FloTorch-core 2.9.11

A Python project for FloTorch

🚀 FloTorch-core

FloTorch-core is a modular and extensible Python framework for building LLM-powered RAG (Retrieval-Augmented Generation) pipelines. It offers plug-and-play components for embeddings, chunking, retrieval, gateway-based LLM calls, and RAG evaluation.

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✨ Features

• 🧩 Text Chunking (Fixed-size, Hierarchical)
• 🧠 Embedding Models (Titan, Cohere, Bedrock)
• 🔍 Document Retrieval (OpenSearch + Vector Storage)
• 💻 Bedrock/sagemaker/gateway inferencer
• 🔌 Unified LLM Gateway (OpenAI, Bedrock, Ollama, etc.)
• 📏 RAG Evaluation (RAGAS Metrics)
• ☁️ AWS Integration (S3, DynamoDB, Lambda)
• 🧢 Built-in Testing Support

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📆 Installation

pip install FloTorch-core

To install development dependencies:

pip install FloTorch-core[dev]

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📂 Project Structure

flotorch/
├── inferencer/         # LLM gateway/bedrock/sagemaker interface
├── embedding/          # Embedding models
├── chunking/           # Text chunking logic
├── evaluator/          # RAG evaluation (RAGAS)
├── storage/            # Vector DB, S3, DynamoDB
├── util/               # Utilities and helpers
├── rerank/             # Ranking documents
├── guardrails/         # Enabling guardrails
├── reader/             # reader for json/pdf

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📖 Usage Example

Reader

from flotorch_core.reader.json_reader import JSONReader
from flotorch_core.storage.s3_storage import S3StorageProvider

json_reader = JSONReader(S3StorageProvider(<S3 bucket>))
json_reader.read(<path>)

Embedding

from flotorch_core.embedding.embedding_registry import embedding_registry

embedding_class = embedding_registry.get_model(<model id>)

# model id example: amazon.titan-text-express-v1, amazon.titan-embed-text-v2:0, cohere.embed-multilingual-v3

Vector storage (opensearch)

from flotorch_core.storage.db.vector.open_search import OpenSearchClient

vector_storage_object = OpenSearchClient(
    <opensearch_host>, 
    <opensearch_port>, 
    <opensearch_username>, 
    <opensearch_password>, 
    <index_id>, 
    <embedding object>
)

Vector storage (bedrock knowledgebase)

from flotorch_core.storage.db.vector.bedrock_knowledgebase_storage import BedrockKnowledgeBaseStorage

vector_storage_object = BedrockKnowledgeBaseStorage(
    knowledge_base_id=<knowledge_base_id>,
    region=<aws_region>
)

Guardrails over vector storage

from flotorch_core.storage.db.vector.guardrails_vector_storage import GuardRailsVectorStorage

base_guardrails = BedrockGuardrail(<guardrail_id>, <guardrail_version>, <aws_region>)            
vector_storage_object = GuardRailsVectorStorage(
    vector_storage_object, 
    base_guardrails,
    <enable_prompt_guardrails(True/False)>,
    <enable_context_guardrails(True/False)>
)

Inferencer

from flotorch_core.inferencer.bedrock_inferencer import BedrockInferencer
from flotorch_core.inferencer.gateway_inferencer import GatewayInferencer
from flotorch_core.inferencer.sagemaker_inferencer import SageMakerInferencer

inferencer = BedrockInferencer(
    <model_id>, 
    <region>, 
    <number of n_shot_prompts>, 
    <temperature>, 
    <n_shot_prompt_guide_obj>
)

inferencer = GatewayInferencer(
    model_id=<model_id>, 
    api_key=<api_key>, 
    base_url=<base_url>, 
    n_shot_prompts=<n_shot_prompts>, 
    n_shot_prompt_guide_obj=<n_shot_prompt_guide_obj>
)

inferencer = SageMakerInferencer(
    <model_id>, 
    <region>, 
    <arn_role>, 
    <n_shot_prompts>, 
    <temperature>, 
    <n_shot_prompt_guide_obj>
)

GuardRail over inferencer

from flotorch_core.inferencer.guardrails.guardrails_inferencer import GuardRailsInferencer

inferencer = GuardRailsInferencer(inferencer, base_guardrails)

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📬 Maintainer

Shiva Krishna
📧 Email: [e-mail]

Adil Raza
📧 Email: [e-mail]

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📄 License

This project is licensed under the MIT License.

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🌐 Links

• GitHub: https://github.com/FissionAI/flotorch-core