How to Generate Embedding Vectors with agent.embed() in Needle 3

Use agent.embed() to generate dense vector representations from text and optional audio inputs, available exclusively in Needle 3 models through a Pythonic wrapper around the low-level C API.

The agent.embed() method in the cactus-compute/needle repository provides a streamlined interface for extracting high-dimensional embedding vectors from Needle 3 generation models. This functionality enables semantic search, similarity comparisons, and multimodal machine learning by converting text prompts—and optionally audio clips—into dense float arrays. According to the source implementation in needle/__init__.py, the method orchestrates generation validation, audio normalization, and C library delegation to return consistent vector representations.

Prerequisites: Needle 3 Generation Requirement

The embedding capability is strictly limited to Needle 3 models (weights with generation ≥ 3). When you invoke agent.embed(), the method first verifies the loaded model's generation status.

In needle/__init__.py at lines 64‑67, the implementation performs an explicit check:

if self._generation < 3:
    raise ValueError("embeddings require a Needle 3 model")

Attempting to call embed() on Needle 1 or Needle 2 weights immediately raises this ValueError, preventing incompatible API usage.

The agent.embed() Execution Pipeline

When you call agent.embed(), the method executes a five-phase pipeline that bridges Python inputs with the native Needle engine.

Generation Validation

The process begins by asserting that self._generation meets the minimum threshold of 3. This gatekeeping ensures that the underlying model architecture includes the necessary embedding table and projection layers defined in needle/model/architecture.py (lines 483‑527).

Audio Normalisation

If you provide an audio parameter, the method normalizes the input via _prepare_audio() and _native_audio()—utilities shared with the complete() generation path. This converts raw WAV bytes or file buffers into a native audio descriptor compatible with the C API. Passing None for audio skips this step but still initializes an empty native descriptor.

Worker Binding

For instances created without fine-tuned weights, _bind() ensures the shared library for the correct generation is dynamically loaded and the inference engine is initialized. This lazy-loading mechanism prevents unnecessary memory allocation until embedding functionality is explicitly requested.

Delegating to the C API

The actual computation branches based on whether a fine-tuned worker exists:

• Fine-tuned path: If self._worker is present (a FineTuneWorker instance), the call delegates to FineTuneWorker.embed() at lines 292‑301 in needle/_worker.py, which forwards the request to the engine and returns the embedding list.

• Base model path: For standard inference, the method interacts directly with the C library through a two-phase protocol (lines 72‑80 in needle/__init__.py):

dim = lib.needle_embed(text.encode("utf-8"), native_audio, None, 0)
output = (ctypes.c_float * dim)()
rc = lib.needle_embed(text.encode("utf-8"), native_audio, output, dim)

The first call retrieves the embedding dimensionality (dim). The second call populates a pre-allocated ctypes buffer with the actual float values. If the return code indicates failure, the method raises a RuntimeError with the specific error code from the underlying implementation.

Result Conversion

Finally, the C float buffer is converted to a standard Python list of floats and returned to the caller. This normalization ensures compatibility with NumPy, PyTorch, and other downstream vector operations.

Practical Code Examples

Generating Text-Only Embeddings

For pure text inference, pass a string prompt directly to agent.embed():

from needle import Needle

# Load a Needle 3 model (defaults to generation 3 if no weights specified)

agent = Needle()

# Generate embedding for a text prompt

embedding = agent.embed("The quick brown fox jumps over the lazy dog")

print(f"Embedding dimension: {len(embedding)}")
print(embedding[:5])  # Display first 5 values: [0.023, -0.145, 0.892, ...]

As verified in tests/test_weights.py (lines 167‑174), the returned object is a plain Python list containing floating-point values suitable for similarity computations.

Creating Multimodal Embeddings with Audio

To incorporate audio context, provide WAV-compatible bytes alongside your text prompt:

from needle import Needle

agent = Needle()

# Load 16-bit PCM WAV file

with open("example.wav", "rb") as f:
    wav_bytes = f.read()

# Generate multimodal embedding

audio_embedding = agent.embed(
    text="Discussing the sound of rain",
    audio=wav_bytes,
    audio_format="wav",
    sample_rate=16000,
    channels=1
)

print(f"Multimodal embedding size: {len(audio_embedding)}")

The _prepare_audio helper handles format validation and resampling before passing the native descriptor to needle_embed.

Implementation Architecture

The embedding functionality spans multiple source files within the repository:

• needle/__init__.py (lines 64‑81): Contains the public embed() method with generation gating and C API orchestration.
• needle/_worker.py (lines 292‑301): Implements FineTuneWorker.embed() for custom-weight inference scenarios.
• needle/model/architecture.py (lines 483‑527): Defines the underlying nn.Embed layers and projection heads that compute the actual vector representations.
• tests/test_weights.py (lines 167‑174): Provides reference implementations demonstrating expected return types and dimensionalities.

Summary

• Generation gate: agent.embed() requires Needle 3 models and raises ValueError for older generations.
• Multimodal support: Accepts optional audio inputs normalized through shared preprocessing utilities.
• Dual path execution: Routes to FineTuneWorker.embed() for custom weights or directly to the needle_embed C API for base models.
• C API protocol: Uses a two-call pattern—first retrieving dimensionality, then populating the output buffer via ctypes.
• Return format: Returns a standard Python list of floats compatible with common ML frameworks.

Frequently Asked Questions

What model generations support agent.embed()?

Only Needle 3 and later generations support the embed() method. The implementation explicitly checks self._generation >= 3 and raises a ValueError stating "embeddings require a Needle 3 model" if you attempt to use it with Needle 1 or 2 weights.

Can I use agent.embed() with fine-tuned weights?

Yes. If you load a fine-tuned model, agent.embed() automatically delegates to FineTuneWorker.embed() in needle/_worker.py. This worker path forwards your text and audio inputs to the customized engine while maintaining the same Python list return format as the base model implementation.

What audio formats are supported for multimodal embeddings?

The method accepts raw WAV bytes (16-bit PCM) through the audio parameter. The internal _prepare_audio and _native_audio functions handle validation and conversion to the native audio descriptor expected by the C library. You must specify audio_format="wav" along with sample_rate and channels parameters for proper normalization.

How do I handle errors when generating embeddings?

The method raises two primary exceptions: ValueError if your model generation is insufficient (caught at lines 64‑67), and RuntimeError if the underlying C API returns a non-zero status code during the embedding computation (caught at lines 72‑80). Always wrap calls in try-except blocks when working with unknown model versions or potentially corrupt audio inputs.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}