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crates/embeddings-engine/README.md

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 # Embeddings Engine
 
-A high-performance text embeddings service that generates vector representations of text using state-of-the-art models. 
-This crate wraps the fastembed crate to provide embeddings and partially adapts the openai specification.  
+A high-performance text embeddings service that generates vector representations of text using state-of-the-art models. This crate wraps the FastEmbed library to provide embeddings with OpenAI-compatible API endpoints.
+
+## Overview
+
+The embeddings-engine provides a standalone service for generating text embeddings that can be used for semantic search, similarity comparisons, and other NLP tasks. It's designed to be compatible with OpenAI's embeddings API format.
+
+## Features
+
+- **OpenAI-Compatible API**: `/v1/embeddings` endpoint matching OpenAI's specification
+- **FastEmbed Integration**: Powered by the FastEmbed library for high-quality embeddings
+- **Multiple Model Support**: Support for various embedding models
+- **High Performance**: Optimized for fast embedding generation
+- **Standalone Service**: Can run independently or as part of the predict-otron-9000 platform
+
+## Building and Running
+
+### Prerequisites
+- Rust toolchain
+- Internet connection for initial model downloads
+
+### Standalone Server
+```bash
+cargo run --bin embeddings-engine --release
+```
+
+The service will start on port 8080 by default.
+
+## API Usage
+
+### Generate Embeddings
+
+**Endpoint**: `POST /v1/embeddings`
+
+**Request Body**:
+```json
+{
+  "input": "Your text to embed",
+  "model": "nomic-embed-text-v1.5"
+}
+```
+
+**Response**:
+```json
+{
+  "object": "list",
+  "data": [
+    {
+      "object": "embedding",
+      "index": 0,
+      "embedding": [0.1, 0.2, 0.3, ...]
+    }
+  ],
+  "model": "nomic-embed-text-v1.5",
+  "usage": {
+    "prompt_tokens": 0,
+    "total_tokens": 0
+  }
+}
+```
+
+### Example Usage
+
+**Using cURL**:
+```bash
+curl -s http://localhost:8080/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -d '{
+    "input": "The quick brown fox jumps over the lazy dog",
+    "model": "nomic-embed-text-v1.5"
+  }' | jq
+```
+
+**Using Python OpenAI Client**:
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8080/v1",
+    api_key="dummy"  # Not validated but required by client
+)
+
+response = client.embeddings.create(
+    input="Your text here",
+    model="nomic-embed-text-v1.5"
+)
+
+print(response.data[0].embedding)
+```
+
+## Configuration
+
+The service can be configured through environment variables:
+- `SERVER_PORT`: Port to run on (default: 8080)
+- `RUST_LOG`: Logging level (default: info)
+
+## Integration
+
+This service is designed to work seamlessly with the predict-otron-9000 main server, but can also be deployed independently for dedicated embeddings workloads.