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.
This commit is contained in:
h-arnold
GitHub
parent
561765c74c
commit
12d1a9a426
5 changed files with 450 additions and 154 deletions
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{file = "lxml-5.2.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0f8c09ed18ecb4ebf23e02b8e7a22a05d6411911e6fabef3a36e4f371f4f2585"},
|
||||
|
|
@ -8794,7 +8811,7 @@ files = [
|
|||
]
|
||||
|
||||
[package.dependencies]
|
||||
greenlet = {version = "!=0.4.17", optional = true, markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\" or extra == \"asyncio\""}
|
||||
greenlet = {version = "!=0.4.17", optional = true, markers = "platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_machine == \"AMD64\" or platform_machine == \"amd64\" or platform_machine == \"x86_64\" or platform_machine == \"ppc64le\" or platform_machine == \"aarch64\" or extra == \"asyncio\""}
|
||||
typing-extensions = ">=4.6.0"
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -9101,120 +9118,130 @@ blobfile = ["blobfile (>=2)"]
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|||
|
||||
[[package]]
|
||||
name = "tokenizers"
|
||||
version = "0.19.1"
|
||||
version = "0.15.2"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = ">=3.7"
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files = [
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[[package]]
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||||
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||||
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|
||||
dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.19)", "urllib3 (<2.0.0)"]
|
||||
dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
|
||||
docs-specific = ["hf-doc-builder"]
|
||||
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
|
||||
flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
|
|
@ -9402,16 +9429,16 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
|
|||
sigopt = ["sigopt"]
|
||||
sklearn = ["scikit-learn"]
|
||||
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
|
||||
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
|
||||
tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
|
||||
tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
|
||||
tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
timm = ["timm"]
|
||||
tokenizers = ["tokenizers (>=0.19,<0.20)"]
|
||||
tokenizers = ["tokenizers (>=0.14,<0.19)"]
|
||||
torch = ["accelerate (>=0.21.0)", "torch"]
|
||||
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch", "tqdm (>=4.27)"]
|
||||
video = ["av (==9.2.0)", "decord (==0.6.0)"]
|
||||
vision = ["Pillow (>=10.0.1,<=15.0)"]
|
||||
|
||||
|
|
@ -10721,4 +10748,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<3.12"
|
||||
content-hash = "3355376a653b6ca7884c09be576958a121ab965927e5e22f20a98ea387497f03"
|
||||
content-hash = "d3ac1c13a79e0c368642bacb2907061361b7865b5136fdc969f5f5a28c7d2322"
|
||||
|
|
|
|||
|
|
@ -85,6 +85,8 @@ zep-python = { version = "^2.0.0rc5", allow-prereleases = true }
|
|||
langchain-google-vertexai = "^1.0.3"
|
||||
langchain-groq = "^0.1.3"
|
||||
langchain-pinecone = "^0.1.0"
|
||||
langchain-mistralai ="^0.1.6"
|
||||
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
types-redis = "^4.6.0.5"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,69 @@
|
|||
from typing import List, Optional
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langchain_mistralai.embeddings import MistralAIEmbeddings
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.field_typing import Embeddings, NestedDict
|
||||
|
||||
class MistralAIEmbeddingsComponent(CustomComponent):
|
||||
display_name = "MistralAI Embeddings"
|
||||
description = "Generate embeddings using MistralAI models."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"model": {
|
||||
"display_name": "Model",
|
||||
"advanced": False,
|
||||
"options": ["mistral-embed"],
|
||||
"value": "mistral-embed",
|
||||
},
|
||||
"mistral_api_key": {
|
||||
"display_name": "Mistral API Key",
|
||||
"password": True,
|
||||
"advanced": False,
|
||||
},
|
||||
"max_concurrent_requests": {
|
||||
"display_name": "Max Concurrent Requests",
|
||||
"advanced": True,
|
||||
"value": 64,
|
||||
},
|
||||
"max_retries": {
|
||||
"display_name": "Max Retries",
|
||||
"advanced": True,
|
||||
"value": 5,
|
||||
},
|
||||
"timeout": {
|
||||
"display_name": "Request Timeout",
|
||||
"advanced": True,
|
||||
"value": 120,
|
||||
},
|
||||
"endpoint": {
|
||||
"display_name": "API Endpoint",
|
||||
"advanced": True,
|
||||
"value": "https://api.mistral.ai/v1/"
|
||||
}
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
mistral_api_key: str,
|
||||
model: str = "mistral-embed",
|
||||
max_concurrent_requests: int = 64,
|
||||
max_retries: int = 5,
|
||||
timeout: int = 120,
|
||||
endpoint: str = "https://api.mistral.ai/v1/"
|
||||
) -> Embeddings:
|
||||
if mistral_api_key:
|
||||
api_key = SecretStr(mistral_api_key)
|
||||
else:
|
||||
api_key = None
|
||||
|
||||
return MistralAIEmbeddings(
|
||||
api_key=api_key,
|
||||
model=model,
|
||||
endpoint=endpoint,
|
||||
max_concurrent_requests=max_concurrent_requests,
|
||||
max_retries=max_retries,
|
||||
timeout=timeout
|
||||
)
|
||||
|
||||
|
|
@ -0,0 +1,87 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_mistralai import ChatMistralAI
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
|
||||
|
||||
class MistralAIModelComponent(CustomComponent):
|
||||
display_name: str = "MistralAI"
|
||||
description: str = "Generate text using MistralAI LLMs."
|
||||
icon = "MistralAI"
|
||||
|
||||
field_order = [
|
||||
"model",
|
||||
"mistral_api_key",
|
||||
"max_tokens",
|
||||
"temperature",
|
||||
"mistral_api_base",
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"model": {
|
||||
"display_name": "Model Name",
|
||||
"options": [
|
||||
"open-mistral-7b",
|
||||
"open-mixtral-8x7b",
|
||||
"open-mixtral-8x22b",
|
||||
"mistral-small-latest",
|
||||
"mistral-medium-latest",
|
||||
"mistral-large-latest"
|
||||
],
|
||||
"info": "Name of the model to use.",
|
||||
"required": True,
|
||||
"value": "open-mistral-7b",
|
||||
},
|
||||
"mistral_api_key": {
|
||||
"display_name": "Mistral API Key",
|
||||
"required": True,
|
||||
"password": True,
|
||||
"info": "Your Mistral API key.",
|
||||
},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"field_type": "int",
|
||||
"advanced": True,
|
||||
"value": 256,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"value": 0.1,
|
||||
},
|
||||
"mistral_api_base": {
|
||||
"display_name": "Mistral API Base",
|
||||
"advanced": True,
|
||||
"info": "Endpoint of the Mistral API. Defaults to 'https://api.mistral.ai' if not specified.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
model: str,
|
||||
mistral_api_key: Optional[str] = None,
|
||||
max_tokens: Optional[int] = None,
|
||||
temperature: Optional[float] = None,
|
||||
mistral_api_base: Optional[str] = None,
|
||||
) -> BaseLanguageModel:
|
||||
# Set default API endpoint if not provided
|
||||
if not mistral_api_base:
|
||||
mistral_api_base = "https://api.mistral.ai"
|
||||
|
||||
try:
|
||||
output = ChatMistralAI(
|
||||
model=model,
|
||||
api_key=(SecretStr(mistral_api_key) if mistral_api_key else None),
|
||||
max_tokens=max_tokens,
|
||||
temperature=temperature,
|
||||
base_url=mistral_api_base,
|
||||
)
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to Mistral API.") from e
|
||||
|
||||
return output
|
||||
111
src/backend/base/langflow/components/models/MistralModel.py
Normal file
111
src/backend/base/langflow/components/models/MistralModel.py
Normal file
|
|
@ -0,0 +1,111 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_mistralai import ChatMistralAI
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow.base.constants import STREAM_INFO_TEXT
|
||||
from langflow.base.models.model import LCModelComponent
|
||||
from langflow.field_typing import NestedDict, Text
|
||||
|
||||
|
||||
class MistralAIModelComponent(LCModelComponent):
|
||||
display_name = "MistralAI"
|
||||
description = "Generates text using MistralAI LLMs."
|
||||
icon = "MistralAI"
|
||||
|
||||
field_order = [
|
||||
"max_tokens",
|
||||
"model_kwargs",
|
||||
"model_name",
|
||||
"mistral_api_base",
|
||||
"mistral_api_key",
|
||||
"temperature",
|
||||
"input_value",
|
||||
"system_message",
|
||||
"stream",
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"input_value": {"display_name": "Input"},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"advanced": True,
|
||||
},
|
||||
"model_kwargs": {
|
||||
"display_name": "Model Kwargs",
|
||||
"advanced": True,
|
||||
},
|
||||
"model_name": {
|
||||
"display_name": "Model Name",
|
||||
"advanced": False,
|
||||
"options": [
|
||||
"open-mistral-7b",
|
||||
"open-mixtral-8x7b",
|
||||
"open-mixtral-8x22b",
|
||||
"mistral-small-latest",
|
||||
"mistral-medium-latest",
|
||||
"mistral-large-latest"
|
||||
],
|
||||
"value": "open-mistral-7b",
|
||||
},
|
||||
"mistral_api_base": {
|
||||
"display_name": "Mistral API Base",
|
||||
"advanced": True,
|
||||
"info": (
|
||||
"The base URL of the Mistral API. Defaults to https://api.mistral.ai.\n\n"
|
||||
"You can change this to use other APIs like JinaChat, LocalAI and Prem."
|
||||
),
|
||||
},
|
||||
"mistral_api_key": {
|
||||
"display_name": "Mistral API Key",
|
||||
"info": "The Mistral API Key to use for the Mistral model.",
|
||||
"advanced": False,
|
||||
"password": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"advanced": False,
|
||||
"value": 0.1,
|
||||
},
|
||||
"stream": {
|
||||
"display_name": "Stream",
|
||||
"info": STREAM_INFO_TEXT,
|
||||
"advanced": True,
|
||||
},
|
||||
"system_message": {
|
||||
"display_name": "System Message",
|
||||
"info": "System message to pass to the model.",
|
||||
"advanced": True,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
input_value: Text,
|
||||
mistral_api_key: str,
|
||||
temperature: float,
|
||||
model_name: str,
|
||||
max_tokens: Optional[int] = 256,
|
||||
model_kwargs: NestedDict = {},
|
||||
mistral_api_base: Optional[str] = None,
|
||||
stream: bool = False,
|
||||
system_message: Optional[str] = None,
|
||||
) -> Text:
|
||||
if not mistral_api_base:
|
||||
mistral_api_base = "https://api.mistral.ai"
|
||||
if mistral_api_key:
|
||||
api_key = SecretStr(mistral_api_key)
|
||||
else:
|
||||
api_key = None
|
||||
|
||||
chat_model = ChatMistralAI(
|
||||
max_tokens=max_tokens,
|
||||
model_kwargs=model_kwargs,
|
||||
model=model_name,
|
||||
base_url=mistral_api_base,
|
||||
api_key=api_key,
|
||||
temperature=temperature,
|
||||
)
|
||||
|
||||
return self.get_chat_result(chat_model, stream, input_value, system_message)
|
||||
Loading…
Add table
Add a link
Reference in a new issue