Short-term Memory

Short-term memory temporarily buffers recent perceptions, it will receive some of the sensory memory, and it can be enhanced by other observations or retrieved memories to enter the long-term memory.

In most cases, short-term memory is analogous to the input information within the context window constrained by the LLM. So you can think of short-term memory will be written into the prompt of the LLM in most cases.

Using Short-term Memory

from dbgpt.agent import AgentMemory, ShortTermMemory

# Create an agent memory, which contains a short-term memory
memory = ShortTermMemory(buffer_size=2)
agent_memory: AgentMemory = AgentMemory(memory=memory)

Like sensory memory, short-term memory is also has a buffer size, when the buffer is full, it will keep the latest buffer_size memories, and some of the discarded memories will be transferred to long-term memory.

The default short-term memory is a FIFO buffered memory, we won't introduce too much here.

Enhanced Short-term Memory

Like human short-term memory, the short-term memory in DB-GPT agents can be enhanced by outside observations. Here we introduce a kind of enhanced short-term memory, which is called EnhancedShortTermMemory, it enhances memories by comparing the similarity between the new observation and the existing memories.

To use EnhancedShortTermMemory, you need to provide a embeddings model.

Prepare Embedding Model

DB-GPT supports a lot of embedding models, here are some of them:

import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

import os
from dbgpt.rag.embedding import DefaultEmbeddingFactory

api_url = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1") + "/embeddings"
api_key = os.getenv("OPENAI_API_KEY")
embeddings = DefaultEmbeddingFactory.openai(api_url=api_url, api_key=api_key)

from dbgpt.rag.embedding import DefaultEmbeddingFactory

embeddings = DefaultEmbeddingFactory.default("/data/models/text2vec-large-chinese")

If you have deployed DB-GPT cluster and API server , you can connect to the API server to get the embeddings.

from dbgpt.rag.embedding import DefaultEmbeddingFactory

embeddings = DefaultEmbeddingFactory.remote(
  api_url="http://localhost:8100/api/v1/embeddings",
  api_key="{your_api_key}",
  model_name="text2vec"
)

Using Enhanced Short-term Memory

from concurrent.futures import ThreadPoolExecutor
from dbgpt.agent import AgentMemory, EnhancedShortTermMemory

# Create an agent memory, which contains a short-term memory
memory = EnhancedShortTermMemory(
    embeddings=embeddings,
    buffer_size=2,
    enhance_similarity_threshold=0.5,
    enhance_threshold=3,
    executor=ThreadPoolExecutor(),
)
agent_memory: AgentMemory = AgentMemory(memory=memory)

In DB-GPT, the core interface is asynchronous and non-blocking, so we use ThreadPoolExecutor to run the similarity calculation in a separate thread for better performance.

In the above code, we set the enhance_similarity_threshold to 0.5, which means if the similarity bigger than 0.7, the new observation has the probability of being enhanced to the short-term memory(there are a random factor in the enhancement process). And we set the enhance_threshold to 3, which means if the memory is enhanced bigger or equal to 3 times, it will be transferred to long-term memory.

Then you can use the enhanced short-term memory in your agent.

Source: docs/i18n/zh-CN/docusaurus-plugin-content-docs/current/agents/modules/memory/short_term_memory.md