---
license: cc-by-nc-4.0
configs:
  - config_name: embeddings
    data_files: "embeddings.parquet"
    default: true
  - config_name: tools
    data_files: "tools.parquet"
task_categories:
  - feature-extraction
  - text-classification
language:
  - "en"
  - "no"
tags:
  - embeddings
  - openapi
  - tripletex
  - accounting
  - api-tools
  - rag
  - lancedb
  - pydantic
size_categories:
  - n<1K
---

# Tripletex API Tool Embeddings

Pre-computed embeddings for 800 Tripletex accounting API tools, extracted from the OpenAPI 3.0.1 spec and embedded with Google `gemini-embedding-001` (3072 dimensions).

Built for RAG-based tool filtering in the [AI Accounting Agent](https://github.com/kuben-labs/nmai) competition project.

## Quick Start

```python
from datasets import load_dataset

# Full embeddings (800 tools, 3072-dim vectors) — ready for RAG
ds = load_dataset("valiantlynxz/tripletex-tool-embeddings")

# Lightweight: tool metadata only, no embedding vectors
ds = load_dataset("valiantlynxz/tripletex-tool-embeddings", name="tools")
```

## Configurations

| Config | Default | Columns | Size | Use case |
|--------|---------|---------|------|----------|
| `embeddings` | Yes | name, description, parameters, embedding | ~9 MB | RAG search, vector index |
| `tools` | | name, description, parameters | ~50 KB | Browsing, filtering, analysis |

## Schema

### `embeddings` config

```python
ds = load_dataset("valiantlynxz/tripletex-tool-embeddings")
example = ds["train"][0]

example["name"]         # "AccountantDashboardNews_get"
example["description"]  # "Get public news articles"
example["parameters"]   # '{"from": "integer", "count": "integer", ...}'  (JSON string)
example["embedding"]    # [3072 floats] — gemini-embedding-001 vector
```

### `tools` config

```python
ds = load_dataset("valiantlynxz/tripletex-tool-embeddings", name="tools")
example = ds["train"][0]

example["name"]         # "AccountantDashboardNews_get"
example["description"]  # "Get public news articles"
example["parameters"]   # '{"from": "integer", "count": "integer", ...}'  (JSON string)
```

## Data Summary

- **800 API tools** from the Tripletex accounting API (OpenAPI 3.0.1)
- **3072-dimensional embeddings** via Google `gemini-embedding-001`
- **Parameters** stored as JSON strings mapping param names to types
- **Source:** `openapi.json` included in this repo (3.5 MB, 546 paths, 2167 schemas)

## Using with LanceDB

```python
from datasets import load_dataset
import lancedb

ds = load_dataset("valiantlynxz/tripletex-tool-embeddings")

# Convert to LanceDB
db = lancedb.connect(".tool_embeddings")
records = [
    {
        "name": row["name"],
        "description": row["description"],
        "parameters": row["parameters"],
        "embedding": row["embedding"],
    }
    for row in ds["train"]
]
table = db.create_table("tools", data=records, mode="overwrite")

# Search
results = table.search(query_embedding).limit(100).to_list()
```

## Using with FAISS

```python
from datasets import load_dataset
import numpy as np
import faiss

ds = load_dataset("valiantlynxz/tripletex-tool-embeddings")

embeddings = np.array(ds["train"]["embedding"], dtype=np.float32)
index = faiss.IndexFlatIP(3072)
faiss.normalize_L2(embeddings)
index.add(embeddings)

# Search
query = np.array([query_embedding], dtype=np.float32)
faiss.normalize_L2(query)
distances, indices = index.search(query, k=100)
tool_names = [ds["train"][int(i)]["name"] for i in indices[0]]
```

## Regenerating Embeddings

The `scripts/` directory contains the original embedding pipeline:

- `scripts/embeddings.py` — Google Gemini embedding provider
- `scripts/rag_tool_filter.py` — OpenAPI-to-embedding pipeline + LanceDB vector store

```python
# Requires: google-genai, lancedb
# Requires: GCP_API_KEY environment variable

from scripts.embeddings import get_embedding_provider
from scripts.rag_tool_filter import ToolEmbedder, ToolVectorStore, index_openapi_tools
import json, asyncio

with open("openapi.json") as f:
    spec = json.load(f)

provider = get_embedding_provider()
embedder = ToolEmbedder(provider)
store = ToolVectorStore(".tool_embeddings")
asyncio.run(index_openapi_tools(spec, store, embedder))
```

## Repo Structure

```
tripletex-tool-embeddings/
├── README.md
├── embeddings.parquet      # 800 tools with 3072-dim embeddings
├── tools.parquet           # 800 tools metadata only (lightweight)
├── openapi.json            # Source Tripletex OpenAPI 3.0.1 spec (3.5 MB)
└── scripts/
    ├── embeddings.py       # Google Gemini embedding provider
    └── rag_tool_filter.py  # OpenAPI extraction + LanceDB indexing
```

## Source Project

Part of the [nmai](https://github.com/kuben-labs/nmai) project — `ai-accounting-agent/`.