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feat: voyageai support (#1799)

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Adding VoyageAI embedding and rerank support
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fzowl
2024-11-08 20:21:20 +01:00
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# Voyage AI Reranker
Voyage AI provides cutting-edge embedding and rerankers.
This re-ranker uses the [VoyageAI](https://docs.voyageai.com/docs/) API to rerank the search results. You can use this re-ranker by passing `VoyageAIReranker()` to the `rerank()` method. Note that you'll either need to set the `VOYAGE_API_KEY` environment variable or pass the `api_key` argument to use this re-ranker.
!!! note
Supported Query Types: Hybrid, Vector, FTS
```python
import numpy
import lancedb
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
from lancedb.rerankers import VoyageAIReranker
embedder = get_registry().get("sentence-transformers").create()
db = lancedb.connect("~/.lancedb")
class Schema(LanceModel):
text: str = embedder.SourceField()
vector: Vector(embedder.ndims()) = embedder.VectorField()
data = [
{"text": "hello world"},
{"text": "goodbye world"}
]
tbl = db.create_table("test", schema=Schema, mode="overwrite")
tbl.add(data)
reranker = VoyageAIReranker(model_name="rerank-2")
# Run vector search with a reranker
result = tbl.search("hello").rerank(reranker=reranker).to_list()
# Run FTS search with a reranker
result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list()
# Run hybrid search with a reranker
tbl.create_fts_index("text", replace=True)
result = tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
```
Accepted Arguments
----------------
| Argument | Type | Default | Description |
| --- | --- | --- | --- |
| `model_name` | `str` | `None` | The name of the reranker model to use. Available models are: rerank-2, rerank-2-lite |
| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
| `api_key` | `str` | `None` | The API key for the Voyage AI API. If not provided, the `VOYAGE_API_KEY` environment variable is used. |
| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
| `truncation` | `bool` | `None` | Whether to truncate the input to satisfy the "context length limit" on the query and the documents. |
## Supported Scores for each query type
You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
### Hybrid Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
### Vector Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
### FTS Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |