Update Mem0 LLM docs (#1497)

docs/llms.mdx

@@ -12,6 +12,8 @@ Mem0 includes built-in support for various popular large language models. Memory
   <Card title="Together" href="#together"></Card>
   <Card title="AWS Bedrock" href="#aws_bedrock"></Card>
   <Card title="Litellm" href="#litellm"></Card>
+  <Card title="Google AI" href="#google-ai"></Card>
+  <Card title="Anthropic" href="#anthropic"></Card>
 </CardGroup>
 
 ## OpenAI
@@ -24,7 +26,7 @@ Once you have obtained the key, you can use it like this:
 import os
 from mem0 import Memory
 
-os.environ['OPENAI_API_KEY'] = 'xxx'
+os.environ["OPENAI_API_KEY"] = "your-api-key"
 
 config = {
     "llm": {
@@ -51,7 +53,7 @@ In order to use LLMs from Groq, go to their [platform](https://console.groq.com/
 import os
 from mem0 import Memory
 
-os.environ['GROQ_API_KEY'] = 'xxx'
+os.environ["GROQ_API_KEY"] = "your-api-key"
 
 config = {
     "llm": {
@@ -78,7 +80,7 @@ Once you have obtained the key, you can use it like this:
 import os
 from mem0 import Memory
 
-os.environ['TOGETHER_API_KEY'] = 'xxx'
+os.environ["TOGETHER_API_KEY"] = "your-api-key"
 
 config = {
     "llm": {
@@ -133,6 +135,8 @@ m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category"
 import os
 from mem0 import Memory
 
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
 config = {
     "llm": {
         "provider": "litellm",
@@ -148,3 +152,55 @@ m = Memory.from_config(config)
 m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
 ```
 
+## Google AI
+
+To use Google AI model, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)
+
+Once you have obtained the key, you can use it like this:
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["GEMINI_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "litellm",
+        "config": {
+            "model": "gemini/gemini-pro",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+
+## Anthropic
+
+To use anthropic's model, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
+
+```python
+import os
+from mem0 import Memory
+
+os.environ["ANTHROPIC_API_KEY"] = "your-api-key"
+
+config = {
+    "llm": {
+        "provider": "litellm",
+        "config": {
+            "model": "claude-3-opus-20240229",
+            "temperature": 0.1,
+            "max_tokens": 2000,
+        }
+    }
+}
+
+m = Memory.from_config(config)
+m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+```
+

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

@@ -167,3 +167,9 @@ app.add("https://www.forbes.com/profile/elon-musk")
 
 app.chat("What is the net worth of Elon Musk?")
 ```
+
+## How Mem0 works:
+- Mem0 saves context derived from each user question into its memory.
+- When a user poses a new question, Mem0 retrieves relevant previous memories.
+- The `top_k` parameter in the memory configuration specifies the number of top memories to consider during retrieval.
+- Mem0 generates the final response by integrating the user's question, context from the data source, and the relevant memories.