feat: support openllm embedding (#1293)

api/core/model_providers/models/embedding/openllm_embedding.py

+from core.third_party.langchain.embeddings.openllm_embedding import OpenLLMEmbeddings
+
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.providers.base import BaseModelProvider
+from core.model_providers.models.embedding.base import BaseEmbedding
+
+
+class OpenLLMEmbedding(BaseEmbedding):
+    def __init__(self, model_provider: BaseModelProvider, name: str):
+        credentials = model_provider.get_model_credentials(
+            model_name=name,
+            model_type=self.type
+        )
+
+        client = OpenLLMEmbeddings(
+            server_url=credentials['server_url']
+        )
+
+        super().__init__(model_provider, client, name)
+
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        return LLMBadRequestError(f"OpenLLM embedding: {str(ex)}")

api/core/model_providers/models/embedding/xinference_embedding.py

 from core.third_party.langchain.embeddings.xinference_embedding import XinferenceEmbedding as XinferenceEmbeddings
-from replicate.exceptions import ModelError, ReplicateError
 
 from core.model_providers.error import LLMBadRequestError
 from core.model_providers.providers.base import BaseModelProvider
@@ -21,7 +20,4 @@ class XinferenceEmbedding(BaseEmbedding):
         super().__init__(model_provider, client, name)
 
     def handle_exceptions(self, ex: Exception) -> Exception:
-        if isinstance(ex, (ModelError, ReplicateError)):
         return LLMBadRequestError(f"Xinference embedding: {str(ex)}")
-        else:
-            return ex

api/core/model_providers/providers/openllm_provider.py

@@ -2,11 +2,13 @@ import json
 from typing import Type
 
 from core.helper import encrypter
+from core.model_providers.models.embedding.openllm_embedding import OpenLLMEmbedding
 from core.model_providers.models.entity.model_params import KwargRule, ModelKwargsRules, ModelType
 from core.model_providers.models.llm.openllm_model import OpenLLMModel
 from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError
 
 from core.model_providers.models.base import BaseProviderModel
+from core.third_party.langchain.embeddings.openllm_embedding import OpenLLMEmbeddings
 from core.third_party.langchain.llms.openllm import OpenLLM
 from models.provider import ProviderType
 
@@ -31,6 +33,8 @@ class OpenLLMProvider(BaseModelProvider):
         """
         if model_type == ModelType.TEXT_GENERATION:
             model_class = OpenLLMModel
+        elif model_type== ModelType.EMBEDDINGS:
+            model_class = OpenLLMEmbedding
         else:
             raise NotImplementedError
 
@@ -69,6 +73,7 @@ class OpenLLMProvider(BaseModelProvider):
                 'server_url': credentials['server_url']
             }
 
+            if model_type == ModelType.TEXT_GENERATION:
                 llm = OpenLLM(
                     llm_kwargs={
                         'max_new_tokens': 10
@@ -77,6 +82,12 @@ class OpenLLMProvider(BaseModelProvider):
                 )
 
                 llm("ping")
+            elif model_type == ModelType.EMBEDDINGS:
+                embedding = OpenLLMEmbeddings(
+                    **credential_kwargs
+                )
+
+                embedding.embed_query("ping")
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 

api/core/third_party/langchain/embeddings/openllm_embedding.py

+"""Wrapper around OpenLLM embedding models."""
+from typing import Any, List, Optional
+
+import requests
+from pydantic import BaseModel, Extra
+
+from langchain.embeddings.base import Embeddings
+
+
+class OpenLLMEmbeddings(BaseModel, Embeddings):
+    """Wrapper around OpenLLM embedding models.
+    """
+
+    client: Any  #: :meta private:
+
+    server_url: Optional[str] = None
+    """Optional server URL that currently runs a LLMServer with 'openllm start'."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Call out to OpenLLM's embedding endpoint.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings = []
+        for text in texts:
+            result = self.invoke_embedding(text=text)
+            embeddings.append(result)
+
+        return [list(map(float, e)) for e in embeddings]
+
+    def invoke_embedding(self, text):
+        params = [
+            text
+        ]
+
+        headers = {"Content-Type": "application/json"}
+        response = requests.post(
+            f'{self.server_url}/v1/embeddings',
+            headers=headers,
+            json=params
+        )
+
+        if not response.ok:
+            raise ValueError(f"OpenLLM HTTP {response.status_code} error: {response.text}")
+
+        json_response = response.json()
+        return json_response[0]["embeddings"][0]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Call out to OpenLLM's embedding endpoint.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        return self.embed_documents([text])[0]

api/tests/integration_tests/models/embedding/test_openllm_embedding.py

+import json
+import os
+from unittest.mock import patch, MagicMock
+
+from core.model_providers.models.embedding.openllm_embedding import OpenLLMEmbedding
+from core.model_providers.models.entity.model_params import ModelType
+from core.model_providers.providers.openllm_provider import OpenLLMProvider
+from models.provider import Provider, ProviderType, ProviderModel
+
+
+def get_mock_provider():
+    return Provider(
+        id='provider_id',
+        tenant_id='tenant_id',
+        provider_name='openllm',
+        provider_type=ProviderType.CUSTOM.value,
+        encrypted_config='',
+        is_valid=True,
+    )
+
+
+def get_mock_embedding_model(mocker):
+    model_name = 'facebook/opt-125m'
+    server_url = os.environ['OPENLLM_SERVER_URL']
+    model_provider = OpenLLMProvider(provider=get_mock_provider())
+
+    mock_query = MagicMock()
+    mock_query.filter.return_value.first.return_value = ProviderModel(
+        provider_name='openllm',
+        model_name=model_name,
+        model_type=ModelType.EMBEDDINGS.value,
+        encrypted_config=json.dumps({
+            'server_url': server_url
+        }),
+        is_valid=True,
+    )
+    mocker.patch('extensions.ext_database.db.session.query', return_value=mock_query)
+
+    return OpenLLMEmbedding(
+        model_provider=model_provider,
+        name=model_name
+    )
+
+
+def decrypt_side_effect(tenant_id, encrypted_api_key):
+    return encrypted_api_key
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_embed_documents(mock_decrypt, mocker):
+    embedding_model = get_mock_embedding_model(mocker)
+    rst = embedding_model.client.embed_documents(['test', 'test1'])
+    assert isinstance(rst, list)
+    assert len(rst) == 2
+    assert len(rst[0]) > 0
+
+
+@patch('core.helper.encrypter.decrypt_token', side_effect=decrypt_side_effect)
+def test_embed_query(mock_decrypt, mocker):
+    embedding_model = get_mock_embedding_model(mocker)
+    rst = embedding_model.client.embed_query('test')
+    assert isinstance(rst, list)
+    assert len(rst) > 0