Custom Prompts

Customize the LLM reranker prompt template in Mem0 to control how search results are ranked and scored.

When using LLM rerankers, you can customize the prompts used for ranking to better suit your specific use case and domain.

Default Prompt

The default LLM reranker prompt scores each memory individually on a 0.0-1.0 scale:

You are a relevance scoring assistant. Given a query and a document, you need to score how relevant the document is to the query.

Score the relevance on a scale from 0.0 to 1.0, where:
- 1.0 = Perfectly relevant and directly answers the query
- 0.8-0.9 = Highly relevant with good information
- 0.6-0.7 = Moderately relevant with some useful information
- 0.4-0.5 = Slightly relevant with limited useful information
- 0.0-0.3 = Not relevant or no useful information

Query: "{query}"
Document: "{document}"

Provide only a single numerical score between 0.0 and 1.0. Do not include any explanation or additional text.

Custom Prompt Configuration

You can provide a custom prompt template using the scoring_prompt parameter:

from mem0 import Memory

custom_prompt = """
You are an expert at evaluating memories for a personal AI assistant.
Given a user query and a memory entry, score how relevant the memory is.
Consider direct relevance, temporal relevance, and actionability.

Query: "{query}"
Memory: "{document}"

Provide only a single numerical score between 0.0 and 1.0.
"""

config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
            "provider": "openai",
            "model": "gpt-4o-mini",
            "api_key": "your-openai-key",
            "scoring_prompt": custom_prompt,
            "top_k": 5
        }
    }
}

memory = Memory.from_config(config)

Prompt Variables

Your custom prompt can use the following variables:

Variable	Description
{query}	The search query
{document}	The memory entry being scored

Both {query} and {document} are required in your custom prompt. The LLM reranker scores each memory individually against the query, so the prompt is called once per candidate memory.

Domain-Specific Examples

Customer Support

customer_support_prompt = """
You are ranking customer support conversation memories.
Prioritize memories that:
- Relate to the current customer issue
- Show previous resolution patterns
- Indicate customer preferences or constraints

Query: "{query}"
Memory: "{document}"

Score relevance from 0.0 to 1.0.
"""

Educational Content

educational_prompt = """
Score this learning memory for relevance to a student query.
Consider:
- Prerequisite knowledge requirements
- Learning progression and difficulty
- Relevance to current learning objectives

Student Query: "{query}"
Memory: "{document}"

Score educational relevance from 0.0 to 1.0.
"""

Personal Assistant

personal_assistant_prompt = """
Score this personal memory for relevance to the user's query.
Consider:
- Recent vs. historical importance
- Personal preferences and habits
- Contextual relationships

Query: "{query}"
Memory: "{document}"

Provide relevance score from 0.0 to 1.0.
"""

Advanced Prompt Techniques

Multi-Criteria Scoring

multi_criteria_prompt = """
Evaluate this memory using multiple criteria:

1. RELEVANCE (40%): How directly related to the query
2. RECENCY (20%): How recent the memory appears to be
3. IMPORTANCE (25%): Personal or business significance
4. ACTIONABILITY (15%): How useful for next steps

Query: "{query}"
Memory: "{document}"

Compute a weighted score from 0.0 to 1.0 based on these criteria.
Provide only the final numerical score.
"""

Chain-of-Thought Scoring

reasoning_prompt = """
Evaluate this memory's relevance step by step:

1. What is the main intent of the query?
2. What key information does the memory contain?
3. How directly does the memory address the query?

Based on this analysis, provide a single relevance score from 0.0 to 1.0.

Query: "{query}"
Memory: "{document}"

Score:
"""

Best Practices

1. Be Specific: Clearly define what makes a memory relevant for your use case
2. Use 0.0-1.0 Scale: The score extractor expects values between 0.0 and 1.0
3. Request Only the Score: Ask for just the numerical score to improve extraction reliability
4. Test Iteratively: Refine your prompt based on actual ranking performance
5. Consider Token Limits: Keep prompts concise while being comprehensive

Prompt Testing

You can test different prompts by comparing ranking results:

# Test multiple prompt variations
prompts = [
    default_prompt,
    custom_prompt_v1,
    custom_prompt_v2
]

for i, prompt in enumerate(prompts):
    config["reranker"]["config"]["scoring_prompt"] = prompt
    memory = Memory.from_config(config)

    results = memory.search("test query", filters={"user_id": "test_user"})
    print(f"Prompt {i+1} results: {results}")

Common Issues

• Too Long: Keep prompts under token limits for your chosen LLM
• Too Vague: Be specific about scoring criteria
• Wrong Scale: Use 0.0-1.0 scale to match the default score extractor
• Extra Output: Ask for only the numeric score: extra text can confuse score extraction