Datasets:

meetara-lab
/

vectorstore-accounting

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

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

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

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

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

# 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:

@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

💡 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:

@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
Hugging Face Profile: @meetara-lab
Issues: Report bugs or request features on GitHub Issues
Documentation: Visit our repository for detailed documentation
Dataset Requests: Want a new domain? Open an issue 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.

Made with ❤️ by the meeTARA Lab team

Part of the meeTARA Vectorstore Collection - Empowering RAG applications with high-quality domain-specific embeddings.