diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..d57e41d --- /dev/null +++ b/.dockerignore @@ -0,0 +1,2 @@ +venv +.venv \ No newline at end of file diff --git a/.env.sample b/.env.sample new file mode 100644 index 0000000..9847a1d --- /dev/null +++ b/.env.sample @@ -0,0 +1 @@ +OPENAI_API_KEY= \ No newline at end of file diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..f792b65 --- /dev/null +++ b/.flake8 @@ -0,0 +1,9 @@ +[flake8] +extend-ignore = C901, W504 +max-line-length = 125 +# NOTE: Update in .pre-commit-config.yaml as well +extend-exclude = .git,__pycache__,old,build,dist,*/migrations/*.py,legacy/,.venv +max-complexity = 10 +per-file-ignores = + /**/tests/*_mock_data.py: E501 + **/snap_test_*.py: E501 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..ea6cd4a --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,31 @@ +default_language_version: + python: python3 + +# NOTE: Update in .flake8 pyproject.toml as well +exclude: | + (?x)^( + \.git| + __pycache__| + .*snap_test_.*\.py| + .+\/.+\/migrations\/.*| + \.venv + ) + + +repos: + - repo: https://github.com/psf/black + rev: 24.4.2 + hooks: + - id: black + # args: ["--check"] + + - repo: https://github.com/PyCQA/isort + rev: 5.13.2 + hooks: + - id: isort + # args: ["--check"] + + - repo: https://github.com/PyCQA/flake8 + rev: 7.0.0 + hooks: + - id: flake8 diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..f43b15b --- /dev/null +++ b/Dockerfile @@ -0,0 +1,24 @@ +FROM python:3.10-slim-buster + +LABEL maintainer="TC Developers" + +ENV PYTHONUNBUFFERED 1 + +WORKDIR /code + +RUN apt-get update -y && \ + rm -rf /var/lib/apt/lists/* + +COPY pyproject.toml poetry.lock /code/ + +# Upgrade pip and install python packages for code +RUN pip install --upgrade --no-cache-dir pip poetry \ + && poetry --version \ + # Configure to use system instead of virtualenvs + && poetry config virtualenvs.create false \ + && poetry install --no-root \ + # Remove installer + && rm -rf /root/.cache/pypoetry \ + && pip uninstall -y poetry virtualenv-clone virtualenv + +COPY . /code/ \ No newline at end of file diff --git a/app.py b/app.py new file mode 100644 index 0000000..0ec5e0e --- /dev/null +++ b/app.py @@ -0,0 +1,71 @@ +from enum import Enum +from typing import List, Optional, Union + +from dotenv import load_dotenv +from fastapi import FastAPI +from pydantic import BaseModel + +from embedding_models import ( + OllamaEmbeddingModel, + OpenAIEmbeddingModel, + SentenceTransformerEmbeddingModel, +) + +load_dotenv() + +app = FastAPI() + + +class EmbeddingModelType(Enum): + """ + Embedding model types + """ + + SENTENCE_TRANSFORMERS = 1 + OLLAMA = 2 + OPENAI = 3 + + +class RequestSchemaForEmbeddings(BaseModel): + """Request Schema""" + + type_model: EmbeddingModelType + name_model: str + texts: Union[str, List[str]] + base_url: Optional[str] = None + + +@app.get("/") +async def home(): + return "Embedding handler using models for texts", 200 + + +@app.post("/get_embeddings") +async def generate_embeddings(item: RequestSchemaForEmbeddings): + """ + Generates the embedding vectors for the text/documents + based on different models + """ + type_model = item.type_model + name_model = item.name_model + base_url = item.base_url + texts = item.texts + + def generate(em_model, texts): + if isinstance(texts, str): + return em_model.embed_query(text=texts) + elif isinstance(texts, list): + return em_model.embed_documents(texts=texts) + return None + + if type_model == EmbeddingModelType.SENTENCE_TRANSFORMERS: + embedding_model = SentenceTransformerEmbeddingModel(model=name_model) + return generate(em_model=embedding_model, texts=texts) + + elif type_model == EmbeddingModelType.OLLAMA: + embedding_model = OllamaEmbeddingModel(model=name_model, base_url=base_url) + return generate(em_model=embedding_model, texts=texts) + + elif type_model == EmbeddingModelType.OPENAI: + embedding_model = OpenAIEmbeddingModel(model=name_model) + return generate(em_model=embedding_model, texts=texts) diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..aa3491b --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,12 @@ +services: + embedding_model: + build: . + volumes: + - embedding_models:/opt/models + command: bash -c 'uvicorn app:app --host=0.0.0.0 --port=8000' + ports: + - "8000:8000" + restart: always + +volumes: + embedding_models: diff --git a/embedding_models.py b/embedding_models.py new file mode 100644 index 0000000..fe25ecc --- /dev/null +++ b/embedding_models.py @@ -0,0 +1,115 @@ +from dataclasses import dataclass, field +from typing import List + +import numpy as np +from langchain_community.embeddings import OllamaEmbeddings +from langchain_core.embeddings import Embeddings +from langchain_openai import OpenAIEmbeddings +from sentence_transformers import SentenceTransformer +from torch import Tensor + +from utils import download_models + + +@dataclass +class SentenceTransformerEmbeddingModel(Embeddings): + """ + Embedding model using Sentence Transformers + """ + + model: str + embedding_model: SentenceTransformer = field(init=False) + + def __post_init__(self): + """ + Post initialization + """ + models_info = download_models(sent_embedding_model=self.model) + self.st_embedding_model = SentenceTransformer(model_name_or_path=models_info["model_path"]) + + def embed_documents(self, texts: list) -> np.ndarray: + """ + Generate embeddings for a list of documents + """ + v_representation = self.st_embedding_model.encode(texts) + return v_representation.tolist() + + def embed_query(self, text: str) -> np.ndarray: + """ + Generate embedding for a piece of text + """ + v_representation = self.st_embedding_model.encode(text) + return v_representation.tolist() + + def check_similarity(self, embeddings_1: np.ndarray, embeddings_2: np.ndarray) -> Tensor: + """ + Computes the cosine similarity between two embeddings + """ + return self.st_embedding_model.similarity(embeddings_1, embeddings_2) + + def get_model(self): + """Returns the model""" + return self.st_embedding_model + + +@dataclass +class OllamaEmbeddingModel(Embeddings): + """ + Embedding model using Ollama (locally deployed) + """ + + model: str + base_url: str + + def __post_init__(self): + """ + Post initialization + """ + self.ollama_embed_model = OllamaEmbeddings(model=self.model, base_url=self.base_url) + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """ + Generate embeddings for a list of documents. + """ + return self.ollama_embed_model.embed_documents(texts) + + def embed_query(self, text: str) -> List[float]: + """ + Generate embedding for a piece of text + """ + return self.ollama_embed_model.embed_query(text=text) + + def get_model(self): + """Returns the model""" + return self.ollama_embed_model + + +@dataclass +class OpenAIEmbeddingModel(Embeddings): + """ + Embedding Model using OpenAI + """ + + model: str + + def 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+fastapi = "^0.114.0" +uvicorn = "^0.22.0" +sentry-sdk = "^1.5.8" +huggingface-hub = "^0.24.7" +python-dotenv = "1.0.1" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.black] +line-length = 125 +# NOTE: Update in .pre-commit-config.yaml as well +extend-exclude = "(__pycache__|.*snap_test_.*\\.py|.+\\/.+\\/migrations\\/.*)" + +[tool.isort] +profile = "black" +multi_line_output = 3 +skip = [ + "**/__pycache__", + "**/snap_test_*.py", + ".venv/", + "**/migrations/*.py", +] + +[tool.pyright] +exclude = [ + "**/__pycache__", + "**/snap_test_*.py", + ".venv/", + "**/migrations/*.py", +] +reportMissingImports = true +reportMissingTypeStubs = false +venvPath = "." +venv = ".venv" diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..e854911 --- /dev/null +++ b/utils.py @@ -0,0 +1,37 @@ +import json +import logging +import os +from pathlib import Path + +from huggingface_hub import snapshot_download + +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + + +def download_models(sent_embedding_model: str): + models_path = Path("/opt/models") + models_info_path = models_path / "model_info.json" + + if not os.path.exists(models_path): + os.makedirs(models_path) + + if not any(os.listdir(models_path)): + logger.info("Downloading the model") + embedding_model_local_path = snapshot_download(repo_id=sent_embedding_model, cache_dir=models_path) + models_info = { + "model": sent_embedding_model, + "model_path": embedding_model_local_path, + } + + with open(models_info_path, "w", encoding="utf-8") as m_info_f: + json.dump(models_info, m_info_f) + + else: + if os.path.exists(models_info_path): + logger.info("Models already exists.") + logger.info(models_info_path) + with open(models_info_path, "r", encoding="utf-8") as m_info_f: + models_info = json.load(m_info_f) + + return models_info