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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:

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.

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")

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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.

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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.

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