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🚀 Embedded Files

Embedding files are a method of converting text into numerical representations, which can be used for various natural language processing tasks. txtai provides a powerful toolset to build and manage these embeddings.

🚀 Quick Start

✨ Features

• Convert text into numerical representations for natural language processing tasks.
• Support multiple storage backends and different embedding models.
• Provide causal boosting mechanism to improve search relevance.
• Support multi - language detection and pattern matching.
• Automatically extract entities from documents and build knowledge graphs.
• Perform community detection on graphs.

📦 Installation

Install Embedding Dependencies

To use the Embedding feature of txtai, install the following dependencies:

pip install sentence-transformers faiss-cpu

Install kb_builder

To install kb_builder, run the following command:

pip install txtai[kb_builder]

💻 Usage Examples

Embedding File Configuration

You can use a YAML file to configure the embedding process. For example, here is a simple configuration file:

# Embedding model path
embedder:
  model: sentence-transformers/nli-mpnet-base-v2
  backend: faiss
  gpu: true

Build a Knowledge Base with Embedding

To build a knowledge base, use the following command:

python -m kb_builder build --input /path/to/documents --config embedding_config.yml

Knowledge Base Storage Location: By default, the knowledge base will be saved in the ~/.txtai/embeddings directory.

Graph Configuration

Embedding files can also be used in combination with graph structures. Here is an example:

graph:
  backend: sqlite
  path: ~/.txtai/graph.db
  similarity: 0.75

Graph Traversal: You can traverse the graph using the following command:

python -m graph_traversal --config graph_config.yml

Causal Boosting Mechanism

Embedding files also support a causal boosting mechanism to improve search relevance. For example, you can configure it as follows:

causal_boosting:
  enabled: true
  boost_factor: 1.5

Multi - Language Support: The causal boosting mechanism supports multi - language detection and pattern matching.

Advanced Features

Automatic Knowledge Graph Construction

Embedding files can automatically extract entities from documents and build knowledge graphs. For example:

python -m kb_builder build --input /path/to/documents --config knowledge_graph.yml

Community Detection

You can perform community detection on the graph in the following way:

python -m community_detection --config graph_config.yml

📚 Documentation

Configuration File Examples

In - Memory Storage

# In - memory storage configuration
path: memory
embedder:
  model: sentence-transformers/nli-mpnet-base-v2
  backend: faiss
  gpu: true

SQLite Storage

# SQLite storage configuration
path: sqlite://~/.txtai/embeddings.db
graph:
  backend: sqlite
  path: ~/.txtai/graph.db
  similarity: 0.75

PostgreSQL Storage

# PostgreSQL storage configuration
path: postgresql://user:password@localhost:5432/kb
embedder:
  model: sentence-transformers/nli-mpnet-base-v2
  backend: faiss
  gpu: true

Graph Analysis Tools

Frequently Asked Questions

• How to check if the knowledge base is built successfully? You can check by running the following command:

  python -m kb_builder check --config config.yml
  

• How to clean up an existing knowledge base? Use the following command to clean up:

  python -m kb_builder clear --config config.yml
  

📄 License

No license information provided in the original document.

Learn More

For more information, please refer to [txtai Documentation](https://github.com/doc2vec txtai).

Get Help

For further assistance, please visit txtai Forum.