Update dataset card

README.md

@@ -1,24 +1,384 @@
 ---
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: embedding
-    list: float32
-    length: 384
-  - name: document
-    dtype: string
-  - name: metadata
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 15595559
-    num_examples: 4838
-  download_size: 10463189
-  dataset_size: 15595559
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: apache-2.0
+task_categories:
+- feature-extraction
+- text-retrieval
+- question-answering
+task_ids:
+- semantic-similarity-scoring
+- document-retrieval
+- open-domain-qa
+language:
+- en
+tags:
+- embeddings
+- vector-database
+- rag
+- retrieval-augmented-generation
+- semantic-search
+- knowledge-base
+- accounting
+size_categories:
+- 1K<n<10K
+annotations_creators:
+- machine-generated
+language_creators:
+- found
+multilinguality: monolingual
+pretty_name: Accounting Vectorstore Dataset
+source_datasets:
+- original
 ---
+
+# Vectorstore Dataset: Accounting
+
+## Overview
+
+This dataset contains pre-computed vector embeddings for the **accounting** domain, ready for use in Retrieval-Augmented Generation (RAG) applications, semantic search, and knowledge base systems. The embeddings are generated from high-quality source documents using state-of-the-art sentence transformers, making it easy to build production-ready RAG applications without the computational overhead of embedding generation.
+
+## Key Features
+
+- ✅ **Pre-computed embeddings**: Ready-to-use vector embeddings, saving computation time
+- ✅ **Production-ready**: Optimized for real-world RAG applications
+- ✅ **Comprehensive metadata**: Includes source file information, page numbers, and document hashes
+- ✅ **LangChain compatible**: Works seamlessly with LangChain and ChromaDB
+- ✅ **Search-optimized**: Designed for fast semantic similarity search
+
+## What's Included
+
+This dataset contains **4,838** text chunks from **2** source documents, each pre-embedded using the `sentence-transformers/all-MiniLM-L6-v2` model. Each chunk includes:
+- **Text content**: The original document text
+- **Embedding vector**: 384-dimensional float32 vector
+- **Rich metadata**: Source file, page numbers, document hash, and more
+
+## Dataset Details
+
+### Dataset Summary
+
+- **Domain**: `accounting`
+- **Total Chunks**: 4,838
+- **Total Documents**: 2
+- **Database Size**: 52.77 MB (8 files)
+- **Embedding Model**: `sentence-transformers/all-MiniLM-L6-v2`
+- **Chunk Size**: 1000
+- **Chunk Overlap**: 200
+
+### Dataset Structure
+
+The dataset contains the following columns:
+
+- **id**: Unique identifier for each chunk
+- **embedding**: Vector embedding (numpy array, dtype=float32)
+- **document**: Original text content of the chunk
+- **metadata**: JSON string containing metadata (file_name, file_hash, page_number, etc.)
+
+### Embedding Model
+
+This dataset uses embeddings from: `sentence-transformers/all-MiniLM-L6-v2`
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("meetara-lab/vectorstore-accounting")
+
+# Access the data
+print(dataset["train"][0])
+# Output:
+# {
+#     'id': '...',
+#     'embedding': array([...], dtype=float32),
+#     'document': '...',
+#     'metadata': '{"file_name": "...", "page": 1, ...}'
+# }
+```
+
+### Loading Back into ChromaDB
+
+```python
+from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+from datasets import load_dataset
+import json
+
+# Load dataset
+dataset = load_dataset("meetara-lab/vectorstore-accounting")["train"]
+
+# Initialize ChromaDB
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+vectorstore = Chroma(
+    persist_directory="./chroma_accounting",
+    embedding_function=embeddings
+)
+
+# Add documents to ChromaDB
+documents = []
+metadatas = []
+ids = []
+embeddings_list = []
+
+for item in dataset:
+    ids.append(item["id"])
+    embeddings_list.append(item["embedding"].tolist())
+    documents.append(item["document"])
+    metadatas.append(json.loads(item["metadata"]))
+
+# Note: You'll need to use ChromaDB's Python client directly for custom embeddings
+import chromadb
+client = chromadb.PersistentClient(path="./chroma_accounting")
+collection = client.create_collection(name="accounting")
+
+collection.add(
+    ids=ids,
+    embeddings=embeddings_list,
+    documents=documents,
+    metadatas=metadatas
+)
+```
+
+### Using with LangChain
+
+```python
+from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+
+# Initialize retriever
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+vectorstore = Chroma(
+    persist_directory="./chroma_accounting",
+    embedding_function=embeddings
+)
+
+# Load from HF Hub first (see above), then use with LangChain
+retriever = vectorstore.as_retriever()
+results = retriever.invoke("your query here")
+```
+
+### Domain-Specific Usage Examples
+
+This vectorstore is optimized for **Accounting** domain queries. Here are practical examples:
+
+#### Example Queries
+
+```python
+from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+
+# Load vectorstore (see "Loading Back into ChromaDB" above)
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+vectorstore = Chroma(
+    persist_directory="./chroma_accounting",
+    embedding_function=embeddings
+)
+retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+
+# Example queries for accounting domain:
+example_queries = [
+    "What information is available about accounting?",
+    "How to get started with accounting?",
+    "Best practices for accounting",
+    "Common questions about accounting",
+    "Resources for accounting"
+]
+
+# Run a query
+query = "What information is available about accounting?"
+results = retriever.invoke(query)
+
+# Display results
+for i, doc in enumerate(results, 1):
+    print(f"\nResult {i}:")
+    print(f"  Source: {doc.metadata.get('file_name', 'Unknown')}")
+    print(f"  Page: {doc.metadata.get('page', 'N/A')}")
+    print(f"  Content: {doc.page_content[:200]}...")
+```
+
+#### Common Use Cases
+
+This dataset is useful for:
+- **Information retrieval**
+- **Educational content lookup**
+- **Best practices reference**
+- **Resource discovery**
+- **Domain-specific queries**
+
+#### Real-World Example
+
+```python
+# Complete example: Query and use results
+from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+
+# 1. Initialize (after loading from HF Hub)
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+vectorstore = Chroma(
+    persist_directory="./chroma_accounting",
+    embedding_function=embeddings
+)
+
+# 2. Create retriever with relevance filtering
+retriever = vectorstore.as_retriever(
+    search_type="similarity",
+    search_kwargs={
+        "k": 5,  # Get top 5 most relevant results
+        "score_threshold": 0.7  # Minimum similarity score
+    }
+)
+
+# 3. Query the vectorstore
+query = "How to get started with accounting?"
+docs = retriever.invoke(query)
+
+# 4. Process results
+for doc in docs:
+    metadata = doc.metadata
+    print(f"📄 File: {metadata.get('file_name', 'Unknown')}")
+    print(f"📃 Page: {metadata.get('page', 'N/A')}")
+    print(f"📝 Content: {doc.page_content[:300]}...\n")
+```
+
+## Dataset Statistics
+
+### Content Statistics
+- **Total Chunks**: 4,838
+- **Total Documents**: 2
+- **Average Chunks per Document**: 2419.0
+- **Database Size**: 52.77 MB (8 files)
+
+### Technical Specifications
+- **Embedding Model**: `sentence-transformers/all-MiniLM-L6-v2` (384 dimensions)
+- **Chunk Size**: 1000 characters
+- **Chunk Overlap**: 200 characters
+- **Format**: Parquet/Arrow (optimized for fast loading)
+
+## Performance Considerations
+
+### Loading Time
+- Full dataset loads in ~5-15 seconds on average hardware
+- Memory usage: ~7.1 MB for embeddings alone
+- Recommended RAM: 2GB+ for full dataset operations
+
+### Search Performance
+- Typical query time: <100ms for similarity search
+- Optimized for retrieval of top-k results (k=5-10)
+- Works best with vector databases like ChromaDB, Pinecone, or Weaviate
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{meetara_vectorstore_accounting,
+  title={meeTARA Vectorstore: Accounting},
+  author={meeTARA Lab},
+  year={2024},
+  url={https://huggingface.co/datasets/meetara-lab/vectorstore-accounting}
+}
+```
+
+## Limitations and Considerations
+
+- **Language**: This dataset is monolingual (English only)
+- **Domain specificity**: Optimized for accounting domain queries
+- **Embedding model**: Uses `sentence-transformers/all-MiniLM-L6-v2` - ensure compatibility if switching models
+- **Update frequency**: Dataset reflects state at time of publication; source documents may have been updated
+
+## Alternatives and Related Datasets
+
+Looking for other domains? Check out the complete meeTARA Vectorstore collection:
+
+### Healthcare Domain
+- 🏥 `meetara-lab/vectorstore-general_health` - General Health (35,556 chunks)
+- 💊 `meetara-lab/vectorstore-healthcare` - Healthcare (Available)
+- 🧠 `meetara-lab/vectorstore-mental_health` - Mental Health (407 chunks)
+- 💊 `meetara-lab/vectorstore-nutrition` - Nutrition (1,165 chunks)
+- 💊 `meetara-lab/vectorstore-senior_health` - Senior Health (410 chunks)
+- 👩 `meetara-lab/vectorstore-women_health` - Women Health (318 chunks)
+
+### Education Domain
+- 📚 `meetara-lab/vectorstore-academic_tutoring` - Academic Tutoring (64,845 chunks)
+- 📚 `meetara-lab/vectorstore-education` - Education (Available)
+
+### Other Domains
+- 📁 `meetara-lab/vectorstore-ai_ml` - Ai Ml (131 chunks)
+- 📁 `meetara-lab/vectorstore-bipolar_disorder` - Bipolar Disorder (65 chunks)
+- 📁 `meetara-lab/vectorstore-chronic_conditions` - Chronic Conditions (1,882 chunks)
+- 📁 `meetara-lab/vectorstore-economics` - Economics (2,195 chunks)
+- 📁 `meetara-lab/vectorstore-emergency_care` - Emergency Care (52 chunks)
+- 📁 `meetara-lab/vectorstore-medication_management` - Medication Management (161 chunks)
+- 📁 `meetara-lab/vectorstore-parenting` - Parenting (460 chunks)
+- 📁 `meetara-lab/vectorstore-preventive_care` - Preventive Care (253 chunks)
+- 📁 `meetara-lab/vectorstore-programming` - Programming (171 chunks)
+- 📁 `meetara-lab/vectorstore-publish` - Publish (Available)
+- 📁 `meetara-lab/vectorstore-software_development` - Software Development (82 chunks)
+- 📁 `meetara-lab/vectorstore-space_technology` - Space Technology (15 chunks)
+- 📁 `meetara-lab/vectorstore-sports_recreation` - Sports Recreation (Available)
+- 📁 `meetara-lab/vectorstore-stress_management` - Stress Management (26 chunks)
+- 📁 `meetara-lab/vectorstore-tech_support` - Tech Support (2,920 chunks)
+
+**🔗 View all meeTARA datasets**: [https://huggingface.co/meetara-lab](https://huggingface.co/meetara-lab)
+
+**💡 Tip**: Combine multiple domain datasets for comprehensive multi-domain RAG applications!
+
+## Maintenance and Updates
+
+This dataset is maintained by the meeTARA Lab team. 
+
+- **Last Updated**: 2026-02-07
+- **Version**: 1.0
+- **Update Policy**: Datasets are updated periodically as source documents are added or improved
+- **Notifications**: Follow the repository to receive updates when new versions are published
+
+For updates, bug reports, or feature requests, please visit our GitHub repository.
+
+## License
+
+This dataset is released under the **Apache 2.0 License**. This means you are free to:
+- Use the dataset commercially and non-commercially
+- Modify and create derivative works
+- Distribute the dataset and modifications
+
+Please see the full license text for complete terms.
+
+## Citation
+
+If you use this dataset in your research or applications, please cite it as:
+
+```bibtex
+@dataset{meetara_vectorstore_accounting,
+  title={meeTARA Vectorstore: Accounting},
+  author={meeTARA Lab},
+  year={2024},
+  url={https://huggingface.co/datasets/meetara-lab/vectorstore-accounting},
+  license={apache-2.0},
+  task={feature-extraction, text-retrieval, rag}
+}
+```
+
+## Contact and Support
+
+- **GitHub**: [meetara-lab/meetara-core](https://github.com/meetara-lab/meetara-core)
+- **Hugging Face Profile**: [@meetara-lab](https://huggingface.co/meetara-lab)
+- **Issues**: Report bugs or request features on [GitHub Issues](https://github.com/meetara-lab/meetara-core/issues)
+- **Documentation**: Visit our repository for detailed documentation
+- **Dataset Requests**: Want a new domain? [Open an issue](https://github.com/meetara-lab/meetara-core/issues) to request it!
+
+## Contributing
+
+We welcome contributions! If you'd like to:
+- 🐛 Report bugs
+- 💡 Suggest new domains
+- 📝 Improve documentation
+- 🔧 Contribute code
+
+Please visit our [GitHub repository](https://github.com/meetara-lab/meetara-core).
+
+---
+
+**Made with ❤️ by the meeTARA Lab team**
+
+**Part of the meeTARA Vectorstore Collection** - Empowering RAG applications with high-quality domain-specific embeddings.