151 lines
5.2 KiB
Markdown
151 lines
5.2 KiB
Markdown
<p align="center">
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<img src="https://gitea.va.reichard.io/evan/minyma/raw/branch/master/resources/logo.png" width="300">
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</p>
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<p align="center">
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<a href="https://gitea.va.reichard.io/evan/minyma/raw/branch/master/resources/loading.png">
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<img src="https://gitea.va.reichard.io/evan/minyma/raw/branch/master/resources/loading.png" width="49%">
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</a>
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<a href="https://gitea.va.reichard.io/evan/minyma/raw/branch/master/resources/response.png">
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<img src="https://gitea.va.reichard.io/evan/minyma/raw/branch/master/resources/response.png" width="49%">
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</a>
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</p>
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---
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AI Chat Bot with Plugins (Home Assistant, Vehicle Lookup, DuckDuckGo Search)
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[](https://drone.va.reichard.io/evan/minyma)
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## Plugins
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### Vehicle Lookup API
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This utilizes Carvana's undocumented API to lookup details on a vehicle.
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```
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User: What vehicle is NY plate HELLO?
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Assistant: The vehicle corresponding to NY plate HELLO is a 2016 MAZDA CX-5
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Grand Touring Sport Utility 4D with VIN JM3KE4DY6G0672552.
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```
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### Home Assistant API
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This utilizes Home Assistants [Conversational API](https://developers.home-assistant.io/docs/intent_conversation_api/).
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```
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User: Turn off the living room lights
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Assistant: The living room lights have been turned off. Is there anything else I can assist you with?
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User: Turn on the living room lights
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Assistant: The living room lights have been turned on successfully.
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```
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### DuckDuckGo
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This utilizes DuckDuckGo Search by scraping the top 5 results.
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```
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User: Tell me about Evan Reichard
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Assistant: Evan Reichard is a Principal Detection and Response Engineer based
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in the Washington DC-Baltimore Area. He has been in this role since
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August 2022. Evan has created a browser extension that helps SOC
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analysts and saves them over 300 hours per month. Additionally,
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there are three professionals named Evan Reichard on LinkedIn and
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there are also profiles of people named Evan Reichard on Facebook.
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```
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## Running Server
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```bash
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# Locally (See "Development" Section)
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export OPENAI_API_KEY=`cat openai_key`
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export HOME_ASSISTANT_API_KEY=`cat ha_key`
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export HOME_ASSISTANT_URL=https://some-url.com
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minyma server run
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# Docker Quick Start
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docker run \
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-p 5000:5000 \
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-e OPENAI_API_KEY=`cat openai_key` \
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-e DATA_PATH=/data \
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-v ./data:/data \
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gitea.va.reichard.io/evan/minyma:latest
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```
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The server will now be accessible at `http://localhost:5000`
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## Normalizing & Loading Data
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Minyma is designed to be extensible. You can add normalizers and vector db's
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using the appropriate interfaces defined in `./minyma/normalizer.py` and
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`./minyma/vdb.py`. At the moment the only supported database is `chroma`
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and the only supported normalizer is the `pubmed` normalizer.
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To normalize data, you can use Minyma's `normalize` CLI command:
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```bash
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minyma normalize \
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--filename ./pubmed_manuscripts.jsonl \
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--normalizer pubmed \
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--database chroma \
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--datapath ./chroma
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```
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The above example does the following:
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- Uses the `pubmed` normalizer
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- Normalizes the `./pubmed_manuscripts.jsonl` raw dataset [0]
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- Loads the output into a `chroma` database and persists the data to the `./chroma` directory
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**NOTE:** The above dataset took about an hour to normalize on my MPB M2 Max
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[0] https://huggingface.co/datasets/TaylorAI/pubmed_author_manuscripts/tree/main
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## Configuration
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| Environment Variable | Default Value | Description |
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| -------------------- | ------------- | ---------------------------------------------------------------------------------- |
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| OPENAI_API_KEY | NONE | Required OpenAI API Key for ChatGPT access. |
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| DATA_PATH | ./data | The path to the data directory. Chroma will store its data in the `chroma` subdir. |
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# Development
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```bash
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# Initiate
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python3 -m venv venv
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. ./venv/bin/activate
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# Local Development
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pip install -e .
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# Creds
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export OPENAI_API_KEY=`cat openai_key`
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# Docker
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make docker_build_local
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```
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# Notes
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This is the first time I'm doing anything LLM related, so it was an adventure.
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Initially I was entertaining OpenAI's Embedding API with plans to load embeddings
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into Pinecone, however initial calculations with `tiktoken` showed that generating
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embeddings would cost roughly $250 USD.
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Fortunately I found [Chroma](https://www.trychroma.com/), which basically solved
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both of those issues. It allowed me to load in the normalized data and automatically
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generated embeddings for me.
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In order to fit into OpenAI ChatGPT's token limit, I limited each document to roughly
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1000 words. I wanted to make sure I could add the top two matches as context while
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still having enough headroom for the actual question from the user.
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A few notes:
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- Context is not carried over from previous messages
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- I "stole" the prompt that is used in LangChain (See `oai.py`). I tried some variations without much (subjective) improvement.
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- 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.
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- Basic web front end with TailwindCSS
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