Change dependency to mem0ai (#1476)

embedchain/docs/api-reference/advanced/configuration.mdx

@@ -250,7 +250,6 @@ Alright, let's dive into what each key means in the yaml config above:
       - `similarity_threshold` (Float): The threshold for similarity evaluation. Defaults to `0.8`.
       - `auto_flush` (Integer): The number of queries after which the cache is flushed. Defaults to `20`.
 7. `memory` Section: (Optional)
-    - `api_key` (String): The API key of mem0.
     - `top_k` (Integer): The number of top-k results to return. Defaults to `10`.
     <Note>
     If you provide a cache section, the app will automatically configure and use a cache to store the results of the language model. This is useful if you want to speed up the response time and save inference cost of your app.

embedchain/docs/api-reference/app/chat.mdx

@@ -149,17 +149,15 @@ app.chat("What is the net worth of Elon Musk?", config=query_config)
 
 Mem0 is a cutting-edge long-term memory for LLMs to enable personalization for the GenAI stack. It enables LLMs to remember past interactions and provide more personalized responses.
 
-Follow these steps to use Mem0  to enable memory for personalization in your apps:
-- Install the [`mem0`](https://docs.mem0.ai/) package using `pip install memzero`. 
-- Get the api_key from [Mem0 Platform](https://app.mem0.ai/).
-- Provide api_key in config under `memory`, refer [Configurations](docs/api-reference/advanced/configuration.mdx).
+In order to use Mem0 to enable memory for personalization in your apps:
+- Install the [`mem0`](https://docs.mem0.ai/) package using `pip install mem0ai`. 
+- Prepare config for `memory`, refer [Configurations](docs/api-reference/advanced/configuration.mdx).
 
 ```python with mem0
 from embedchain import App
 
 config = {
   "memory": {
-    "api_key": "m0-xxx",
     "top_k": 5
   }
 }