Prompt Embeddings

Dynamo supports prompt embeddings (also known as prompt embeds) as a secure alternative input method to traditional text prompts. By allowing applications to use pre-computed embeddings for inference, this feature not only offers greater flexibility in prompt engineering but also significantly enhances privacy and data security. With prompt embeddings, sensitive user data can be transformed into embeddings before ever reaching the inference server, reducing the risk of exposing confidential information during the AI workflow.

How It Works

Path	What Happens
Text prompt	Tokenize → Embedding Layer → Transformer
Prompt embeds	Validate → Bypass Embedding → Transformer

Architecture

        flowchart LR
    subgraph FE["Frontend (Rust)"]
        A[Request] --> B{prompt_embeds?}
        B -->|No| C[🔴 Tokenize text]
        B -->|Yes| D[🟢 Validate base64+size]
        C --> E[token_ids, ISL=N]
        D --> F[token_ids=empty, skip ISL]
    end

    subgraph RT["Router (NATS)"]
        G[Route PreprocessedRequest]
    end

    subgraph WK["Worker (Python)"]
        H[TokensPrompt#40;token_ids#41;]
        I[Decode → EmbedsPrompt#40;tensor#41;]
    end

    subgraph VLLM["vLLM Engine"]
        J[🔴 Embedding Layer]
        K[🟢 Bypass Embedding]
        L[Transformer Layers]
        M[LM Head → Response]
    end

    E --> G
    F --> G
    G -->|Normal| H
    G -->|Embeds| I
    H --> J --> L
    I --> K --> L
    L --> M
    

Layer	Normal Flow	Prompt Embeds
Frontend (Rust)	🔴 Tokenize text → token_ids, compute ISL	🟢 Validate base64+size, skip tokenization
Router (NATS)	Forward token_ids in PreprocessedRequest	Forward prompt_embeds string
Worker (Python)	TokensPrompt(token_ids)	Decode base64 → EmbedsPrompt(tensor)
vLLM Engine	🔴 Embedding Layer → Transformer	🟢 Bypass Embedding → Transformer

Quick Start

Send pre-computed prompt embeddings directly to vLLM, bypassing tokenization.

1. Enable Feature

python -m dynamo.vllm --model <model-name> --enable-prompt-embeds

Required: The --enable-prompt-embeds flag must be set or requests will fail.

2. Send Request

import torch
import base64
import io
from openai import OpenAI

# Prepare embeddings (sequence_length, hidden_dim)
embeddings = torch.randn(10, 4096, dtype=torch.float32)

# Encode
buffer = io.BytesIO()
torch.save(embeddings, buffer)
buffer.seek(0)
embeddings_base64 = base64.b64encode(buffer.read()).decode()

# Send
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    prompt="",  # Can be empty or present; prompt_embeds takes precedence
    max_tokens=100,
    extra_body={"prompt_embeds": embeddings_base64}
)

Configuration

Docker Compose

vllm-worker:
  command:
    - python
    - -m
    - dynamo.vllm
    - --model
    - meta-llama/Meta-Llama-3.1-8B-Instruct
    - --enable-prompt-embeds  # Add this

Kubernetes

extraPodSpec:
  mainContainer:
    args:
      - "--model"
      - "meta-llama/Meta-Llama-3.1-8B-Instruct"
      - "--enable-prompt-embeds"  # Add this

NATS Configuration

NATS needs 15MB payload limit (already configured in default deployments):

# Docker Compose - deploy/docker-compose.yml
nats-server:
  command: ["-js", "--trace", "-m", "8222", "--max_payload", "15728640"]

# Kubernetes - deploy/cloud/helm/platform/values.yaml
nats:
  config:
    merge:
      max_payload: 15728640

API Reference

Request

{
  "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
  "prompt": "",
  "prompt_embeds": "<base64-encoded-pytorch-tensor>",
  "max_tokens": 100
}

Requirements:

• Format: PyTorch tensor serialized with torch.save() and base64-encoded

• Size: 100 bytes - 10MB (decoded)

• Shape: (seq_len, hidden_dim) or (batch, seq_len, hidden_dim)

• Dtype: torch.float32 (recommended)

Field Precedence:

• Both prompt and prompt_embeds can be provided in the same request

• When both are present, prompt_embeds takes precedence and prompt is ignored

• The prompt field can be empty ("") when using prompt_embeds

Response

Standard OpenAI format with accurate usage:

{
  "usage": {
    "prompt_tokens": 10,        // Extracted from embedding shape
    "completion_tokens": 15,
    "total_tokens": 25
  }
}

Errors

Error	Fix
ValueError: You must set --enable-prompt-embeds	Add --enable-prompt-embeds to worker
prompt_embeds must be valid base64	Use .decode('utf-8') after base64.b64encode()
decoded data must be at least 100 bytes	Increase sequence length
exceeds maximum size of 10MB	Reduce sequence length
must be a torch.Tensor	Use torch.save() not NumPy
size of tensor must match	Use correct hidden dimension for model

Examples

Streaming

stream = client.completions.create(
    model="meta-llama/Meta-Llama-3.1-8B-Instruct",
    prompt="",
    max_tokens=100,
    stream=True,
    extra_body={"prompt_embeds": embeddings_base64}
)

for chunk in stream:
    if chunk.choices:
        print(chunk.choices[0].text, end="", flush=True)

Load from File

embeddings = torch.load("embeddings.pt")

buffer = io.BytesIO()
torch.save(embeddings, buffer)
buffer.seek(0)
embeddings_base64 = base64.b64encode(buffer.read()).decode()

# Use in request...

Limitations

• ❌ Requires --enable-prompt-embeds flag (disabled by default)

• ❌ PyTorch format only (NumPy not supported)

• ❌ 10MB decoded size limit

• ❌ Cannot mix with multimodal data (images/video)

Testing

Comprehensive test coverage ensures reliability:

• Unit Tests: 31 tests (11 Rust + 20 Python)

  • Validation, decoding, format handling, error cases, usage statistics

• Integration Tests: 21 end-to-end tests

  • Core functionality, performance, formats, concurrency, usage statistics

Run integration tests:

# Start worker with flag
python -m dynamo.vllm --model Qwen/Qwen3-0.6B --enable-prompt-embeds

# Run tests
pytest tests/integration/test_prompt_embeds_integration.py -v

See Also

• vLLM Backend

• vLLM Configuration