Custom Prompts

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.

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)

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_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_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.
"""

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.
"""

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:
"""

# 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", user_id="test_user")
    print(f"Prompt {i+1} results: {results}")