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docs(personal_memory): update documentation for Reme's personal memory system
- Capitalize 'Reme' in the documentation - Remove unnecessary sections and simplify content - Update file names to be more concise - Remove redundant information and improve readability
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# Personal Memory in reme
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# Personal Memory in Reme
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## Configuration Logic
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reme's personal memory system consists of two main components: retrieval and summarization. The configuration for these components is defined in the default.yaml file.
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Reme's personal memory system consists of two main components: retrieval and summarization. The configuration for these components is defined in the default.yaml file.
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### Retrieval Configuration (`retrieve_personal_memory`)
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## Complete Example
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For a complete working example, refer to `/cookbook/simple_demo/use_personal_memory_demo.py` in the reme repository.
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For a complete working example, refer to `/cookbook/simple_demo/use_personal_memory_demo.py` in the Reme repository.
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# Personal Memory Retrieve Operations
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This document describes the operations used in the personal memory retrieval flow of MemoryScope. The retrieval flow is defined in the configuration as:
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```yaml
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retrieve_personal_memory:
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flow_content: set_query_op >> (extract_time_op | (retrieve_memory_op >> semantic_rank_op)) >> fuse_rerank_op
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```
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# Personal Memory Retrieve Ops
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## SetQueryOp
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3. Flattens the messages into a single list
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4. Sorts messages by creation time if available
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5. Stores the filtered messages back in the context
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## Complete Flow Execution
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When the personal memory retrieval flow executes:
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1. `SetQueryOp` prepares the query with timestamp
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2. Two parallel paths execute:
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- `ExtractTimeOp` extracts time information from the query
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- `RetrieveMemoryOp` followed by `SemanticRankOp` retrieves and ranks memories
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3. `FuseRerankOp` combines the results, considering both semantic relevance and time information
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4. The final output is a ranked list of memories relevant to the query
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This flow provides a sophisticated memory retrieval system that considers semantic relevance, memory types, and temporal context to deliver the most appropriate memories for a given query.
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# Personal Memory Summary Operations
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This document provides a detailed overview of the operations (ops) used in the personal memory summarization flow in MemoryScope. Each operation plays a specific role in processing, filtering, and storing personal memories.
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## Overview
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As defined in the configuration, the personal memory summarization flow follows this sequence:
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```yaml
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summary_personal_memory:
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flow_content: info_filter_op >> (get_observation_op | get_observation_with_time_op | load_today_memory_op) >> contra_repeat_op >> update_vector_store_op
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```
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This document describes each operation in detail, including its purpose, parameters, and configuration options.
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# Personal Memory Summary Ops
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## InfoFilterOp
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3. Records the number of deleted and inserted memories
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This ensures that the vector store remains up-to-date with the latest processed memories.
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## Configuration Example
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Here's an example of how to configure these operations in your YAML configuration:
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```yaml
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op:
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info_filter_op:
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params:
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preserved_scores: "2,3"
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info_filter_msg_max_size: 200
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load_today_memory_op:
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params:
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top_k: 50
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contra_repeat_op:
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params:
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contra_repeat_max_count: 50
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enable_contra_repeat: true
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long_contra_repeat_op:
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params:
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long_contra_repeat_max_count: 50
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enable_long_contra_repeat: true
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update_insight_op:
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params:
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update_insight_threshold: 0.3
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update_insight_max_count: 5
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get_reflection_subject_op:
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params:
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reflect_obs_cnt_threshold: 10
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reflect_num_questions: 3
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```
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This configuration can be adjusted based on your specific requirements for personal memory processing.
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