Adding the Basic Agent Memory tool

integrations/basic-agent-memory.md

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+---
+layout: integration
+name: Basic Agent Memory Tppl
+description: A working memory that stores the Agent's conversation memory
+authors:
+ - name: Roland Tannous
+ socials:
+ github: rolandtannous
+ twitter: rolandtannous
+ - name: Xceron
+ socials:
+ github: Xceron
+pypi: https://pypi.org/project/haystack-memory/
+repo: https://github.com/rolandtannous/haystack-memory
+type: Agent Tool
+report_issue: https://github.com/rolandtannous/haystack-memory/issues
+---
+
+# Basic Haystack Memory Tool
+
+This library implements a working memory that stores the Agent's conversation memory 
+and a sensory memory that stores the agent's short-term sensory memory. The working memory can be utilized in-memory or through Redis, with the 
+
+Redis implementation featuring a sliding window. On the other hand, the sensory memory is an in-memory implementation that mimics 
+a human's brief sensory memory, lasting only for the duration of one interaction.. 
+
+## Installation
+
+- Python pip: ```pip install --upgrade haystack-memory``` . This method will attempt to install the dependencies (farm-haystack>=1.15.0, redis)
+- Python pip (skip dependency installation): Use ```pip install --upgrade haystack-memory --no-deps```
+- Using git: ```pip install git+https://github.com/rolandtannous/haystack-memory.git@main#egg=haystack-memory```
+
+
+## Usage
+
+To use memory in your agent, you need three components:
+- `MemoryRecallNode`: This node is added to the agent as a tool. It will allow the agent to remember the conversation and make query-memory associations.
+- `MemoryUtils`: This class should be used to save the queries and the final agent answers to the conversation memory.
+- `chat`: This is a method of the MemoryUtils class. It is used to chat with the agent. It will save the query and the answer to the memory. It also returns the full result for further usage.
+
+```py
+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
+working_memory = []
+sensory_memory = []
+memory_node = MemoryRecallNode(memory=working_memory)
+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(working_memory=working_memory,sensory_memory=sensory_memory, agent=memory_agent)
+result = memory_utils.chat("<Your Question>")
+print(working_memory)
+```
+
+### Redis
+
+The working 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 k messages.
+
+```py
+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
+
+sensory_memory = []
+# Initialize the memory and the memory tool so the agent can retrieve the memory
+redis_memory_node = RedisMemoryRecallNode(memory_id="working_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,
+ sensory_memory=sensory_memory,
+ memory_id="working_memory",
+ host="localhost",
+ port=6379,
+ db=0)
+result = redis_utils.chat("<Your Question>")
+```
+
+
+## Examples
+
+Examples can be found in the `examples/` folder. They contain usage examples for both in-memory and Redis memory types.
+To open the examples in colab, click on the following links:
+- Basic Memory: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rolandtannous/HaystackAgentBasicMemory/blob/main/examples/example_basic_memory.ipynb)
+- Redis Memory: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/rolandtannous/HaystackAgentBasicMemory/blob/main/examples/example_redis_memory.ipynb)
+
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