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Evan Reichard 2023-10-15 18:02:44 -04:00
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__pycache__
.direnv
data
venv
openai_key
minyma.egg-info/

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use nix

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[flake8]
max-line-length = 80

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__pycache__
.DS_Store
.direnv
data
venv
openai_key
minyma.egg-info/

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repos:
- repo: https://github.com/psf/black
rev: 23.9.1
hooks:
- id: black
name: black
language_version: python3.10
files: "^minyma/|^setup.py|^tests/minyma/"
- repo: https://github.com/pycqa/flake8
rev: 6.1.0
hooks:
- id: flake8
name: flake8
args: ["--config=.flake8"]
files: "^minyma/|^setup.py|^tests/minyma/"
- repo: https://github.com/pycqa/isort
rev: 5.12.0
hooks:
- id: isort
name: isort
args: ["--profile", "black", "--filter-files"]
files: "^minyma/|^setup.py|^tests/minyma/"

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# Build Container
FROM python:3.11-slim
# Install App
WORKDIR /app
COPY . /app
# Install Curl
RUN apt-get update -y
RUN apt-get install curl -y
# Install Chroma Dependencies
RUN mkdir -p /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/
RUN curl https://chroma-onnx-models.s3.amazonaws.com/all-MiniLM-L6-v2/onnx.tar.gz --output /root/.cache/chroma/onnx_models/all-MiniLM-L6-v2/onnx.tar.gz
# Install App & Gunicorn
RUN pip install .
RUN pip3 install gunicorn
# Cleanup
RUN rm -rf /app
# Start Application
ENTRYPOINT ["gunicorn"]
EXPOSE 5000
CMD ["minyma:create_app()", "--bind", "0.0.0.0:5000", "--threads=4", "--access-logfile", "-"]

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GNU GENERAL PUBLIC LICENSE
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recursive-include minyma/api *.py
recursive-include minyma/templates *

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docker_build_local:
docker build -t minyma:latest .

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<p align="center">
<img src="https://gitea.va.reichard.io/evan/minyma/raw/branch/master/resources/logo.png" width="300">
</p>
<p align="center">
<a href="https://gitea.va.reichard.io/evan/minyma/raw/branch/master/resources/loading.png">
<img src="https://gitea.va.reichard.io/evan/minyma/raw/branch/master/resources/loading.png" width="49%">
</a>
<a href="https://gitea.va.reichard.io/evan/minyma/raw/branch/master/resources/response.png">
<img src="https://gitea.va.reichard.io/evan/minyma/raw/branch/master/resources/response.png" width="49%">
</a>
</p>
---
AI Chat Bot with Vector / Embedding DB Context
## Running Server
```bash
# Locally (See "Development" Section)
export OPENAI_API_KEY=`cat openai_key`
minyma server run
# Docker Quick Start
make docker_build_local
docker run \
-p 5000:5000 \
-e OPENAI_API_KEY=`cat openai_key` \
-e DATA_PATH=/data \
-v ./data:/data \
minyma:latest
```
The server will now be accessible at `http://localhost:5000`
## Normalizing & Loading Data
Minyma is designed to be extensible. You can add normalizers and vector db's
using the appropriate interfaces defined in `./minyma/normalizer.py` and
`./minyma/vdb.py`. At the moment the only supported database is `chroma`
and the only supported normalizer is the `pubmed` normalizer.
To normalize data, you can use Minyma's `normalize` CLI command:
```bash
minyma normalize --filename ./pubmed_manuscripts.jsonl --normalizer pubmed --database chroma --datapath ./chroma
```
The above example does the following:
- Uses the `pubmed` normalizer
- Normalizes the `./pubmed_manuscripts.jsonl` raw dataset [0]
- Loads the output into a `chroma` database and persists the data to the `./chroma` directory
**NOTE:** The above dataset took about an hour to normalize on my MPB M2 Max
[0] https://huggingface.co/datasets/TaylorAI/pubmed_author_manuscripts/tree/main
## Configuration
| Environment Variable | Default Value | Description |
| -------------------- | ------------- | ---------------------------------------------------------------------------------- |
| OPENAI_API_KEY | NONE | Required OpenAI API Key for ChatGPT access. |
| DATA_PATH | ./data | The path to the data directory. Chroma will store its data in the `chroma` subdir. |
# Development
```bash
# Initiate
python3 -m venv venv
. ./venv/bin/activate
# Local Development
pip install -e .
# Creds
export OPENAI_API_KEY=`cat openai_key`
```
# Notes
This is the first time I'm doing anything LLM related, so it was an adventure.
Initially I was entertaining OpenAI's Embedding API with plans to load embeddings
into Pinecone, however initial calculations with `tiktoken` showed that generating
embeddings would cost roughly $250 USD.
Fortunately I found [Chroma](https://www.trychroma.com/), which basically solved
both of those issues. It allowed me to load in the normalized data and automatically
generated embeddings for me.
In order to fit into OpenAI ChatGPT's token limit, I limited each document to roughly
1000 words. I wanted to make sure I could add the top two matches as context while
still having enough headroom for the actual question from the user.
A few notes:
- Context is not carried over from previous messages
- I "stole" the prompt that is used in LangChain (See `oai.py`). I tried some variations without much (subjective) improvement.
- A generalized normalizer format. This should make it fairly easy to use completely different data. Just add a new normalizer that implements the super class.
- Basic web front end with TailwindCSS

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import click
import signal
import sys
from importlib.metadata import version
from minyma.config import Config
from minyma.oai import OpenAIConnector
from minyma.vdb import ChromaDB
from flask import Flask
from flask.cli import FlaskGroup
__version__ = version("minyma")
def signal_handler(sig, frame):
sys.exit(0)
def create_app():
global oai, cdb
import minyma.api.common as api_common
import minyma.api.v1 as api_v1
app = Flask(__name__)
cdb = ChromaDB(Config.DATA_PATH)
oai = OpenAIConnector(Config.OPENAI_API_KEY, cdb)
app.register_blueprint(api_common.bp)
app.register_blueprint(api_v1.bp)
return app
@click.group()
def cli():
"""Minyma CLI"""
@cli.group(cls=FlaskGroup, create_app=create_app)
def server():
"""Minyma flask server"""
@cli.command()
@click.option('--filename', type=click.File('r'), required=True)
@click.option('--normalizer', help="pubmed", required=True)
@click.option('--database', help="chroma", required=True)
@click.option('--datapath', type=click.Path(), help="database datapath", required=False)
def normalize(filename, normalizer, database, datapath):
"""Minyma data normalizer & loader"""
database = database.lower()
normalizer = normalizer.lower()
# Validate Database
if database == "chroma":
if datapath is None:
return print("INVALID DATAPATH")
vdb = ChromaDB(datapath)
else:
return print("INVALID DATABASE:", database)
# Select Normalizer
if normalizer == "pubmed":
from minyma.normalizer import PubMedNormalizer
norm = PubMedNormalizer(filename)
else:
return print("INVALID NORMALIZER:", normalizer)
# Process Data
vdb.load_documents(norm)
signal.signal(signal.SIGINT, signal_handler)

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from flask import make_response, render_template, send_from_directory
from flask import Blueprint
bp = Blueprint("common", __name__)
@bp.route("/", methods=["GET"])
def main_entry():
return make_response(render_template("index.html"))

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import minyma
from flask import Blueprint, request
bp = Blueprint("v1", __name__, url_prefix="/api/v1")
"""
Return OpenAI LLM final response with vector db embedding
context
"""
@bp.route("/query", methods=["POST"])
def get_response():
data = request.get_json()
if not data:
return {"error": "Missing Message"}
message = str(data.get("message"))
if message == "":
return {"error": "Empty Message"}
oai_response = minyma.oai.query(message)
return oai_response
"""
Return the raw vector db related response
"""
@bp.route("/related", methods=["POST"])
def get_related():
data = request.get_json()
if not data:
return {"error": "Missing Message"}
message = str(data.get("message"))
if message == "":
return {"error": "Empty Message"}
related_documents = minyma.cdb.get_related(message)
return related_documents

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import os
def get_env(key, default=None, required=False) -> str:
"""Wrapper for gathering env vars."""
if required:
assert key in os.environ, "Missing Environment Variable: %s" % key
return str(os.environ.get(key, default))
class Config:
"""Wrap application configurations
Attributes
----------
DATA_PATH : str
The path where to store any resources (default: ./)
OPENAI_API_KEY : str
OpenAI API Key - Required
"""
DATA_PATH: str = get_env("DATA_PATH", default="./data")
OPENAI_API_KEY: str = get_env("OPENAI_API_KEY", required=True)

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from io import TextIOWrapper
import json
class DataNormalizer:
def __init__(self, file: TextIOWrapper):
pass
def __iter__(self):
pass
class PubMedNormalizer(DataNormalizer):
"""
Iterator class that takes a file and iterates over each line. Data is
normalized inside the iterator.
"""
def __init__(self, file: TextIOWrapper):
self.file = file
def __iter__(self):
count = 0
# Iterate over each line in self.file, normalize, increment counter,
# and yield the normalized data.
while True:
line = self.file.readline()
# EOF
if not line:
break
# Load JSON
l = json.loads(line, strict=False)
norm_text = l.get("text").lower()
# Using the second occurance of "text mining" as a break point. We
# only capture what follows. Initially tried using regular
# expressions, but this is significantly faster.
split_data = norm_text.split("text mining")
norm_text = "text mining".join(split_data[2:])
norm_text = "\n".join(norm_text.split("\n")[1:])
count += 1
# ID = Line Number
yield { "doc": norm_text, "id": str(count - 1) }

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from typing import Any
import openai
from minyma.vdb import VectorDB
# Stolen LangChain Prompt
PROMPT_TEMPLATE = """
Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to
make up an answer.
{context}
Question: {question}
Helpful Answer:
"""
class OpenAIConnector:
def __init__(self, api_key: str, vdb: VectorDB):
self.vdb = vdb
self.model = "gpt-3.5-turbo"
openai.api_key = api_key
def query(self, question: str) -> Any:
# Get related documents from vector db
related = self.vdb.get_related(question)
# Validate results
all_docs = related.get("docs", [])
if len(all_docs) == 0:
return { "error": "No Context Found" }
# Join on new line, generate main prompt
context = '\n'.join(all_docs)
prompt = PROMPT_TEMPLATE.format(context = context, question = question)
# Query OpenAI ChatCompletion
response = openai.ChatCompletion.create(
model=self.model,
messages=[{"role": "user", "content": prompt}]
)
# Return Response
return response

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<title>Minyma - Chat</title>
<script src="https://cdn.tailwindcss.com"></script>
</head>
<body class="bg-slate-900 h-screen p-5 flex flex-col justify-between">
<header class="w-full">
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<script>
const LOADING_SVG = `<svg
width="24"
height="24"
viewBox="0 0 24 24"
xmlns="http://www.w3.org/2000/svg"
fill="currentColor"
>
<style>
.spinner_qM83 {
animation: spinner_8HQG 1.05s infinite;
}
.spinner_oXPr {
animation-delay: 0.1s;
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.spinner_ZTLf {
animation-delay: 0.2s;
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0%,
57.14% {
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<circle class="spinner_qM83 spinner_ZTLf" cx="20" cy="12" r="3"></circle>
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/**
* Wrapper API Call
**/
function apiCall(data) {
return fetch(data.url, {
method: data.method || "GET",
headers: {
"Content-Type": "application/json",
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body: JSON.stringify(data.data || {}),
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}
function appendMessageElement(name, content) {
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wrapEl.innerHTML = `<div class="flex">
<span class="font-bold w-24 grow-0 shrink-0"></span>
<span class="whitespace-break-spaces w-full"></span>
</div>`;
// Get Elements
let nameEl = wrapEl.querySelector("span");
let contentEl = nameEl.nextElementSibling;
// Prevent XSS
nameEl.innerText = name + ":";
contentEl.innerText = content;
// Add to DOM
let newEl = wrapEl.querySelector("div");
document.querySelector("#messages").prepend(newEl);
// Return References (Used in sendMessage)
return { name: nameEl, content: contentEl };
}
function sendMessage(message) {
// Set Loading
let { name, content } = appendMessageElement("Assistant", "");
content.innerHTML = LOADING_SVG;
// Request API
apiCall({
url: "./api/v1/query",
method: "POST",
data: { message },
})
.then((data) => {
console.log("SUCCESS:", data);
content.innerText = data.choices[0].message.content;
})
.catch((e) => {
console.log("ERROR:", e);
content.innerText = "[API ERROR]";
});
}
function initListeners() {
let messageBox = document.querySelector("[contenteditable]");
messageBox.addEventListener("keydown", (evt) => {
if (evt.keyCode != 13) return;
// Send Message & Add to DOM
let textContent = evt.target.innerText;
appendMessageElement("User", textContent);
sendMessage(textContent);
// Reset
evt.target.innerHTML = "";
evt.preventDefault();
});
}
initListeners();
</script>
</body>
</html>

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from chromadb.api import API
from itertools import islice
from os import path
from tqdm.auto import tqdm
from typing import Any, cast
import chromadb
from minyma.normalizer import DataNormalizer
"""
Given an iterable, chunk it by `chunk_size`
"""
def chunk(iterable, chunk_size: int):
iterator = iter(iterable)
while batch := list(islice(iterator, chunk_size)):
yield batch
"""
VectorDB Interface
"""
class VectorDB:
def load_documents(self, normalizer: DataNormalizer):
pass
def get_related(self, question: str) -> Any:
pass
"""
ChromaDV VectorDB Type
"""
class ChromaDB(VectorDB):
def __init__(self, base_path: str):
chroma_path = path.join(base_path, "chroma")
self.client: API = chromadb.PersistentClient(path=chroma_path)
self.word_limit = 1000
self.collection_name: str = "vdb"
self.collection: chromadb.Collection = self.client.create_collection(name=self.collection_name, get_or_create=True)
def get_related(self, question) -> Any:
"""Returns line separated related docs"""
results = self.collection.query(
query_texts=[question],
n_results=2
)
all_docs: list = cast(list, results.get("documents", [[]]))[0]
all_distances: list = cast(list, results.get("distances", [[]]))[0]
all_ids: list = cast(list, results.get("ids", [[]]))[0]
return {
"distances":all_distances,
"docs": all_docs,
"ids": all_ids
}
def load_documents(self, normalizer: DataNormalizer):
# 10 Item Chunking
for items in tqdm(chunk(normalizer, 50)):
ids = []
documents = []
# Limit words per document to accommodate context token limits
for item in items:
doc = " ".join(item.get("doc").split()[:self.word_limit])
documents.append(doc)
ids.append(item.get("id"))
# Ideally we parse out metadata from each document
# and pass to the metadata kwarg. However, each
# document appears to have a slightly different format,
# so it's difficult to parse out.
self.collection.add(
documents=documents,
ids=ids
)

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[project]
name = "minyma"
version = "0.0.1"
description = "AI Chat Bot with Vector DB Context"
authors = [
{ name = "Evan Reichard", email = "evan@reichard.io" },
]
license = { file = "LICENSE" }
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"Flask>=3.0",
"openai==0.28.1",
"openai[datalib]==0.28.1",
"tqdm",
"chromadb",
"sqlite-utils",
"click"
]
[project.scripts]
minyma = "minyma:cli"
[tool.setuptools.packages]
find = {}

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{ pkgs ? import <nixpkgs> { } }:
pkgs.mkShell {
packages = with pkgs; [
python311
];
}

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