merging origin/maun

.github/workflows/test.yml

@@ -8,7 +8,6 @@ on:
 
 jobs:
   build:
-    if: false
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:

README.md

@@ -1,4 +1,4 @@
-# Universal LLMs
+# Universal LLM
 
 This module should manage interactions with AI language models. For starters: Openai, Huggingface, Ollama and Elevenlabs.
 
@@ -10,24 +10,6 @@ Install [ffmpeg](https://ffmpeg.org/) for audio and video processing.
 npm install
 ```
 
-Create in the root directory .env file with the following content:
-
-```
-OPENAI_API_KEY=<string?>
-HUGGINGFACE_TOKEN=<string?>
-ELEVENLABS_API_KEY=<string?>
-```
-
-Fill the .env file with the following data:
-
-- Register free openai account and get the API key (for developers of LLM module)
-- Register free huggingface account and get the token (for developers of LLM module)
-- Register free elevenlabs account and get the API key (for developers using Elevenlabs speech-to-text module)
-
-Check periodically changes of the package.json file and install the new dependencies.
-
-All non private global settings, like port numbers etc. you should store in config.json
-
 ### LLM
 
 **Openai** - there is the connector class managing many kinds of interactions with the Openai API, mostly self explanatory.
@@ -40,7 +22,7 @@ To compare manually performance and quality of open source models in parallel yo
 **Ollama** - there is the connector class managing many kinds of interactions with multiple local LLMs.
 You should install [Ollama](https://github.com/ollama/ollama) on your local machine to use this module. Then run following commands (with your preferred model - in this case llama2):
 
-```
+```bash
 ollama serve
 ollama run llama2
 ```
@@ -49,13 +31,31 @@ ollama run llama2
 
 ## Testing
 
+Running for all tests:
+
 ```bash
 npm t
 ```
 
-Alternatively, you can check manually if a method works by placing following code in the main.js file in the root (in this case we testing Elevenlabs module):
+Running for single test file with environments file:
+
+```bash
+node --env-file=.env --test 'test/openai.js'
+```
+
+Running for single test in a test file:
+
+```bash
+node --env-file=.env --test --test-name-pattern="/Text completion/" 'test/openai.js'
+```
+
+## Usage
+
+**OpenAi Example**
 
 ```javascript
+'use strict';
+
 const { openai } = require('./lib');
 
 const { Chat } = openai;
@@ -74,14 +74,6 @@ If LOG_API_CALL_TIME config variable is set to true, each method should measure
 
 If LOG_API_CALL_RESULT config variable is set to true, each method should print the full api responce to pick required portion of data to return from a method.
 
-For those who unfamiliar with backend, you run following command in the terminal:
-
-```bash
-node --env-file=.env main.js
-```
-
-Just don't forget to remove the code from the index.js file after testing.
-
 ## Diagnostics
 
 I would like to use [Clinic.js](https://clinicjs.org/) for diagnostics, but we may set other systems in parrallel.
@@ -107,15 +99,16 @@ Peak a module you see a potential in and start working on it.
 
 ## TODO list
 
-- Write tests for openaAI assisitants
-- Finish Huggingface module and write JEST tests
-- Add price calculation for all methods that return usage statistics
-- Add price estimation function for all methods, that do not return usage statistics
-- Add "my\_" prefix to all generated dusting tests files, so git will ignore them
-- Make frontend allowing to interact with multiple LLMs in parralel and compare output
-- Connect the frontend to openai, huggingface and ollama modules
-- Make frontend to visualise dialogues and all other interactions with LLMs
-- Allow dinamic API_KEY definition (stored on frontend side)
+- [x] Make LLM to be separate subrepository, with all dependencies and tests.
+- [ ] Write tests for openaAI assisitants
+- [ ] Finish Huggingface module and write tests
+- [ ] Add price calculation for all methods that return usage statistics
+- [ ] Add price estimation function for all methods, that do not return usage statistics
+- [x] Add "my\_" prefix to all generated dusting tests files, so git will ignore them
+- [ ] Make frontend allowing to interact with multiple LLMs in parralel and compare output
+- [ ] Connect the frontend to openai, huggingface and ollama modules
+- [ ] Make frontend to visualise dialogues and all other interactions with LLMs
+- [x] Allow dynamic API_KEY definition (stored on frontend side)
 
 ##
 

lib/huggingface/config.json

@@ -0,0 +1,42 @@
+{
+  "DEFAULT_MODELS": {
+    "language": {
+      "fillMask": "bert-base-uncased",
+      "summarization": "facebook/bart-large-cnn",
+      "questionAnswering": "deepset/roberta-base-squad2",
+      "tableQuestionAnswering": "google/tapas-base-finetuned-wtq",
+      "textClassification": "distilbert-base-uncased-finetuned-sst-2-english",
+      "textGeneration": "gpt2",
+      "textGenerationStream": "google/flan-t5-xxl",
+      "tokenClassification": "dbmdz/bert-large-cased-finetuned-conll03-english",
+      "translation": "t5-base",
+      "zeroShotClassification": "facebook/bart-large-mnli",
+      "sentenceSimilarity": "sentence-transformers/paraphrase-xlm-r-multilingual-v1"
+    },
+    "audio": {
+      "automaticSpeechRecognition": "facebook/wav2vec2-large-960h-lv60-self",
+      "audioClassification": "superb/hubert-large-superb-er",
+      "textToSpeech": "espnet/kan-bayashi_ljspeech_vits",
+      "audioToAudio": "speechbrain/sepformer-wham"
+    },
+    "computerVision": {
+      "imageClassification": "google/vit-base-patch16-224",
+      "objectDetection": "facebook/detr-resnet-50",
+      "imageSegmentation": "facebook/detr-resnet-50-panoptic",
+      "imageToText": "nlpconnect/vit-gpt2-image-captioning",
+      "textToImage": "stabilityai/stable-diffusion-2",
+      "imageToImage": "lllyasviel/sd-controlnet-depth",
+      "zeroShotImageClassification": "openai/clip-vit-large-patch14-336"
+    },
+    "multimodal": {
+      "featureExtraction": "sentence-transformers/distilbert-base-nli-mean-tokens",
+      "visualQuestionAnswering": "dandelin/vilt-b32-finetuned-vqa",
+      "documentQuestionAnswering": "impira/layoutlm-document-qa"
+    },
+    "tabular": {
+      "tabularRegression": "scikit-learn/Fish-Weight",
+      "tabularClassification": "vvmnnnkv/wine-quality"
+    }
+  },
+  "DEFAULT_VOICE": "onyx"
+}