haystack-memory 0.3

Basic Memory library for Haystack NLP agents

Haystack Memory

Memory for haystack Agents. Currently, the memory of agents can be used in memory or with redis. The latter supports a sliding window.

Installation

• Python pip: python3 -m pip install haystack-memory . This package will attempt to install the dependencies (farm-haystack>=1.15.0)
• Python pip (skip dependency installation: Use python3 -m pip install haystack-memory --no-deps
• Using git: pip install git+https://github.com/rolandtannous/HaystackAgentBasicMemory.git@main#egg=HaystackAgentBasicMemory

Usage

To use memory in your agent, you need two parts:

• MemoryRecallNode: This node is passed to the agent to be used as a tool. It will be passed to the agent to let it remember the conversation.
• MemoryUtils: This class has to be used to save the query and the answers to the memory.

from haystack.agents import Agent, Tool
from haystack.nodes import PromptNode
from haystack_memory.prompt_templates import memory_template
from haystack_memory.memory import MemoryRecallNode
from haystack_memory.utils import MemoryUtils

# Initialize the memory and the memory tool so the agent can retrieve the memory
memory_database = []
memory_node = MemoryRecallNode(memory=memory_database)
memory_tool = Tool(name="Memory",
                   pipeline_or_node=memory_node,
                   description="Your memory. Always access this tool first to remember what you have learned.")

prompt_node = PromptNode(model_name_or_path="text-davinci-003", 
                         api_key="<YOUR_OPENAI_KEY>", 
                         max_length=1024,
                         stop_words=["Observation:"])
memory_agent = Agent(prompt_node=prompt_node, prompt_template=memory_template)
memory_agent.add_tool(memory_tool)

# Initialize the utils to save the query and the answers to the memory
memory_utils = MemoryUtils(memory_database=memory_database, agent=memory_agent)
memory_utils.chat("<Your Question>")

Redis

The memory can also be stored in a redis database which makes it possible to use different memories at the same time to be used with multiple agents. Additionally, it supports a sliding window to only utilize the last messages.

from haystack.agents import Agent, Tool
from haystack.nodes import PromptNode
from haystack_memory.memory import RedisMemoryRecallNode
from haystack_memory.prompt_templates import memory_template
from haystack_memory.utils import RedisUtils

# Initialize the memory and the memory tool so the agent can retrieve the memory
redis_memory_node = RedisMemoryRecallNode(memory_id="agent_memory",
                                          host="localhost",
                                          port=6379,
                                          db=0)
memory_tool = Tool(name="Memory",
                   pipeline_or_node=redis_memory_node,
                   description="Your memory. Always access this tool first to remember what you have learned.")
prompt_node = PromptNode(model_name_or_path="text-davinci-003",
                         api_key="<YOUR_OPENAI_KEY>",
                         max_length=1024,
                         stop_words=["Observation:"])
memory_agent = Agent(prompt_node=prompt_node, prompt_template=memory_template)
# Initialize the utils to save the query and the answers to the memory
redis_utils = RedisUtils(agent=memory_agent,
                         memory_id="agent_memory",
                         host="localhost",
                         port=6379,
                         db=0)
redis_utils.chat("<Your Question>")

Examples

Examples can be found in the examples/ folder. It contains the usage for all memory types. To open the examples in colab, click on the following links:

• Basic Memory:
• Redis Memory: