> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neuraltrust.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# In memory

The `InMemoryKnowledgeBase` class is an in-memory implementation of the `KnowledgeBase` interface. It provides fast, local storage for documents with support for topic-based organization. This implementation is ideal for lightweight applications that do not require a persistent database or cloud-based storage. Unlike cloud-based solutions, this implementation stores all documents in memory, making it extremely fast but limited by system memory.

### Key Features

* **In-Memory Storage**: Stores all documents in Python dictionaries for quick access.
* **Topic-Based Organization**: Documents are grouped by topics for easy categorization.
* **Fast Document Retrieval**: Provides quick access to retrieving documents by ID.
* **Random Document Selection**: Supports random selection of documents from the filtered set.
* **Basic Similarity Search**: Returns random documents from the same topic as a given seed document.

## Usage Example

```python theme={null}
from trusttest.knowledge_base.in_memory_knowledge_base import InMemoryKnowledgeBase
from trusttest.knowledge_base.base import Document

docs = [
    Document(id="1", content="AI is transforming the world", topic="AI"),
    Document(id="2", content="Python is great for data science", topic="Programming"),
]

kb = InMemoryKnowledgeBase(docs)
kb.initialize_topics()
print("Topics:", kb.topics)
print("Random Document:", kb.choose_document())
```