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JzoNg 2024-09-27 16:27:00 +08:00
commit 8fd04e5313
171 changed files with 1964 additions and 489 deletions
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46
.github/workflows/web-tests.yml vendored Normal file
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@ -0,0 +1,46 @@
name: Web Tests
on:
pull_request:
branches:
- main
paths:
- web/**
concurrency:
group: web-tests-${{ github.head_ref || github.run_id }}
cancel-in-progress: true
jobs:
test:
name: Web Tests
runs-on: ubuntu-latest
defaults:
run:
working-directory: ./web
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Check changed files
id: changed-files
uses: tj-actions/changed-files@v45
with:
files: web/**
- name: Setup Node.js
uses: actions/setup-node@v4
if: steps.changed-files.outputs.any_changed == 'true'
with:
node-version: 20
cache: yarn
cache-dependency-path: ./web/package.json
- name: Install dependencies
if: steps.changed-files.outputs.any_changed == 'true'
run: yarn install --frozen-lockfile
- name: Run tests
if: steps.changed-files.outputs.any_changed == 'true'
run: yarn test

8
api/app.py
View File

@ -53,11 +53,9 @@ from services.account_service import AccountService
warnings.simplefilter("ignore", ResourceWarning)
# fix windows platform
if os.name == "nt":
os.system('tzutil /s "UTC"')
else:
os.environ["TZ"] = "UTC"
os.environ["TZ"] = "UTC"
# windows platform not support tzset
if hasattr(time, "tzset"):
time.tzset()

2
api/core/app/apps/base_app_runner.py
View File

@ -309,7 +309,7 @@ class AppRunner:
if not prompt_messages:
prompt_messages = result.prompt_messages
if not usage and result.delta.usage:
if result.delta.usage:
usage = result.delta.usage
if not usage:

9
api/core/embedding/cached_embedding.py
View File

@ -5,6 +5,7 @@ from typing import Optional, cast
import numpy as np
from sqlalchemy.exc import IntegrityError
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_manager import ModelInstance
from core.model_runtime.entities.model_entities import ModelPropertyKey
from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
@ -56,7 +57,9 @@ class CacheEmbedding(Embeddings):
for i in range(0, len(embedding_queue_texts), max_chunks):
batch_texts = embedding_queue_texts[i : i + max_chunks]
embedding_result = self._model_instance.invoke_text_embedding(texts=batch_texts, user=self._user)
embedding_result = self._model_instance.invoke_text_embedding(
texts=batch_texts, user=self._user, input_type=EmbeddingInputType.DOCUMENT
)
for vector in embedding_result.embeddings:
try:
@ -100,7 +103,9 @@ class CacheEmbedding(Embeddings):
redis_client.expire(embedding_cache_key, 600)
return list(np.frombuffer(base64.b64decode(embedding), dtype="float"))
try:
embedding_result = self._model_instance.invoke_text_embedding(texts=[text], user=self._user)
embedding_result = self._model_instance.invoke_text_embedding(
texts=[text], user=self._user, input_type=EmbeddingInputType.QUERY
)
embedding_results = embedding_result.embeddings[0]
embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist()

10
api/core/embedding/embedding_constant.py Normal file
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@ -0,0 +1,10 @@
from enum import Enum
class EmbeddingInputType(Enum):
"""
Enum for embedding input type.
"""
DOCUMENT = "document"
QUERY = "query"

7
api/core/model_manager.py
View File

@ -3,6 +3,7 @@ import os
from collections.abc import Callable, Generator, Sequence
from typing import IO, Optional, Union, cast
from core.embedding.embedding_constant import EmbeddingInputType
from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
from core.entities.provider_entities import ModelLoadBalancingConfiguration
from core.errors.error import ProviderTokenNotInitError
@ -158,12 +159,15 @@ class ModelInstance:
tools=tools,
)
def invoke_text_embedding(self, texts: list[str], user: Optional[str] = None) -> TextEmbeddingResult:
def invoke_text_embedding(
self, texts: list[str], user: Optional[str] = None, input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT
) -> TextEmbeddingResult:
"""
Invoke large language model
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
if not isinstance(self.model_type_instance, TextEmbeddingModel):
@ -176,6 +180,7 @@ class ModelInstance:
credentials=self.credentials,
texts=texts,
user=user,
input_type=input_type,
)
def get_text_embedding_num_tokens(self, texts: list[str]) -> int:

23
api/core/model_runtime/model_providers/__base/text_embedding_model.py
View File

@ -4,6 +4,7 @@ from typing import Optional
from pydantic import ConfigDict
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType
from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
from core.model_runtime.model_providers.__base.ai_model import AIModel
@ -20,35 +21,47 @@ class TextEmbeddingModel(AIModel):
model_config = ConfigDict(protected_namespaces=())
def invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke large language model
Invoke text embedding model
:param model: model name
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
self.started_at = time.perf_counter()
try:
return self._invoke(model, credentials, texts, user)
return self._invoke(model, credentials, texts, user, input_type)
except Exception as e:
raise self._transform_invoke_error(e)
@abstractmethod
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke large language model
Invoke text embedding model
:param model: model name
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
raise NotImplementedError

18
api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py
View File

@ -7,6 +7,7 @@ import numpy as np
import tiktoken
from openai import AzureOpenAI
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import AIModelEntity, PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.validate import CredentialsValidateFailedError
@ -17,8 +18,23 @@ from core.model_runtime.model_providers.azure_openai._constant import EMBEDDING_
class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
:param model: model name
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
base_model_name = credentials["base_model_name"]
credentials_kwargs = self._to_credential_kwargs(credentials)
client = AzureOpenAI(**credentials_kwargs)

9
api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py
View File

@ -4,6 +4,7 @@ from typing import Optional
from requests import post
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.invoke import (
@ -35,7 +36,12 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
api_base: str = "http://api.baichuan-ai.com/v1/embeddings"
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -44,6 +50,7 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
api_key = credentials["api_key"]

9
api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py
View File

@ -13,6 +13,7 @@ from botocore.exceptions import (
UnknownServiceError,
)
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.invoke import (
@ -30,7 +31,12 @@ logger = logging.getLogger(__name__)
class BedrockTextEmbeddingModel(TextEmbeddingModel):
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -39,6 +45,7 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
client_config = Config(region_name=credentials["aws_region"])

9
api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py
View File

@ -5,6 +5,7 @@ import cohere
import numpy as np
from cohere.core import RequestOptions
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.invoke import (
@ -25,7 +26,12 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
"""
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -34,6 +40,7 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
# get model properties

1
api/core/model_runtime/model_providers/fireworks/fireworks.yaml
View File

@ -15,6 +15,7 @@ help:
en_US: https://fireworks.ai/account/api-keys
supported_model_types:
- llm
- text-embedding
configurate_methods:
- predefined-model
provider_credential_schema:

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-11b-vision-instruct.yaml Normal file
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@ -0,0 +1,46 @@
model: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
label:
zh_Hans: Llama 3.2 11B Vision Instruct
en_US: Llama 3.2 11B Vision Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.2'
output: '0.2'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-1b-instruct.yaml Normal file
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@ -0,0 +1,46 @@
model: accounts/fireworks/models/llama-v3p2-1b-instruct
label:
zh_Hans: Llama 3.2 1B Instruct
en_US: Llama 3.2 1B Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.1'
output: '0.1'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-3b-instruct.yaml Normal file
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@ -0,0 +1,46 @@
model: accounts/fireworks/models/llama-v3p2-3b-instruct
label:
zh_Hans: Llama 3.2 3B Instruct
en_US: Llama 3.2 3B Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.1'
output: '0.1'
unit: '0.000001'
currency: USD

46
api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-90b-vision-instruct.yaml Normal file
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@ -0,0 +1,46 @@
model: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
label:
zh_Hans: Llama 3.2 90B Vision Instruct
en_US: Llama 3.2 90B Vision Instruct
model_type: llm
features:
- agent-thought
- tool-call
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
- name: max_tokens
use_template: max_tokens
- name: context_length_exceeded_behavior
default: None
label:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
help:
zh_Hans: 上下文长度超出行为
en_US: Context Length Exceeded Behavior
type: string
options:
- None
- truncate
- error
- name: response_format
use_template: response_format
pricing:
input: '0.9'
output: '0.9'
unit: '0.000001'
currency: USD

12
api/core/model_runtime/model_providers/fireworks/text_embedding/UAE-Large-V1.yaml Normal file
View File

@ -0,0 +1,12 @@
model: WhereIsAI/UAE-Large-V1
label:
zh_Hans: UAE-Large-V1
en_US: UAE-Large-V1
model_type: text-embedding
model_properties:
context_size: 512
max_chunks: 1
pricing:
input: '0.008'
unit: '0.000001'
currency: 'USD'

0
api/core/model_runtime/model_providers/fireworks/text_embedding/__init__.py Normal file
View File

12
api/core/model_runtime/model_providers/fireworks/text_embedding/gte-base.yaml Normal file
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@ -0,0 +1,12 @@
model: thenlper/gte-base
label:
zh_Hans: GTE-base
en_US: GTE-base
model_type: text-embedding
model_properties:
context_size: 512
max_chunks: 1
pricing:
input: '0.008'
unit: '0.000001'
currency: 'USD'

12
api/core/model_runtime/model_providers/fireworks/text_embedding/gte-large.yaml Normal file
View File

@ -0,0 +1,12 @@
model: thenlper/gte-large
label:
zh_Hans: GTE-large
en_US: GTE-large
model_type: text-embedding
model_properties:
context_size: 512
max_chunks: 1
pricing:
input: '0.008'
unit: '0.000001'
currency: 'USD'

12
api/core/model_runtime/model_providers/fireworks/text_embedding/nomic-embed-text-v1.5.yaml Normal file
View File

@ -0,0 +1,12 @@
model: nomic-ai/nomic-embed-text-v1.5
label:
zh_Hans: nomic-embed-text-v1.5
en_US: nomic-embed-text-v1.5
model_type: text-embedding
model_properties:
context_size: 8192
max_chunks: 16
pricing:
input: '0.008'
unit: '0.000001'
currency: 'USD'

12
api/core/model_runtime/model_providers/fireworks/text_embedding/nomic-embed-text-v1.yaml Normal file
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@ -0,0 +1,12 @@
model: nomic-ai/nomic-embed-text-v1
label:
zh_Hans: nomic-embed-text-v1
en_US: nomic-embed-text-v1
model_type: text-embedding
model_properties:
context_size: 8192
max_chunks: 16
pricing:
input: '0.008'
unit: '0.000001'
currency: 'USD'

151
api/core/model_runtime/model_providers/fireworks/text_embedding/text_embedding.py Normal file
View File

@ -0,0 +1,151 @@
import time
from collections.abc import Mapping
from typing import Optional, Union
import numpy as np
from openai import OpenAI
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
from core.model_runtime.model_providers.fireworks._common import _CommonFireworks
class FireworksTextEmbeddingModel(_CommonFireworks, TextEmbeddingModel):
"""
Model class for Fireworks text embedding model.
"""
def _invoke(
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
:param model: model name
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
credentials_kwargs = self._to_credential_kwargs(credentials)
client = OpenAI(**credentials_kwargs)
extra_model_kwargs = {}
if user:
extra_model_kwargs["user"] = user
extra_model_kwargs["encoding_format"] = "float"
context_size = self._get_context_size(model, credentials)
max_chunks = self._get_max_chunks(model, credentials)
inputs = []
indices = []
used_tokens = 0
for i, text in enumerate(texts):
# Here token count is only an approximation based on the GPT2 tokenizer
# TODO: Optimize for better token estimation and chunking
num_tokens = self._get_num_tokens_by_gpt2(text)
if num_tokens >= context_size:
cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
# if num tokens is larger than context length, only use the start
inputs.append(text[0:cutoff])
else:
inputs.append(text)
indices += [i]
batched_embeddings = []
_iter = range(0, len(inputs), max_chunks)
for i in _iter:
embeddings_batch, embedding_used_tokens = self._embedding_invoke(
model=model,
client=client,
texts=inputs[i : i + max_chunks],
extra_model_kwargs=extra_model_kwargs,
)
used_tokens += embedding_used_tokens
batched_embeddings += embeddings_batch
usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
return TextEmbeddingResult(embeddings=batched_embeddings, usage=usage, model=model)
def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
"""
Get number of tokens for given prompt messages
:param model: model name
:param credentials: model credentials
:param texts: texts to embed
:return:
"""
return sum(self._get_num_tokens_by_gpt2(text) for text in texts)
def validate_credentials(self, model: str, credentials: Mapping) -> None:
"""
Validate model credentials
:param model: model name
:param credentials: model credentials
:return:
"""
try:
# transform credentials to kwargs for model instance
credentials_kwargs = self._to_credential_kwargs(credentials)
client = OpenAI(**credentials_kwargs)
# call embedding model
self._embedding_invoke(model=model, client=client, texts=["ping"], extra_model_kwargs={})
except Exception as ex:
raise CredentialsValidateFailedError(str(ex))
def _embedding_invoke(
self, model: str, client: OpenAI, texts: Union[list[str], str], extra_model_kwargs: dict
) -> tuple[list[list[float]], int]:
"""
Invoke embedding model
:param model: model name
:param client: model client
:param texts: texts to embed
:param extra_model_kwargs: extra model kwargs
:return: embeddings and used tokens
"""
response = client.embeddings.create(model=model, input=texts, **extra_model_kwargs)
return [data.embedding for data in response.data], response.usage.total_tokens
def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
"""
Calculate response usage
:param model: model name
:param credentials: model credentials
:param tokens: input tokens
:return: usage
"""
input_price_info = self.get_price(
model=model, credentials=credentials, tokens=tokens, price_type=PriceType.INPUT
)
usage = EmbeddingUsage(
tokens=tokens,
total_tokens=tokens,
unit_price=input_price_info.unit_price,
price_unit=input_price_info.unit,
total_price=input_price_info.total_amount,
currency=input_price_info.currency,
latency=time.perf_counter() - self.started_at,
)
return usage

48
api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-001.yaml Normal file
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@ -0,0 +1,48 @@
model: gemini-1.5-flash-001
label:
en_US: Gemini 1.5 Flash 001
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 1048576
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

48
api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-002.yaml Normal file
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@ -0,0 +1,48 @@
model: gemini-1.5-flash-002
label:
en_US: Gemini 1.5 Flash 002
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 1048576
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

9
api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml
View File

@ -32,6 +32,15 @@ parameter_rules:
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'

48
api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0924.yaml Normal file
View File

@ -0,0 +1,48 @@
model: gemini-1.5-flash-8b-exp-0924
label:
en_US: Gemini 1.5 Flash 8B 0924
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 1048576
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

9
api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml
View File

@ -32,6 +32,15 @@ parameter_rules:
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'

11
api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml
View File

@ -1,6 +1,6 @@
model: gemini-1.5-flash-latest
label:
en_US: Gemini 1.5 Flash
en_US: Gemini 1.5 Flash Latest
model_type: llm
features:
- agent-thought
@ -32,6 +32,15 @@ parameter_rules:
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'

48
api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash.yaml Normal file
View File

@ -0,0 +1,48 @@
model: gemini-1.5-flash
label:
en_US: Gemini 1.5 Flash
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 1048576
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

48
api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-001.yaml Normal file
View File

@ -0,0 +1,48 @@
model: gemini-1.5-pro-001
label:
en_US: Gemini 1.5 Pro 001
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 2097152
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

48
api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-002.yaml Normal file
View File

@ -0,0 +1,48 @@
model: gemini-1.5-pro-002
label:
en_US: Gemini 1.5 Pro 002
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 2097152
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

9
api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml
View File

@ -32,6 +32,15 @@ parameter_rules:
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'

9
api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml
View File

@ -32,6 +32,15 @@ parameter_rules:
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'

11
api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml
View File

@ -1,6 +1,6 @@
model: gemini-1.5-pro-latest
label:
en_US: Gemini 1.5 Pro
en_US: Gemini 1.5 Pro Latest
model_type: llm
features:
- agent-thought
@ -32,6 +32,15 @@ parameter_rules:
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'

48
api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro.yaml Normal file
View File

@ -0,0 +1,48 @@
model: gemini-1.5-pro
label:
en_US: Gemini 1.5 Pro
model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 2097152
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: top_k
label:
zh_Hans: 取样数量
en_US: Top k
type: int
help:
zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
en_US: Only sample from the top K options for each subsequent token.
required: false
- name: max_tokens_to_sample
use_template: max_tokens
required: true
default: 8192
min: 1
max: 8192
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'
unit: '0.000001'
currency: USD

9
api/core/model_runtime/model_providers/google/llm/gemini-pro-vision.yaml
View File

@ -27,6 +27,15 @@ parameter_rules:
default: 4096
min: 1
max: 4096
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'

9
api/core/model_runtime/model_providers/google/llm/gemini-pro.yaml
View File

@ -31,6 +31,15 @@ parameter_rules:
max: 2048
- name: response_format
use_template: response_format
- name: stream
label:
zh_Hans: 流式输出
en_US: Stream
type: boolean
help:
zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
default: false
pricing:
input: '0.00'
output: '0.00'

14
api/core/model_runtime/model_providers/google/llm/llm.py
View File

@ -9,8 +9,8 @@ import google.ai.generativelanguage as glm
import google.generativeai as genai
import requests
from google.api_core import exceptions
from google.generativeai import client
from google.generativeai.types import ContentType, GenerateContentResponse, HarmBlockThreshold, HarmCategory
from google.generativeai.client import _ClientManager
from google.generativeai.types import ContentType, GenerateContentResponse
from google.generativeai.types.content_types import to_part
from PIL import Image
@ -200,24 +200,16 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
history.append(content)
# Create a new ClientManager with tenant's API key
new_client_manager = client._ClientManager()
new_client_manager = _ClientManager()
new_client_manager.configure(api_key=credentials["google_api_key"])
new_custom_client = new_client_manager.make_client("generative")
google_model._client = new_custom_client
safety_settings = {
HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
}
response = google_model.generate_content(
contents=history,
generation_config=genai.types.GenerationConfig(**config_kwargs),
stream=stream,
safety_settings=safety_settings,
tools=self._convert_tools_to_glm_tool(tools) if tools else None,
request_options={"timeout": 600},
)

25
api/core/model_runtime/model_providers/groq/llm/llama-3.2-11b-text-preview.yaml Normal file
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@ -0,0 +1,25 @@
model: llama-3.2-11b-text-preview
label:
zh_Hans: Llama 3.2 11B Text (Preview)
en_US: Llama 3.2 11B Text (Preview)
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: max_tokens
use_template: max_tokens
default: 512
min: 1
max: 8192
pricing:
input: '0.05'
output: '0.1'
unit: '0.000001'
currency: USD

25
api/core/model_runtime/model_providers/groq/llm/llama-3.2-1b-preview.yaml Normal file
View File

@ -0,0 +1,25 @@
model: llama-3.2-1b-preview
label:
zh_Hans: Llama 3.2 1B Text (Preview)
en_US: Llama 3.2 1B Text (Preview)
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: max_tokens
use_template: max_tokens
default: 512
min: 1
max: 8192
pricing:
input: '0.05'
output: '0.1'
unit: '0.000001'
currency: USD

25
api/core/model_runtime/model_providers/groq/llm/llama-3.2-3b-preview.yaml Normal file
View File

@ -0,0 +1,25 @@
model: llama-3.2-3b-preview
label:
zh_Hans: Llama 3.2 3B Text (Preview)
en_US: Llama 3.2 3B Text (Preview)
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: max_tokens
use_template: max_tokens
default: 512
min: 1
max: 8192
pricing:
input: '0.05'
output: '0.1'
unit: '0.000001'
currency: USD

25
api/core/model_runtime/model_providers/groq/llm/llama-3.2-90b-text-preview.yaml Normal file
View File

@ -0,0 +1,25 @@
model: llama-3.2-90b-text-preview
label:
zh_Hans: Llama 3.2 90B Text (Preview)
en_US: Llama 3.2 90B Text (Preview)
model_type: llm
features:
- agent-thought
model_properties:
mode: chat
context_size: 131072
parameter_rules:
- name: temperature
use_template: temperature
- name: top_p
use_template: top_p
- name: max_tokens
use_template: max_tokens
default: 512
min: 1
max: 8192
pricing:
input: '0.05'
output: '0.1'
unit: '0.000001'
currency: USD

18
api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py
View File

@ -6,6 +6,7 @@ import numpy as np
import requests
from huggingface_hub import HfApi, InferenceClient
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType, PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@ -18,8 +19,23 @@ HUGGINGFACE_ENDPOINT_API = "https://api.endpoints.huggingface.cloud/v2/endpoint/
class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel):
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
:param model: model name
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
client = InferenceClient(token=credentials["huggingfacehub_api_token"])
execute_model = model

9
api/core/model_runtime/model_providers/huggingface_tei/text_embedding/text_embedding.py
View File

@ -1,6 +1,7 @@
import time
from typing import Optional
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@ -23,7 +24,12 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
"""
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -38,6 +44,7 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
server_url = credentials["server_url"]

9
api/core/model_runtime/model_providers/hunyuan/text_embedding/text_embedding.py
View File

@ -9,6 +9,7 @@ from tencentcloud.common.profile.client_profile import ClientProfile
from tencentcloud.common.profile.http_profile import HttpProfile
from tencentcloud.hunyuan.v20230901 import hunyuan_client, models
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.invoke import (
@ -26,7 +27,12 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
"""
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -35,6 +41,7 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""

43
api/core/model_runtime/model_providers/jina/jina.yaml
View File

@ -67,46 +67,3 @@ model_credential_schema:
required: false
type: text-input
default: '8192'
- variable: task
label:
zh_Hans: 下游任务
en_US: Downstream task
placeholder:
zh_Hans: 选择将使用向量模型的下游任务。模型将返回针对该任务优化的向量。
en_US: Select the downstream task for which the embeddings will be used. The model will return the optimized embeddings for that task.
required: false
type: select
options:
- value: retrieval.query
label:
en_US: retrieval.query
- value: retrieval.passage
label:
en_US: retrieval.passage
- value: separation
label:
en_US: separation
- value: classification
label:
en_US: classification
- value: text-matching
label:
en_US: text-matching
- variable: dimensions
label:
zh_Hans: 输出维度
en_US: Output dimensions
placeholder:
zh_Hans: 输入您的输出维度
en_US: Enter output dimensions
required: false
type: text-input
- variable: late_chunking
label:
zh_Hans: 后期分块
en_US: Late chunking
placeholder:
zh_Hans: 应用后期分块技术来利用模型的长上下文功能来生成上下文块向量化。
en_US: Apply the late chunking technique to leverage the model's long-context capabilities for generating contextual chunk embeddings.
required: false
type: switch

29
api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py
View File

@ -4,6 +4,7 @@ from typing import Optional
from requests import post
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@ -27,7 +28,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
api_base: str = "https://api.jina.ai/v1"
def _to_payload(self, model: str, texts: list[str], credentials: dict) -> dict:
def _to_payload(self, model: str, texts: list[str], credentials: dict, input_type: EmbeddingInputType) -> dict:
"""
Parse model credentials
@ -44,23 +45,20 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
data = {"model": model, "input": [transform_jina_input_text(model, text) for text in texts]}
task = credentials.get("task")
dimensions = credentials.get("dimensions")
late_chunking = credentials.get("late_chunking")
if task is not None:
data["task"] = task
if dimensions is not None:
data["dimensions"] = int(dimensions)
if late_chunking is not None:
data["late_chunking"] = late_chunking
# model specific parameters
if model == "jina-embeddings-v3":
# set `task` type according to input type for the best performance
data["task"] = "retrieval.query" if input_type == EmbeddingInputType.QUERY else "retrieval.passage"
return data
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -69,6 +67,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
api_key = credentials["api_key"]
@ -81,7 +80,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
url = base_url + "/embeddings"
headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
data = self._to_payload(model=model, texts=texts, credentials=credentials)
data = self._to_payload(model=model, texts=texts, credentials=credentials, input_type=input_type)
try:
response = post(url, headers=headers, data=dumps(data))

11
api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py
View File

@ -5,6 +5,7 @@ from typing import Optional
from requests import post
from yarl import URL
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@ -22,11 +23,16 @@ from core.model_runtime.model_providers.__base.text_embedding_model import TextE
class LocalAITextEmbeddingModel(TextEmbeddingModel):
"""
Model class for Jina text embedding model.
Model class for LocalAI text embedding model.
"""
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -35,6 +41,7 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
if len(texts) != 1:

9
api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py
View File

@ -4,6 +4,7 @@ from typing import Optional
from requests import post
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.invoke import (
@ -34,7 +35,12 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
api_base: str = "https://api.minimax.chat/v1/embeddings"
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -43,6 +49,7 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
api_key = credentials["minimax_api_key"]

9
api/core/model_runtime/model_providers/mixedbread/text_embedding/text_embedding.py
View File

@ -4,6 +4,7 @@ from typing import Optional
import requests
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@ -27,7 +28,12 @@ class MixedBreadTextEmbeddingModel(TextEmbeddingModel):
api_base: str = "https://api.mixedbread.ai/v1"
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -36,6 +42,7 @@ class MixedBreadTextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
api_key = credentials["api_key"]

3
api/core/model_runtime/model_providers/nomic/text_embedding/text_embedding.py
View File

@ -5,6 +5,7 @@ from typing import Optional
from nomic import embed
from nomic import login as nomic_login
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import (
EmbeddingUsage,
@ -46,6 +47,7 @@ class NomicTextEmbeddingModel(_CommonNomic, TextEmbeddingModel):
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -54,6 +56,7 @@ class NomicTextEmbeddingModel(_CommonNomic, TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
embeddings, prompt_tokens, total_tokens = self.embed_text(

9
api/core/model_runtime/model_providers/nvidia/text_embedding/text_embedding.py
View File

@ -4,6 +4,7 @@ from typing import Optional
from requests import post
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.invoke import (
@ -27,7 +28,12 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel):
models: list[str] = ["NV-Embed-QA"]
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -36,6 +42,7 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
api_key = credentials["api_key"]

9
api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py
View File

@ -6,6 +6,7 @@ from typing import Optional
import numpy as np
import oci
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.invoke import (
@ -41,7 +42,12 @@ class OCITextEmbeddingModel(TextEmbeddingModel):
"""
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -50,6 +56,7 @@ class OCITextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
# get model properties

19
api/core/model_runtime/model_providers/ollama/llm/llm.py
View File

@ -364,14 +364,21 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
if chunk_json["done"]:
# calculate num tokens
if "prompt_eval_count" in chunk_json and "eval_count" in chunk_json:
# transform usage
if "prompt_eval_count" in chunk_json:
prompt_tokens = chunk_json["prompt_eval_count"]
completion_tokens = chunk_json["eval_count"]
else:
# calculate num tokens
prompt_tokens = self._get_num_tokens_by_gpt2(prompt_messages[0].content)
completion_tokens = self._get_num_tokens_by_gpt2(full_text)
prompt_message_content = prompt_messages[0].content
if isinstance(prompt_message_content, str):
prompt_tokens = self._get_num_tokens_by_gpt2(prompt_message_content)
else:
content_text = ""
for message_content in prompt_message_content:
if message_content.type == PromptMessageContentType.TEXT:
message_content = cast(TextPromptMessageContent, message_content)
content_text += message_content.data
prompt_tokens = self._get_num_tokens_by_gpt2(content_text)
completion_tokens = chunk_json.get("eval_count", self._get_num_tokens_by_gpt2(full_text))
# transform usage
usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)

9
api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py
View File

@ -8,6 +8,7 @@ from urllib.parse import urljoin
import numpy as np
import requests
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import (
AIModelEntity,
@ -38,7 +39,12 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
"""
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -47,6 +53,7 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""

9
api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py
View File

@ -6,6 +6,7 @@ import numpy as np
import tiktoken
from openai import OpenAI
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.validate import CredentialsValidateFailedError
@ -19,7 +20,12 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
"""
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -28,6 +34,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
# transform credentials to kwargs for model instance

9
api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py
View File

@ -7,6 +7,7 @@ from urllib.parse import urljoin
import numpy as np
import requests
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import (
AIModelEntity,
@ -28,7 +29,12 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
"""
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -37,6 +43,7 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""

9
api/core/model_runtime/model_providers/openllm/text_embedding/text_embedding.py
View File

@ -5,6 +5,7 @@ from typing import Optional
from requests import post
from requests.exceptions import ConnectionError, InvalidSchema, MissingSchema
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.model_entities import PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
from core.model_runtime.errors.invoke import (
@ -25,7 +26,12 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
"""
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -34,6 +40,7 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
server_url = credentials["server_url"]

9
api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py
View File

@ -7,6 +7,7 @@ from urllib.parse import urljoin
import numpy as np
import requests
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import (
AIModelEntity,
@ -28,7 +29,12 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
"""
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -37,6 +43,7 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""

18
api/core/model_runtime/model_providers/replicate/text_embedding/text_embedding.py
View File

@ -4,6 +4,7 @@ from typing import Optional
from replicate import Client as ReplicateClient
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType, PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@ -14,8 +15,23 @@ from core.model_runtime.model_providers.replicate._common import _CommonReplicat
class ReplicateEmbeddingModel(_CommonReplicate, TextEmbeddingModel):
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
:param model: model name
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
client = ReplicateClient(api_token=credentials["replicate_api_token"], timeout=30)
if "model_version" in credentials:

16
api/core/model_runtime/model_providers/sagemaker/llm/llm.py
View File

@ -84,8 +84,9 @@ class SageMakerLargeLanguageModel(LargeLanguageModel):
Model class for Cohere large language model.
"""
sagemaker_client: Any = None
sagemaker_session: Any = None
predictor: Any = None
sagemaker_endpoint: str = None
def _handle_chat_generate_response(
self,
@ -211,7 +212,7 @@ class SageMakerLargeLanguageModel(LargeLanguageModel):
:param user: unique user id
:return: full response or stream response chunk generator result
"""
if not self.sagemaker_client:
if not self.sagemaker_session:
access_key = credentials.get("aws_access_key_id")
secret_key = credentials.get("aws_secret_access_key")
aws_region = credentials.get("aws_region")
@ -226,11 +227,14 @@ class SageMakerLargeLanguageModel(LargeLanguageModel):
else:
boto_session = boto3.Session()
self.sagemaker_client = boto_session.client("sagemaker")
sagemaker_session = Session(boto_session=boto_session, sagemaker_client=self.sagemaker_client)
sagemaker_client = boto_session.client("sagemaker")
self.sagemaker_session = Session(boto_session=boto_session, sagemaker_client=sagemaker_client)
if self.sagemaker_endpoint != credentials.get("sagemaker_endpoint"):
self.sagemaker_endpoint = credentials.get("sagemaker_endpoint")
self.predictor = Predictor(
endpoint_name=credentials.get("sagemaker_endpoint"),
sagemaker_session=sagemaker_session,
endpoint_name=self.sagemaker_endpoint,
sagemaker_session=self.sagemaker_session,
serializer=serializers.JSONSerializer(),
)

9
api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py
View File

@ -6,6 +6,7 @@ from typing import Any, Optional
import boto3
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.common_entities import I18nObject
from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@ -53,7 +54,12 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
return embeddings
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
@ -62,6 +68,7 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
# get model properties

39
api/core/model_runtime/model_providers/siliconflow/llm/_position.yaml
View File

@ -1,25 +1,38 @@
- Qwen/Qwen2.5-7B-Instruct
- Qwen/Qwen2.5-14B-Instruct
- Qwen/Qwen2.5-32B-Instruct
- Qwen/Qwen2.5-72B-Instruct
- Qwen/Qwen2.5-Math-72B-Instruct
- Qwen/Qwen2.5-32B-Instruct
- Qwen/Qwen2.5-14B-Instruct
- Qwen/Qwen2.5-7B-Instruct
- Qwen/Qwen2.5-Coder-7B-Instruct
- deepseek-ai/DeepSeek-V2.5
- Qwen/Qwen2-72B-Instruct
- Qwen/Qwen2-57B-A14B-Instruct
- Qwen/Qwen2-7B-Instruct
- Qwen/Qwen2-1.5B-Instruct
- 01-ai/Yi-1.5-34B-Chat
- 01-ai/Yi-1.5-9B-Chat-16K
- 01-ai/Yi-1.5-6B-Chat
- THUDM/glm-4-9b-chat
- deepseek-ai/DeepSeek-V2.5
- deepseek-ai/DeepSeek-V2-Chat
- deepseek-ai/DeepSeek-Coder-V2-Instruct
- THUDM/glm-4-9b-chat
- THUDM/chatglm3-6b
- 01-ai/Yi-1.5-34B-Chat-16K
- 01-ai/Yi-1.5-9B-Chat-16K
- 01-ai/Yi-1.5-6B-Chat
- internlm/internlm2_5-20b-chat
- internlm/internlm2_5-7b-chat
- google/gemma-2-27b-it
- google/gemma-2-9b-it
- meta-llama/Meta-Llama-3-70B-Instruct
- meta-llama/Meta-Llama-3-8B-Instruct
- meta-llama/Meta-Llama-3.1-405B-Instruct
- meta-llama/Meta-Llama-3.1-70B-Instruct
- meta-llama/Meta-Llama-3.1-8B-Instruct
- mistralai/Mixtral-8x7B-Instruct-v0.1
- meta-llama/Meta-Llama-3-70B-Instruct
- meta-llama/Meta-Llama-3-8B-Instruct
- google/gemma-2-27b-it
- google/gemma-2-9b-it
- mistralai/Mistral-7B-Instruct-v0.2
- Pro/Qwen/Qwen2-7B-Instruct
- Pro/Qwen/Qwen2-1.5B-Instruct
- Pro/THUDM/glm-4-9b-chat
- Pro/THUDM/chatglm3-6b
- Pro/01-ai/Yi-1.5-9B-Chat-16K
- Pro/01-ai/Yi-1.5-6B-Chat
- Pro/internlm/internlm2_5-7b-chat
- Pro/meta-llama/Meta-Llama-3.1-8B-Instruct
- Pro/meta-llama/Meta-Llama-3-8B-Instruct
- Pro/google/gemma-2-9b-it

1
api/core/model_runtime/model_providers/siliconflow/llm/mistral-7b-instruct-v0.2.yaml
View File

@ -28,3 +28,4 @@ pricing:
output: '0'
unit: '0.000001'
currency: RMB
deprecated: true

1
api/core/model_runtime/model_providers/siliconflow/llm/mistral-8x7b-instruct-v0.1.yaml
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@ -28,3 +28,4 @@ pricing:
output: '1.26'
unit: '0.000001'
currency: RMB
deprecated: true

18
api/core/model_runtime/model_providers/siliconflow/text_embedding/text_embedding.py
View File

@ -1,5 +1,6 @@
from typing import Optional
from core.embedding.embedding_constant import EmbeddingInputType
from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
from core.model_runtime.model_providers.openai_api_compatible.text_embedding.text_embedding import (
OAICompatEmbeddingModel,
@ -16,8 +17,23 @@ class SiliconflowTextEmbeddingModel(OAICompatEmbeddingModel):
super().validate_credentials(model, credentials)
def _invoke(
self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
self,
model: str,
credentials: dict,
texts: list[str],
user: Optional[str] = None,
input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
) -> TextEmbeddingResult:
"""
Invoke text embedding model
:param model: model name
:param credentials: model credentials
:param texts: texts to embed
:param user: unique user id
:param input_type: input type
:return: embeddings result
"""
self._add_custom_parameters(credentials)
return super()._invoke(model, credentials, texts, user)

9
api/core/model_runtime/model_providers/spark/llm/llm.py
View File

@ -213,18 +213,21 @@ class SparkLargeLanguageModel(LargeLanguageModel):
:param prompt_messages: prompt messages
:return: llm response chunk generator result
"""
completion = ""
for index, content in enumerate(client.subscribe()):
if isinstance(content, dict):
delta = content["data"]
else:
delta = content
completion += delta
assistant_prompt_message = AssistantPromptMessage(
content=delta or "",
)
temp_assistant_prompt_message = AssistantPromptMessage(
content=completion,
)
prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
completion_tokens = self.get_num_tokens(model, credentials, [temp_assistant_prompt_message])
# transform usage
usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)

1
api/core/model_runtime/model_providers/tongyi/llm/farui-plus.yaml
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@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: farui-plus
label:
en_US: farui-plus

102
api/core/model_runtime/model_providers/tongyi/llm/llm.py
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@ -18,7 +18,7 @@ from dashscope.common.error import (
UnsupportedModel,
)
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
ImagePromptMessageContent,
@ -35,6 +35,7 @@ from core.model_runtime.entities.model_entities import (
FetchFrom,
I18nObject,
ModelFeature,
ModelPropertyKey,
ModelType,
ParameterRule,
ParameterType,
@ -97,6 +98,11 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
:param tools: tools for tool calling
:return:
"""
# Check if the model was added via get_customizable_model_schema
if self.get_customizable_model_schema(model, credentials) is not None:
# For custom models, tokens are not calculated.
return 0
if model in {"qwen-turbo-chat", "qwen-plus-chat"}:
model = model.replace("-chat", "")
if model == "farui-plus":
@ -537,55 +543,51 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
:param credentials: model credentials
:return: AIModelEntity or None
"""
rules = [
ParameterRule(
name="temperature",
type=ParameterType.FLOAT,
use_template="temperature",
label=I18nObject(zh_Hans="温度", en_US="Temperature"),
),
ParameterRule(
name="top_p",
type=ParameterType.FLOAT,
use_template="top_p",
label=I18nObject(zh_Hans="Top P", en_US="Top P"),
),
ParameterRule(
name="top_k",
type=ParameterType.INT,
min=0,
max=99,
label=I18nObject(zh_Hans="top_k", en_US="top_k"),
),
ParameterRule(
name="max_tokens",
type=ParameterType.INT,
min=1,
max=128000,
default=1024,
label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"),
),
ParameterRule(
name="seed",
type=ParameterType.INT,
default=1234,
label=I18nObject(zh_Hans="随机种子", en_US="Random Seed"),
),
ParameterRule(
name="repetition_penalty",
type=ParameterType.FLOAT,
default=1.1,
label=I18nObject(zh_Hans="重复惩罚", en_US="Repetition Penalty"),
),
]
entity = AIModelEntity(
return AIModelEntity(
model=model,
label=I18nObject(en_US=model),
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
label=I18nObject(en_US=model, zh_Hans=model),
model_type=ModelType.LLM,
model_properties={},
parameter_rules=rules,
features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL]
if credentials.get("function_calling_type") == "tool_call"
else [],
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={
ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 8000)),
ModelPropertyKey.MODE: LLMMode.CHAT.value,
},
parameter_rules=[
ParameterRule(
name="temperature",
use_template="temperature",
label=I18nObject(en_US="Temperature", zh_Hans="温度"),
type=ParameterType.FLOAT,
),
ParameterRule(
name="max_tokens",
use_template="max_tokens",
default=512,
min=1,
max=int(credentials.get("max_tokens", 1024)),
label=I18nObject(en_US="Max Tokens", zh_Hans="最大标记"),
type=ParameterType.INT,
),
ParameterRule(
name="top_p",
use_template="top_p",
label=I18nObject(en_US="Top P", zh_Hans="Top P"),
type=ParameterType.FLOAT,
),
ParameterRule(
name="top_k",
use_template="top_k",
label=I18nObject(en_US="Top K", zh_Hans="Top K"),
type=ParameterType.FLOAT,
),
ParameterRule(
name="frequency_penalty",
use_template="frequency_penalty",
label=I18nObject(en_US="Frequency Penalty", zh_Hans="重复惩罚"),
type=ParameterType.FLOAT,
),
],
)
return entity

1
api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-0919.yaml
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@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-coder-turbo-0919
label:
en_US: qwen-coder-turbo-0919

1
api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-latest.yaml
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@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-coder-turbo-latest
label:
en_US: qwen-coder-turbo-latest

1
api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo.yaml
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@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-coder-turbo
label:
en_US: qwen-coder-turbo

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-long.yaml
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@ -1,4 +1,4 @@
# model docs: https://help.aliyun.com/zh/model-studio/getting-started/models#27b2b3a15d5c6
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-long
label:
en_US: qwen-long

1
api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0816.yaml
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@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-math-plus-0816
label:
en_US: qwen-math-plus-0816

1
api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0919.yaml
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@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-math-plus-0919
label:
en_US: qwen-math-plus-0919

1
api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-latest.yaml
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@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-math-plus-latest
label:
en_US: qwen-math-plus-latest

1
api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus.yaml
View File

@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-math-plus
label:
en_US: qwen-math-plus

1
api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-0919.yaml
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@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-math-turbo-0919
label:
en_US: qwen-math-turbo-0919

1
api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-latest.yaml
View File

@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-math-turbo-latest
label:
en_US: qwen-math-turbo-latest

1
api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo.yaml
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@ -1,3 +1,4 @@
# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models
model: qwen-math-turbo
label:
en_US: qwen-math-turbo

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0107.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-max, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
model: qwen-max-0107
label:
en_US: qwen-max-0107

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0403.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-max-0403, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
model: qwen-max-0403
label:
en_US: qwen-max-0403

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0428.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-max-0428, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
model: qwen-max-0428
label:
en_US: qwen-max-0428

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0919.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-max-0919, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
model: qwen-max-0919
label:
en_US: qwen-max-0919

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-max-1201.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-max, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
model: qwen-max-1201
label:
en_US: qwen-max-1201

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-max-latest.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-max, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
model: qwen-max-latest
label:
en_US: qwen-max-latest

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-max-longcontext.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-max, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
model: qwen-max-longcontext
label:
en_US: qwen-max-longcontext

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-max.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-max, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf)
model: qwen-max
label:
en_US: qwen-max

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0206.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-plus-0206, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
model: qwen-plus-0206
label:
en_US: qwen-plus-0206

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0624.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-plus-0624, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
model: qwen-plus-0624
label:
en_US: qwen-plus-0624

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0723.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-plus-0723, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
model: qwen-plus-0723
label:
en_US: qwen-plus-0723

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0806.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-plus-0806, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
model: qwen-plus-0806
label:
en_US: qwen-plus-0806

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0919.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-plus-0919, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
model: qwen-plus-0919
label:
en_US: qwen-plus-0919

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-chat.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-plus, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
model: qwen-plus-chat
label:
en_US: qwen-plus-chat

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-latest.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-plus-latest, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
model: qwen-plus-latest
label:
en_US: qwen-plus-latest

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-plus.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-plus, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk)
model: qwen-plus
label:
en_US: qwen-plus

3
api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0206.yaml
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@ -1,3 +1,6 @@
# this model corresponds to qwen-turbo-0206, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub)
model: qwen-turbo-0206
label:
en_US: qwen-turbo-0206

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0624.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-turbo-0624, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub)
model: qwen-turbo-0624
label:
en_US: qwen-turbo-0624

2
api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0919.yaml
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@ -1,3 +1,5 @@
# this model corresponds to qwen-turbo-0919, for more details
# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub)
model: qwen-turbo-0919
label:
en_US: qwen-turbo-0919

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