docs: fix LLM reranker config examples and field names (#4539)
Co-authored-by: utkarsh240799 <utkarsh240799@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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utkarsh240799
Claude Opus 4.6
utkarsh240799
Claude Opus 4.6
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1aecfadf45
commit
7fff26f374
@@ -7,58 +7,51 @@ When using LLM rerankers, you can customize the prompts used for ranking to bett
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## Default Prompt
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The default LLM reranker prompt is designed to be general-purpose:
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The default LLM reranker prompt scores each memory individually on a 0.0-1.0 scale:
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```
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Given a query and a list of memory entries, rank the memory entries based on their relevance to the query.
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Rate each memory on a scale of 1-10 where 10 is most relevant.
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You are a relevance scoring assistant. Given a query and a document, you need to score how relevant the document is to the query.
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Query: {query}
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Score the relevance on a scale from 0.0 to 1.0, where:
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- 1.0 = Perfectly relevant and directly answers the query
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- 0.8-0.9 = Highly relevant with good information
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- 0.6-0.7 = Moderately relevant with some useful information
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- 0.4-0.5 = Slightly relevant with limited useful information
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- 0.0-0.3 = Not relevant or no useful information
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Memory entries:
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{memories}
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Query: "{query}"
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Document: "{document}"
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Provide your ranking as a JSON array with scores for each memory.
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Provide only a single numerical score between 0.0 and 1.0. Do not include any explanation or additional text.
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```
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## Custom Prompt Configuration
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You can provide a custom prompt template when configuring the LLM reranker:
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You can provide a custom prompt template using the `scoring_prompt` parameter:
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```python
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from mem0 import Memory
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custom_prompt = """
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You are an expert at ranking memories for a personal AI assistant.
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Given a user query and a list of memory entries, rank each memory based on:
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1. Direct relevance to the query
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2. Temporal relevance (recent memories may be more important)
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3. Emotional significance
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4. Actionability
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You are an expert at evaluating memories for a personal AI assistant.
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Given a user query and a memory entry, score how relevant the memory is.
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Consider direct relevance, temporal relevance, and actionability.
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Query: {query}
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User Context: {user_context}
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Query: "{query}"
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Memory: "{document}"
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Memory entries:
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{memories}
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Rate each memory from 1-10 and provide reasoning.
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Return as JSON: {{"rankings": [{{"index": 0, "score": 8, "reason": "..."}}]}}
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Provide only a single numerical score between 0.0 and 1.0.
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"""
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config = {
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"reranker": {
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"provider": "llm_reranker",
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"config": {
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"llm": {
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"provider": "openai",
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"config": {
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"model": "gpt-4.1-nano-2025-04-14",
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"api_key": "your-openai-key"
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}
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},
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"custom_prompt": custom_prompt,
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"top_n": 5
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"provider": "openai",
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"model": "gpt-4o-mini",
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"api_key": "your-openai-key",
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"scoring_prompt": custom_prompt,
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"top_k": 5
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}
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}
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}
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@@ -70,12 +63,14 @@ memory = Memory.from_config(config)
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Your custom prompt can use the following variables:
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| Variable | Description |
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| ---------------- | ------------------------------------- |
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| `{query}` | The search query |
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| `{memories}` | The list of memory entries to rank |
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| `{user_id}` | The user ID (if available) |
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| `{user_context}` | Additional user context (if provided) |
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| Variable | Description |
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| ------------ | ----------------------------- |
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| `{query}` | The search query |
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| `{document}` | The memory entry being scored |
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<Note>
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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.
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</Note>
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## Domain-Specific Examples
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@@ -89,13 +84,10 @@ Prioritize memories that:
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- Show previous resolution patterns
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- Indicate customer preferences or constraints
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Query: {query}
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Customer Context: Previous interactions with this customer
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Query: "{query}"
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Memory: "{document}"
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Memories:
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{memories}
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Rank each memory 1-10 based on support relevance.
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Score relevance from 0.0 to 1.0.
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"""
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```
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@@ -103,19 +95,16 @@ Rank each memory 1-10 based on support relevance.
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```python
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educational_prompt = """
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Rank these learning memories for a student query.
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Score this learning memory for relevance to a student query.
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Consider:
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- Prerequisite knowledge requirements
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- Learning progression and difficulty
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- Relevance to current learning objectives
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Student Query: {query}
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Learning Context: {user_context}
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Student Query: "{query}"
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Memory: "{document}"
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Available memories:
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{memories}
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Score each memory for educational value (1-10).
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Score educational relevance from 0.0 to 1.0.
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"""
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```
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@@ -123,73 +112,64 @@ Score each memory for educational value (1-10).
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```python
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personal_assistant_prompt = """
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Rank personal memories for relevance to the user's query.
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Score this personal memory for relevance to the user's query.
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Consider:
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- Recent vs. historical importance
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- Personal preferences and habits
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- Contextual relationships between memories
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- Contextual relationships
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Query: {query}
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Personal context: {user_context}
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Query: "{query}"
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Memory: "{document}"
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Memories to rank:
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{memories}
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Provide relevance scores (1-10) with brief explanations.
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Provide relevance score from 0.0 to 1.0.
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"""
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```
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## Advanced Prompt Techniques
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### Multi-Criteria Ranking
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### Multi-Criteria Scoring
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```python
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multi_criteria_prompt = """
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Evaluate memories using multiple criteria:
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Evaluate this memory using multiple criteria:
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1. RELEVANCE (40%): How directly related to the query
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2. RECENCY (20%): How recent the memory is
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2. RECENCY (20%): How recent the memory appears to be
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3. IMPORTANCE (25%): Personal or business significance
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4. ACTIONABILITY (15%): How useful for next steps
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Query: {query}
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Context: {user_context}
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Query: "{query}"
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Memory: "{document}"
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Memories:
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{memories}
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For each memory, provide:
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- Overall score (1-10)
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- Breakdown by criteria
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- Final ranking recommendation
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Format: JSON with detailed scoring
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Compute a weighted score from 0.0 to 1.0 based on these criteria.
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Provide only the final numerical score.
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"""
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```
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### Contextual Ranking
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### Chain-of-Thought Scoring
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```python
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contextual_prompt = """
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Consider the following context when ranking memories:
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- Current user situation: {user_context}
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- Time of day: {current_time}
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- Recent activities: {recent_activities}
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reasoning_prompt = """
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Evaluate this memory's relevance step by step:
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Query: {query}
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1. What is the main intent of the query?
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2. What key information does the memory contain?
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3. How directly does the memory address the query?
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Rank these memories considering both direct relevance and contextual appropriateness:
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{memories}
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Based on this analysis, provide a single relevance score from 0.0 to 1.0.
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Provide contextually-aware relevance scores (1-10).
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Query: "{query}"
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Memory: "{document}"
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Score:
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"""
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```
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## Best Practices
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1. **Be Specific**: Clearly define what makes a memory relevant for your use case
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2. **Use Examples**: Include examples in your prompt for better model understanding
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3. **Structure Output**: Specify the exact JSON format you want returned
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2. **Use 0.0-1.0 Scale**: The score extractor expects values between 0.0 and 1.0
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3. **Request Only the Score**: Ask for just the numerical score to improve extraction reliability
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4. **Test Iteratively**: Refine your prompt based on actual ranking performance
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5. **Consider Token Limits**: Keep prompts concise while being comprehensive
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@@ -206,7 +186,7 @@ prompts = [
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]
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for i, prompt in enumerate(prompts):
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config["reranker"]["config"]["custom_prompt"] = prompt
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config["reranker"]["config"]["scoring_prompt"] = prompt
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memory = Memory.from_config(config)
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results = memory.search("test query", user_id="test_user")
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@@ -216,6 +196,6 @@ for i, prompt in enumerate(prompts):
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## Common Issues
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- **Too Long**: Keep prompts under token limits for your chosen LLM
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- **Too Vague**: Be specific about ranking criteria
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- **Inconsistent Format**: Ensure JSON output format is clearly specified
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- **Missing Context**: Include relevant variables for your use case
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- **Too Vague**: Be specific about scoring criteria
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- **Wrong Scale**: Use 0.0-1.0 scale to match the default score extractor
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- **Extra Output**: Ask for only the numeric score — extra text can confuse score extraction
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@@ -18,13 +18,9 @@ config = {
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"reranker": {
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"provider": "llm_reranker",
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"config": {
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"llm": {
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"provider": "openai",
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"config": {
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"model": "gpt-4",
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"api_key": "your-openai-api-key"
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}
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}
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"provider": "openai",
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"model": "gpt-4o-mini",
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"api_key": "your-openai-api-key"
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}
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}
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}
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@@ -36,11 +32,14 @@ m = Memory.from_config(config)
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `llm` | dict | Required | LLM configuration object |
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| `top_k` | int | 10 | Number of results to rerank |
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| `provider` | str | `"openai"` | LLM provider (openai, anthropic, etc.) |
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| `model` | str | `"gpt-4o-mini"` | LLM model to use for reranking |
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| `api_key` | str | None | API key for the LLM provider |
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| `top_k` | int | None | Number of top documents to return after reranking |
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| `temperature` | float | 0.0 | LLM temperature for consistency |
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| `custom_prompt` | str | None | Custom reranking prompt |
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| `score_range` | tuple | (0, 10) | Score range for relevance |
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| `max_tokens` | int | 100 | Maximum tokens for LLM response |
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| `scoring_prompt` | str | None | Custom prompt template for scoring documents |
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| `llm` | dict | None | Optional nested LLM config for provider-specific fields (e.g., `ollama_base_url`, `azure_endpoint`). Overrides top-level `provider`/`model`/`api_key` when provided. |
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### Advanced Configuration
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@@ -49,20 +48,19 @@ config = {
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"reranker": {
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"provider": "llm_reranker",
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"config": {
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"llm": {
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"provider": "anthropic",
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"config": {
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"model": "claude-3-sonnet-20240229",
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"api_key": "your-anthropic-api-key"
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}
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},
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"provider": "anthropic",
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"model": "claude-sonnet-4-20250514",
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"api_key": "your-anthropic-api-key",
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"top_k": 15,
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"temperature": 0.0,
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"score_range": (1, 5),
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"custom_prompt": """
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Rate the relevance of each memory to the query on a scale of 1-5.
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"scoring_prompt": """
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Rate the relevance of each memory to the query on a scale of 0.0-1.0.
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Consider semantic similarity, context, and practical utility.
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Only provide the numeric score.
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Query: "{query}"
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Document: "{document}"
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Score:
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"""
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}
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}
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@@ -78,14 +76,10 @@ config = {
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"reranker": {
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"provider": "llm_reranker",
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"config": {
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"llm": {
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"provider": "openai",
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"config": {
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"model": "gpt-4",
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"api_key": "your-openai-api-key",
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"temperature": 0.0
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}
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}
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"provider": "openai",
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"model": "gpt-4o-mini",
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"api_key": "your-openai-api-key",
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"temperature": 0.0
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}
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}
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}
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@@ -98,13 +92,9 @@ config = {
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"reranker": {
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"provider": "llm_reranker",
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"config": {
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"llm": {
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"provider": "anthropic",
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"config": {
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"model": "claude-3-sonnet-20240229",
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"api_key": "your-anthropic-api-key"
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}
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}
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"provider": "anthropic",
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"model": "claude-sonnet-4-20250514",
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"api_key": "your-anthropic-api-key"
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}
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}
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}
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@@ -117,10 +107,12 @@ config = {
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"reranker": {
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"provider": "llm_reranker",
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"config": {
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"provider": "ollama",
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"model": "llama3.2",
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"llm": {
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"provider": "ollama",
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"config": {
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"model": "llama2",
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"model": "llama3.2",
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"ollama_base_url": "http://localhost:11434"
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}
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}
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@@ -129,6 +121,10 @@ config = {
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}
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```
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<Note>
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For providers like Ollama that need extra fields (e.g., `ollama_base_url`), use the optional nested `llm` key to pass provider-specific configuration. The nested `llm` config overrides top-level `provider`/`model`/`api_key` when provided.
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</Note>
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### Azure OpenAI
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```python
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@@ -136,13 +132,16 @@ config = {
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"reranker": {
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"provider": "llm_reranker",
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"config": {
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"provider": "azure_openai",
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"model": "gpt-4o-mini",
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"api_key": "your-azure-api-key",
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"llm": {
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"provider": "azure_openai",
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"config": {
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"model": "gpt-4",
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"model": "gpt-4o-mini",
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"api_key": "your-azure-api-key",
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"azure_endpoint": "https://your-resource.openai.azure.com/",
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"azure_deployment": "gpt-4-deployment"
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"azure_deployment": "gpt-4o-mini-deployment"
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}
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}
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}
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@@ -154,16 +153,22 @@ config = {
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### Default Prompt Behavior
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The default prompt asks the LLM to score relevance on a 0-10 scale:
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The default prompt asks the LLM to score relevance on a 0.0-1.0 scale:
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```
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Given a query and a memory, rate how relevant the memory is to answering the query.
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Score from 0 (completely irrelevant) to 10 (perfectly relevant).
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Only provide the numeric score.
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You are a relevance scoring assistant. Given a query and a document, you need to score how relevant the document is to the query.
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Query: {query}
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Memory: {memory}
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Score:
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Score the relevance on a scale from 0.0 to 1.0, where:
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- 1.0 = Perfectly relevant and directly answers the query
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- 0.8-0.9 = Highly relevant with good information
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- 0.6-0.7 = Moderately relevant with some useful information
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- 0.4-0.5 = Slightly relevant with limited useful information
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- 0.0-0.3 = Not relevant or no useful information
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Query: "{query}"
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Document: "{document}"
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Provide only a single numerical score between 0.0 and 1.0. Do not include any explanation or additional text.
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```
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### Custom Prompt Examples
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@@ -174,14 +179,14 @@ Score:
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custom_prompt = """
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You are a medical information specialist. Rate how relevant each memory is for answering the medical query.
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Consider clinical accuracy, specificity, and practical applicability.
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Rate from 1-10 where:
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- 1-3: Irrelevant or potentially harmful
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- 4-6: Somewhat relevant but incomplete
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- 7-8: Relevant and helpful
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- 9-10: Highly relevant and clinically useful
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Rate from 0.0 to 1.0 where:
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- 0.0-0.3: Irrelevant or potentially harmful
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- 0.4-0.6: Somewhat relevant but incomplete
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- 0.7-0.8: Relevant and helpful
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- 0.9-1.0: Highly relevant and clinically useful
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Query: {query}
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Memory: {memory}
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Query: "{query}"
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Document: "{document}"
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Score:
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"""
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@@ -189,14 +194,10 @@ config = {
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"reranker": {
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"provider": "llm_reranker",
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"config": {
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"llm": {
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"provider": "openai",
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"config": {
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"model": "gpt-4",
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"api_key": "your-api-key"
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}
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},
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"custom_prompt": custom_prompt
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"provider": "openai",
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"model": "gpt-4o-mini",
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"api_key": "your-api-key",
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"scoring_prompt": custom_prompt
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}
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}
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}
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@@ -213,15 +214,15 @@ Consider:
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- Recency and accuracy
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- Practical usefulness
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Rate 1-5:
|
||||
1 = Not relevant
|
||||
2 = Slightly relevant
|
||||
3 = Moderately relevant
|
||||
4 = Very relevant
|
||||
5 = Perfectly answers the question
|
||||
Rate 0.0-1.0:
|
||||
0.0 = Not relevant
|
||||
0.25 = Slightly relevant
|
||||
0.5 = Moderately relevant
|
||||
0.75 = Very relevant
|
||||
1.0 = Perfectly answers the question
|
||||
|
||||
Query: {query}
|
||||
Memory: {memory}
|
||||
Query: "{query}"
|
||||
Document: "{document}"
|
||||
Score:
|
||||
"""
|
||||
```
|
||||
@@ -239,13 +240,17 @@ Consider:
|
||||
- Factual accuracy
|
||||
- Conversation flow
|
||||
|
||||
Rate 0-10:
|
||||
Query: {query}
|
||||
Memory: {memory}
|
||||
Rate 0.0-1.0:
|
||||
Query: "{query}"
|
||||
Document: "{document}"
|
||||
Score:
|
||||
"""
|
||||
```
|
||||
|
||||
<Note>
|
||||
Custom prompts must include `{query}` and `{document}` placeholders. The LLM response should contain a numerical score which is automatically extracted.
|
||||
</Note>
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Usage
|
||||
@@ -295,9 +300,9 @@ results = safe_llm_rerank_search("What are my preferences?", "alice")
|
||||
|
||||
| Model Type | Speed | Quality | Cost | Best For |
|
||||
|------------|-------|---------|------|----------|
|
||||
| GPT-3.5 Turbo | Fast | Good | Low | High-volume applications |
|
||||
| GPT-4 | Medium | Excellent | Medium | Quality-critical applications |
|
||||
| Claude 3 Sonnet | Medium | Excellent | Medium | Balanced performance |
|
||||
| GPT-4o mini | Fast | Good | Low | High-volume applications |
|
||||
| GPT-4o | Medium | Excellent | Medium | Quality-critical applications |
|
||||
| Claude Sonnet | Medium | Excellent | Medium | Balanced performance |
|
||||
| Ollama Local | Variable | Good | Free | Privacy-sensitive applications |
|
||||
|
||||
### Optimization Strategies
|
||||
@@ -308,14 +313,10 @@ fast_config = {
|
||||
"reranker": {
|
||||
"provider": "llm_reranker",
|
||||
"config": {
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "gpt-3.5-turbo",
|
||||
"api_key": "your-api-key"
|
||||
}
|
||||
},
|
||||
"top_k": 5, # Limit candidates
|
||||
"provider": "openai",
|
||||
"model": "gpt-4o-mini",
|
||||
"api_key": "your-api-key",
|
||||
"top_k": 5,
|
||||
"temperature": 0.0
|
||||
}
|
||||
}
|
||||
@@ -326,13 +327,9 @@ quality_config = {
|
||||
"reranker": {
|
||||
"provider": "llm_reranker",
|
||||
"config": {
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "gpt-4",
|
||||
"api_key": "your-api-key"
|
||||
}
|
||||
},
|
||||
"provider": "openai",
|
||||
"model": "gpt-4o",
|
||||
"api_key": "your-api-key",
|
||||
"top_k": 15,
|
||||
"temperature": 0.0
|
||||
}
|
||||
@@ -353,10 +350,10 @@ Evaluate this memory's relevance using multi-step reasoning:
|
||||
3. How directly does the memory address the query?
|
||||
4. What additional context might be needed?
|
||||
|
||||
Based on this analysis, rate relevance 1-10:
|
||||
Based on this analysis, rate relevance 0.0-1.0:
|
||||
|
||||
Query: {query}
|
||||
Memory: {memory}
|
||||
Query: "{query}"
|
||||
Document: "{document}"
|
||||
|
||||
Analysis:
|
||||
Step 1 (Intent):
|
||||
@@ -367,42 +364,6 @@ Final Score:
|
||||
"""
|
||||
```
|
||||
|
||||
### Comparative Ranking
|
||||
|
||||
```python
|
||||
comparative_prompt = """
|
||||
You will see a query and multiple memories. Rank them in order of relevance.
|
||||
Consider which memories best answer the question and would be most helpful.
|
||||
|
||||
Query: {query}
|
||||
|
||||
Memories to rank:
|
||||
{memories}
|
||||
|
||||
Provide scores 1-10 for each memory, considering their relative usefulness.
|
||||
"""
|
||||
```
|
||||
|
||||
### Emotional Intelligence
|
||||
|
||||
```python
|
||||
emotional_prompt = """
|
||||
Consider both factual relevance and emotional appropriateness.
|
||||
Rate how suitable this memory is for responding to the user's query.
|
||||
|
||||
Factors to consider:
|
||||
- Factual accuracy and relevance
|
||||
- Emotional tone and sensitivity
|
||||
- User's likely emotional state
|
||||
- Appropriateness of response
|
||||
|
||||
Query: {query}
|
||||
Memory: {memory}
|
||||
Emotional Context: {context}
|
||||
Score (1-10):
|
||||
"""
|
||||
```
|
||||
|
||||
## Error Handling and Fallbacks
|
||||
|
||||
```python
|
||||
@@ -433,14 +394,22 @@ class RobustLLMReranker:
|
||||
primary_config = {
|
||||
"reranker": {
|
||||
"provider": "llm_reranker",
|
||||
"config": {"llm": {"provider": "openai", "config": {"model": "gpt-4"}}}
|
||||
"config": {
|
||||
"provider": "openai",
|
||||
"model": "gpt-4o",
|
||||
"api_key": "your-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fallback_config = {
|
||||
"reranker": {
|
||||
"provider": "llm_reranker",
|
||||
"config": {"llm": {"provider": "openai", "config": {"model": "gpt-3.5-turbo"}}}
|
||||
"config": {
|
||||
"provider": "openai",
|
||||
"model": "gpt-4o-mini",
|
||||
"api_key": "your-api-key"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -486,4 +455,4 @@ results = reranker.search("What are my preferences?", "alice")
|
||||
<Card title="Performance Optimization" icon="bolt" href="/components/rerankers/optimization">
|
||||
Optimize LLM reranker performance
|
||||
</Card>
|
||||
</CardGroup>
|
||||
</CardGroup>
|
||||
|
||||
@@ -123,13 +123,9 @@ config = {
|
||||
"reranker": {
|
||||
"provider": "llm_reranker",
|
||||
"config": {
|
||||
"llm": {
|
||||
"provider": "openai",
|
||||
"config": {
|
||||
"model": "gpt-4",
|
||||
"api_key": "your-openai-api-key"
|
||||
}
|
||||
},
|
||||
"provider": "openai",
|
||||
"model": "gpt-4o-mini",
|
||||
"api_key": "your-openai-api-key",
|
||||
"top_k": 5
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user