Added MistralAI LLM and Embedding (#1865)
* Added Mistral AI LLM Model and added MistralAI embeddings. * Added tracking to the new MistralAIEmbeddings.py and MistalModel.py * Added MistralAI model specs and added the correct models list to the Mistral Model LLM component. * Actually added the MistralAI model specs this time.
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h-arnold
GitHub
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12d1a9a426
5 changed files with 450 additions and 154 deletions
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from typing import List, Optional
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from pydantic.v1 import SecretStr
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from langchain_mistralai.embeddings import MistralAIEmbeddings
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from langflow.interface.custom.custom_component import CustomComponent
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from langflow.field_typing import Embeddings, NestedDict
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class MistralAIEmbeddingsComponent(CustomComponent):
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display_name = "MistralAI Embeddings"
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description = "Generate embeddings using MistralAI models."
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def build_config(self):
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return {
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"model": {
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"display_name": "Model",
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"advanced": False,
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"options": ["mistral-embed"],
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"value": "mistral-embed",
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},
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"mistral_api_key": {
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"display_name": "Mistral API Key",
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"password": True,
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"advanced": False,
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},
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"max_concurrent_requests": {
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"display_name": "Max Concurrent Requests",
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"advanced": True,
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"value": 64,
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},
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"max_retries": {
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"display_name": "Max Retries",
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"advanced": True,
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"value": 5,
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},
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"timeout": {
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"display_name": "Request Timeout",
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"advanced": True,
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"value": 120,
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},
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"endpoint": {
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"display_name": "API Endpoint",
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"advanced": True,
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"value": "https://api.mistral.ai/v1/"
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}
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}
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def build(
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self,
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mistral_api_key: str,
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model: str = "mistral-embed",
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max_concurrent_requests: int = 64,
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max_retries: int = 5,
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timeout: int = 120,
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endpoint: str = "https://api.mistral.ai/v1/"
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) -> Embeddings:
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if mistral_api_key:
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api_key = SecretStr(mistral_api_key)
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else:
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api_key = None
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return MistralAIEmbeddings(
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api_key=api_key,
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model=model,
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endpoint=endpoint,
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max_concurrent_requests=max_concurrent_requests,
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max_retries=max_retries,
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timeout=timeout
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)
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from typing import Optional
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from langchain_mistralai import ChatMistralAI
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from pydantic.v1 import SecretStr
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from langflow.custom import CustomComponent
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from langflow.field_typing import BaseLanguageModel
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class MistralAIModelComponent(CustomComponent):
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display_name: str = "MistralAI"
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description: str = "Generate text using MistralAI LLMs."
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icon = "MistralAI"
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field_order = [
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"model",
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"mistral_api_key",
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"max_tokens",
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"temperature",
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"mistral_api_base",
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]
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def build_config(self):
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return {
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"model": {
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"display_name": "Model Name",
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"options": [
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"open-mistral-7b",
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"open-mixtral-8x7b",
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"open-mixtral-8x22b",
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"mistral-small-latest",
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"mistral-medium-latest",
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"mistral-large-latest"
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],
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"info": "Name of the model to use.",
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"required": True,
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"value": "open-mistral-7b",
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},
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"mistral_api_key": {
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"display_name": "Mistral API Key",
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"required": True,
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"password": True,
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"info": "Your Mistral API key.",
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},
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"max_tokens": {
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"display_name": "Max Tokens",
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"field_type": "int",
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"advanced": True,
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"value": 256,
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},
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"temperature": {
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"display_name": "Temperature",
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"field_type": "float",
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"value": 0.1,
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},
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"mistral_api_base": {
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"display_name": "Mistral API Base",
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"advanced": True,
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"info": "Endpoint of the Mistral API. Defaults to 'https://api.mistral.ai' if not specified.",
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},
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"code": {"show": False},
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}
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def build(
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self,
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model: str,
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mistral_api_key: Optional[str] = None,
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max_tokens: Optional[int] = None,
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temperature: Optional[float] = None,
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mistral_api_base: Optional[str] = None,
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) -> BaseLanguageModel:
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# Set default API endpoint if not provided
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if not mistral_api_base:
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mistral_api_base = "https://api.mistral.ai"
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try:
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output = ChatMistralAI(
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model=model,
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api_key=(SecretStr(mistral_api_key) if mistral_api_key else None),
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max_tokens=max_tokens,
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temperature=temperature,
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base_url=mistral_api_base,
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)
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except Exception as e:
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raise ValueError("Could not connect to Mistral API.") from e
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return output
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111
src/backend/base/langflow/components/models/MistralModel.py
Normal file
111
src/backend/base/langflow/components/models/MistralModel.py
Normal file
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from typing import Optional
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from langchain_mistralai import ChatMistralAI
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from pydantic.v1 import SecretStr
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from langflow.base.constants import STREAM_INFO_TEXT
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from langflow.base.models.model import LCModelComponent
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from langflow.field_typing import NestedDict, Text
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class MistralAIModelComponent(LCModelComponent):
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display_name = "MistralAI"
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description = "Generates text using MistralAI LLMs."
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icon = "MistralAI"
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field_order = [
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"max_tokens",
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"model_kwargs",
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"model_name",
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"mistral_api_base",
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"mistral_api_key",
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"temperature",
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"input_value",
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"system_message",
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"stream",
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]
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def build_config(self):
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return {
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"input_value": {"display_name": "Input"},
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"max_tokens": {
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"display_name": "Max Tokens",
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"advanced": True,
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},
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"model_kwargs": {
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"display_name": "Model Kwargs",
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"advanced": True,
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},
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"model_name": {
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"display_name": "Model Name",
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"advanced": False,
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"options": [
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"open-mistral-7b",
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"open-mixtral-8x7b",
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"open-mixtral-8x22b",
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"mistral-small-latest",
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"mistral-medium-latest",
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"mistral-large-latest"
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],
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"value": "open-mistral-7b",
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},
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"mistral_api_base": {
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"display_name": "Mistral API Base",
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"advanced": True,
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"info": (
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"The base URL of the Mistral API. Defaults to https://api.mistral.ai.\n\n"
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"You can change this to use other APIs like JinaChat, LocalAI and Prem."
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),
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},
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"mistral_api_key": {
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"display_name": "Mistral API Key",
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"info": "The Mistral API Key to use for the Mistral model.",
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"advanced": False,
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"password": True,
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},
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"temperature": {
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"display_name": "Temperature",
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"advanced": False,
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"value": 0.1,
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},
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"stream": {
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"display_name": "Stream",
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"info": STREAM_INFO_TEXT,
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"advanced": True,
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},
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"system_message": {
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"display_name": "System Message",
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"info": "System message to pass to the model.",
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"advanced": True,
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},
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}
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def build(
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self,
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input_value: Text,
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mistral_api_key: str,
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temperature: float,
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model_name: str,
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max_tokens: Optional[int] = 256,
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model_kwargs: NestedDict = {},
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mistral_api_base: Optional[str] = None,
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stream: bool = False,
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system_message: Optional[str] = None,
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) -> Text:
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if not mistral_api_base:
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mistral_api_base = "https://api.mistral.ai"
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if mistral_api_key:
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api_key = SecretStr(mistral_api_key)
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else:
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api_key = None
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chat_model = ChatMistralAI(
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max_tokens=max_tokens,
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model_kwargs=model_kwargs,
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model=model_name,
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base_url=mistral_api_base,
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api_key=api_key,
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temperature=temperature,
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)
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return self.get_chat_result(chat_model, stream, input_value, system_message)
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