ella-sdk 1.1.1

Medical Triage & Clinical RAG Engine

⭐ If Ella's RAG-based medical triage architecture gave you ideas — a star helps other health-AI builders find it. Takes 2 seconds.

ELLA

Medical Triage & Clinical RAG Engine

Ella is a production-grade Retrieval-Augmented Generation (RAG) system purpose-built for medical triage. She ingests 90,000+ clinical text chunks, embeds them via NVIDIA NIM, stores them in Pinecone, and retrieves context-grounded answers through a multi-stage pipeline — eliminating hallucinations in healthcare workflows.

Install • Quick Start • Architecture • Live Demo • Benchmark

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Install

pip install ella-sdk

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Quick Start

from ella_medical import Ella

client = Ella()
response = client.query("What are the symptoms of a heart attack?")

print(response.intent)         # "TRIAGE"
print(response.response)       # Grounded clinical response

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Usage

Basic Query

from ella_medical import Ella

client = Ella()
response = client.query("What are the symptoms of a heart attack?")

print(response.intent)              # "TRIAGE"
print(response.priority)            # Priority level
print(response.thought_process)     # Router's reasoning
print(response.response)            # Ella's response
print(response.retrieved_context)   # Retrieved medical documents

Multi-Turn Conversation

from ella_medical import Ella

client = Ella()

# First message
r1 = client.query("I have chest pain")

# Follow-up
r2 = client.query(
    "What about treatment options?",
    history=f"Patient: I have chest pain\nElla: {r1.response}"
)

print(r2.response)

Context Manager

from ella_medical import Ella

with Ella() as client:
    response = client.query("What are the symptoms of diabetes?")
    print(response.response)

Response Object

@dataclass
class QueryResponse:
    intent: str            # EMERGENCY | TRIAGE | BOOKING | GENERAL_INFO | CLOSING
    priority: str          # Priority level
    thought_process: str   # Router's reasoning
    justification: str     # Clinical justification
    response: str          # Ella's response
    retrieved_context: str # Retrieved medical documents

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Live Demo

→ Launch Ella on HuggingFace Spaces

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Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                        PATIENT INPUT                                │
└────────────────────────────┬────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────────┐
│  INTENT ROUTER (Groq llama-3.1-8b-instant + Pydantic Schema)      │
│  Classifies: EMERGENCY │ TRIAGE │ BOOKING │ GENERAL_INFO │ CLOSING │
└────────────────────────────┬────────────────────────────────────────┘
                             │
              ┌──────────────┼──────────────┐
              ▼              ▼              ▼
      ┌──────────┐  ┌──────────────┐  ┌──────────┐
      │ EMERGENCY │  │    TRIAGE    │  │ BOOKING  │
      │ GUARDRAIL │  │  RAG SEARCH  │  │ HANDLER  │
      └──────────┘  └──────┬───────┘  └──────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────────────┐
│                    RETRIEVAL PIPELINE                               │
│                                                                     │
│  ┌─────────────┐   ┌─────────────┐   ┌─────────────────────────┐  │
│  │  NVIDIA NIM  │   │   BM25      │   │  CrossEncoder Reranker  │  │
│  │  Embeddings  │ + │  Keyword    │ → │  ms-marco-MiniLM-L-6   │  │
│  │  (Semantic)  │   │  Matching   │   │  (Top-10 → Top-3)      │  │
│  └──────┬──────┘   └──────┬──────┘   └───────────┬─────────────┘  │
│         │                 │                      │                  │
│         ▼                 ▼                      ▼                  │
│  ┌─────────────────────────────────────────────────────────────┐   │
│  │              PINECONE VECTOR DATABASE                       │   │
│  │         90,306 vectors • cosine • 1024 dimensions           │   │
│  └─────────────────────────────────────────────────────────────┘   │
└────────────────────────────┬────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────────┐
│  SYNTHESIS (Groq llama-3.1-8b-instant)                             │
│  Grounded response + clinical justification + source attribution    │
└────────────────────────────┬────────────────────────────────────────┘
                             │
                             ▼
┌─────────────────────────────────────────────────────────────────────┐
│                       PATIENT RESPONSE                              │
└─────────────────────────────────────────────────────────────────────┘

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Benchmark

Metric	Value
Intent Accuracy	96.0%
Avg Latency	9.26s
Avg Retrieval Score	0.92
Records in DB	90,306

Intent	Correct	Total	Accuracy
EMERGENCY	10	10	100%
TRIAGE	18	20	90%
BOOKING	10	10	100%
GENERAL_INFO	5	5	100%
CLOSING	5	5	100%

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Tech Stack

Layer	Technology	Purpose
SDK	ella-sdk (PyPI)	Python client
Embeddings	NVIDIA NIM (nv-embedqa-e5-v5)	1024-dim semantic vectors
Vector DB	Pinecone (Serverless, AWS)	Cosine similarity search
LLM	Groq (llama-3.1-8b-instant)	Intent classification + response generation
Reranker	CrossEncoder (ms-marco-MiniLM-L-6-v2)	Precision reranking
Orchestration	LangChain + LangGraph	Agent pipeline
Validation	Pydantic	Schema-validated outputs

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License

MIT License — see LICENSE for details.

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