[Docs] Added documentation for benchmarks

docs/key_modules/Benchmark.md

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+---
+title: "BenchMarks"
+description: "Learn about CAMEL's Benchmark module."
+---
+
+## Overview
+
+The **Benchmark** module in CAMEL provides a framework for evaluating AI agents and language models across various tasks and domains. It includes implementations of multiple benchmarks and provides a interface for running evaluations, measuring performance, and generating detailed reports.
+
+The module supports benchmarks for:
+
+- **API calling and tool use** (APIBank, APIBench, Nexus)
+- **General AI assistance** (GAIA)
+- **Browser-based comprehension** (BrowseComp)
+- **Retrieval-Augmented Generation** (RAGBench)
+
+## Architecture
+
+### Base Class: `BaseBenchmark`
+
+All benchmarks inherit from the `BaseBenchmark` abstract class, which provides a common interface for downloading data, loading datasets, running evaluations, and accessing results.
+
+#### BaseBenchmark Methods
+
+| Method       | Description                  | Parameters                                                                                                                                       |
+| ------------ | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `__init__()` | Initialize the benchmark     | `name`: Benchmark name<br>`data_dir`: Data directory path<br>`save_to`: Results save path<br>`processes`: Number of parallel processes           |
+| `download()` | Download benchmark data      | None                                                                                                                                             |
+| `load()`     | Load benchmark data          | `force_download`: Force re-download                                                                                                              |
+| `run()`      | Run the benchmark evaluation | `agent`: ChatAgent to evaluate<br>`on`: Data split ("train", "valid", "test")<br>`randomize`: Shuffle data<br>`subset`: Limit number of examples |
+| `train`      | Get training data            | None                                                                                                                                             |
+| `valid`      | Get validation data          | None                                                                                                                                             |
+| `test`       | Get test data                | None                                                                                                                                             |
+| `results`    | Get evaluation results       | None                                                                                                                                             |
+
+## Available Benchmarks
+
+### 1. GAIA Benchmark
+
+**GAIA (General AI Assistants)** is a benchmark for evaluating general-purpose AI assistants on real-world tasks requiring multiple steps, tool use, and reasoning.
+
+### 2. APIBank Benchmark
+
+**APIBank** evaluates the ability of LLMs to make correct API calls and generate appropriate responses in multi-turn conversations.
+
+### 3. APIBench Benchmark
+
+**APIBench (Gorilla)** tests the ability to generate correct API calls for various machine learning frameworks (HuggingFace, TensorFlow Hub, Torch Hub).
+
+### 4. Nexus Benchmark
+
+**Nexus** evaluates function calling capabilities across multiple domains including security APIs, location services, and climate data.
+
+#### Available Tasks
+
+| Task                         | Description                   |
+| ---------------------------- | ----------------------------- |
+| `"NVDLibrary"`               | CVE and CPE API calls         |
+| `"VirusTotal"`               | Malware and security analysis |
+| `"OTX"`                      | Open Threat Exchange API      |
+| `"PlacesAPI"`                | Location and mapping services |
+| `"ClimateAPI"`               | Weather and climate data      |
+| `"VirusTotal-ParallelCalls"` | Multiple parallel API calls   |
+| `"VirusTotal-NestedCalls"`   | Nested API calls              |
+| `"NVDLibrary-NestedCalls"`   | Nested CVE/CPE calls          |
+
+### 5. BrowseComp Benchmark
+
+**BrowseComp** evaluates browser-based comprehension by testing agents on questions that require understanding web content.
+
+### 6. RAGBench Benchmark
+
+**RAGBench** evaluates Retrieval-Augmented Generation systems using context relevancy and faithfulness metrics.
+
+#### Available Subsets
+
+| Subset       | Description                                        |
+| ------------ | -------------------------------------------------- |
+| `"hotpotqa"` | Multi-hop question answering                       |
+| `"covidqa"`  | COVID-19 related questions                         |
+| `"finqa"`    | Financial question answering                       |
+| `"cuad"`     | Contract understanding                             |
+| `"msmarco"`  | Microsoft Machine Reading Comprehension            |
+| `"pubmedqa"` | Biomedical questions                               |
+| `"expertqa"` | Expert-level questions                             |
+| `"techqa"`   | Technical questions                                |
+| Others       | `"emanual"`, `"delucionqa"`, `"hagrid"`, `"tatqa"` |
+
+## Common Usage Pattern
+
+All benchmarks follow a similar pattern:
+
+```python
+from camel.benchmarks import <BenchmarkName>
+from camel.agents import ChatAgent
+
+# 1. Initialize
+benchmark = <BenchmarkName>(
+    data_dir="./data",
+    save_to="./results.json",
+    processes=4
+)
+
+# 2. Load data
+benchmark.load(force_download=False)
+
+# 3. Create agent
+agent = ChatAgent(...)
+
+# 4. Run evaluation
+results = benchmark.run(
+    agent=agent,
+    # benchmark-specific parameters
+    randomize=False,
+    subset=None  # or number of examples
+)
+
+# 5. Access results
+print(results)  # Summary metrics
+print(benchmark.results)  # Detailed per-example results
+```
+
+## Implementing Custom Benchmarks
+
+To create a custom benchmark, inherit from `BaseBenchmark` and implement:
+
+1. `download()`: Download benchmark data
+2. `load()`: Load data into `self._data` dictionary
+3. `run()`: Execute benchmark and populate `self._results`
+4. Optional: Override `train`, `valid`, `test` properties
+
+## References
+
+- **GAIA**: https://huggingface.co/datasets/gaia-benchmark/GAIA
+- **APIBank**: https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/api-bank
+- **APIBench (Gorilla)**: https://huggingface.co/datasets/gorilla-llm/APIBench
+- **Nexus**: https://huggingface.co/collections/Nexusflow/nexusraven-v2
+- **RAGBench**: https://arxiv.org/abs/2407.11005
+
+## Other Resources
+
+- Explore the [Agents](./agents.md) module for creating custom agents