Environment | Version |
---|---|
Production | 0.0.1 |
Development | 0.0.1 |
The cognitive-space package offers tools for abstracting and storing Recall Space Cognitive Algorithms💫. It allows you to customize different components of the 🧠AI brain abstraction to suit your application's requirements, ranging from basic vector search to advanced engram modeling.
- note: Requires recall-space-benchmark, agent-builder packages.
- Build your Recall Space AI Brain using the composite design pattern.
- Run the recall-space-benchmark on your agents with AI Brain.
- Develop innovative models of the AI Brain that can be easily integrated into the Recall Space ecosystem.
classDiagram
class Brain {
+CognitiveEncode : CognitiveEncode
+CognitiveRecall : CognitiveRecall
+CognitiveSynthesis : CognitiveSynthesis
}
class CognitiveEncode {
+encode(input: Data) : EncodedData
}
class CognitiveRecall {
+recall(input: EncodedData) : Data
}
class CognitiveSynthesis {
+synthesize(input1: EncodedData, input2: EncodedData) : SynthesizedData
}
class Storage {
+create_or_update(data: Data) : Boolean
+read(identifier: String) : Data
+delete(identifier: String) : Boolean
+search(query: Any) : Data
+recall(query: Any) : Data
}
class AzureAISearchStorage{
+ endpoint: String
+ api_key: String
+ index_name: String
+ index_client: String
+ search_client: String
}
class MongoStorage{
+ db_name: String
+ collection_name: String
+ uri: String
}
class GravitationalEncode {
+encode(input: Data) : EncodedData
}
class GravitationalRecall {
+recall(input: EncodedData) : Data
}
class EmbeddingEncode {
+encode(input: Data) : EncodedData
}
class EmbeddingRecall {
+recall(input: EncodedData) : Data
}
Brain *-- CognitiveEncode
Brain *-- CognitiveRecall
Brain *-- CognitiveSynthesis
CognitiveEncode *-- Storage
CognitiveRecall *-- Storage
CognitiveSynthesis *-- Storage
Storage <|-- AzureAISearchStorage
Storage <|-- MongoStorage
CognitiveEncode <|-- GravitationalEncode
CognitiveRecall <|-- GravitationalRecall
CognitiveEncode <|-- EmbeddingEncode
CognitiveRecall <|-- EmbeddingRecall
-
Embedding Model: It is the simplest
EmbeddingEncode: CognitiveEncode
andEmbeddingRecall: CognitiveRecall
algorithms that we provide, and it's perfect for simple cases. Visit README -
Gravitational Model: This model is inspired by classical gravitational theory.
GravitationalEncode: CognitiveEncode
optimizes the potential gravitational energy of the memory planetary system, where point masses are replaced by vector embeddings.GravitationalRecall: CognitiveRecall
employs gradient descent to collapse an incoming memory into the strongest gravitational field of the existing memories. Visit README -
Engram Model: soon.
- AzureAISearchStorage: Used Azure AI search client to serve as interface to azure vector indexes. Visit README
- MongoStorage: Used mongo client to serve as interface to key value pair storage. Visit README
- Visit colors notebook -> 4/4
- Visit jokes notebook -> 2/4
- Visit colors notebook -> 3/4
- Visit jokes notebook -> 1/4