---
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.

### What This Knowledge Base Covers

This knowledge base covers core accounting concepts, principles, and practices. It includes topics such as financial statements, bookkeeping, GAAP/IFRS standards, auditing, tax fundamentals, and corporate finance. Use it to answer questions about accounting terminology, reporting standards, financial analysis, and practical accounting workflows.

## 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**: 52.78 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 are the main components of a balance sheet?",
    "How to record journal entries for accruals?",
    "Explain GAAP vs IFRS accounting standards",
    "What is the double-entry bookkeeping system?",
    "How to prepare a cash flow statement?"
]

# Run a query
query = "What are the main components of a balance sheet?"
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:
- **Accounting concept lookup and explanation**
- **Financial reporting and standards reference**
- **Bookkeeping and journal entry guidance**
- **Audit and compliance information**
- **Corporate finance and analysis**

#### 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 record journal entries for accruals?"
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**: 52.78 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.