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Add Fireworks AI as new model provider (#8428)
This commit is contained in:
ice yao
GitHub
parent
c8b9bdebfe
commit
0665268578
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@ -37,3 +37,4 @@
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- siliconflow
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- perfxcloud
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- zhinao
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- fireworks
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<svg width="130" role="graphics-symbol" aria-label="Fireworks AI Home" viewBox="0 0 835 130" xmlns="http://www.w3.org/2000/svg">
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<path fill-rule="evenodd" clip-rule="evenodd" d="M112.65 0L91.33 51.09L69.99 0H56.3L79.69 55.85C81.63 60.51 86.18 63.52 91.25 63.52C96.32 63.52 100.86 60.51 102.81 55.87L126.34 0H112.65ZM121.76 77.84L160.76 38.41L155.44 25.86L112.84 69.01C109.28 72.62 108.26 77.94 110.23 82.6C112.19 87.22 116.72 90.21 121.77 90.21L121.79 90.23L182.68 90.08L177.36 77.53L121.77 77.84H121.76ZM21.92 38.38L27.24 25.83L69.84 68.98C73.4 72.58 74.43 77.92 72.45 82.57C70.49 87.2 65.94 90.18 60.91 90.18L0.02 90.04L0 90.06L5.32 77.51L60.91 77.82L21.92 38.38Z" fill="#6720FF"></path>
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After Width: | Height: | Size: 4.2 KiB |
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<svg width="638" height="315" viewBox="0 0 638 315" fill="none" xmlns="http://www.w3.org/2000/svg">
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<path d="M318.563 221.755C300.863 221.755 284.979 211.247 278.206 194.978L196.549 0H244.342L318.842 178.361L393.273 0H441.066L358.92 195.048C352.112 211.247 336.263 221.755 318.563 221.755Z" fill="#6720FF"/>
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<path d="M425.111 314.933C407.481 314.933 391.667 304.494 384.824 288.366C377.947 272.097 381.507 253.524 393.936 240.921L542.657 90.2803L561.229 134.094L425.076 271.748L619.147 270.666L637.72 314.479L425.146 315.003L425.076 314.933H425.111Z" fill="#6720FF"/>
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<path d="M0 314.408L18.5727 270.595L212.643 271.677L76.525 133.988L95.0977 90.1748L243.819 240.816C256.247 253.384 259.843 272.026 252.93 288.26C246.088 304.424 230.203 314.827 212.643 314.827L0.0698221 314.339L0 314.408Z" fill="#6720FF"/>
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</svg>
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After Width: | Height: | Size: 815 B |
52
api/core/model_runtime/model_providers/fireworks/_common.py
Normal file
52
api/core/model_runtime/model_providers/fireworks/_common.py
Normal file
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from collections.abc import Mapping
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import openai
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from core.model_runtime.errors.invoke import (
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InvokeAuthorizationError,
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InvokeBadRequestError,
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InvokeConnectionError,
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InvokeError,
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InvokeRateLimitError,
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InvokeServerUnavailableError,
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)
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class _CommonFireworks:
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def _to_credential_kwargs(self, credentials: Mapping) -> dict:
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"""
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Transform credentials to kwargs for model instance
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:param credentials:
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:return:
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"""
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credentials_kwargs = {
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"api_key": credentials["fireworks_api_key"],
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"base_url": "https://api.fireworks.ai/inference/v1",
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"max_retries": 1,
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}
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return credentials_kwargs
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@property
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def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
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"""
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Map model invoke error to unified error
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The key is the error type thrown to the caller
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The value is the error type thrown by the model,
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which needs to be converted into a unified error type for the caller.
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:return: Invoke error mapping
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"""
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return {
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InvokeConnectionError: [openai.APIConnectionError, openai.APITimeoutError],
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InvokeServerUnavailableError: [openai.InternalServerError],
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InvokeRateLimitError: [openai.RateLimitError],
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InvokeAuthorizationError: [openai.AuthenticationError, openai.PermissionDeniedError],
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InvokeBadRequestError: [
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openai.BadRequestError,
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openai.NotFoundError,
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openai.UnprocessableEntityError,
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openai.APIError,
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],
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}
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import logging
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from core.model_runtime.entities.model_entities import ModelType
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from core.model_runtime.errors.validate import CredentialsValidateFailedError
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from core.model_runtime.model_providers.__base.model_provider import ModelProvider
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logger = logging.getLogger(__name__)
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class FireworksProvider(ModelProvider):
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def validate_provider_credentials(self, credentials: dict) -> None:
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"""
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Validate provider credentials
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if validate failed, raise exception
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:param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
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"""
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try:
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model_instance = self.get_model_instance(ModelType.LLM)
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model_instance.validate_credentials(
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model="accounts/fireworks/models/llama-v3p1-8b-instruct", credentials=credentials
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)
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except CredentialsValidateFailedError as ex:
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raise ex
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except Exception as ex:
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logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
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raise ex
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provider: fireworks
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label:
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zh_Hans: Fireworks AI
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en_US: Fireworks AI
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icon_small:
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en_US: icon_s_en.svg
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icon_large:
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en_US: icon_l_en.svg
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background: "#FCFDFF"
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help:
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title:
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en_US: Get your API Key from Fireworks AI
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zh_Hans: 从 Fireworks AI 获取 API Key
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url:
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en_US: https://fireworks.ai/account/api-keys
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supported_model_types:
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- llm
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configurate_methods:
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- predefined-model
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provider_credential_schema:
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credential_form_schemas:
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- variable: fireworks_api_key
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label:
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en_US: API Key
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type: secret-input
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required: true
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placeholder:
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zh_Hans: 在此输入您的 API Key
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en_US: Enter your API Key
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@ -0,0 +1,16 @@
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- llama-v3p1-405b-instruct
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- llama-v3p1-70b-instruct
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- llama-v3p1-8b-instruct
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- llama-v3-70b-instruct
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- mixtral-8x22b-instruct
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- mixtral-8x7b-instruct
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- firefunction-v2
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- firefunction-v1
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- gemma2-9b-it
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- llama-v3-70b-instruct-hf
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- llama-v3-8b-instruct
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- llama-v3-8b-instruct-hf
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- mixtral-8x7b-instruct-hf
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- mythomax-l2-13b
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- phi-3-vision-128k-instruct
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- yi-large
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@ -0,0 +1,46 @@
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model: accounts/fireworks/models/firefunction-v1
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label:
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zh_Hans: Firefunction V1
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en_US: Firefunction V1
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model_type: llm
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features:
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- agent-thought
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- tool-call
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model_properties:
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mode: chat
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context_size: 32768
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parameter_rules:
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- name: temperature
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use_template: temperature
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- name: top_p
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use_template: top_p
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- name: top_k
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label:
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zh_Hans: 取样数量
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en_US: Top k
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type: int
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help:
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zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
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en_US: Only sample from the top K options for each subsequent token.
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- name: max_tokens
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use_template: max_tokens
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- name: context_length_exceeded_behavior
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default: None
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label:
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zh_Hans: 上下文长度超出行为
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en_US: Context Length Exceeded Behavior
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help:
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zh_Hans: 上下文长度超出行为
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en_US: Context Length Exceeded Behavior
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type: string
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options:
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- None
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- truncate
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- error
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- name: response_format
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use_template: response_format
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pricing:
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input: '0.5'
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output: '0.5'
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unit: '0.000001'
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currency: USD
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@ -0,0 +1,46 @@
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model: accounts/fireworks/models/firefunction-v2
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label:
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zh_Hans: Firefunction V2
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en_US: Firefunction V2
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model_type: llm
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features:
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- agent-thought
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- tool-call
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model_properties:
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mode: chat
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context_size: 8192
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parameter_rules:
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- name: temperature
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use_template: temperature
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- name: top_p
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use_template: top_p
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- name: top_k
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label:
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zh_Hans: 取样数量
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en_US: Top k
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type: int
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help:
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zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
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en_US: Only sample from the top K options for each subsequent token.
|
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- name: max_tokens
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use_template: max_tokens
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- name: context_length_exceeded_behavior
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default: None
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label:
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zh_Hans: 上下文长度超出行为
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en_US: Context Length Exceeded Behavior
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help:
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zh_Hans: 上下文长度超出行为
|
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en_US: Context Length Exceeded Behavior
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type: string
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options:
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- None
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- truncate
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- error
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- name: response_format
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use_template: response_format
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pricing:
|
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input: '0.9'
|
||||
output: '0.9'
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unit: '0.000001'
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currency: USD
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@ -0,0 +1,45 @@
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model: accounts/fireworks/models/gemma2-9b-it
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label:
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zh_Hans: Gemma2 9B Instruct
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en_US: Gemma2 9B Instruct
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model_type: llm
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features:
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- agent-thought
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model_properties:
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mode: chat
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context_size: 8192
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parameter_rules:
|
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- name: temperature
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use_template: temperature
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||||
- name: top_p
|
||||
use_template: top_p
|
||||
- name: top_k
|
||||
label:
|
||||
zh_Hans: 取样数量
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||||
en_US: Top k
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||||
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'
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||||
currency: USD
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||||
|
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@ -0,0 +1,46 @@
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model: accounts/fireworks/models/llama-v3-70b-instruct-hf
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label:
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zh_Hans: Llama3 70B Instruct(HF version)
|
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en_US: Llama3 70B Instruct(HF version)
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
- tool-call
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 8192
|
||||
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
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/llama-v3-70b-instruct
|
||||
label:
|
||||
zh_Hans: Llama3 70B Instruct
|
||||
en_US: Llama3 70B Instruct
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
- tool-call
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 8192
|
||||
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
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/llama-v3-8b-instruct-hf
|
||||
label:
|
||||
zh_Hans: Llama3 8B Instruct(HF version)
|
||||
en_US: Llama3 8B Instruct(HF version)
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
- tool-call
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 8192
|
||||
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
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/llama-v3-8b-instruct
|
||||
label:
|
||||
zh_Hans: Llama3 8B Instruct
|
||||
en_US: Llama3 8B Instruct
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
- tool-call
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 8192
|
||||
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
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/llama-v3p1-405b-instruct
|
||||
label:
|
||||
zh_Hans: Llama3.1 405B Instruct
|
||||
en_US: Llama3.1 405B 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: '3'
|
||||
output: '3'
|
||||
unit: '0.000001'
|
||||
currency: USD
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/llama-v3p1-70b-instruct
|
||||
label:
|
||||
zh_Hans: Llama3.1 70B Instruct
|
||||
en_US: Llama3.1 70B 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
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/llama-v3p1-8b-instruct
|
||||
label:
|
||||
zh_Hans: Llama3.1 8B Instruct
|
||||
en_US: Llama3.1 8B 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
|
||||
610
api/core/model_runtime/model_providers/fireworks/llm/llm.py
Normal file
610
api/core/model_runtime/model_providers/fireworks/llm/llm.py
Normal file
|
|
@ -0,0 +1,610 @@
|
|||
import logging
|
||||
from collections.abc import Generator
|
||||
from typing import Optional, Union, cast
|
||||
|
||||
from openai import OpenAI, Stream
|
||||
from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessageToolCall
|
||||
from openai.types.chat.chat_completion_chunk import ChoiceDeltaFunctionCall, ChoiceDeltaToolCall
|
||||
from openai.types.chat.chat_completion_message import FunctionCall
|
||||
|
||||
from core.model_runtime.callbacks.base_callback import Callback
|
||||
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
|
||||
from core.model_runtime.entities.message_entities import (
|
||||
AssistantPromptMessage,
|
||||
ImagePromptMessageContent,
|
||||
PromptMessage,
|
||||
PromptMessageContentType,
|
||||
PromptMessageTool,
|
||||
SystemPromptMessage,
|
||||
TextPromptMessageContent,
|
||||
ToolPromptMessage,
|
||||
UserPromptMessage,
|
||||
)
|
||||
from core.model_runtime.errors.validate import CredentialsValidateFailedError
|
||||
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
|
||||
from core.model_runtime.model_providers.fireworks._common import _CommonFireworks
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
FIREWORKS_BLOCK_MODE_PROMPT = """You should always follow the instructions and output a valid {{block}} object.
|
||||
The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
|
||||
if you are not sure about the structure.
|
||||
|
||||
<instructions>
|
||||
{{instructions}}
|
||||
</instructions>
|
||||
""" # noqa: E501
|
||||
|
||||
|
||||
class FireworksLargeLanguageModel(_CommonFireworks, LargeLanguageModel):
|
||||
"""
|
||||
Model class for Fireworks large language model.
|
||||
"""
|
||||
|
||||
def _invoke(
|
||||
self,
|
||||
model: str,
|
||||
credentials: dict,
|
||||
prompt_messages: list[PromptMessage],
|
||||
model_parameters: dict,
|
||||
tools: Optional[list[PromptMessageTool]] = None,
|
||||
stop: Optional[list[str]] = None,
|
||||
stream: bool = True,
|
||||
user: Optional[str] = None,
|
||||
) -> Union[LLMResult, Generator]:
|
||||
"""
|
||||
Invoke large language model
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:param prompt_messages: prompt messages
|
||||
:param model_parameters: model parameters
|
||||
:param tools: tools for tool calling
|
||||
:param stop: stop words
|
||||
:param stream: is stream response
|
||||
:param user: unique user id
|
||||
:return: full response or stream response chunk generator result
|
||||
"""
|
||||
|
||||
return self._chat_generate(
|
||||
model=model,
|
||||
credentials=credentials,
|
||||
prompt_messages=prompt_messages,
|
||||
model_parameters=model_parameters,
|
||||
tools=tools,
|
||||
stop=stop,
|
||||
stream=stream,
|
||||
user=user,
|
||||
)
|
||||
|
||||
def _code_block_mode_wrapper(
|
||||
self,
|
||||
model: str,
|
||||
credentials: dict,
|
||||
prompt_messages: list[PromptMessage],
|
||||
model_parameters: dict,
|
||||
tools: Optional[list[PromptMessageTool]] = None,
|
||||
stop: Optional[list[str]] = None,
|
||||
stream: bool = True,
|
||||
user: Optional[str] = None,
|
||||
callbacks: Optional[list[Callback]] = None,
|
||||
) -> Union[LLMResult, Generator]:
|
||||
"""
|
||||
Code block mode wrapper for invoking large language model
|
||||
"""
|
||||
if "response_format" in model_parameters and model_parameters["response_format"] in {"JSON", "XML"}:
|
||||
stop = stop or []
|
||||
self._transform_chat_json_prompts(
|
||||
model=model,
|
||||
credentials=credentials,
|
||||
prompt_messages=prompt_messages,
|
||||
model_parameters=model_parameters,
|
||||
tools=tools,
|
||||
stop=stop,
|
||||
stream=stream,
|
||||
user=user,
|
||||
response_format=model_parameters["response_format"],
|
||||
)
|
||||
model_parameters.pop("response_format")
|
||||
|
||||
return self._invoke(
|
||||
model=model,
|
||||
credentials=credentials,
|
||||
prompt_messages=prompt_messages,
|
||||
model_parameters=model_parameters,
|
||||
tools=tools,
|
||||
stop=stop,
|
||||
stream=stream,
|
||||
user=user,
|
||||
)
|
||||
|
||||
def _transform_chat_json_prompts(
|
||||
self,
|
||||
model: str,
|
||||
credentials: dict,
|
||||
prompt_messages: list[PromptMessage],
|
||||
model_parameters: dict,
|
||||
tools: list[PromptMessageTool] | None = None,
|
||||
stop: list[str] | None = None,
|
||||
stream: bool = True,
|
||||
user: str | None = None,
|
||||
response_format: str = "JSON",
|
||||
) -> None:
|
||||
"""
|
||||
Transform json prompts
|
||||
"""
|
||||
if stop is None:
|
||||
stop = []
|
||||
if "```\n" not in stop:
|
||||
stop.append("```\n")
|
||||
if "\n```" not in stop:
|
||||
stop.append("\n```")
|
||||
|
||||
if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
|
||||
prompt_messages[0] = SystemPromptMessage(
|
||||
content=FIREWORKS_BLOCK_MODE_PROMPT.replace("{{instructions}}", prompt_messages[0].content).replace(
|
||||
"{{block}}", response_format
|
||||
)
|
||||
)
|
||||
prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}\n"))
|
||||
else:
|
||||
prompt_messages.insert(
|
||||
0,
|
||||
SystemPromptMessage(
|
||||
content=FIREWORKS_BLOCK_MODE_PROMPT.replace(
|
||||
"{{instructions}}", f"Please output a valid {response_format} object."
|
||||
).replace("{{block}}", response_format)
|
||||
),
|
||||
)
|
||||
prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
|
||||
|
||||
def get_num_tokens(
|
||||
self,
|
||||
model: str,
|
||||
credentials: dict,
|
||||
prompt_messages: list[PromptMessage],
|
||||
tools: Optional[list[PromptMessageTool]] = None,
|
||||
) -> int:
|
||||
"""
|
||||
Get number of tokens for given prompt messages
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:param prompt_messages: prompt messages
|
||||
:param tools: tools for tool calling
|
||||
:return:
|
||||
"""
|
||||
return self._num_tokens_from_messages(model, prompt_messages, tools)
|
||||
|
||||
def validate_credentials(self, model: str, credentials: dict) -> None:
|
||||
"""
|
||||
Validate model credentials
|
||||
|
||||
:param model: model name
|
||||
:param credentials: model credentials
|
||||
:return:
|
||||
"""
|
||||
try:
|
||||
credentials_kwargs = self._to_credential_kwargs(credentials)
|
||||
client = OpenAI(**credentials_kwargs)
|
||||
|
||||
client.chat.completions.create(
|
||||
messages=[{"role": "user", "content": "ping"}], model=model, temperature=0, max_tokens=10, stream=False
|
||||
)
|
||||
except Exception as e:
|
||||
raise CredentialsValidateFailedError(str(e))
|
||||
|
||||
def _chat_generate(
|
||||
self,
|
||||
model: str,
|
||||
credentials: dict,
|
||||
prompt_messages: list[PromptMessage],
|
||||
model_parameters: dict,
|
||||
tools: Optional[list[PromptMessageTool]] = None,
|
||||
stop: Optional[list[str]] = None,
|
||||
stream: bool = True,
|
||||
user: Optional[str] = None,
|
||||
) -> Union[LLMResult, Generator]:
|
||||
credentials_kwargs = self._to_credential_kwargs(credentials)
|
||||
client = OpenAI(**credentials_kwargs)
|
||||
|
||||
extra_model_kwargs = {}
|
||||
|
||||
if tools:
|
||||
extra_model_kwargs["functions"] = [
|
||||
{"name": tool.name, "description": tool.description, "parameters": tool.parameters} for tool in tools
|
||||
]
|
||||
|
||||
if stop:
|
||||
extra_model_kwargs["stop"] = stop
|
||||
|
||||
if user:
|
||||
extra_model_kwargs["user"] = user
|
||||
|
||||
# chat model
|
||||
response = client.chat.completions.create(
|
||||
messages=[self._convert_prompt_message_to_dict(m) for m in prompt_messages],
|
||||
model=model,
|
||||
stream=stream,
|
||||
**model_parameters,
|
||||
**extra_model_kwargs,
|
||||
)
|
||||
|
||||
if stream:
|
||||
return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages, tools)
|
||||
return self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
|
||||
|
||||
def _handle_chat_generate_response(
|
||||
self,
|
||||
model: str,
|
||||
credentials: dict,
|
||||
response: ChatCompletion,
|
||||
prompt_messages: list[PromptMessage],
|
||||
tools: Optional[list[PromptMessageTool]] = None,
|
||||
) -> LLMResult:
|
||||
"""
|
||||
Handle llm chat response
|
||||
|
||||
:param model: model name
|
||||
:param credentials: credentials
|
||||
:param response: response
|
||||
:param prompt_messages: prompt messages
|
||||
:param tools: tools for tool calling
|
||||
:return: llm response
|
||||
"""
|
||||
assistant_message = response.choices[0].message
|
||||
# assistant_message_tool_calls = assistant_message.tool_calls
|
||||
assistant_message_function_call = assistant_message.function_call
|
||||
|
||||
# extract tool calls from response
|
||||
# tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
|
||||
function_call = self._extract_response_function_call(assistant_message_function_call)
|
||||
tool_calls = [function_call] if function_call else []
|
||||
|
||||
# transform assistant message to prompt message
|
||||
assistant_prompt_message = AssistantPromptMessage(content=assistant_message.content, tool_calls=tool_calls)
|
||||
|
||||
# calculate num tokens
|
||||
if response.usage:
|
||||
# transform usage
|
||||
prompt_tokens = response.usage.prompt_tokens
|
||||
completion_tokens = response.usage.completion_tokens
|
||||
else:
|
||||
# calculate num tokens
|
||||
prompt_tokens = self._num_tokens_from_messages(model, prompt_messages, tools)
|
||||
completion_tokens = self._num_tokens_from_messages(model, [assistant_prompt_message])
|
||||
|
||||
# transform usage
|
||||
usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
|
||||
|
||||
# transform response
|
||||
response = LLMResult(
|
||||
model=response.model,
|
||||
prompt_messages=prompt_messages,
|
||||
message=assistant_prompt_message,
|
||||
usage=usage,
|
||||
system_fingerprint=response.system_fingerprint,
|
||||
)
|
||||
|
||||
return response
|
||||
|
||||
def _handle_chat_generate_stream_response(
|
||||
self,
|
||||
model: str,
|
||||
credentials: dict,
|
||||
response: Stream[ChatCompletionChunk],
|
||||
prompt_messages: list[PromptMessage],
|
||||
tools: Optional[list[PromptMessageTool]] = None,
|
||||
) -> Generator:
|
||||
"""
|
||||
Handle llm chat stream response
|
||||
|
||||
:param model: model name
|
||||
:param response: response
|
||||
:param prompt_messages: prompt messages
|
||||
:param tools: tools for tool calling
|
||||
:return: llm response chunk generator
|
||||
"""
|
||||
full_assistant_content = ""
|
||||
delta_assistant_message_function_call_storage: Optional[ChoiceDeltaFunctionCall] = None
|
||||
prompt_tokens = 0
|
||||
completion_tokens = 0
|
||||
final_tool_calls = []
|
||||
final_chunk = LLMResultChunk(
|
||||
model=model,
|
||||
prompt_messages=prompt_messages,
|
||||
delta=LLMResultChunkDelta(
|
||||
index=0,
|
||||
message=AssistantPromptMessage(content=""),
|
||||
),
|
||||
)
|
||||
|
||||
for chunk in response:
|
||||
if len(chunk.choices) == 0:
|
||||
if chunk.usage:
|
||||
# calculate num tokens
|
||||
prompt_tokens = chunk.usage.prompt_tokens
|
||||
completion_tokens = chunk.usage.completion_tokens
|
||||
continue
|
||||
|
||||
delta = chunk.choices[0]
|
||||
has_finish_reason = delta.finish_reason is not None
|
||||
|
||||
if (
|
||||
not has_finish_reason
|
||||
and (delta.delta.content is None or delta.delta.content == "")
|
||||
and delta.delta.function_call is None
|
||||
):
|
||||
continue
|
||||
|
||||
# assistant_message_tool_calls = delta.delta.tool_calls
|
||||
assistant_message_function_call = delta.delta.function_call
|
||||
|
||||
# extract tool calls from response
|
||||
if delta_assistant_message_function_call_storage is not None:
|
||||
# handle process of stream function call
|
||||
if assistant_message_function_call:
|
||||
# message has not ended ever
|
||||
delta_assistant_message_function_call_storage.arguments += assistant_message_function_call.arguments
|
||||
continue
|
||||
else:
|
||||
# message has ended
|
||||
assistant_message_function_call = delta_assistant_message_function_call_storage
|
||||
delta_assistant_message_function_call_storage = None
|
||||
else:
|
||||
if assistant_message_function_call:
|
||||
# start of stream function call
|
||||
delta_assistant_message_function_call_storage = assistant_message_function_call
|
||||
if delta_assistant_message_function_call_storage.arguments is None:
|
||||
delta_assistant_message_function_call_storage.arguments = ""
|
||||
if not has_finish_reason:
|
||||
continue
|
||||
|
||||
# tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
|
||||
function_call = self._extract_response_function_call(assistant_message_function_call)
|
||||
tool_calls = [function_call] if function_call else []
|
||||
if tool_calls:
|
||||
final_tool_calls.extend(tool_calls)
|
||||
|
||||
# transform assistant message to prompt message
|
||||
assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
|
||||
|
||||
full_assistant_content += delta.delta.content or ""
|
||||
|
||||
if has_finish_reason:
|
||||
final_chunk = LLMResultChunk(
|
||||
model=chunk.model,
|
||||
prompt_messages=prompt_messages,
|
||||
system_fingerprint=chunk.system_fingerprint,
|
||||
delta=LLMResultChunkDelta(
|
||||
index=delta.index,
|
||||
message=assistant_prompt_message,
|
||||
finish_reason=delta.finish_reason,
|
||||
),
|
||||
)
|
||||
else:
|
||||
yield LLMResultChunk(
|
||||
model=chunk.model,
|
||||
prompt_messages=prompt_messages,
|
||||
system_fingerprint=chunk.system_fingerprint,
|
||||
delta=LLMResultChunkDelta(
|
||||
index=delta.index,
|
||||
message=assistant_prompt_message,
|
||||
),
|
||||
)
|
||||
|
||||
if not prompt_tokens:
|
||||
prompt_tokens = self._num_tokens_from_messages(model, prompt_messages, tools)
|
||||
|
||||
if not completion_tokens:
|
||||
full_assistant_prompt_message = AssistantPromptMessage(
|
||||
content=full_assistant_content, tool_calls=final_tool_calls
|
||||
)
|
||||
completion_tokens = self._num_tokens_from_messages(model, [full_assistant_prompt_message])
|
||||
|
||||
# transform usage
|
||||
usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
|
||||
final_chunk.delta.usage = usage
|
||||
|
||||
yield final_chunk
|
||||
|
||||
def _extract_response_tool_calls(
|
||||
self, response_tool_calls: list[ChatCompletionMessageToolCall | ChoiceDeltaToolCall]
|
||||
) -> list[AssistantPromptMessage.ToolCall]:
|
||||
"""
|
||||
Extract tool calls from response
|
||||
|
||||
:param response_tool_calls: response tool calls
|
||||
:return: list of tool calls
|
||||
"""
|
||||
tool_calls = []
|
||||
if response_tool_calls:
|
||||
for response_tool_call in response_tool_calls:
|
||||
function = AssistantPromptMessage.ToolCall.ToolCallFunction(
|
||||
name=response_tool_call.function.name, arguments=response_tool_call.function.arguments
|
||||
)
|
||||
|
||||
tool_call = AssistantPromptMessage.ToolCall(
|
||||
id=response_tool_call.id, type=response_tool_call.type, function=function
|
||||
)
|
||||
tool_calls.append(tool_call)
|
||||
|
||||
return tool_calls
|
||||
|
||||
def _extract_response_function_call(
|
||||
self, response_function_call: FunctionCall | ChoiceDeltaFunctionCall
|
||||
) -> AssistantPromptMessage.ToolCall:
|
||||
"""
|
||||
Extract function call from response
|
||||
|
||||
:param response_function_call: response function call
|
||||
:return: tool call
|
||||
"""
|
||||
tool_call = None
|
||||
if response_function_call:
|
||||
function = AssistantPromptMessage.ToolCall.ToolCallFunction(
|
||||
name=response_function_call.name, arguments=response_function_call.arguments
|
||||
)
|
||||
|
||||
tool_call = AssistantPromptMessage.ToolCall(
|
||||
id=response_function_call.name, type="function", function=function
|
||||
)
|
||||
|
||||
return tool_call
|
||||
|
||||
def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict:
|
||||
"""
|
||||
Convert PromptMessage to dict for Fireworks API
|
||||
"""
|
||||
if isinstance(message, UserPromptMessage):
|
||||
message = cast(UserPromptMessage, message)
|
||||
if isinstance(message.content, str):
|
||||
message_dict = {"role": "user", "content": message.content}
|
||||
else:
|
||||
sub_messages = []
|
||||
for message_content in message.content:
|
||||
if message_content.type == PromptMessageContentType.TEXT:
|
||||
message_content = cast(TextPromptMessageContent, message_content)
|
||||
sub_message_dict = {"type": "text", "text": message_content.data}
|
||||
sub_messages.append(sub_message_dict)
|
||||
elif message_content.type == PromptMessageContentType.IMAGE:
|
||||
message_content = cast(ImagePromptMessageContent, message_content)
|
||||
sub_message_dict = {
|
||||
"type": "image_url",
|
||||
"image_url": {"url": message_content.data, "detail": message_content.detail.value},
|
||||
}
|
||||
sub_messages.append(sub_message_dict)
|
||||
|
||||
message_dict = {"role": "user", "content": sub_messages}
|
||||
elif isinstance(message, AssistantPromptMessage):
|
||||
message = cast(AssistantPromptMessage, message)
|
||||
message_dict = {"role": "assistant", "content": message.content}
|
||||
if message.tool_calls:
|
||||
# message_dict["tool_calls"] = [tool_call.dict() for tool_call in
|
||||
# message.tool_calls]
|
||||
function_call = message.tool_calls[0]
|
||||
message_dict["function_call"] = {
|
||||
"name": function_call.function.name,
|
||||
"arguments": function_call.function.arguments,
|
||||
}
|
||||
elif isinstance(message, SystemPromptMessage):
|
||||
message = cast(SystemPromptMessage, message)
|
||||
message_dict = {"role": "system", "content": message.content}
|
||||
elif isinstance(message, ToolPromptMessage):
|
||||
message = cast(ToolPromptMessage, message)
|
||||
# message_dict = {
|
||||
# "role": "tool",
|
||||
# "content": message.content,
|
||||
# "tool_call_id": message.tool_call_id
|
||||
# }
|
||||
message_dict = {"role": "function", "content": message.content, "name": message.tool_call_id}
|
||||
else:
|
||||
raise ValueError(f"Got unknown type {message}")
|
||||
|
||||
if message.name:
|
||||
message_dict["name"] = message.name
|
||||
|
||||
return message_dict
|
||||
|
||||
def _num_tokens_from_messages(
|
||||
self,
|
||||
model: str,
|
||||
messages: list[PromptMessage],
|
||||
tools: Optional[list[PromptMessageTool]] = None,
|
||||
credentials: dict = None,
|
||||
) -> int:
|
||||
"""
|
||||
Approximate num tokens with GPT2 tokenizer.
|
||||
"""
|
||||
|
||||
tokens_per_message = 3
|
||||
tokens_per_name = 1
|
||||
|
||||
num_tokens = 0
|
||||
messages_dict = [self._convert_prompt_message_to_dict(m) for m in messages]
|
||||
for message in messages_dict:
|
||||
num_tokens += tokens_per_message
|
||||
for key, value in message.items():
|
||||
# Cast str(value) in case the message value is not a string
|
||||
# This occurs with function messages
|
||||
# TODO: The current token calculation method for the image type is not implemented,
|
||||
# which need to download the image and then get the resolution for calculation,
|
||||
# and will increase the request delay
|
||||
if isinstance(value, list):
|
||||
text = ""
|
||||
for item in value:
|
||||
if isinstance(item, dict) and item["type"] == "text":
|
||||
text += item["text"]
|
||||
|
||||
value = text
|
||||
|
||||
if key == "tool_calls":
|
||||
for tool_call in value:
|
||||
for t_key, t_value in tool_call.items():
|
||||
num_tokens += self._get_num_tokens_by_gpt2(t_key)
|
||||
if t_key == "function":
|
||||
for f_key, f_value in t_value.items():
|
||||
num_tokens += self._get_num_tokens_by_gpt2(f_key)
|
||||
num_tokens += self._get_num_tokens_by_gpt2(f_value)
|
||||
else:
|
||||
num_tokens += self._get_num_tokens_by_gpt2(t_key)
|
||||
num_tokens += self._get_num_tokens_by_gpt2(t_value)
|
||||
else:
|
||||
num_tokens += self._get_num_tokens_by_gpt2(str(value))
|
||||
|
||||
if key == "name":
|
||||
num_tokens += tokens_per_name
|
||||
|
||||
# every reply is primed with <im_start>assistant
|
||||
num_tokens += 3
|
||||
|
||||
if tools:
|
||||
num_tokens += self._num_tokens_for_tools(tools)
|
||||
|
||||
return num_tokens
|
||||
|
||||
def _num_tokens_for_tools(self, tools: list[PromptMessageTool]) -> int:
|
||||
"""
|
||||
Calculate num tokens for tool calling with tiktoken package.
|
||||
|
||||
:param tools: tools for tool calling
|
||||
:return: number of tokens
|
||||
"""
|
||||
num_tokens = 0
|
||||
for tool in tools:
|
||||
num_tokens += self._get_num_tokens_by_gpt2("type")
|
||||
num_tokens += self._get_num_tokens_by_gpt2("function")
|
||||
num_tokens += self._get_num_tokens_by_gpt2("function")
|
||||
|
||||
# calculate num tokens for function object
|
||||
num_tokens += self._get_num_tokens_by_gpt2("name")
|
||||
num_tokens += self._get_num_tokens_by_gpt2(tool.name)
|
||||
num_tokens += self._get_num_tokens_by_gpt2("description")
|
||||
num_tokens += self._get_num_tokens_by_gpt2(tool.description)
|
||||
parameters = tool.parameters
|
||||
num_tokens += self._get_num_tokens_by_gpt2("parameters")
|
||||
if "title" in parameters:
|
||||
num_tokens += self._get_num_tokens_by_gpt2("title")
|
||||
num_tokens += self._get_num_tokens_by_gpt2(parameters.get("title"))
|
||||
num_tokens += self._get_num_tokens_by_gpt2("type")
|
||||
num_tokens += self._get_num_tokens_by_gpt2(parameters.get("type"))
|
||||
if "properties" in parameters:
|
||||
num_tokens += self._get_num_tokens_by_gpt2("properties")
|
||||
for key, value in parameters.get("properties").items():
|
||||
num_tokens += self._get_num_tokens_by_gpt2(key)
|
||||
for field_key, field_value in value.items():
|
||||
num_tokens += self._get_num_tokens_by_gpt2(field_key)
|
||||
if field_key == "enum":
|
||||
for enum_field in field_value:
|
||||
num_tokens += 3
|
||||
num_tokens += self._get_num_tokens_by_gpt2(enum_field)
|
||||
else:
|
||||
num_tokens += self._get_num_tokens_by_gpt2(field_key)
|
||||
num_tokens += self._get_num_tokens_by_gpt2(str(field_value))
|
||||
if "required" in parameters:
|
||||
num_tokens += self._get_num_tokens_by_gpt2("required")
|
||||
for required_field in parameters["required"]:
|
||||
num_tokens += 3
|
||||
num_tokens += self._get_num_tokens_by_gpt2(required_field)
|
||||
|
||||
return num_tokens
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/mixtral-8x22b-instruct
|
||||
label:
|
||||
zh_Hans: Mixtral MoE 8x22B Instruct
|
||||
en_US: Mixtral MoE 8x22B Instruct
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
- tool-call
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 65536
|
||||
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: '1.2'
|
||||
output: '1.2'
|
||||
unit: '0.000001'
|
||||
currency: USD
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/mixtral-8x7b-instruct-hf
|
||||
label:
|
||||
zh_Hans: Mixtral MoE 8x7B Instruct(HF version)
|
||||
en_US: Mixtral MoE 8x7B Instruct(HF version)
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
- tool-call
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 32768
|
||||
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: '0.5'
|
||||
output: '0.5'
|
||||
unit: '0.000001'
|
||||
currency: USD
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/mixtral-8x7b-instruct
|
||||
label:
|
||||
zh_Hans: Mixtral MoE 8x7B Instruct
|
||||
en_US: Mixtral MoE 8x7B Instruct
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
- tool-call
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 32768
|
||||
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: '0.5'
|
||||
output: '0.5'
|
||||
unit: '0.000001'
|
||||
currency: USD
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/mythomax-l2-13b
|
||||
label:
|
||||
zh_Hans: MythoMax L2 13b
|
||||
en_US: MythoMax L2 13b
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
- tool-call
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 4096
|
||||
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
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
model: accounts/fireworks/models/phi-3-vision-128k-instruct
|
||||
label:
|
||||
zh_Hans: Phi3.5 Vision Instruct
|
||||
en_US: Phi3.5 Vision Instruct
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
- vision
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 8192
|
||||
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
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
model: accounts/yi-01-ai/models/yi-large
|
||||
label:
|
||||
zh_Hans: Yi-Large
|
||||
en_US: Yi-Large
|
||||
model_type: llm
|
||||
features:
|
||||
- agent-thought
|
||||
model_properties:
|
||||
mode: chat
|
||||
context_size: 32768
|
||||
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: '3'
|
||||
output: '3'
|
||||
unit: '0.000001'
|
||||
currency: USD
|
||||
|
|
@ -100,6 +100,7 @@ exclude = [
|
|||
[tool.pytest_env]
|
||||
OPENAI_API_KEY = "sk-IamNotARealKeyJustForMockTestKawaiiiiiiiiii"
|
||||
UPSTAGE_API_KEY = "up-aaaaaaaaaaaaaaaaaaaa"
|
||||
FIREWORKS_API_KEY = "fw_aaaaaaaaaaaaaaaaaaaa"
|
||||
AZURE_OPENAI_API_BASE = "https://difyai-openai.openai.azure.com"
|
||||
AZURE_OPENAI_API_KEY = "xxxxb1707exxxxxxxxxxaaxxxxxf94"
|
||||
ANTHROPIC_API_KEY = "sk-ant-api11-IamNotARealKeyJustForMockTestKawaiiiiiiiiii-NotBaka-ASkksz"
|
||||
|
|
|
|||
186
api/tests/integration_tests/model_runtime/fireworks/test_llm.py
Normal file
186
api/tests/integration_tests/model_runtime/fireworks/test_llm.py
Normal file
|
|
@ -0,0 +1,186 @@
|
|||
import os
|
||||
from collections.abc import Generator
|
||||
|
||||
import pytest
|
||||
|
||||
from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
|
||||
from core.model_runtime.entities.message_entities import (
|
||||
AssistantPromptMessage,
|
||||
PromptMessageTool,
|
||||
SystemPromptMessage,
|
||||
UserPromptMessage,
|
||||
)
|
||||
from core.model_runtime.entities.model_entities import AIModelEntity
|
||||
from core.model_runtime.errors.validate import CredentialsValidateFailedError
|
||||
from core.model_runtime.model_providers.fireworks.llm.llm import FireworksLargeLanguageModel
|
||||
|
||||
"""FOR MOCK FIXTURES, DO NOT REMOVE"""
|
||||
from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
|
||||
|
||||
|
||||
def test_predefined_models():
|
||||
model = FireworksLargeLanguageModel()
|
||||
model_schemas = model.predefined_models()
|
||||
|
||||
assert len(model_schemas) >= 1
|
||||
assert isinstance(model_schemas[0], AIModelEntity)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
|
||||
def test_validate_credentials_for_chat_model(setup_openai_mock):
|
||||
model = FireworksLargeLanguageModel()
|
||||
|
||||
with pytest.raises(CredentialsValidateFailedError):
|
||||
# model name to gpt-3.5-turbo because of mocking
|
||||
model.validate_credentials(model="gpt-3.5-turbo", credentials={"fireworks_api_key": "invalid_key"})
|
||||
|
||||
model.validate_credentials(
|
||||
model="accounts/fireworks/models/llama-v3p1-8b-instruct",
|
||||
credentials={"fireworks_api_key": os.environ.get("FIREWORKS_API_KEY")},
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
|
||||
def test_invoke_chat_model(setup_openai_mock):
|
||||
model = FireworksLargeLanguageModel()
|
||||
|
||||
result = model.invoke(
|
||||
model="accounts/fireworks/models/llama-v3p1-8b-instruct",
|
||||
credentials={"fireworks_api_key": os.environ.get("FIREWORKS_API_KEY")},
|
||||
prompt_messages=[
|
||||
SystemPromptMessage(
|
||||
content="You are a helpful AI assistant.",
|
||||
),
|
||||
UserPromptMessage(content="Hello World!"),
|
||||
],
|
||||
model_parameters={
|
||||
"temperature": 0.0,
|
||||
"top_p": 1.0,
|
||||
"presence_penalty": 0.0,
|
||||
"frequency_penalty": 0.0,
|
||||
"max_tokens": 10,
|
||||
},
|
||||
stop=["How"],
|
||||
stream=False,
|
||||
user="foo",
|
||||
)
|
||||
|
||||
assert isinstance(result, LLMResult)
|
||||
assert len(result.message.content) > 0
|
||||
|
||||
|
||||
@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
|
||||
def test_invoke_chat_model_with_tools(setup_openai_mock):
|
||||
model = FireworksLargeLanguageModel()
|
||||
|
||||
result = model.invoke(
|
||||
model="accounts/fireworks/models/llama-v3p1-8b-instruct",
|
||||
credentials={"fireworks_api_key": os.environ.get("FIREWORKS_API_KEY")},
|
||||
prompt_messages=[
|
||||
SystemPromptMessage(
|
||||
content="You are a helpful AI assistant.",
|
||||
),
|
||||
UserPromptMessage(
|
||||
content="what's the weather today in London?",
|
||||
),
|
||||
],
|
||||
model_parameters={"temperature": 0.0, "max_tokens": 100},
|
||||
tools=[
|
||||
PromptMessageTool(
|
||||
name="get_weather",
|
||||
description="Determine weather in my location",
|
||||
parameters={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
|
||||
"unit": {"type": "string", "enum": ["c", "f"]},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
),
|
||||
PromptMessageTool(
|
||||
name="get_stock_price",
|
||||
description="Get the current stock price",
|
||||
parameters={
|
||||
"type": "object",
|
||||
"properties": {"symbol": {"type": "string", "description": "The stock symbol"}},
|
||||
"required": ["symbol"],
|
||||
},
|
||||
),
|
||||
],
|
||||
stream=False,
|
||||
user="foo",
|
||||
)
|
||||
|
||||
assert isinstance(result, LLMResult)
|
||||
assert isinstance(result.message, AssistantPromptMessage)
|
||||
assert len(result.message.tool_calls) > 0
|
||||
|
||||
|
||||
@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
|
||||
def test_invoke_stream_chat_model(setup_openai_mock):
|
||||
model = FireworksLargeLanguageModel()
|
||||
|
||||
result = model.invoke(
|
||||
model="accounts/fireworks/models/llama-v3p1-8b-instruct",
|
||||
credentials={"fireworks_api_key": os.environ.get("FIREWORKS_API_KEY")},
|
||||
prompt_messages=[
|
||||
SystemPromptMessage(
|
||||
content="You are a helpful AI assistant.",
|
||||
),
|
||||
UserPromptMessage(content="Hello World!"),
|
||||
],
|
||||
model_parameters={"temperature": 0.0, "max_tokens": 100},
|
||||
stream=True,
|
||||
user="foo",
|
||||
)
|
||||
|
||||
assert isinstance(result, Generator)
|
||||
|
||||
for chunk in result:
|
||||
assert isinstance(chunk, LLMResultChunk)
|
||||
assert isinstance(chunk.delta, LLMResultChunkDelta)
|
||||
assert isinstance(chunk.delta.message, AssistantPromptMessage)
|
||||
assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True
|
||||
if chunk.delta.finish_reason is not None:
|
||||
assert chunk.delta.usage is not None
|
||||
assert chunk.delta.usage.completion_tokens > 0
|
||||
|
||||
|
||||
def test_get_num_tokens():
|
||||
model = FireworksLargeLanguageModel()
|
||||
|
||||
num_tokens = model.get_num_tokens(
|
||||
model="accounts/fireworks/models/llama-v3p1-8b-instruct",
|
||||
credentials={"fireworks_api_key": os.environ.get("FIREWORKS_API_KEY")},
|
||||
prompt_messages=[UserPromptMessage(content="Hello World!")],
|
||||
)
|
||||
|
||||
assert num_tokens == 10
|
||||
|
||||
num_tokens = model.get_num_tokens(
|
||||
model="accounts/fireworks/models/llama-v3p1-8b-instruct",
|
||||
credentials={"fireworks_api_key": os.environ.get("FIREWORKS_API_KEY")},
|
||||
prompt_messages=[
|
||||
SystemPromptMessage(
|
||||
content="You are a helpful AI assistant.",
|
||||
),
|
||||
UserPromptMessage(content="Hello World!"),
|
||||
],
|
||||
tools=[
|
||||
PromptMessageTool(
|
||||
name="get_weather",
|
||||
description="Determine weather in my location",
|
||||
parameters={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
|
||||
"unit": {"type": "string", "enum": ["c", "f"]},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
),
|
||||
],
|
||||
)
|
||||
|
||||
assert num_tokens == 77
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
import os
|
||||
|
||||
import pytest
|
||||
|
||||
from core.model_runtime.errors.validate import CredentialsValidateFailedError
|
||||
from core.model_runtime.model_providers.fireworks.fireworks import FireworksProvider
|
||||
from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock
|
||||
|
||||
|
||||
@pytest.mark.parametrize("setup_openai_mock", [["chat"]], indirect=True)
|
||||
def test_validate_provider_credentials(setup_openai_mock):
|
||||
provider = FireworksProvider()
|
||||
|
||||
with pytest.raises(CredentialsValidateFailedError):
|
||||
provider.validate_provider_credentials(credentials={})
|
||||
|
||||
provider.validate_provider_credentials(credentials={"fireworks_api_key": os.environ.get("FIREWORKS_API_KEY")})
|
||||
|
|
@ -6,5 +6,5 @@ pytest api/tests/integration_tests/model_runtime/anthropic \
|
|||
api/tests/integration_tests/model_runtime/openai api/tests/integration_tests/model_runtime/chatglm \
|
||||
api/tests/integration_tests/model_runtime/google api/tests/integration_tests/model_runtime/xinference \
|
||||
api/tests/integration_tests/model_runtime/huggingface_hub/test_llm.py \
|
||||
api/tests/integration_tests/model_runtime/upstage
|
||||
|
||||
api/tests/integration_tests/model_runtime/upstage \
|
||||
api/tests/integration_tests/model_runtime/fireworks
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user