Update README.md

README.md

@@ -46,29 +46,40 @@ containing the embeddings for all images in the [imageomics/TreeOfLife-10M](http
 - **Paper:** 
 
 
-<!-- Provide a longer summary of what this dataset is. -->
-[More Information Needed]
+This dataset contains the generated vector database built using ChromaDb as the backend vector database solution for the entire TreeOfLife-10M dataset.
 
+The rationale behind creating a vector database was to enable blazingly fast nearest neighbor search. 
+The vector database is loaded into memory and for a given query embedding in the same dimension as the vectors in the database, we calculate the inner product (ip) to calculate distances.
+In this case all vectors are of the same dimension i.e. 512 dims.
 
+The files in this dataset are binaries and cannot be read in raw format. The files in this dataset are intended to be used with chromadb.
 
-## Dataset Structure
-
+Below is an example usage:
+```py
+import chromadb
 
+client = chromadb.PersistentClient(path=self._collection_dir)
 
-### Data Fields
-[More Information Needed]
-<!--
-Describe the types of the data files or the columns in a CSV with metadata.
-
-Ex: 
-**metadata.csv**:
-  - `img_id`: Unique identifier for the dataset. 
-  - `specimen_id`: ID of specimen in the image, provided by museum data source. There are multiple images of a single specimen.
-  - `species`: Species of the specimen in the image. There are N different species of <genus> of <animal>.
-  - `view`: View of the specimen in the image (e.g., `ventral` or `dorsal` OR `top` or `bottom`, etc.; specify options where reasonable).
-  - `file_name`: Relative path to image from the root of the directory (`<species>/<img_id>.png`); allows for image to be displayed in the dataset viewer alongside its associated metadata.
--->
+collection = client.get_or_create_collection(
+    name="imageomics/TreeOfLife-10M",
+    metadata={"hnsw:space": "ip", "hnsw:search_ef": 10},
+)
+# returns ID of 10 nearest neighbor. If an exact match exists,
+# that will be the first returned result with a distance of 0.
+collection.query(query_embeddings=[query_embedding], n_results=10)
+```
 
+## Dataset Structure
+```
+tol_vector_db/
+├── 97283350-138c-49b4-a4d4-9b93a33960da/
+│   ├── header.bin
+│   ├── index_metadata.pickle
+│   ├── data_level0.bin
+│   ├── link_lists.bin
+│   └── length.bin
+└── chroma.sqlite3
+```
 
 ## Dataset Creation
 
@@ -78,16 +89,6 @@ Ex:
 [imageomics/TreeOfLife-10M](https://huggingface.co/datasets/imageomics/TreeOfLife-10M) dataset was used as the source to generate all the embeddings in the vector database. 
 
 
-
-#### Who are the source data producers?
-[More Information Needed]
-<!-- This section describes the people or systems who originally created the data.
-
-Ex: This dataset is a collection of images taken of the butterfly collection housed at the Ohio State University Museum of Biological Diversity. The associated labels and metadata are the information provided with the collection from biologists that study butterflies and supplied the specimens to the museum.
- -->
-
-
-
 ## Citation
 
 **BibTeX:**
@@ -100,62 +101,20 @@ Ex: This dataset is a collection of images taken of the butterfly collection hou
   url = {https://huggingface.co/datasets/imageomics/tree-of-life-vector-db},
 }
 ```
-<!--
-If you want to include BibTex, replace "<>"s with your info 
-
-**Data**
-```
-@misc{<ref_code>,
-  author = {sreejithmenon},
-  title = {<title>},
-  year = {<year>},
-  url = {https://huggingface.co/datasets/imageomics/tree-of-life-vector-db},
-  doi = {<doi once generated>},
-  publisher = {Hugging Face}
-}
-```
-
--for an associated paper:
-**Paper**
-```
-@article{<ref_code>,
-  title    = {<title>},
-  author   = {<author1 and author2>},
-  journal  = {<journal_name>},
-  year     =  <year>,
-  url      = {<DOI_URL>},
-  doi      = {<DOI>}
-}
-```
--->
-
-<!---
-If the data is modified from another source, add the following. 
-
-Please be sure to also cite the original data source(s):
-<citation>
--->
 
 
 ## Acknowledgements
 
 This work was supported by the [Imageomics Institute](https://imageomics.org), which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
 
-<!-- You may also want to credit the source of your data, i.e., if you went to a museum or nature preserve to collect it. -->
-
-## Glossary 
-
-<!-- [optional] If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-## More Information 
+Speical thanks to:
+* [Matt Thompson](https://github.com/thompsonmj)
 
-<!-- [optional] Any other relevant information that doesn't fit elsewhere. -->
 
 ## Dataset Card Authors 
 
-[More Information Needed]
+[Sreejith Menon](https://github.com/smenon8)
 
 ## Dataset Card Contact
 
-[More Information Needed--optional]
-<!-- Could include who to contact with questions, but this is also what the "Discussions" tab is for. -->
+[Sreejith Menon](https://github.com/smenon8)