diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index a79fa60595315943591ad5ec4eb2a34e39a8c50c..5efc730398a45fc3a9de5f234a6d43a0e37911be 100644
--- a/semantic_router/encoders/__init__.py
+++ b/semantic_router/encoders/__init__.py
@@ -1,3 +1,5 @@
+from typing import List, Optional
+
from semantic_router.encoders.base import BaseEncoder
from semantic_router.encoders.bm25 import BM25Encoder
from semantic_router.encoders.clip import CLIPEncoder
@@ -11,6 +13,7 @@ from semantic_router.encoders.openai import OpenAIEncoder
from semantic_router.encoders.tfidf import TfidfEncoder
from semantic_router.encoders.vit import VitEncoder
from semantic_router.encoders.zure import AzureOpenAIEncoder
+from semantic_router.schema import EncoderType
__all__ = [
"BaseEncoder",
@@ -27,3 +30,45 @@ __all__ = [
"CLIPEncoder",
"GoogleEncoder",
]
+
+
+class AutoEncoder:
+ type: EncoderType
+ name: Optional[str]
+ model: BaseEncoder
+
+ def __init__(self, type: str, name: Optional[str]):
+ self.type = EncoderType(type)
+ self.name = name
+ if self.type == EncoderType.AZURE:
+ # TODO should change `model` to `name` JB
+ self.model = AzureOpenAIEncoder(model=name)
+ elif self.type == EncoderType.COHERE:
+ self.model = CohereEncoder(name=name)
+ elif self.type == EncoderType.OPENAI:
+ self.model = OpenAIEncoder(name=name)
+ elif self.type == EncoderType.BM25:
+ if name is None:
+ name = "bm25"
+ self.model = BM25Encoder(name=name)
+ elif self.type == EncoderType.TFIDF:
+ if name is None:
+ name = "tfidf"
+ self.model = TfidfEncoder(name=name)
+ elif self.type == EncoderType.FASTEMBED:
+ self.model = FastEmbedEncoder(name=name)
+ elif self.type == EncoderType.HUGGINGFACE:
+ self.model = HuggingFaceEncoder(name=name)
+ elif self.type == EncoderType.MISTRAL:
+ self.model = MistralEncoder(name=name)
+ elif self.type == EncoderType.VIT:
+ self.model = VitEncoder(name=name)
+ elif self.type == EncoderType.CLIP:
+ self.model = CLIPEncoder(name=name)
+ elif self.type == EncoderType.GOOGLE:
+ self.model = GoogleEncoder(name=name)
+ else:
+ raise ValueError(f"Encoder type '{type}' not supported")
+
+ def __call__(self, texts: List[str]) -> List[List[float]]:
+ return self.model(texts)
diff --git a/semantic_router/encoders/openai.py b/semantic_router/encoders/openai.py
index 14b9bb7bb4549443373429cb8bfb6a79530a578f..d56a1e71ecd0ff520edd69ff0d709524617db964 100644
--- a/semantic_router/encoders/openai.py
+++ b/semantic_router/encoders/openai.py
@@ -1,6 +1,7 @@
import os
from time import sleep
from typing import Any, List, Optional, Union
+from pydantic.v1 import PrivateAttr
import openai
from openai import OpenAIError
@@ -16,28 +17,18 @@ from semantic_router.utils.logger import logger
model_configs = {
"text-embedding-ada-002": EncoderInfo(
- name="text-embedding-ada-002",
- type="openai",
- token_limit=4000
+ name="text-embedding-ada-002", token_limit=8192
),
- "text-embed-3-small": EncoderInfo(
- name="text-embed-3-small",
- type="openai",
- token_limit=8192
- ),
- "text-embed-3-large": EncoderInfo(
- name="text-embed-3-large",
- type="openai",
- token_limit=8192
- )
+ "text-embed-3-small": EncoderInfo(name="text-embed-3-small", token_limit=8192),
+ "text-embed-3-large": EncoderInfo(name="text-embed-3-large", token_limit=8192),
}
class OpenAIEncoder(BaseEncoder):
client: Optional[openai.Client]
dimensions: Union[int, NotGiven] = NotGiven()
- token_limit: Optional[int] = None
- token_encoder: Optional[Any] = None
+ token_limit: int = 8192 # default value, should be replaced by config
+ _token_encoder: Any = PrivateAttr()
type: str = "openai"
def __init__(
@@ -71,11 +62,11 @@ class OpenAIEncoder(BaseEncoder):
if name in model_configs:
self.token_limit = model_configs[name].token_limit
# get token encoder
- self.token_encoder = tiktoken.encoding_for_model(name)
+ self._token_encoder = tiktoken.encoding_for_model(name)
def __call__(self, docs: List[str], truncate: bool = True) -> List[List[float]]:
"""Encode a list of text documents into embeddings using OpenAI API.
-
+
:param docs: List of text documents to encode.
:param truncate: Whether to truncate the documents to token limit. If
False and a document exceeds the token limit, an error will be
@@ -121,15 +112,15 @@ class OpenAIEncoder(BaseEncoder):
embeddings = [embeds_obj.embedding for embeds_obj in embeds.data]
return embeddings
-
+
def _truncate(self, text: str) -> str:
- tokens = self.token_encoder.encode(text)
+ tokens = self._token_encoder.encode(text)
if len(tokens) > self.token_limit:
logger.warning(
f"Document exceeds token limit: {len(tokens)} > {self.token_limit}"
"\nTruncating document..."
)
- text = self.token_encoder.decode(tokens[:self.token_limit-1])
- logger.info(f"Trunc length: {len(self.token_encoder.encode(text))}")
+ text = self._token_encoder.decode(tokens[: self.token_limit - 1])
+ logger.info(f"Trunc length: {len(self._token_encoder.encode(text))}")
return text
return text
diff --git a/semantic_router/encoders/zure.py b/semantic_router/encoders/zure.py
index ee1b1fa67fdf917b8c9113b1e89fcd7e82dd6486..df2bf858aa32578e4751fac90d698e4db111f84b 100644
--- a/semantic_router/encoders/zure.py
+++ b/semantic_router/encoders/zure.py
@@ -26,7 +26,7 @@ class AzureOpenAIEncoder(BaseEncoder):
deployment_name: Optional[str] = None,
azure_endpoint: Optional[str] = None,
api_version: Optional[str] = None,
- model: Optional[str] = None,
+ model: Optional[str] = None, # TODO we should change to `name` JB
score_threshold: float = 0.82,
):
name = deployment_name
diff --git a/semantic_router/layer.py b/semantic_router/layer.py
index d9781820bf6bdc7af5c6eac32be87e982a6d04df..02e626f4979b945258b0ef3223e547e05c0218bd 100644
--- a/semantic_router/layer.py
+++ b/semantic_router/layer.py
@@ -8,12 +8,12 @@ import numpy as np
import yaml # type: ignore
from tqdm.auto import tqdm
-from semantic_router.encoders import BaseEncoder, OpenAIEncoder
+from semantic_router.encoders import AutoEncoder, BaseEncoder, OpenAIEncoder
from semantic_router.index.base import BaseIndex
from semantic_router.index.local import LocalIndex
from semantic_router.llms import BaseLLM, OpenAILLM
from semantic_router.route import Route
-from semantic_router.schema import Encoder, EncoderType, RouteChoice
+from semantic_router.schema import EncoderType, RouteChoice
from semantic_router.utils.defaults import EncoderDefault
from semantic_router.utils.logger import logger
@@ -337,18 +337,18 @@ class RouteLayer:
@classmethod
def from_json(cls, file_path: str):
config = LayerConfig.from_file(file_path)
- encoder = Encoder(type=config.encoder_type, name=config.encoder_name).model
+ encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model
return cls(encoder=encoder, routes=config.routes)
@classmethod
def from_yaml(cls, file_path: str):
config = LayerConfig.from_file(file_path)
- encoder = Encoder(type=config.encoder_type, name=config.encoder_name).model
+ encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model
return cls(encoder=encoder, routes=config.routes)
@classmethod
def from_config(cls, config: LayerConfig, index: Optional[BaseIndex] = None):
- encoder = Encoder(type=config.encoder_type, name=config.encoder_name).model
+ encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model
return cls(encoder=encoder, routes=config.routes, index=index)
def add(self, route: Route):
diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index daf608131f6bc107c47e60644b93073d1fbf8102..60f61536e08840e2c023fd12867cc960a14d3608 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -5,19 +5,24 @@ from pydantic.v1 import BaseModel
class EncoderType(Enum):
- HUGGINGFACE = "huggingface"
- FASTEMBED = "fastembed"
- OPENAI = "openai"
+ AZURE = "azure"
COHERE = "cohere"
+ OPENAI = "openai"
+ BM25 = "bm25"
+ TFIDF = "tfidf"
+ FASTEMBED = "fastembed"
+ HUGGINGFACE = "huggingface"
MISTRAL = "mistral"
+ VIT = "vit"
+ CLIP = "clip"
GOOGLE = "google"
class EncoderInfo(BaseModel):
name: str
- type: EncoderType
token_limit: int
+
class RouteChoice(BaseModel):
name: Optional[str] = None
function_call: Optional[dict] = None
diff --git a/tests/unit/encoders/test_openai.py b/tests/unit/encoders/test_openai.py
index de3594f1838ebf787b2a2d78c2738bb7eae772db..508e9e9e197a8a0b87fb179554d43c18686c1b44 100644
--- a/tests/unit/encoders/test_openai.py
+++ b/tests/unit/encoders/test_openai.py
@@ -5,6 +5,7 @@ from openai.types.create_embedding_response import Usage
from semantic_router.encoders import OpenAIEncoder
+
@pytest.fixture
def openai_encoder(mocker):
mocker.patch("openai.Client")
diff --git a/tests/unit/test_schema.py b/tests/unit/test_schema.py
index 90f3c949b5009bdb894367fa3ad5d37a1beed889..d0fce781be2bbf948b43b7a48d3047ab7318b92a 100644
--- a/tests/unit/test_schema.py
+++ b/tests/unit/test_schema.py
@@ -2,46 +2,10 @@ import pytest
from pydantic.v1 import ValidationError
from semantic_router.schema import (
- CohereEncoder,
- Encoder,
- EncoderType,
Message,
- OpenAIEncoder,
)
-class TestEncoderDataclass:
- def test_encoder_initialization_openai(self, mocker):
- mocker.patch.dict("os.environ", {"OPENAI_API_KEY": "test"})
- encoder = Encoder(type="openai", name="test-engine")
- assert encoder.type == EncoderType.OPENAI
- assert isinstance(encoder.model, OpenAIEncoder)
-
- def test_encoder_initialization_cohere(self, mocker):
- mocker.patch.dict("os.environ", {"COHERE_API_KEY": "test"})
- encoder = Encoder(type="cohere", name="test-engine")
- assert encoder.type == EncoderType.COHERE
- assert isinstance(encoder.model, CohereEncoder)
-
- def test_encoder_initialization_unsupported_type(self):
- with pytest.raises(ValueError):
- Encoder(type="unsupported", name="test-engine")
-
- def test_encoder_initialization_huggingface(self):
- with pytest.raises(NotImplementedError):
- Encoder(type="huggingface", name="test-engine")
-
- def test_encoder_call_method(self, mocker):
- mocker.patch.dict("os.environ", {"OPENAI_API_KEY": "test"})
- mocker.patch(
- "semantic_router.encoders.openai.OpenAIEncoder.__call__",
- return_value=[0.1, 0.2, 0.3],
- )
- encoder = Encoder(type="openai", name="test-engine")
- result = encoder(["test"])
- assert result == [0.1, 0.2, 0.3]
-
-
class TestMessageDataclass:
def test_message_creation(self):
message = Message(role="user", content="Hello!")