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A memory framework for Large Language Models and Agents.
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REmote Dictionary cache - Artificial Intelligence (RedCache-AI). While developing a chat application, every solution I came across was either expensive, closed source or lacked extensive support for external dependencies. Redcache-ai provides a dynamic memory framework for Large Language Models, enabling developers to build wide ranging applications, from AI-powered dating apps to healthcare diagnostics platforms.
Install redcache-ai as a python package
pip install redcache-ai
Initialize Redcache-ai
Redcache-ai provides two options. Initialize to disk or sqlite. By default redcache-ai initializes to disk. This where memories are stored.
Option 1: initialize to disk
Make sure to import all the required dependencies
from redcache_ai import RedCache, load_config, set_openai_api_key
from redcache_ai.storage import DiskStorage, SQLiteStorage
storage = Disktorage()
Option 2: Initialize to sqlite
storage = SQLiteStorage(db_path='my_cache.db')
Store text as memory
Note: the output visual representation below is for disk storage only. The input is the same for sqlite
Input
memory_cache = storage.add("England is nice country", user_id = "James", metadata={"category": "facts"})
Output
{"James": {"7bbfbcbf-da9e-44ca-9cbb-ab558c64b36a": {"id": "7bbfbcbf-da9e-44ca-9cbb-ab558c64b36a", "text": "\"England is a nice country\"", "metadata": {"data": "\"England is a nice country\"", "category": "facts"}, "vector": [0.4472135954999579]}}}
Retrieve Memory
memories = storage(user_id = "James")
print(all_memories)
Output:
[
{
"id": "7bbfbcbf-da9e-44ca-9cbb-ab558c64b36a",
"text": "England is a nice country",
"metadata": {
"category": "facts"
}
}
]
Search Memories
results = storage.search("country", user_id="James", num_results=1)
Output:
[
{
"id": "7bbfbcbf-da9e-44ca-9cbb-ab558c64b36a",
"text": "England is a nice country",
"metadata": {
"category": "facts"
},
"score": 0.849
}
]
Update Memory
updated_memory = storage.update("7bbfbcbf-da9e-44ca-9cbb-ab558c64b36a", "England is a beautiful country", user_id="James")
Output:
{
"id": "7bbfbcbf-da9e-44ca-9cbb-ab558c64b36a",
"text": "England is a beautiful country",
"metadata": {
"category": "facts"
}
}
Delete Memory
storage.delete("7bbfbcbf-da9e-44ca-9cbb-ab558c64b36a", user_id="James")
Output:
Delete all memories
storage.delete_all(user_id="James")
Enhance a memory using LLMs 🔥
Large Langugae Model Integradtion
For now, redcache-ai only supports OpenAI. To integrate redcache-ai memories into openAI, you have to set an OPENAI API Key.
Once you have the key, you'll integrate your text memories like this:
from redcache_ai.config import set_openai_api_key
set_openai_api_key("your-openai-api-key-here")
from redcache_ai import RedCache, load_config
config = load_config()
redcache = RedCache.from_config(config)
The output for load_config is below:
{
"llm": {
"provider": "openai",
"config": {
"model": "gpt-4",
"temperature": 0.2,
"max_tokens": 1500,
}
}
}
enhanced_memory = redcache.enhance_memory("England has a rich history", user_id="James", category="facts")
summary = redcache.generate_summary(user_id="James")
These examples demonstrate the basic usage of redcache-ai. For more detailed information and advanced features, please refer to the full documentation. If implementation details are unclear please see the test_redcache.py file in examples. For more information, visit the discord channel.
What's Next ✈️
Redcache-ai is still early. Priority is to fix any and all existing bugs. Improve documentation then:
1.Add integration with more LLM providers including Llama, Mixtral, Claude etc.
2.Add functionality for AI Agents.
3.Provide support for a hosted version.
Happy memory building. ❤️
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