Added custom prompt support (#1849)

docs/features/custom-prompts.mdx

@@ -0,0 +1,109 @@
+---
+title: Custom Prompts
+description: 'Enhance your product experience by adding custom prompts tailored to your needs'
+---
+
+## Introduction to Custom Prompts
+
+Custom prompts allow you to tailor the behavior of your Mem0 instance to specific use cases or domains. 
+By defining a custom prompt, you can control how information is extracted, processed, and stored in your memory system.
+
+To create an effective custom prompt:
+1. Be specific about the information to extract.
+2. Provide few-shot examples to guide the LLM.
+3. Ensure examples follow the format shown below.
+
+Example of a custom prompt:
+
+```python
+custom_prompt = """
+Please only extract entities containing customer support information, order details, and user information. 
+Here are some few shot examples:
+
+Input: Hi.
+Output: {{"facts" : []}}
+
+Input: The weather is nice today.
+Output: {{"facts" : []}}
+
+Input: My order #12345 hasn't arrived yet.
+Output: {{"facts" : ["Order #12345 not received"]}}
+
+Input: I'm John Doe, and I'd like to return the shoes I bought last week.
+Output: {{"facts" : ["Customer name: John Doe", "Wants to return shoes", "Purchase made last week"]}}
+
+Input: I ordered a red shirt, size medium, but received a blue one instead.
+Output: {{"facts" : ["Ordered red shirt, size medium", "Received blue shirt instead"]}}
+
+Return the facts and customer information in a json format as shown above.
+"""
+
+```
+
+Here we initialize the custom prompt in the config.
+
+```python
+from mem0 import Memory
+
+config = {
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o",
+            "temperature": 0.2,
+            "max_tokens": 1500,
+        }
+    },
+    "custom_prompt": custom_prompt,
+    "version": "v1.1"
+}
+
+m = Memory.from_config(config_dict=config, user_id="alice")
+```
+
+### Example 1
+
+In this example, we are adding a memory of a user ordering a laptop. As seen in the output, the custom prompt is used to extract the relevant information from the user's message.
+
+<CodeGroup>
+```python Code
+m.add("Yesterday, I ordered a laptop, the order id is 12345", user_id="alice")
+```
+
+```json Output
+{
+  "results": [
+    {
+      "memory": "Ordered a laptop",
+      "event": "ADD"
+    },
+    {
+      "memory": "Order ID: 12345",
+      "event": "ADD"
+    },
+    {
+      "memory": "Order placed yesterday",
+      "event": "ADD"
+    }
+  ],
+  "relations": []
+}
+```
+</CodeGroup>
+
+### Example 2
+
+In this example, we are adding a memory of a user liking to go on hikes. This add message is not specific to the use-case mentioned in the custom prompt. 
+Hence, the memory is not added.
+<CodeGroup>
+```python Code
+m.add("I like going to hikes", user_id="alice")
+```
+
+```json Output
+{
+  "results": [],
+  "relations": []
+}
+```
+</CodeGroup>

docs/mint.json

@@ -131,7 +131,7 @@
         },
         {
           "group": "Features",
-          "pages": ["features/openai_compatibility"]
+          "pages": ["features/openai_compatibility", "features/custom-prompts"]
         }
       ]
     },

mem0/configs/base.py

@@ -56,6 +56,10 @@ class MemoryConfig(BaseModel):
         description="The version of the API",
         default="v1.0",
     )
+    custom_prompt: Optional[str] = Field(
+        description="Custom prompt for the memory",
+        default=None,
+    )
     
 
 class AzureConfig(BaseModel):

mem0/memory/main.py

@@ -28,6 +28,8 @@ logger = logging.getLogger(__name__)
 class Memory(MemoryBase):
     def __init__(self, config: MemoryConfig = MemoryConfig()):
         self.config = config
+
+        self.custom_prompt = self.config.custom_prompt
         self.embedding_model = EmbedderFactory.create(
             self.config.embedder.provider, self.config.embedder.config
         )
@@ -131,7 +133,11 @@ class Memory(MemoryBase):
     def _add_to_vector_store(self, messages, metadata, filters):
         parsed_messages = parse_messages(messages)
 
-        system_prompt, user_prompt = get_fact_retrieval_messages(parsed_messages)
+        if self.custom_prompt:
+            system_prompt=self.custom_prompt
+            user_prompt=f"Input: {parsed_messages}"
+        else:
+            system_prompt, user_prompt = get_fact_retrieval_messages(parsed_messages)
 
         response = self.llm.generate_response(
             messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}],

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "mem0ai"
-version = "0.1.12"
+version = "0.1.13"
 description = "Long-term memory for AI Agents"
 authors = ["Mem0 <founders@mem0.ai>"]
 exclude = [