Adding docs for the CLI

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docs/cli.md

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+# Encoderfile CLI Documentation
+
+## Overview
+
+Encoderfile is a command-line tool for running machine learning inference tasks using transformer models. It supports multiple model types including embeddings, sequence classification, and token classification. The tool can operate in two modes: as a server (HTTP/gRPC) or as a standalone inference tool.
+
+## Architecture
+
+The CLI is built with the following components:
+- **Server Mode**: Hosts models via HTTP and/or gRPC endpoints
+- **Inference Mode**: Performs one-off inference operations from the command line
+- **Multi-Model Support**: Automatically detects and routes to the appropriate model type
+
+## Commands
+
+### `serve`
+
+Starts the encoderfile server with HTTP and/or gRPC endpoints for model inference.
+
+#### Usage
+
+```bash
+encoderfile serve [OPTIONS]
+```
+
+#### Options
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `--grpc-hostname` | String | `[::]` | Hostname/IP address for the gRPC server |
+| `--grpc-port` | String | `50051` | Port for the gRPC server |
+| `--http-hostname` | String | `0.0.0.0` | Hostname/IP address for the HTTP server |
+| `--http-port` | String | `8080` | Port for the HTTP server |
+| `--disable-grpc` | Boolean | `false` | Disable the gRPC server |
+| `--disable-http` | Boolean | `false` | Disable the HTTP server |
+
+#### Examples
+
+**Start both HTTP and gRPC servers (default):**
+```bash
+encoderfile serve
+```
+
+**Start only HTTP server:**
+```bash
+encoderfile serve --disable-grpc
+```
+
+**Start only gRPC server:**
+```bash
+encoderfile serve --disable-http
+```
+
+**Custom ports and hostnames:**
+```bash
+encoderfile serve \
+  --http-hostname 127.0.0.1 \
+  --http-port 3000 \
+  --grpc-hostname localhost \
+  --grpc-port 50052
+```
+
+#### Notes
+
+- At least one server type (HTTP or gRPC) must be enabled
+- The server will display a banner upon successful startup
+- Both servers run concurrently using async tasks
+
+---
+
+### `infer`
+
+Performs inference on input text using the configured model. The model type is automatically detected based on configuration.
+
+#### Usage
+
+```bash
+encoderfile infer <INPUTS>... [OPTIONS]
+```
+
+#### Arguments
+
+| Argument | Required | Description |
+|----------|----------|-------------|
+| `<INPUTS>` | Yes | One or more text strings to process |
+
+#### Options
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `--normalize` | Boolean | `true` | Normalize embeddings (embedding models only) |
+| `-f, --format` | Enum | `json` | Output format (currently only JSON supported) |
+| `-o, --out-dir` | String | None | Output file path; if not provided, prints to stdout |
+
+#### Model Types
+
+The inference behavior depends on the model type configured:
+
+##### 1. Embedding Models
+Generates vector embeddings for input text.
+
+**Example:**
+```bash
+encoderfile infer "Hello world" "Another sentence"
+```
+
+**With normalization disabled:**
+```bash
+encoderfile infer "Hello world" --normalize=false
+```
+
+##### 2. Sequence Classification Models
+Classifies entire sequences (e.g., sentiment analysis, topic classification).
+
+**Example:**
+```bash
+encoderfile infer "This product is amazing!" "I'm very disappointed"
+```
+
+##### 3. Token Classification Models
+Labels individual tokens (e.g., Named Entity Recognition, Part-of-Speech tagging).
+
+**Example:**
+```bash
+encoderfile infer "Apple Inc. is located in Cupertino, California"
+```
+
+#### Output Formats
+
+Currently, only JSON format is supported (`--format json`). The output structure varies by model type:
+
+##### Embedding Output
+```json
+{
+  "embeddings": [
+    [0.123, -0.456, 0.789, ...],
+    [0.321, -0.654, 0.987, ...]
+  ],
+  "metadata": null
+}
+```
+
+##### Sequence Classification Output
+```json
+{
+  "predictions": [
+    {
+      "label": "POSITIVE",
+      "score": 0.9876
+    },
+    {
+      "label": "NEGATIVE",
+      "score": 0.8765
+    }
+  ],
+  "metadata": null
+}
+```
+
+##### Token Classification Output
+```json
+{
+  "predictions": [
+    {
+      "tokens": ["Apple", "Inc.", "is", "located", "in", "Cupertino", ",", "California"],
+      "labels": ["B-ORG", "I-ORG", "O", "O", "O", "B-LOC", "O", "B-LOC"]
+    }
+  ],
+  "metadata": null
+}
+```
+
+#### Saving Output to File
+
+**Save results to a file:**
+```bash
+encoderfile infer "Sample text" -o results.json
+```
+
+**Process multiple inputs and save:**
+```bash
+encoderfile infer "First input" "Second input" "Third input" --out-dir output.json
+```
+
+## Configuration
+
+The CLI relies on external configuration to determine:
+- Model type (Embedding, SequenceClassification, TokenClassification)
+- Model path and parameters
+- Server settings
+
+Ensure your configuration is properly set before running commands. Refer to the main encoderfile configuration documentation for details.
+
+## Error Handling
+
+The CLI will return appropriate error messages for:
+- Invalid configuration (e.g., both servers disabled)
+- Missing required arguments
+- Model loading failures
+- Inference errors
+- File I/O errors
+
+## Examples
+
+### Basic Inference Workflow
+
+```bash
+# Set up configuration (example)
+export MODEL_PATH=/path/to/model
+export MODEL_TYPE=embedding
+
+# Run inference
+encoderfile infer "Hello world"
+
+# Save to file
+encoderfile infer "Hello world" -o embedding.json
+```
+
+### Server Workflow
+
+```bash
+# Terminal 1: Start server
+encoderfile serve --http-port 8080
+
+# Terminal 2: Make HTTP requests (using curl)
+curl -X POST http://localhost:8080/infer \
+  -H "Content-Type: application/json" \
+  -d '{"inputs": ["Hello world"], "normalize": true}'
+```
+
+### Batch Processing
+
+```bash
+# Process multiple inputs at once
+encoderfile infer \
+  "First document to analyze" \
+  "Second document to analyze" \
+  "Third document to analyze" \
+  --out-dir batch_results.json
+```
+
+### Custom Server Configuration
+
+```bash
+# Run on specific network interface with custom ports
+encoderfile serve \
+  --http-hostname 192.168.1.100 \
+  --http-port 3000 \
+  --grpc-hostname 192.168.1.100 \
+  --grpc-port 50052
+```
+
+## Troubleshooting
+
+### Both servers cannot be disabled
+**Error**: "Cannot disable both gRPC and HTTP"
+
+**Solution**: Enable at least one server type:  
+```bash
+encoderfile serve --disable-grpc  # Keep HTTP enabled
+# OR
+encoderfile serve --disable-http  # Keep gRPC enabled
+```
+
+### Output not appearing
+If output isn't visible, check:
+1. Ensure you're not redirecting output to a file unintentionally
+2. Check file permissions if using `--out-dir`
+3. Verify the model is correctly configured
+
+### Model type detection
+The CLI automatically detects model type from configuration. If inference behaves unexpectedly:
+1. Verify your model configuration
+2. Ensure the model type matches your use case
+3. Check model compatibility
+
+## Additional Resources
+
+- GitHub Repository: [mozilla-ai/encoderfile](https://github.com/mozilla-ai/encoderfile)
+- API Documentation: Refer to the server API docs for HTTP/gRPC endpoint specifications
+- Model Configuration: See configuration documentation for model setup