, LlmFactory, VectorDBFactory
 from embedchain.helpers.json_serializable import register_deserializable
 from embedchain.llm.base import BaseLlm
@@ -55,6 +60,7 @@ class App(EmbedChain):
         auto_deploy: bool = False,
         chunker: ChunkerConfig = None,
         cache_config: CacheConfig = None,
+        memory_config: Mem0Config = None,
         log_level: int = logging.WARN,
     ):
         """
@@ -95,6 +101,7 @@ class App(EmbedChain):
         self.id = None
         self.chunker = ChunkerConfig(**chunker) if chunker else None
         self.cache_config = cache_config
+        self.memory_config = memory_config
 
         self.config = config or AppConfig()
         self.name = self.config.name
@@ -123,6 +130,11 @@ class App(EmbedChain):
         if self.cache_config is not None:
             self._init_cache()
 
+        # If memory_config is provided, initializing the memory ...
+        self.mem0_client = None
+        if self.memory_config is not None:
+            self.mem0_client = Mem0(api_key=self.memory_config.api_key)
+
         # Send anonymous telemetry
         self._telemetry_props = {"class": self.__class__.__name__}
         self.telemetry = AnonymousTelemetry(enabled=self.config.collect_metrics)
@@ -365,11 +377,13 @@ class App(EmbedChain):
         app_config_data = config_data.get("app", {}).get("config", {})
         vector_db_config_data = config_data.get("vectordb", {})
         embedding_model_config_data = config_data.get("embedding_model", config_data.get("embedder", {}))
+        memory_config_data = config_data.get("memory", {})
         llm_config_data = config_data.get("llm", {})
         chunker_config_data = config_data.get("chunker", {})
         cache_config_data = config_data.get("cache", None)
 
         app_config = AppConfig(**app_config_data)
+        memory_config = Mem0Config(**memory_config_data) if memory_config_data else None
 
         vector_db_provider = vector_db_config_data.get("provider", "chroma")
         vector_db = VectorDBFactory.create(vector_db_provider, vector_db_config_data.get("config", {}))
@@ -403,6 +417,7 @@ class App(EmbedChain):
             auto_deploy=auto_deploy,
             chunker=chunker_config_data,
             cache_config=cache_config,
+            memory_config=memory_config,
         )
 
     def _eval(self, dataset: list[EvalData], metric: Union[BaseMetric, str]):