Agent Memory Mechanisms and Dify Knowledge Base Segmentation & Retrieval Details

1. Agent Memory Issues

Agent memory refers to the capability of an AI agent to store and manage information such as interaction history, task state, and user preferences, thereby extending the limited context window of large language models (typically 16K‑2M tokens).

The memory can be categorized into three forms: internal trial information (step‑by‑step interaction logs), cross‑trial information (aggregated successes/failures across runs), and external knowledge (retrieved via APIs). The memory operations include writing, managing, and reading (CRUD), which together support learning and decision‑making.

Different memory types suggest different storage designs. Short‑term memory holds immediate dialogue context and transient results and can be implemented with lightweight structures like queues or Redis caches, possibly compressed when exceeding the model window. Long‑term memory persists user behavior, knowledge bases, and experience data; typical solutions involve vector databases for semantic similarity search, knowledge graphs for structured reasoning, or hybrid approaches combining vectors with relational databases (e.g., PostgreSQL). Working memory is a temporary, non‑persistent store for intermediate states in multi‑step tasks, often realized with in‑memory dictionaries.

2. Dify Knowledge‑Base Segmentation and Retrieval Logic

Dify is an open‑source LLM application platform that organizes complex tasks into workflows (Chatflow for conversational scenarios and Workflow for batch/automation tasks). Within Dify, a knowledge base is a core RAG component.

The knowledge base supports two segmentation modes:

General mode : splits the document into independent chunks.

Parent‑child mode : creates a two‑level hierarchy where a parent chunk (e.g., a paragraph) contains multiple child chunks (e.g., sentences).

Segmentation parameters include a delimiter (default "\n"), maximum chunk length (default 500 Tokens, up to 4000 Tokens), and overlap length (recommended 10‑25 % of the chunk size). Proper configuration improves retrieval relevance.

After segmentation, Dify builds indexes. Two index quality levels exist:

High‑quality mode : offers vector search, full‑text search, and hybrid search. Top‑K (default 3) controls how many segments are returned, and a score threshold (default 0.5) filters low‑similarity results.

Economic mode : provides only an inverted index for fast keyword lookup.

Hybrid search can combine vector and full‑text results or employ a rerank model (disabled by default) to reorder retrieved chunks for better LLM output.

Dify also supports a Q&A segmentation mode, where each chunk is automatically paired with generated questions and answers. This uses a Q‑to‑Q matching strategy, producing roughly 20 QA pairs per document and storing them in a vector database for similarity‑based retrieval.

3. Summary

The article introduces the challenges of agent memory and provides a practical guide to Dify’s knowledge‑base configuration, helping practitioners design effective memory systems and RAG pipelines.

References

1. A Survey on the Memory Mechanism of Large Language Model based Agents

2. Dify Documentation