From 79eedd003cb502e28b39238ab03318b9681cf913 Mon Sep 17 00:00:00 2001 From: Evan Reichard Date: Sun, 15 Oct 2023 18:02:44 -0400 Subject: [PATCH] Initial Commit --- .dockerignore | 6 + .envrc | 1 + .flake8 | 2 + .gitignore | 7 + .pre-commit-config.yaml | 22 +++ Dockerfile | 26 +++ LICENSE | 339 ++++++++++++++++++++++++++++++++++++ MANIFEST.in | 2 + Makefile | 2 + README.md | 101 +++++++++++ minyma/__init__.py | 73 ++++++++ minyma/api/__init__.py | 0 minyma/api/common.py | 7 + minyma/api/v1.py | 38 ++++ minyma/config.py | 23 +++ minyma/normalizer.py | 45 +++++ minyma/oai.py | 44 +++++ minyma/templates/index.html | 184 +++++++++++++++++++ minyma/vdb.py | 75 ++++++++ pyproject.toml | 25 +++ resources/loading.png | Bin 0 -> 54413 bytes resources/logo.png | Bin 0 -> 20597 bytes resources/response.png | Bin 0 -> 117752 bytes shell.nix | 7 + test.html | 43 +++++ 25 files changed, 1072 insertions(+) create mode 100644 .dockerignore create mode 100644 .envrc create mode 100644 .flake8 create mode 100644 .gitignore create mode 100644 .pre-commit-config.yaml create mode 100644 Dockerfile create mode 100644 LICENSE create mode 100644 MANIFEST.in create mode 100644 Makefile create mode 100644 README.md create mode 100644 minyma/__init__.py create mode 100644 minyma/api/__init__.py create mode 100644 minyma/api/common.py create mode 100644 minyma/api/v1.py create mode 100644 minyma/config.py create mode 100644 minyma/normalizer.py create mode 100644 minyma/oai.py create mode 100644 minyma/templates/index.html create mode 100644 minyma/vdb.py create mode 100644 pyproject.toml create mode 100644 resources/loading.png create mode 100644 resources/logo.png create mode 100644 resources/response.png create mode 100644 shell.nix create mode 100644 test.html diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..052a345 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,6 @@ +__pycache__ +.direnv +data +venv +openai_key +minyma.egg-info/ diff --git a/.envrc b/.envrc new file mode 100644 index 0000000..1d953f4 --- /dev/null +++ b/.envrc @@ -0,0 +1 @@ +use nix diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..15fc7e3 --- /dev/null +++ b/.flake8 @@ -0,0 +1,2 @@ +[flake8] +max-line-length = 80 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..0fc361c --- /dev/null +++ b/.gitignore @@ -0,0 +1,7 @@ +__pycache__ +.DS_Store +.direnv +data +venv +openai_key +minyma.egg-info/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..c9e35c9 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,22 @@ +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/" diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..b0edfb7 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,26 @@ +# 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", "-"] diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..4522ba0 --- /dev/null +++ b/LICENSE @@ -0,0 +1,339 @@ +GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General +Public License instead of this License. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..266c5d1 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,2 @@ +recursive-include minyma/api *.py +recursive-include minyma/templates * diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..2e6f6fe --- /dev/null +++ b/Makefile @@ -0,0 +1,2 @@ +docker_build_local: + docker build -t minyma:latest . diff --git a/README.md b/README.md new file mode 100644 index 0000000..24b8101 --- /dev/null +++ b/README.md @@ -0,0 +1,101 @@ +

+ +

+ +

+ + + + + + +

+ +--- + +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 diff --git a/minyma/__init__.py b/minyma/__init__.py new file mode 100644 index 0000000..b85bf5f --- /dev/null +++ b/minyma/__init__.py @@ -0,0 +1,73 @@ +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) diff --git a/minyma/api/__init__.py b/minyma/api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/minyma/api/common.py b/minyma/api/common.py new file mode 100644 index 0000000..adc0df4 --- /dev/null +++ b/minyma/api/common.py @@ -0,0 +1,7 @@ +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")) diff --git a/minyma/api/v1.py b/minyma/api/v1.py new file mode 100644 index 0000000..4edb3a2 --- /dev/null +++ b/minyma/api/v1.py @@ -0,0 +1,38 @@ +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 diff --git a/minyma/config.py b/minyma/config.py new file mode 100644 index 0000000..15d8b58 --- /dev/null +++ b/minyma/config.py @@ -0,0 +1,23 @@ +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) diff --git a/minyma/normalizer.py b/minyma/normalizer.py new file mode 100644 index 0000000..42de7b0 --- /dev/null +++ b/minyma/normalizer.py @@ -0,0 +1,45 @@ +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) } diff --git a/minyma/oai.py b/minyma/oai.py new file mode 100644 index 0000000..1c3fc13 --- /dev/null +++ b/minyma/oai.py @@ -0,0 +1,44 @@ +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 diff --git a/minyma/templates/index.html b/minyma/templates/index.html new file mode 100644 index 0000000..21af51a --- /dev/null +++ b/minyma/templates/index.html @@ -0,0 +1,184 @@ + + + + + Minyma - Chat + + + +
+ + + + + + + + + + + + + + + + + + + + +
+
+
+
+
+ + + diff --git a/minyma/vdb.py b/minyma/vdb.py new file mode 100644 index 0000000..47f0770 --- /dev/null +++ b/minyma/vdb.py @@ -0,0 +1,75 @@ +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 + ) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..26f8be2 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,25 @@ +[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 = {} diff --git a/resources/loading.png b/resources/loading.png new file mode 100644 index 0000000000000000000000000000000000000000..a876bcc9170146f0e8e3d0809f76e11899c6a0db GIT binary patch literal 54413 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diff --git a/shell.nix b/shell.nix new file mode 100644 index 0000000..8e4c91c --- /dev/null +++ b/shell.nix @@ -0,0 +1,7 @@ +{ pkgs ? import { } }: + +pkgs.mkShell { + packages = with pkgs; [ + python311 + ]; +} diff --git a/test.html b/test.html new file mode 100644 index 0000000..87954c8 --- /dev/null +++ b/test.html @@ -0,0 +1,43 @@ + + + + + + + + + + + + + + + + + + + +