Add langchain embedding, update langchain LLM and version bump -> 0.1.84 (#2510)

docs/components/llms/config.mdx

@@ -109,7 +109,6 @@ Here's a comprehensive list of all parameters that can be used across different
     | `deepseek_base_url`  | Base URL for DeepSeek API                     | DeepSeek          |
     | `xai_base_url`       | Base URL for XAI API                          | XAI               |
     | `lmstudio_base_url`  | Base URL for LM Studio API                    | LM Studio         |
-    | `langchain_provider` | Provider for Langchain                      | Langchain         |
   </Tab>
   <Tab title="TypeScript">
     | Parameter            | Description                                   | Provider          |

docs/components/llms/models/langchain.mdx

@@ -12,19 +12,24 @@ For a complete list of available chat models supported by LangChain, refer to th
 ```python Python
 import os
 from mem0 import Memory
+from langchain_openai import ChatOpenAI
 
 # Set necessary environment variables for your chosen LangChain provider
-# For example, if using OpenAI through LangChain:
 os.environ["OPENAI_API_KEY"] = "your-api-key"
 
+# Initialize a LangChain model directly
+openai_model = ChatOpenAI(
+    model="gpt-4o",
+    temperature=0.2,
+    max_tokens=2000
+)
+
+# Pass the initialized model to the config
 config = {
     "llm": {
         "provider": "langchain",
         "config": {
-            "langchain_provider": "OpenAI",
-            "model": "gpt-4o",
-            "temperature": 0.2,
-            "max_tokens": 2000,
+            "model": openai_model
         }
     }
 }
@@ -53,15 +58,15 @@ LangChain supports a wide range of LLM providers, including:
 - HuggingFace (`HuggingFaceChatEndpoint`)
 - And many more
 
-You can specify any supported provider in the `langchain_provider` parameter. For a complete and up-to-date list of available providers, refer to the [LangChain Chat Models documentation](https://python.langchain.com/docs/integrations/chat).
+You can use any of these model instances directly in your configuration. For a complete and up-to-date list of available providers, refer to the [LangChain Chat Models documentation](https://python.langchain.com/docs/integrations/chat).
 
 ## Provider-Specific Configuration
 
 When using LangChain as a provider, you'll need to:
 
 1. Set the appropriate environment variables for your chosen LLM provider
-2. Specify the LangChain provider class name in the `langchain_provider` parameter
-3. Include any additional configuration parameters required by the specific provider
+2. Import and initialize the specific model class you want to use
+3. Pass the initialized model instance to the config
 
 <Note>
   Make sure to install the necessary LangChain packages and any provider-specific dependencies.