Add whichmodel-haystack integration — cost-aware LLM model selection

integrations/whichmodel.md

@@ -0,0 +1,102 @@
+---
+layout: integration
+name: WhichModel
+description: Cost-aware LLM model selection for Haystack pipelines using the WhichModel recommendation engine
+authors:
+    - name: WhichModel
+      socials:
+        github: Which-Model
+pypi: https://pypi.org/project/whichmodel-haystack/
+repo: https://github.com/Which-Model/whichmodel-mcp/tree/main/integrations/haystack-whichmodel
+type: Custom Component
+report_issue: https://github.com/Which-Model/whichmodel-mcp/issues
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+
+## Overview
+
+`whichmodel-haystack` adds a `WhichModelRouter` pipeline component that calls the [WhichModel](https://whichmodel.app) recommendation engine before routing to a generator. It picks the most cost-effective model for your task — balancing quality, latency, and price — without requiring an API key.
+
+No API key required. The component calls the public WhichModel MCP server at `https://mcp.whichmodel.app/mcp`.
+
+## Installation
+
+```bash
+pip install whichmodel-haystack
+```
+
+## Usage
+
+### Standalone recommendation
+
+```python
+from haystack_integrations.components.routers.whichmodel import WhichModelRouter
+
+router = WhichModelRouter()
+result = router.run(task_type="code_generation", complexity="high")
+
+print(result["model_id"])       # e.g. "anthropic/claude-sonnet-4"
+print(result["provider"])       # e.g. "anthropic"
+print(result["confidence"])     # "high", "medium", or "low"
+print(result["recommendation"]) # full dict with pricing, score, reasoning
+```
+
+### Dynamic model selection with budget constraints
+
+```python
+from haystack_integrations.components.routers.whichmodel import WhichModelRouter
+
+router = WhichModelRouter()
+result = router.run(
+    task_type="code_generation",
+    complexity="high",
+    estimated_input_tokens=2000,
+    estimated_output_tokens=1000,
+    budget_per_call=0.01,
+    requirements={"tool_calling": True},
+)
+
+print(f"Using {result['model_id']} (confidence: {result['confidence']})")
+print(f"Estimated cost: ${result['recommendation']['cost_estimate_usd']:.6f}")
+```
+
+### Parameters
+
+**Init parameters:**
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `mcp_endpoint` | `str` | `https://mcp.whichmodel.app/mcp` | WhichModel MCP server URL |
+| `timeout` | `float` | `30.0` | HTTP request timeout in seconds |
+| `default_task_type` | `str` | `None` | Default task type for `run()` |
+| `default_complexity` | `str` | `"medium"` | Default complexity level |
+
+**Run parameters:**
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `task_type` | `str` | Task type: `chat`, `code_generation`, `code_review`, `summarisation`, `translation`, `data_extraction`, `tool_calling`, `creative_writing`, `research`, `classification`, `embedding`, `vision`, `reasoning` |
+| `complexity` | `str` | `"low"`, `"medium"`, or `"high"` |
+| `estimated_input_tokens` | `int` | Expected input size in tokens |
+| `estimated_output_tokens` | `int` | Expected output size in tokens |
+| `budget_per_call` | `float` | Max USD per call |
+| `requirements` | `dict` | Capability requirements: `tool_calling`, `json_output`, `streaming`, `context_window_min`, `providers_include`, `providers_exclude` |
+
+**Outputs:**
+
+| Key | Type | Description |
+|-----|------|-------------|
+| `model_id` | `str` | Recommended model ID (e.g. `anthropic/claude-sonnet-4`) |
+| `provider` | `str` | Provider name |
+| `recommendation` | `dict` | Full recommendation with score, reasoning, pricing |
+| `alternative` | `dict` | Alternative model from different provider/tier |
+| `budget_model` | `dict` | Cheapest viable option |
+| `confidence` | `str` | `"high"`, `"medium"`, or `"low"` |
+| `data_freshness` | `str` | When pricing data was last updated |