Improve dataset card: Add paper link, project page, task category, and sample usage

This PR enhances the dataset card by:
- Adding `task_categories: ['text-retrieval']` to the metadata, improving the dataset's discoverability for relevant tasks.
- Including a direct link to the associated paper: [Citegeist: Automated Generation of Related Work Analysis on the arXiv Corpus](https://huggingface.co/papers/2503.23229).
- Adding a link to the project's website: [https://citegeist.org](https://citegeist.org).
- Incorporating a "Sample Usage" section with Python code snippets from the GitHub README, demonstrating how to use the Citegeist tool with the dataset, including instructions for both hosted and local Milvus database setups.

README.md

@@ -1,10 +1,97 @@
 ---
-license: cc0-1.0
 language:
 - en
+license: cc0-1.0
 tags:
 - arxiv
+task_categories:
+- text-retrieval
 ---
-Contains the entire arXiv metadata dataset, including 768-dimensional embeddings of all abstracts generated using sentence-transformer/all-MiniLM-L6-v2.
 
-This database is an artifact of the Citegeist project ([Github](https://github.com/chenneking/citegeist))
+# Citegeist: Automated Generation of Related Work Analysis on the arXiv Corpus
+
+This repository contains the entire arXiv metadata dataset, including 768-dimensional embeddings of all abstracts generated using `sentence-transformer/all-MiniLM-L6-v2`. This dataset is a core artifact of the [Citegeist project](https://github.com/chenneking/citegeist).
+
+The dataset and associated project were presented in the paper:
+[Citegeist: Automated Generation of Related Work Analysis on the arXiv Corpus](https://huggingface.co/papers/2503.23229)
+
+Project Page: [https://citegeist.org](https://citegeist.org)
+Code: [https://github.com/chenneking/citegeist](https://github.com/chenneking/citegeist)
+
+## Sample Usage
+
+To generate a related work section for a given abstract, you can use the `citegeist` library's `Generator` class.
+
+First, install the package:
+```bash
+pip install citegeist-arxiv
+```
+
+### Option 1: Using the Hosted Milvus Database
+
+As of March 2025, a hosted Milvus database is provided. You can use it by setting the following environment variables:
+
+```python
+import os
+from citegeist import Generator
+
+os.environ["MILVUS_URI"] = "http://49.12.219.90:19530"
+os.environ["MILVUS_TOKEN"] = "citegeist:citegeist"
+
+# Set your API key for the chosen LLM provider (e.g., for Gemini)
+os.environ["GEMINI_API_KEY"] = "YOUR_GEMINI_API_KEY"
+
+generator = Generator(
+   llm_provider="gemini",  # Choice of: "azure" (OpenAI Studio), "anthropic", "gemini", "mistral", and "openai"
+   api_key=os.environ.get("GEMINI_API_KEY"), # Here, you will need to set the respective API key
+   model_name="gemini-2.0-flash", # Choose the model that the provider supports
+   database_uri=os.environ.get("MILVUS_URI"),  # Set the path (local) / url (remote) for the Milvus DB connection
+   database_token=os.environ.get("MILVUS_TOKEN"),  # Optionally, also set the access token
+)
+# Define input abstract and breadth (5-20), depth (1-5), and diversity (0.0-1.0) parameters.
+abstract = "Large Language Models provide significant new opportunities for the generation of high-quality written works. However, their employment in the research community is inhibited by their tendency to hallucinate invalid sources and lack of direct access to a knowledge base of relevant scientific articles."
+breadth = 10
+depth = 2
+diversity = 0.0
+related_works, citations = generator.generate_related_work(abstract, breadth, depth, diversity)
+
+print("Generated Related Works:\
+", related_works)
+print("\
+Citations:\
+", citations)
+```
+
+### Option 2: Running the Milvus Database Locally
+
+Alternatively, you can run the Milvus database locally. Download [database.db](https://huggingface.co/datasets/chenneking/citegeist-milvus-db/blob/main/database.db) and provide its absolute path as the value for `MILVUS_URI`. You do not need to set `MILVUS_TOKEN` for this local setup.
+
+```python
+import os
+from citegeist import Generator
+
+# Replace <path_to_database.db_goes_here> with the actual absolute path to your database.db file
+os.environ["MILVUS_URI"] = "<path_to_database.db_goes_here>"
+
+# Set your API key for the chosen LLM provider (e.g., for Gemini)
+os.environ["GEMINI_API_KEY"] = "YOUR_GEMINI_API_KEY"
+
+generator = Generator(
+   llm_provider="gemini",  # Choice of: "azure" (OpenAI Studio), "anthropic", "gemini", "mistral", and "openai"
+   api_key=os.environ.get("GEMINI_API_KEY"), # Here, you will need to set the respective API key
+   model_name="gemini-2.0-flash", # Choose the model that the provider supports
+   database_uri=os.environ.get("MILVUS_URI"),  # Set the path (local) database.db file
+)
+# Define input abstract and breadth (5-20), depth (1-5), and diversity (0.0-1.0) parameters.
+abstract = "Large Language Models provide significant new opportunities for the generation of high-quality written works. However, their employment in the research community is inhibited by their tendency to hallucinate invalid sources and lack of direct access to a knowledge base of relevant scientific articles."
+breadth = 10
+depth = 2
+diversity = 0.0
+related_works, citations = generator.generate_related_work(abstract, breadth, depth, diversity)
+
+print("Generated Related Works:\
+", related_works)
+print("\
+Citations:\
+", citations)
+```