feat: support native embedding generation

docs/concepts/columns.md

@@ -64,6 +64,22 @@ Define scoring rubrics (relevance, accuracy, fluency, helpfulness) and the judge
 
 Use judge columns for data quality filtering (e.g., keep only 4+ rated responses), A/B testing generation strategies, and quality monitoring over time.
 
+### 🧬 Embedding Columns
+
+Embedding columns generate vector embeddings (numerical representations) for text content using embedding models. These embeddings capture semantic meaning, enabling similarity search, clustering, and semantic analysis.
+
+Specify a `target_column` containing text, and Data Designer generates embeddings for that content. The target column can contain either a single text string or a list of text strings in stringified JSON format—in the latter case, embeddings are generated for each text string in the list.
+
+Common use cases:
+
+- **Semantic search**: Generate embeddings for documents, then find similar content by vector similarity
+- **Clustering**: Group similar texts based on embedding proximity
+- **Recommendation systems**: Match content by semantic similarity
+- **Anomaly detection**: Identify outliers in embedding space
+
+!!! note "Embedding Models"
+    Embedding columns require an embedding model configured with `EmbeddingInferenceParams`. These models differ from chat completion models—they output vectors rather than text. The generation type is automatically determined by the inference parameters type.
+
 ### 🧩 Expression Columns
 
 Expression columns handle simple transformations using **Jinja2 templates**—concatenate first and last names, calculate numerical totals, format date strings. No LLM overhead needed.

docs/concepts/models/configure-model-settings-with-the-cli.md

@@ -130,6 +130,6 @@ The CLI will show which configuration files exist and ask for confirmation befor
 ## See Also
 
 - **[Model Providers](model-providers.md)**: Learn about the `ModelProvider` class and provider configuration
-- **[Model Configurations](model-configs.md)**: Learn about `ModelConfig` and `InferenceParameters`
+- **[Model Configurations](model-configs.md)**: Learn about `ModelConfig`
 - **[Default Model Settings](default-model-settings.md)**: Pre-configured providers and model settings included with Data Designer
 - **[Quick Start Guide](../../quick-start.md)**: Get started with a simple example

docs/concepts/models/inference-parameters.md

@@ -0,0 +1,147 @@
+# Inference Parameters
+
+Inference parameters control how models generate responses during synthetic data generation. Data Designer provides two types of inference parameters: `ChatCompletionInferenceParams` for text/code/structured generation and `EmbeddingInferenceParams` for embedding generation.
+
+## Overview
+
+When you create a `ModelConfig`, you can specify inference parameters to fine-tune model behavior. These parameters control aspects like randomness (temperature), diversity (top_p), context size (max_tokens), and more. Data Designer supports both static values and dynamic distribution-based sampling for certain parameters.
+
+## Chat Completion Inference Parameters
+
+The `ChatCompletionInferenceParams` class controls how models generate text completions (for text, code, and structured data generation). It provides fine-grained control over generation behavior and supports both static values and dynamic distribution-based sampling.
+
+!!! warning "InferenceParameters is Deprecated"
+    The `InferenceParameters` class is deprecated and will be removed in a future version. Use `ChatCompletionInferenceParams` instead. The old `InferenceParameters` class now shows a deprecation warning when used.
+
+### Fields
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `temperature` | `float` or `Distribution` | No | Controls randomness in generation (0.0 to 2.0). Higher values = more creative/random |
+| `top_p` | `float` or `Distribution` | No | Nucleus sampling parameter (0.0 to 1.0). Controls diversity by filtering low-probability tokens |
+| `max_tokens` | `int` | No | Maximum number of tokens for the request, including both input and output tokens (≥ 1) |
+| `max_parallel_requests` | `int` | No | Maximum concurrent API requests (default: 4, ≥ 1) |
+| `timeout` | `int` | No | API request timeout in seconds (≥ 1) |
+| `extra_body` | `dict[str, Any]` | No | Additional parameters to include in the API request body |
+
+!!! note "Default Values"
+    If `temperature`, `top_p`, or `max_tokens` are not provided, the model provider's default values will be used. Different providers and models may have different defaults.
+
+!!! tip "Controlling Reasoning Effort for GPT-OSS Models"
+    For gpt-oss models like `gpt-oss-20b` and `gpt-oss-120b`, you can control the reasoning effort using the `extra_body` parameter:
+
+    ```python
+    from data_designer.essentials import ChatCompletionInferenceParams
+
+    # High reasoning effort (more thorough, slower)
+    inference_parameters = ChatCompletionInferenceParams(
+        extra_body={"reasoning_effort": "high"}
+    )
+
+    # Medium reasoning effort (balanced)
+    inference_parameters = ChatCompletionInferenceParams(
+        extra_body={"reasoning_effort": "medium"}
+    )
+
+    # Low reasoning effort (faster, less thorough)
+    inference_parameters = ChatCompletionInferenceParams(
+        extra_body={"reasoning_effort": "low"}
+    )
+    ```
+
+### Temperature and Top P Guidelines
+
+- **Temperature**:
+    - `0.0-0.3`: Highly deterministic, focused outputs (ideal for structured/reasoning tasks)
+    - `0.4-0.7`: Balanced creativity and coherence (general purpose)
+    - `0.8-1.0`: Creative, diverse outputs (ideal for creative writing)
+    - `1.0+`: Highly random and experimental
+
+- **Top P**:
+    - `0.1-0.5`: Very focused, only most likely tokens
+    - `0.6-0.9`: Balanced diversity
+    - `0.95-1.0`: Maximum diversity, including less likely tokens
+
+!!! tip "Adjusting Temperature and Top P Together"
+    When tuning both parameters simultaneously, consider these combinations:
+
+    - **For deterministic/structured outputs**: Low temperature (`0.0-0.3`) + moderate-to-high top_p (`0.8-0.95`)
+        - The low temperature ensures focus, while top_p allows some token diversity
+    - **For balanced generation**: Moderate temperature (`0.5-0.7`) + high top_p (`0.9-0.95`)
+        - This is a good starting point for most use cases
+    - **For creative outputs**: Higher temperature (`0.8-1.0`) + high top_p (`0.95-1.0`)
+        - Both parameters work together to maximize diversity
+
+    **Avoid**: Setting both very low (overly restrictive) or adjusting both dramatically at once. When experimenting, adjust one parameter at a time to understand its individual effect.
+
+## Distribution-Based Inference Parameters
+
+For `temperature` and `top_p` in `ChatCompletionInferenceParams`, you can specify distributions instead of fixed values. This allows Data Designer to sample different values for each generation request, introducing controlled variability into your synthetic data.
+
+### Uniform Distribution
+
+Samples values uniformly between a low and high bound:
+
+```python
+from data_designer.essentials import (
+    ChatCompletionInferenceParams,
+    UniformDistribution,
+    UniformDistributionParams,
+)
+
+inference_params = ChatCompletionInferenceParams(
+    temperature=UniformDistribution(
+        params=UniformDistributionParams(low=0.7, high=1.0)
+    ),
+)
+```
+
+### Manual Distribution
+
+Samples from a discrete set of values with optional weights:
+
+```python
+from data_designer.essentials import (
+    ChatCompletionInferenceParams,
+    ManualDistribution,
+    ManualDistributionParams,
+)
+
+# Equal probability for each value
+inference_params = ChatCompletionInferenceParams(
+    temperature=ManualDistribution(
+        params=ManualDistributionParams(values=[0.5, 0.7, 0.9])
+    ),
+)
+
+# Weighted probabilities (normalized automatically)
+inference_params = ChatCompletionInferenceParams(
+    top_p=ManualDistribution(
+        params=ManualDistributionParams(
+            values=[0.8, 0.9, 0.95],
+            weights=[0.2, 0.5, 0.3]  # 20%, 50%, 30% probability
+        )
+    ),
+)
+```
+
+## Embedding Inference Parameters
+
+The `EmbeddingInferenceParams` class controls how models generate embeddings. This is used when working with embedding models for tasks like semantic search or similarity analysis.
+
+### Fields
+
+| Field | Type | Required | Description |
+|-------|------|----------|-------------|
+| `encoding_format` | `Literal["float", "base64"]` | No | Format of the embedding encoding (default: "float") |
+| `dimensions` | `int` | No | Number of dimensions for the embedding |
+| `max_parallel_requests` | `int` | No | Maximum concurrent API requests (default: 4, ≥ 1) |
+| `timeout` | `int` | No | API request timeout in seconds (≥ 1) |
+| `extra_body` | `dict[str, Any]` | No | Additional parameters to include in the API request body |
+
+
+## See Also
+
+- **[Model Configurations](model-configs.md)**: Learn about configuring model settings
+- **[Model Providers](model-providers.md)**: Learn about configuring model providers
+- **[Default Model Settings](default-model-settings.md)**: Pre-configured model settings included with Data Designer