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raphaelsty committed Sep 11, 2024
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21 changes: 21 additions & 0 deletions .gitignore
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Expand Up @@ -8,6 +8,7 @@ __pycache__/
*.ipynb
*.duckdb
duckdb_tmp/
*.block

evaluation_datasets/

Expand Down Expand Up @@ -168,3 +169,23 @@ cython_debug/
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
arguana
benchmark_bm25s.py
benchmark_crud.py
climate-fever
fever.tmp/duckdb_temp_block-4611686018432402649.block
fever.tmp/duckdb_temp_block-4611686018432404521.block
fever.tmp/duckdb_temp_block-4611686018432404963.block
fever.tmp/duckdb_temp_storage-4.tmp
metrics.json
metrics_20K.json
metrics_bm25s.json
mmetrics_30K.json
msmarco
nfcorpus
nq
quora
scidocs
scifact
trec-covid
webis-touche2020
22 changes: 8 additions & 14 deletions Makefile
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Expand Up @@ -10,28 +10,22 @@ tests:
@echo "Removing test.duckdb if it exists..."
rm -rf test.duckdb
rm -rf test.duckdb.wal
pytest ducksearch/tables/create.py
pytest ducksearch/tables/insert.py
pytest ducksearch/tables/select.py
pytest ducksearch/tables/create.py --disable-warnings
pytest ducksearch/tables/insert.py --disable-warnings
pytest ducksearch/tables/select.py --disable-warnings
rm -rf test.duckdb
rm -rf test.duckdb.wal
pytest ducksearch/hf/insert.py
pytest ducksearch/hf/insert.py --disable-warnings
rm -rf test.duckdb
rm -rf test.duckdb.wal
pytest ducksearch/delete/documents.py
pytest ducksearch/evaluation/evaluation.py --disable-warnings
rm -rf test.duckdb
rm -rf test.duckdb.wal
pytest ducksearch/evaluation/evaluation.py
pytest ducksearch/search/create.py --disable-warnings
pytest ducksearch/search/select.py --disable-warnings
rm -rf test.duckdb
rm -rf test.duckdb.wal
pytest ducksearch/upload/upload.py
rm -rf test.duckdb
rm -rf test.duckdb.wal
pytest ducksearch/search/create.py
pytest ducksearch/search/select.py
rm -rf test.duckdb
rm -rf test.duckdb.wal
pytest ducksearch/search/graphs.py
pytest ducksearch/search/graphs.py --disable-warnings
rm -rf test.duckdb
rm -rf test.duckdb.wal

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129 changes: 41 additions & 88 deletions README.md
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Expand Up @@ -13,30 +13,26 @@
</div>

<p align="justify">
DuckSearch is a lightweight and easy-to-use library that allows to index and search documents. DuckSearch is built on top of DuckDB, a high-performance analytical database. DuckDB is designed to execute analytical SQL queries fast, and DuckSearch leverages this to provide efficient and scallable search / filtering capabilities.
DuckSearch is a lightweight and easy-to-use library to search documents. DuckSearch is built on top of DuckDB, a high-performance analytical database. DuckDB is designed to execute analytical SQL queries fast, and DuckSearch leverages this to provide efficient search and filtering features. DuckSearch index can be updated with new documents and documents can be deleted as well.

DuckSearch also supports HuggingFace datasets, allowing to index datasets directly from the HuggingFace Hub.
</p>

## Installation

We can install DuckSearch using pip:
Install DuckSearch using pip:

```bash
pip install ducksearch
```

For evaluation dependencies, we can install DuckSearch with the `eval` extra:

```bash
pip install "ducksearch[eval]"
```

## Documentation

The complete documentation is available [here](https://lightonai.github.io/ducksearch/), which includes in-depth guides, examples, and API references.

### Upload

We can upload documents to DuckDB using the `upload.documents` function. The documents are stored in a DuckDB database, and the fields are indexed with BM25.
We can upload documents to DuckDB using the `upload.documents` function. The documents are stored in a DuckDB database, and the `fields` are indexed with BM25.

```python
from ducksearch import upload
Expand Down Expand Up @@ -79,7 +75,7 @@ upload.documents(

## Search

We can search documents using the `search.documents` function. The function returns the documents that match the query, sorted by the BM25 score. The `top_k` parameter controls the number of documents to return. We can also filter the results using SQL syntax which will be evaluated by DuckDB, therefore all DuckDB functions are available.
`search.documents` returns a list of list of documents ordered by relevance. We can control the number of documents to return using the `top_k` parameter. The following example demonstrates how to search for documents with the queries "punk" and "california" while filtering the results to include only documents with a date after 1970 and a popularity score greater than 8.

```python
from ducksearch import search
Expand Down Expand Up @@ -117,7 +113,22 @@ search.documents(
]
```

List of DuckDB functions such as date functions can be found [here](https://duckdb.org/docs/sql/functions/date).
Filters are SQL expressions that are applied to the search results. We can use every filtering function DuckDB provides such as [date functions](https://duckdb.org/docs/sql/functions/date).

## Delete and update index

We can delete documents and update the BM25 weights accordingly using the `delete.documents` function.

```python
from ducksearch import delete

delete.documents(
database="ducksearch.duckdb",
ids=[0, 1],
)
```

To update the index, we should first delete the documents and then upload the updated documents.

## Extra features

Expand Down Expand Up @@ -152,7 +163,6 @@ search.documents(
database="fineweb.duckdb",
queries="earth science",
top_k=2,
filters="token_count > 200",
)
```

Expand Down Expand Up @@ -180,82 +190,25 @@ search.documents(
]
```

### Graphs

The `search.graphs` function can be used to search documents with a graph query. This function is useful if we have paired documents and queries. The search will retrieve the set of documents and queries that match the input query. Then it will build a graph and compute the weight of each document using a graph-based scoring function.

```python
from ducksearch import search, upload

documents = [
{
"id": 0,
"title": "Hotel California",
"style": "rock",
"date": "1977-02-22",
"popularity": 9,
},
{
"id": 1,
"title": "Here Comes the Sun",
"style": "rock",
"date": "1969-06-10",
"popularity": 10,
},
{
"id": 2,
"title": "Alive",
"style": "electro, punk",
"date": "2007-11-19",
"popularity": 9,
},
]

upload.documents(
database="ducksearch.duckdb",
key="id",
fields=["title", "style", "date", "popularity"],
documents=documents,
dtypes={
"date": "DATE",
"popularity": "INT",
},
)

# Mapping between documents ids and queries
documents_queries = {
0: ["the beatles", "rock band"],
1: ["rock band", "california"],
2: ["daft"],
}

upload.queries(
database="ducksearch.duckdb",
documents_queries=documents_queries,
)

search.graphs(
database="ducksearch.duckdb",
queries="daft punk",
top_k=10,
)
```

```python
[
{
"id": "2",
"title": "Alive",
"style": "electro, punk",
"date": Timestamp("2007-11-19 00:00:00"),
"popularity": 9,
"score": 2.877532958984375,
}
]
```

## Lightning fast

## Benchmark


| Dataset | ndcg@10 | hits@1 | hits@10 | mrr@10 | map@10 | r-precision | qps | Indexation Time (s) | Number of Documents and Queries |
|-------------------|-----------|---------|----------|----------|---------|-------------|----------------|---------------------|--------------------------------|
| arguana | 0.3779 | 0.0 | 0.8267 | 0.2491 | 0.2528 | 0.0108 | 117.80 | 1.42 | 1,406 queries, 8.67K documents |
| climate-fever | 0.1184 | 0.1068 | 0.3648 | 0.1644 | 0.0803 | 0.0758 | 5.88 | 302.39 | 1,535 queries, 5.42M documents |
| dbpedia-entity | 0.6046 | 0.7669 | 5.6241 | 0.8311 | 0.0649 | 0.0741 | 113.20 | 181.42 | 400 queries, 4.63M documents |
| fever | 0.3861 | 0.2583 | 0.5826 | 0.3525 | 0.3329 | 0.2497 | 74.40 | 329.70 | 6,666 queries, 5.42M documents |
| fiqa | 0.2445 | 0.2207 | 0.6790 | 0.3002 | 0.1848 | 0.1594 | 545.77 | 6.04 | 648 queries, 57K documents |
| hotpotqa | 0.4487 | 0.5059 | 0.9699 | 0.5846 | 0.3642 | 0.3388 | 48.15 | 163.14 | 7,405 queries, 5.23M documents |
| msmarco | 0.8951 | 1.0 | 8.6279 | 1.0 | 0.0459 | 0.0473 | 35.11 | 202.37 | 6,980 queries, 8.84M documents |
| nfcorpus | 0.3301 | 0.4396 | 2.4087 | 0.5292 | 0.1233 | 0.1383 | 3464.66 | 0.99 | 323 queries, 3.6K documents |
| nq | 0.2451 | 0.1272 | 0.4574 | 0.2099 | 0.1934 | 0.1240 | 150.23 | 71.43 | 3,452 queries, 2.68M documents |
| quora | 0.7705 | 0.6783 | 1.1749 | 0.7606 | 0.7206 | 0.6502 | 741.13 | 3.78 | 10,000 queries, 523K documents |
| scidocs | 0.1025 | 0.1790 | 0.8240 | 0.2754 | 0.0154 | 0.0275 | 879.11 | 4.46 | 1,000 queries, 25K documents |
| scifact | 0.6908 | 0.5533 | 0.9133 | 0.6527 | 0.6416 | 0.5468 | 2153.64 | 1.22 | 300 queries, 5K documents |
| trec-covid | 0.9533 | 1.0 | 9.4800 | 1.0 | 0.0074 | 0.0077 | 112.38 | 22.15 | 50 queries, 171K documents |
| webis-touche2020 | 0.4130 | 0.5510 | 3.7347 | 0.7114 | 0.0564 | 0.0827 | 104.65 | 44.14 | 49 queries, 382K documents |

## License

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31 changes: 16 additions & 15 deletions benchmark.py
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@@ -1,39 +1,40 @@
import time
from nltk import download
from nltk.corpus import stopwords

from ducksearch import evaluation, search, upload

download("stopwords")

stopword = list(stopwords.words("english"))

dataset_name = "quora"

documents, queries, qrels = evaluation.load_beir(
dataset_name=dataset_name, split="test"
dataset_name=dataset_name,
split="test",
)

upload.documents(
database=dataset_name, documents=documents, key="id", fields=["title", "text"]
database=dataset_name,
documents=documents,
key="id",
fields=["title", "text"],
stopwords=stopword,
)

upload.indexes(database=dataset_name)


start = time.time()

scores = search.documents(
database=dataset_name,
queries=queries,
top_k=10,
top_k_token=10_000,
batch_size=30,
top_k_token=30_000,
batch_size=32,
)

end = time.time()

print(f"Search took {end - start:.2f} seconds, QPS: {len(queries) / (end - start):.2f}")

evaluation_scores = evaluation.evaluate(
scores=scores,
qrels=qrels,
queries=queries,
metrics=["ndcg@10", "hits@1", "hits@2", "hits@3", "hits@4", "hits@5", "hits@10"],
metrics=["ndcg@10", "hits@1", "hits@10", "mrr@10", "map@10", "r-precision"],
)

print(evaluation_scores)
11 changes: 0 additions & 11 deletions docs/api/evaluation/evaluate.md
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Expand Up @@ -47,16 +47,5 @@ Evaluate the performance of document retrieval using relevance judgments.
... queries=queries,
... top_k=10,
... )

>>> evaluation_scores = evaluation.evaluate(
... scores=scores,
... qrels=qrels,
... queries=queries,
... metrics=["ndcg@10", "hits@1", "hits@2", "hits@3", "hits@4", "hits@5", "hits@10"],
... )

>>> assert evaluation_scores["ndcg@10"] > 0.68
>>> assert evaluation_scores["hits@1"] > 0.54
>>> assert evaluation_scores["hits@10"] > 0.90
```

21 changes: 13 additions & 8 deletions docs/api/search/documents.md
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Expand Up @@ -14,15 +14,15 @@ Search for documents in the documents table using specified queries.

A string or list of query strings to search for.

- **batch_size** (*int*) – defaults to `30`
- **batch_size** (*int*) – defaults to `32`

The batch size for query processing.

- **top_k** (*int*) – defaults to `10`

The number of top documents to retrieve for each query.

- **top_k_token** (*int*) – defaults to `10000`
- **top_k_token** (*int*) – defaults to `30000`

The number of documents to score per token.

Expand All @@ -38,17 +38,22 @@ Search for documents in the documents table using specified queries.

Optional SQL filters to apply during the search.

- **kwargs**



## Examples

```python
>>> from ducksearch import evaluation, upload, search
>>> documents, queries, qrels = evaluation.load_beir("scifact", split="test")
>>> scores = search.documents(database="test.duckdb", queries=queries, top_k_token=1000)
>>> evaluation_scores = evaluation.evaluate(scores=scores, qrels=qrels, queries=queries)
>>> assert evaluation_scores["ndcg@10"] > 0.68

>>> documents, queries, qrels = evaluation.load_beir(
... "scifact",
... split="test",
... )

>>> scores = search.documents(
... database="test.duckdb",
... queries=queries,
... top_k_token=1000,
... )
```

13 changes: 1 addition & 12 deletions docs/api/search/graphs.md
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Expand Up @@ -22,7 +22,7 @@ Search for graphs in DuckDB using the provided queries.

The number of top documents to retrieve for each query.

- **top_k_token** (*int*) – defaults to `10000`
- **top_k_token** (*int*) – defaults to `30000`

The number of top tokens to retrieve.

Expand Down Expand Up @@ -65,16 +65,5 @@ Search for graphs in DuckDB using the provided queries.
... queries=queries,
... top_k=10,
... )

>>> assert len(scores) > 0

>>> evaluation_scores = evaluation.evaluate(
... scores=scores,
... qrels=qrels,
... queries=queries,
... metrics=["ndcg@10", "hits@1", "hits@10"]
... )

>>> assert evaluation_scores["ndcg@10"] > 0.74
```

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