Add MongoDB docs chunks embedded with voyage-4-large

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+mongodb_docs_voyage_4_large.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,56 @@
+---
+license: cc-by-3.0
+task_categories:
+- question-answering
+language:
+- en
+tags:
+- vector search
+- retrieval augmented generation
+size_categories:
+- <1K
+---
+
+## Overview
+
+This dataset consists of chunked and embedded versions of a small subset of MongoDB's technical documentation. It contains 795 chunks from 498 documentation pages, sampled from 50 documentation sources.
+
+## Dataset Structure
+
+The dataset consists of the following fields:
+
+- chunk_id: Unique identifier for the chunk.
+- page_id: Identifier of the documentation page the chunk came from.
+- source_name: The documentation source, such as `pymongo` or `atlas-cli`.
+- product_name: The product the page documents, when available.
+- url: Link to the documentation page.
+- title: Title of the documentation page.
+- text: Content of the chunk in Markdown format.
+- embedding: The embedding of the chunk's content, created using Voyage AI's [voyage-4-large](https://www.mongodb.com/docs/voyageai/models/text-embeddings/) model with 1024 dimensions.
+
+Pages were split into chunks of up to ~2,000 tokens at Markdown heading boundaries. Because the Voyage 4 models share an embedding space, you can embed queries for this dataset with `voyage-4-large`, `voyage-4`, or `voyage-4-lite`.
+
+## Usage
+
+This dataset can be useful for prototyping RAG applications. It is derived from [mongodb-eai/docs](https://huggingface.co/datasets/mongodb-eai/docs), and was built with [this script](https://github.com/mongodb-developer/GenAI-Showcase/tree/main/partners/openrouter/reliability-patterns/scripts/build_dataset.py).
+
+## Ingest Data
+
+To experiment with this dataset using MongoDB Atlas, first [create a MongoDB Atlas account](https://www.mongodb.com/cloud/atlas/register?utm_campaign=devrel&utm_source=community&utm_medium=organic_social&utm_content=Hugging%20Face%20Dataset&utm_term=apoorva.joshi).
+
+You can then use the following script to load this dataset into your MongoDB Atlas cluster:
+
+```python
+import os
+
+from datasets import load_dataset
+from pymongo import MongoClient
+
+client = MongoClient(os.environ["MONGODB_URI"])
+collection = client["your_database_name"]["mongodb_docs_voyage_4_large"]
+
+dataset = load_dataset("MongoDB/mongodb-docs-voyage-4-large", split="train")
+collection.insert_many(dataset.to_list())
+
+print(f"Ingested {len(dataset)} documents")
+```

mongodb_docs_voyage_4_large.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7985d43559f567db1cc0175d0e7b8a4cf86b2c7fc69e9c1541847ffab63875f9
+size 22292835