migrate from old repo

.gitignore

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+src/config/load_env.py
+volumes/*
+*.pyc
+*.csv
+*.json
+.env
+.DS_Store

README.md

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+# ViMedicine: An Vietnamese LLMs agent for drug and illness suggestion
+
+## Introduction
+In this project, we present a medicine agent for Vietnamese medical suggestion. 
+The medicine agent can:
+1. Predict illness based on patient's symptoms
+2. Suggest medicine based on disease
+3. Check specific medicine's suitability based on disease and patient's symptoms
+4. Predict medicines' side effects or interact or violate patient's medical record
+
+We use OpenAI GPT-3.5 model to read documents and generate medical entities, but the agent flow is not restricted to OpenAI GPT model or to Vietnamese medical domain.
+
+> ⚠️ Notes: The full document is comming soon. 
+
+## Install requirement
+Input your chat model key in `config/.env` file.
+Install via `pip`:
+```bash
+pip install -r <requirements.txt>
+```
+
+## Initualize Milvus server and deploy API
+**Initualize milvus server and create collection**
+```bash
+docker-compose up -d
+python setup_milvus.py
+```
+Notes: for the data, check out [Here](https://github.com/nmd2k/vi-medicine/releases/tag/data_v1)
+
+**Deploy API server**
+```bash
+uvicorn main:app  --reload --port=<port> --workers=<n_worker>
+```
+
+## Acknowledge
+This is a research project, so we do not guarantee the accuracy of the model.
+Since the performance heavily depends on database (in vector store) quality, the current model precision might not meet the expectation for production use.
+
+# Citation
+```
+@misc{vimedicine,
+  author = {Tran Tuan Anh, Phan Quang Hung, Dung Manh Nguyen},
+  title = {ViMedicine: An Vietnamese LLMs agent for drug and illness suggestion},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/nmd2k/vi-medicine/}},
+}
+```
+**Our team:**
+- Tran Tuan Anh*
+- Phan Quang Hung*
+- Dung Manh Nguyen*
+
+*: Equals contribution

app/business/functions.py

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+import sys
+sys.path.append(["../"])
+
+from pathlib import Path
+from typing import Any, List, Dict
+
+from langchain.schema import HumanMessage, SystemMessage
+
+from utils.prompts import *
+from utils.chatmodel import ChatModel
+from app.exception.custom_exception import CustomException
+
+class Functions:
+    "Simple generate function and embedding functions"
+    def __init__(self,
+        top_p: float = 1,
+        max_tokens: int = 512,
+        temperature: float = 0,
+        n_retry: int = 2,
+        request_timeout: int = 30, **kwargs) -> None:
+
+        self.chatmodel = ChatModel(
+            temperature=temperature,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            n_retry=n_retry,
+            request_timeout=request_timeout,
+            **kwargs
+        )
+
+    async def generate(self, message, prompt=None):
+        """
+        Chat model generate function
+        Args:
+            - message (str): human query/message
+            - prompt (str): optional system message
+        Return:
+            - str: Generated output
+        """
+        try:
+            messages = []
+            if prompt:
+                messages.append(SystemMessage(content=prompt))
+            messages.append(HumanMessage(content=message))
+            generate = self.chatmodel.generate(messages)
+
+            return generate
+        except Exception as exc:
+            raise CustomException(exc)
+
+    async def embed(self, message):
+        """
+        Embedding string input
+        Args:
+            - message (str): message to embed
+        Return:
+            - List: List of embedding output
+        """
+        try:
+            assert type(message) == str
+            embed = self.chatmodel.embed(message=message)
+            print(len(embed))
+            return embed
+        except Exception as exc:
+            raise CustomException(exc)