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fix: remove heavy local embedding model

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swve 2024-01-28 20:39:42 +00:00
parent 1b57195a7a
commit 15a2c6ab4b
6 changed files with 13 additions and 860 deletions
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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 (>=1.10,!=1.12.0)", "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)"]
ftfy = ["ftfy"]
integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
modelcreation = ["cookiecutter (==1.7.3)"]
natten = ["natten (>=0.14.6)"]
onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
optuna = ["optuna"]
quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
ray = ["ray[tune] (>=2.7.0)"]
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
sagemaker = ["sagemaker (>=2.31.0)"]
sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
serving = ["fastapi", "pydantic (<2)", "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 (<2)", "pytest (>=7.2.0)", "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 (>=0.14,<0.19)"]
torch = ["accelerate (>=0.21.0)", "torch (>=1.10,!=1.12.0)"]
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 (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow (>=10.0.1,<=15.0)"]
[[package]]
name = "triton"
version = "2.1.0"
description = "A language and compiler for custom Deep Learning operations"
optional = false
python-versions = "*"
files = [
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]
[package.dependencies]
filelock = "*"
[package.extras]
build = ["cmake (>=3.18)", "lit"]
tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
tutorials = ["matplotlib", "pandas", "tabulate"]
[[package]]
name = "typer"
version = "0.9.0"
@ -4441,4 +3597,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
content-hash = "76237f0e04218f9ca9a2593ccf952452bd6d45657066feec87373279fb7fe6a2"
content-hash = "f1dfc749e30bbf23ec06ac8b236ef6c2072591fe9a8a56f26e05416151ca97bb"

1
apps/api/pyproject.toml
View file

@ -34,7 +34,6 @@ langchain = "0.1.0"
tiktoken = "^0.5.2"
openai = "^1.7.1"
chromadb = "^0.4.22"
sentence-transformers = "^2.2.2"
python-dotenv = "^1.0.0"
redis = "^5.0.1"
langchain-community = "^0.0.11"

1
apps/api/requirements.txt
View file

@ -24,6 +24,5 @@ langchain-openai
tiktoken
openai
chromadb
sentence-transformers
python-dotenv
redis

4
apps/api/src/db/organization_config.py
View file

@ -21,8 +21,8 @@ class AIConfig(BaseModel):
enabled : bool = True
limits: AILimitsSettings = AILimitsSettings()
embeddings: Literal[
"text-embedding-ada-002", "all-MiniLM-L6-v2"
] = "all-MiniLM-L6-v2"
"text-embedding-ada-002",
] = "text-embedding-ada-002"
ai_model: Literal["gpt-3.5-turbo", "gpt-4-1106-preview"] = "gpt-3.5-turbo"
features: AIEnabledFeatures = AIEnabledFeatures()

15
apps/api/src/services/ai/base.py
View file

@ -2,7 +2,6 @@ from typing import Optional
from uuid import uuid4
from langchain.agents import AgentExecutor
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain.prompts import MessagesPlaceholder
@ -45,7 +44,6 @@ def ask_ai(
texts = text_splitter.split_documents(documents)
embedding_models = {
"all-MiniLM-L6-v2": SentenceTransformerEmbeddings,
"text-embedding-ada-002": OpenAIEmbeddings,
}
@ -53,11 +51,11 @@ def ask_ai(
if embedding_model_name in embedding_models:
if embedding_model_name == "text-embedding-ada-002":
embedding_function = embedding_models[embedding_model_name](model=embedding_model_name, api_key=openai_api_key)
if embedding_model_name == "all-MiniLM-L6-v2":
embedding_function = embedding_models[embedding_model_name](model_name=embedding_model_name)
embedding_function = embedding_models[embedding_model_name](
model=embedding_model_name, api_key=openai_api_key
)
else:
embedding_function = embedding_models[embedding_model_name](model_name=embedding_model_name)
raise Exception("Embedding model not found")
# load it into Chroma and use it as a retriever
db = Chroma.from_documents(texts, embedding_function)
@ -75,7 +73,10 @@ def ask_ai(
memory_key = "history"
memory = AgentTokenBufferMemory(
memory_key=memory_key, llm=llm, chat_memory=message_history, max_token_limit=1000
memory_key=memory_key,
llm=llm,
chat_memory=message_history,
max_token_limit=1000,
)
system_message = SystemMessage(content=(message_for_the_prompt))

4
apps/api/src/services/orgs/orgs.py
View file

@ -169,7 +169,7 @@ async def create_org(
limits_enabled=False,
max_asks=0,
),
embeddings="all-MiniLM-L6-v2",
embeddings="text-embedding-ada-002",
ai_model="gpt-3.5-turbo",
features=AIEnabledFeatures(
editor=False,
@ -531,8 +531,6 @@ async def get_org_join_mechanism(
return signup_mechanism
## 🔒 RBAC Utils ##