Q & A Comparison Instructions
- Ask a question and rank the answers across the models. Drag each answer to
- rank it, in ascending order, from left-to-right. Optionally, select the
- rank for each via the corresponding dropdown.
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new file mode 100644
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deleted file mode 100644
index f38bb83..0000000
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+ *
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+ *
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+ * LICENSE.md file in the root directory of this source tree.
+ *
+ * @license MIT
+ */function pt(e,t){return typeof e=="function"?e(t):e}function je(e,t){return n=>{t.setState(r=>({...r,[e]:pt(n,r[e])}))}}function fr(e){return e instanceof Function}function xh(e){return Array.isArray(e)&&e.every(t=>typeof t=="number")}function Th(e,t){const n=[],r=i=>{i.forEach(o=>{n.push(o);const l=t(o);l!=null&&l.length&&r(l)})};return r(e),n}function G(e,t,n){let r=[],i;return()=>{let o;n.key&&n.debug&&(o=Date.now());const l=e();if(!(l.length!==r.length||l.some((u,c)=>r[c]!==u)))return i;r=l;let s;if(n.key&&n.debug&&(s=Date.now()),i=t(...l),n==null||n.onChange==null||n.onChange(i),n.key&&n.debug&&n!=null&&n.debug()){const u=Math.round((Date.now()-o)*100)/100,c=Math.round((Date.now()-s)*100)/100,f=c/16,d=(g,p)=>{for(g=String(g);g.length{let g=d;for(const m of s.split(".")){var p;g=(p=g)==null?void 0:p[m]}return g}:c=d=>d[a.accessorKey]),!u)throw new Error;let f={id:`${String(u)}`,accessorFn:c,parent:r,depth:n,columnDef:a,columns:[],getFlatColumns:G(()=>[!0],()=>{var 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Dl(){return(e,t)=>G(()=>[e.getPreFilteredRowModel(),e.getState().columnFilters,e.getState().globalFilter,e.getFilteredRowModel()],(n,r,i)=>{if(!n.rows.length||!(r!=null&&r.length)&&!i)return n;const o=[...r.map(a=>a.id).filter(a=>a!==t),i?"__global__":void 0].filter(Boolean),l=a=>{for(let s=0;s{var n;return(n=e.options.debugAll)!=null?n:e.options.debugTable},onChange:()=>{}})}function Il(){return e=>G(()=>[e.getState().sorting,e.getPreSortedRowModel()],(t,n)=>{if(!n.rows.length||!(t!=null&&t.length))return n;const r=e.getState().sorting,i=[],o=r.filter(s=>{var u;return(u=e.getColumn(s.id))==null?void 0:u.getCanSort()}),l={};o.forEach(s=>{const u=e.getColumn(s.id);u&&(l[s.id]={sortUndefined:u.columnDef.sortUndefined,invertSorting:u.columnDef.invertSorting,sortingFn:u.getSortingFn()})});const a=s=>{const u=[...s];return u.sort((c,f)=>{for(let g=0;g{var f;i.push(c),(f=c.subRows)!=null&&f.length&&(c.subRows=a(c.subRows))}),u};return{rows:a(n.rows),flatRows:i,rowsById:n.rowsById}},{key:!1,debug:()=>{var t;return(t=e.options.debugAll)!=null?t:e.options.debugTable},onChange:()=>{e._autoResetPageIndex()}})}function _p(e){const t=[],n=r=>{var i;t.push(r),(i=r.subRows)!=null&&i.length&&r.getIsExpanded()&&r.subRows.forEach(n)};return e.rows.forEach(n),{rows:t,flatRows:e.flatRows,rowsById:e.rowsById}}function Nl(e){return t=>G(()=>[t.getState().pagination,t.getPrePaginationRowModel(),t.options.paginateExpandedRows?void 0:t.getState().expanded],(n,r)=>{if(!r.rows.length)return r;const{pageSize:i,pageIndex:o}=n;let{rows:l,flatRows:a,rowsById:s}=r;const u=i*o,c=u+i;l=l.slice(u,c);let f;t.options.paginateExpandedRows?f={rows:l,flatRows:a,rowsById:s}:f=_p({rows:l,flatRows:a,rowsById:s}),f.flatRows=[];const d=g=>{f.flatRows.push(g),g.subRows.length&&g.subRows.forEach(d)};return f.rows.forEach(d),f},{key:!1,debug:()=>{var n;return(n=t.options.debugAll)!=null?n:t.options.debugTable}})}/**
+ * svelte-table
+ *
+ * Copyright (c) TanStack
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE.md file in the root directory of this source tree.
+ *
+ * @license MIT
+ */function vp(e){let t;return{c(){t=z(e[0])},l(n){t=za(n,e[0])},m(n,r){xa(n,t,r)},p(n,[r]){r&1&&Q(t,n[0])},i:H,o:H,d(n){n&&x(t)}}}function bp(e,t,n){let{content:r}=t;return e.$$set=i=>{"content"in i&&n(0,r=i.content)},[r]}class wp extends ie{constructor(t){super(),se(this,t,bp,vp,re,{content:0})}}const yp=gs((e,t,n,r)=>`${tu(t.content)}`);var Sp=typeof document>"u"?yp:wp;function Cp(e,t,n){let r,i;return r=new t({props:n,$$inline:!0}),{c(){Z(r.$$.fragment)},l(o){ru(r.$$.fragment,o)},m(o,l){K(r,o,l),i=!0},p:H,i(o){i||(N(r.$$.fragment,o),i=!0)},o(o){V(r.$$.fragment,o),i=!1},d(o){J(r,o)}}}function kp(e,t){return class extends ie{constructor(r){super(),se(this,r,null,i=>Cp(i,e,t),re,{},void 0)}}}function $p(e,t){return gs((r,i,o,l)=>`${nu(e,"TableComponent").$$render(r,t,{},{})}`)}const gi=typeof window>"u"?$p:kp;function Rp(e){return typeof e=="object"&&typeof e.$$render=="function"&&typeof e.render=="function"}function Ap(e){var t,n;let r="__SVELTE_HMR"in window;return e.prototype instanceof ie||r&&((t=e.name)==null?void 0:t.startsWith("Proxy<"))&&((n=e.name)==null?void 0:n.endsWith(">"))}function Pl(e){return typeof document>"u"?Rp(e):Ap(e)}function xl(e){return gi(Sp,{content:e})}function dr(e,t){if(!e)return null;if(Pl(e))return gi(e,t);if(typeof e=="function"){const n=e(t);return n==null?null:Pl(n)?gi(n,t):xl(n)}return xl(e)}function Tl(e){let t;"subscribe"in e?t=e:t=Ur(e);let n={state:{},onStateChange:()=>{},renderFallbackValue:null,...Aa(t)},r=dp(n),i=ke(r.initialState),o=ou([i,t],a=>a);return Ur(r,function(s){const u=o.subscribe(c=>{let[f,d]=c;r.setOptions(g=>({...g,...d,state:{...f,...d.state},onStateChange:p=>{p instanceof Function?i.update(p):i.set(p),n.onStateChange==null||n.onStateChange(p)}})),s(r)});return function(){u()}})}/**
+ * match-sorter-utils
+ *
+ * Copyright (c) TanStack
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE.md file in the root directory of this source tree.
+ *
+ * @license MIT
+ */const Ca={Ã:"A",Ã:"A",Ã:"A",Ã:"A",Ã:"A",Ã
:"A",áē¤:"A",áēŽ:"A",áē˛:"A",áē´:"A",áēļ:"A",Ã:"AE",áēĻ:"A",áē°:"A",Č:"A",Ã:"C",á¸:"C",Ã:"E",Ã:"E",Ã:"E",Ã:"E",áēž:"E",á¸:"E",áģ:"E",á¸:"E",á¸:"E",Č:"E",Ã:"I",Ã:"I",Ã:"I",Ã:"I",Ḏ:"I",Č:"I",Ã:"D",Ã:"N",Ã:"O",Ã:"O",Ã:"O",Ã:"O",Ã:"O",Ã:"O",áģ:"O",áš:"O",áš:"O",Č:"O",Ã:"U",Ã:"U",Ã:"U",Ã:"U",Ã:"Y",à :"a",ÃĄ:"a",Ãĸ:"a",ÃŖ:"a",ä:"a",ÃĨ:"a",áēĨ:"a",áē¯:"a",áēŗ:"a",áēĩ:"a",áēˇ:"a",ÃĻ:"ae",áē§:"a",áēą:"a",Č:"a",ç:"c",á¸:"c",è:"e",Ê:"e",ÃĒ:"e",ÃĢ:"e",áēŋ:"e",á¸:"e",áģ:"e",á¸:"e",á¸:"e",Č:"e",ÃŦ:"i",Ã:"i",ÃŽ:"i",ï:"i",ḯ:"i",Č:"i",ð:"d",Ãą:"n",Ã˛:"o",Ãŗ:"o",ô:"o",Ãĩ:"o",Ãļ:"o",ø:"o",áģ:"o",áš:"o",áš:"o",Č:"o",Ú:"u",Ãē:"u",Ãģ:"u",Ãŧ:"u",ÃŊ:"y",Ãŋ:"y",Ä:"A",Ä:"a",Ä:"A",Ä:"a",Ä:"A",Ä
:"a",Ä:"C",Ä:"c",Ä:"C",Ä:"c",Ä:"C",Ä:"c",Ä:"C",Ä:"c",CĖ:"C",cĖ:"c",Ä:"D",Ä:"d",Ä:"D",Ä:"d",Ä:"E",Ä:"e",Ä:"E",Ä:"e",Ä:"E",Ä:"e",Ä:"E",Ä:"e",Ä:"E",Ä:"e",Ä:"G",Į´:"G",Ä:"g",Įĩ:"g",Ä:"G",Ä:"g",Ä :"G",ÄĄ:"g",Äĸ:"G",ÄŖ:"g",Ĥ:"H",ÄĨ:"h",ÄĻ:"H",ħ:"h",á¸Ē:"H",á¸Ģ:"h",Ĩ:"I",ÄŠ:"i",ÄĒ:"I",ÄĢ:"i",ÄŦ:"I",Ä:"i",ÄŽ:"I",į:"i",Ä°:"I",Äą:"i",IJ:"IJ",Äŗ:"ij",Ä´:"J",Äĩ:"j",Äļ:"K",ġ:"k",Ḱ:"K",ḹ:"k",KĖ:"K",kĖ:"k",Äš:"L",Äē:"l",Äģ:"L",Äŧ:"l",ÄŊ:"L",Äž:"l",Äŋ:"L",Å:"l",Å:"l",Å:"l",Ḟ:"M",á¸ŋ:"m",MĖ:"M",mĖ:"m",Å:"N",Å:"n",Å
:"N",Å:"n",Å:"N",Å:"n",Å:"n",NĖ:"N",nĖ:"n",Å:"O",Å:"o",Å:"O",Å:"o",Å:"O",Å:"o",Å:"OE",Å:"oe",PĖ:"P",pĖ:"p",Å:"R",Å:"r",Å:"R",Å:"r",Å:"R",Å:"r",RĖ:"R",rĖ:"r",Č:"R",Č:"r",Å:"S",Å:"s",Å:"S",Å:"s",Å:"S",Č:"S",Č:"s",Å:"s",Å :"S",ÅĄ:"s",Åĸ:"T",ÅŖ:"t",Č:"t",Č:"T",Ť:"T",ÅĨ:"t",ÅĻ:"T",ŧ:"t",TĖ:"T",tĖ:"t",Ũ:"U",ÅŠ:"u",ÅĒ:"U",ÅĢ:"u",ÅŦ:"U",Å:"u",ÅŽ:"U",ů:"u",Å°:"U",Åą:"u",Å˛:"U",Åŗ:"u",Č:"U",Č:"u",VĖ:"V",vĖ:"v",Å´:"W",Åĩ:"w",áē:"W",áē:"w",XĖ:"X",xĖ:"x",Åļ:"Y",Åˇ:"y",Ÿ:"Y",YĖ:"Y",yĖ:"y",Åš:"Z",Åē:"z",Åģ:"Z",Åŧ:"z",ÅŊ:"Z",Åž:"z",Åŋ:"s",Æ:"f",Æ :"O",ÆĄ:"o",Ư:"U",Æ°:"u",Į:"A",Į:"a",Į:"I",Į:"i",Į:"O",Į:"o",Į:"U",Į:"u",Į:"U",Į:"u",Į:"U",Į:"u",Į:"U",Į:"u",Į:"U",Į:"u",áģ¨:"U",áģŠ:"u",ᚸ:"U",ášš:"u",Įē:"A",Įģ:"a",Įŧ:"AE",ĮŊ:"ae",Įž:"O",Įŋ:"o",Ã:"TH",Þ:"th",áš:"P",áš:"p",ᚤ:"S",ášĨ:"s",XĖ:"X",xĖ:"x",Đ:"Đ",Ņ:"Đŗ",Đ:"Đ",Ņ:"Đē",AĖ:"A",aĖ:"a",EĖ:"E",eĖ:"e",IĖ:"I",iĖ:"i",Į¸:"N",Įš:"n",áģ:"O",áģ:"o",áš:"O",áš:"o",áģĒ:"U",áģĢ:"u",áē:"W",áē:"w",áģ˛:"Y",áģŗ:"y",Č:"A",Č:"a",Č:"E",Č
:"e",Č:"I",Č:"i",Č:"O",Č:"o",Č:"R",Č:"r",Č:"U",Č:"u",BĖ:"B",bĖ:"b",ÄĖŖ:"C",ÄĖŖ:"c",ÃĖ:"E",ÃĒĖ:"e",FĖ:"F",fĖ:"f",ĮĻ:"G",Į§:"g",Č:"H",Č:"h",JĖ:"J",Į°:"j",Į¨:"K",ĮŠ:"k",MĖ:"M",mĖ:"m",PĖ:"P",pĖ:"p",QĖ:"Q",qĖ:"q",ÅĖŠ:"R",ÅĖŠ:"r",ášĻ:"S",ᚧ:"s",VĖ:"V",vĖ:"v",WĖ:"W",wĖ:"w",XĖ:"X",xĖ:"x",YĖ:"Y",yĖ:"y",AĖ§:"A",aĖ§:"a",BĖ§:"B",bĖ§:"b",á¸:"D",á¸:"d",Ȩ:"E",ČŠ:"e",ÆĖ§:"E",ÉĖ§:"e",Ḩ:"H",Ḋ:"h",IĖ§:"I",iĖ§:"i",ÆĖ§:"I",ɨĖ§:"i",MĖ§:"M",mĖ§:"m",OĖ§:"O",oĖ§:"o",QĖ§:"Q",qĖ§:"q",UĖ§:"U",uĖ§:"u",XĖ§:"X",xĖ§:"x",ZĖ§:"Z",zĖ§:"z"},Ep=Object.keys(Ca).join("|"),Mp=new RegExp(Ep,"g");function Fp(e){return e.replace(Mp,t=>Ca[t])}/**
+ * @name match-sorter
+ * @license MIT license.
+ * @copyright (c) 2099 Kent C. Dodds
+ * @author Kent C. Dodds (https://kentcdodds.com)
+ */const Pe={CASE_SENSITIVE_EQUAL:7,EQUAL:6,STARTS_WITH:5,WORD_STARTS_WITH:4,CONTAINS:3,ACRONYM:2,MATCHES:1,NO_MATCH:0};function Dp(e,t,n){var r;if(n=n||{},n.threshold=(r=n.threshold)!=null?r:Pe.MATCHES,!n.accessors){const l=Ol(e,t,n);return{rankedValue:e,rank:l,accessorIndex:-1,accessorThreshold:n.threshold,passed:l>=n.threshold}}const i=xp(e,n.accessors),o={rankedValue:e,rank:Pe.NO_MATCH,accessorIndex:-1,accessorThreshold:n.threshold,passed:!1};for(let l=0;l=Pe.MATCHES?s=u:s>c&&(s=c),s=Math.min(s,c),s>=f&&s>o.rank&&(o.rank=s,o.passed=!0,o.accessorIndex=l,o.accessorThreshold=f,o.rankedValue=a.itemValue)}return o}function Ol(e,t,n){return e=Vl(e,n),t=Vl(t,n),t.length>e.length?Pe.NO_MATCH:e===t?Pe.CASE_SENSITIVE_EQUAL:(e=e.toLowerCase(),t=t.toLowerCase(),e===t?Pe.EQUAL:e.startsWith(t)?Pe.STARTS_WITH:e.includes(` ${t}`)?Pe.WORD_STARTS_WITH:e.includes(t)?Pe.CONTAINS:t.length===1?Pe.NO_MATCH:Ip(e).includes(t)?Pe.ACRONYM:Np(e,t))}function Ip(e){let t="";return e.split(" ").forEach(r=>{r.split("-").forEach(o=>{t+=o.substr(0,1)})}),t}function Np(e,t){let n=0,r=0;function i(s,u,c){for(let f=c,d=u.length;f-1))return Pe.NO_MATCH}const a=r-l;return o(a)}function Vl(e,t){let{keepDiacritics:n}=t;return e=`${e}`,n||(e=Fp(e)),e}function Pp(e,t){let n=t;typeof t=="object"&&(n=t.accessor);const r=n(e);return r==null?[]:Array.isArray(r)?r:[String(r)]}function xp(e,t){const n=[];for(let r=0,i=t.length;r 
- 
+ 
+ 
-- EBS: at least 100G
- 
+On your GPU instance terminal, create a conda environment using:
+```
+conda create -n pykoi python=3.10 -y && source activate pykoi
+```
-Next, on your GPU instance terminal, create a conda environment using:
+Then install `pykoi` and [pytorch based on your cuda version](https://pytorch.org/get-started/locally/). You can find your CUDA version via `nvcc -V`.
+```
+pip3 install "pykoi[rag, huggingface]"
+
+pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121 # cu121 means cuda 12.1
+```
+
+### Option 3: RLHF (GPU)
+This option allows you to train LLM via RLHF on a GPU. Here's a quick [tutorial](#ec2-dev-setup) on setting up an EC2 GPU instance for the installation below.
+
+On your GPU instance terminal, create a conda environment using:
```
conda create -n pykoi python=3.10 -y && source activate pykoi
```
-Then install `pykoi` and [pytorch based on your cuda version](https://pytorch.org/get-started).
+Then install `pykoi` and [pytorch based on your cuda version](https://pytorch.org/get-started/locally/). You can find your CUDA version via `nvcc -V`.
```
-pip3 install pykoi
+pip3 install "pykoi[rlhf]"
-# install torch based on cuda (e.g. cu118 means cuda 11.8)
-pip3 install torch --index-url https://download.pytorch.org/whl/cu118
+pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121 # cu121 means cuda 12.1
```
Congrats you have finished the installation!
@@ -91,13 +100,41 @@ conda create -n pykoi python=3.10
conda activate pykoi
cd pykoi
pip3 install poetry
-poetry install --no-root
```
+Then, based the feature you need to develop, run one or more installation options below. We recommend install all the options below although it may take ~3 minutes longer.
+
+- Option 1: RAG (CPU)
+ ```
+ poetry install --no-root --extras rag
+ ```
+- Option 2: RAG (GPU)
+ ```
+ poetry install --no-root --extras "rag huggingface"
+ ```
+- Option 3: RLHF (GPU)
+ ```
+ poetry install --no-root --extras rlhf
+ ```
+
+Finally, if you are on a GPU, install [pytorch based on your cuda version](https://pytorch.org/get-started/locally/). You can find your CUDA version via `nvcc -V`.
+```
+pip3 install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121 # cu121 means cuda 12.1
+```
+
+
### Frontend Dev Setup
-Frontend:
```
cd pykoi/pykoi/frontend
npm install
npm run build
```
+
+### EC2 Dev Setup
+If you are on EC2, you can launch a GPU instance with the following config:
+- EC2 `g4dn.xlarge` (if you want to run a pretrained LLM with 7B parameters)
+- Deep Learning AMI PyTorch GPU 2.0.1 (Ubuntu 20.04)
+ ![](\"../image/comparisonDemoSmall_2x.gif\")
\n",
""
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/example/chatbot/demo_model_comparator_cpu_openai.py b/example/comparator/demo_model_comparator_cpu_openai.py
similarity index 80%
rename from example/chatbot/demo_model_comparator_cpu_openai.py
rename to example/comparator/demo_model_comparator_cpu_openai.py
index b5c45f9..483c3f3 100644
--- a/example/chatbot/demo_model_comparator_cpu_openai.py
+++ b/example/comparator/demo_model_comparator_cpu_openai.py
@@ -1,4 +1,18 @@
-"""Demo for the chatbot application using multiple model endpoint."""
+"""
+Demo for the chatbot application using multiple OpenAI models.
+
+- Prerequisites:
+ To run this jupyter notebook, you need a `pykoi` environment with the `rag` option.
+ You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-1-rag-cpu)
+ to set up the environment.
+- Run the demo:
+ 1. Enter your OpenAI API key in the `api_key` below.
+ 2. On terminal and `~/pykoi` directory, run
+ ```
+ python -m example.comparator.demo_model_comparator_cpu_openai
+ ```
+"""
+
from pykoi import Application
from pykoi.chat import ModelFactory
from pykoi.component import Compare
diff --git a/example/chatbot/demo_model_comparator_gpu_huggingface.ipynb b/example/comparator/demo_model_comparator_gpu_huggingface.ipynb
similarity index 76%
rename from example/chatbot/demo_model_comparator_gpu_huggingface.ipynb
rename to example/comparator/demo_model_comparator_gpu_huggingface.ipynb
index 92614e9..1799762 100644
--- a/example/chatbot/demo_model_comparator_gpu_huggingface.ipynb
+++ b/example/comparator/demo_model_comparator_gpu_huggingface.ipynb
@@ -1,5 +1,23 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Demo: LLMs Comparison between Open-source LLMs\n",
+ "\n",
+ "`pykoi` provides simple API to compare between LLMs, including your own finetuned LLM, a pretrained LLM from huggingface, or OpenAI/Anthropic/Bedrock APIs. This demo shows how to create and launch an LLM comparison app between Open-source LLMs from huggingface. Let's get started!\n",
+ "\n",
+ "### Prerequisites\n",
+ "To run this jupyter notebook, you need a `pykoi` environment with the `huggingface` option. You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-2-rag-gpu) to set up the environment. \n",
+ "\n",
+ "You may also need `pip install ipykernel` to run the kernel environment.\n",
+ "\n",
+ "\n",
+ "### (Optional) Developer setup\n",
+ "If you are a normal user of `pykoi`, you can skip this step. However, if you modify the pykoi code and want to test your changes, you can uncomment the code below."
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -9,19 +27,18 @@
"# %reload_ext autoreload\n",
"# %autoreload 2\n",
"\n",
- "# import os\n",
"# import sys\n",
"\n",
- "# # Add the root folder to the module search path\n",
- "# # Get the current directory\n",
- "# current_directory = os.getcwd()\n",
- "\n",
- "# # Move two levels up (go to the parent directory of the parent directory)\n",
- "# two_levels_up_directory = os.path.dirname(os.path.dirname(current_directory))\n",
- "\n",
- "# print(two_levels_up_directory)\n",
- "\n",
- "# sys.path.append(two_levels_up_directory)"
+ "# sys.path.append(\".\")\n",
+ "# sys.path.append(\"..\")\n",
+ "# sys.path.append(\"../..\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import Libraries"
]
},
{
@@ -42,21 +59,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Demo: LLMs Comparison\n",
- "\n",
- "`pykoi` provides simple API to compare between LLMs, including your own finetuned LLM, a pretrained LLM from huggingface, or OpenAI/Anthropic/Bedrock APIs.\n",
- "\n",
- "This demo shows how to create and launch an LLM comparison app. Let's get started!\n",
- "\n",
- "## Load LLMs\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Creating a Huggingface model (requires at least EC2 `g4.2xlarge` or GPU with 16G memory)"
+ "### Load LLMs\n",
+ "#### Creating a Huggingface model (requires at least EC2 `g4dn.xlarge` or GPU with at least 16G memory)"
]
},
{
@@ -65,7 +69,7 @@
"metadata": {},
"outputs": [],
"source": [
- "## requires a GPU with at least 16GB memory (e.g. g4.2xlarge)\n",
+ "## requires a GPU with at least 16GB memory (e.g. g4dn.xlarge)\n",
"huggingface_model_1 = ModelFactory.create_model(\n",
" model_source=\"huggingface\",\n",
" pretrained_model_name_or_path=\"tiiuae/falcon-rw-1b\",\n",
@@ -78,7 +82,7 @@
"metadata": {},
"outputs": [],
"source": [
- "## requires a GPU with at least 16GB memory (e.g. g4.2xlarge)\n",
+ "## requires a GPU with at least 16GB memory (e.g. g4dn.2xlarge)\n",
"huggingface_model_2 = ModelFactory.create_model(\n",
" model_source=\"huggingface\",\n",
" pretrained_model_name_or_path=\"databricks/dolly-v2-3b\",\n",
@@ -102,9 +106,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Create a chatbot comparator\n",
+ "### Create a chatbot comparator\n",
"\n",
- "### Add `nest_asyncio` \n",
+ "#### Add `nest_asyncio` \n",
"Add `nest_asyncio` to avoid error. Since we're running another interface inside a Jupyter notebook where an asyncio event loop is already running, we'll encounter the error. (since The uvicorn.run() function uses asyncio.run(), which isn't compatible with a running event loop.)"
]
},
@@ -134,7 +138,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Add ngrok auth (TODO: change to bash file)"
+ "#### Add ngrok auth (TODO: change to bash file)"
]
},
{
diff --git a/example/chatbot/demo_model_comparator_gpu_huggingface.py b/example/comparator/demo_model_comparator_gpu_huggingface.py
similarity index 81%
rename from example/chatbot/demo_model_comparator_gpu_huggingface.py
rename to example/comparator/demo_model_comparator_gpu_huggingface.py
index d033143..7ddec16 100644
--- a/example/chatbot/demo_model_comparator_gpu_huggingface.py
+++ b/example/comparator/demo_model_comparator_gpu_huggingface.py
@@ -1,4 +1,16 @@
-"""Demo for the chatbot application using multiple model endpoint."""
+"""
+Demo for the chatbot application using multiple open source LLMs from Huggingface.
+
+- Prerequisites:
+ To run this jupyter notebook, you need a `pykoi` environment with the `huggingface` option.
+ You can follow [the installation guide](https://github.com/CambioML/pykoi/tree/install#option-2-rag-gpu)
+ to set up the environment.
+- Run the demo:
+ 1. On terminal and `~/pykoi` directory, run
+ ```
+ python -m example.comparator.demo_model_comparator_gpu_huggingface
+ ```
+"""
from pykoi import Application
from pykoi.chat import ModelFactory
from pykoi.component import Compare
diff --git a/example/flex/flex_demo.py b/example/flex/flex_demo.py
index 13c0d09..60bf66c 100644
--- a/example/flex/flex_demo.py
+++ b/example/flex/flex_demo.py
@@ -8,6 +8,7 @@
from pykoi.retrieval import RetrievalFactory
from pykoi.retrieval import VectorDbFactory
from pykoi.component import Chatbot, Dashboard, RetrievalQA
+from pykoi.chat import RAGDatabase
load_dotenv()
@@ -32,9 +33,9 @@ def main(**kargs):
)
# retrieval, chatbot, and dashboard pykoi components
- retriever = RetrievalQA(retrieval_model=retrieval_model, vector_db=vector_db)
- chatbot = Chatbot(None, feedback="vote", is_retrieval=True)
- dashboard = Dashboard(QuestionAnswerDatabase())
+ retriever = RetrievalQA(retrieval_model=retrieval_model, vector_db=vector_db, feedback="rag")
+ chatbot = Chatbot(None, feedback="rag", is_retrieval=True)
+ dashboard = Dashboard(RAGDatabase(), feedback="rag")
############################################################
# Starting the application and retrieval qa as a component #
diff --git a/pykoi/__init__.py b/pykoi/__init__.py
index 86c1474..827a46b 100644
--- a/pykoi/__init__.py
+++ b/pykoi/__init__.py
@@ -1,4 +1,3 @@
from pykoi.application import Application
-
-__version__ = "0.0.6"
+__version__ = "0.0.7"
diff --git a/pykoi/application.py b/pykoi/application.py
index 50d2850..0bdbf1a 100644
--- a/pykoi/application.py
+++ b/pykoi/application.py
@@ -20,6 +20,7 @@
from pykoi.interactives.chatbot import Chatbot
from pykoi.telemetry.telemetry import Telemetry
from pykoi.telemetry.events import AppStartEvent, AppStopEvent
+from pykoi.chat.db.constants import QA_LIST_SEPARATOR
oauth_scheme = HTTPBasic()
@@ -29,6 +30,17 @@ class UpdateQATable(BaseModel):
id: int
vote_status: str
+class UpdateRAGTable(BaseModel):
+ id: int
+ vote_status: str
+
+class UpdateQATableAnswer(BaseModel):
+ id: int
+ new_answer: str
+
+class UpdateRAGTableAnswer(BaseModel):
+ id: int
+ new_answer: str
class RankingTableUpdate(BaseModel):
question: str
@@ -212,6 +224,7 @@ async def update_qa_table(
user: Union[None, UserInDB] = Depends(self.get_auth_dependency()),
):
try:
+ print("updating QA vote")
component["component"].database.update_vote_status(
request_body.id, request_body.vote_status
)
@@ -219,6 +232,20 @@ async def update_qa_table(
except Exception as ex:
return {"log": f"Table update failed: {ex}", "status": "500"}
+ @app.post("/chat/qa_table/update_answer")
+ async def update_qa_table_response(
+ request_body: UpdateQATableAnswer,
+ user: Union[None, UserInDB] = Depends(self.get_auth_dependency()),
+ ):
+ try:
+ component["component"].database.update_answer(
+ request_body.id, request_body.new_answer
+ )
+ print("/chat/qa_table/update_answer", request_body.id, request_body.new_answer)
+ return {"log": "Table response updated", "new_answer": request_body.new_answer, "status": "200"}
+ except Exception as ex:
+ return {"log": f"Table update failed: {ex}", "status": "500"}
+
@app.get("/chat/qa_table/close")
async def close_qa_table(
user: Union[None, UserInDB] = Depends(self.get_auth_dependency())
@@ -274,6 +301,51 @@ async def retrieve_ranking_table(
except Exception as ex:
return {"log": f"Table retrieval failed: {ex}", "status": "500"}
+ @app.post("/chat/rag_table/update")
+ async def update_rag_table(
+ request_body: UpdateRAGTable,
+ user: Union[None, UserInDB] = Depends(self.get_auth_dependency()),
+ ):
+ try:
+ print("updating RAG vote")
+ component["component"].database.update_vote_status(
+ request_body.id, request_body.vote_status
+ )
+ return {"log": "Table updated", "status": "200"}
+ except Exception as ex:
+ return {"log": f"Table update failed: {ex}", "status": "500"}
+
+ @app.post("/chat/rag_table/update_answer")
+ async def update_rag_table_response(
+ request_body: UpdateRAGTableAnswer,
+ user: Union[None, UserInDB] = Depends(self.get_auth_dependency()),
+ ):
+ try:
+ component["component"].database.update_answer(
+ request_body.id, request_body.new_answer
+ )
+ print("/chat/rag_table/update_answer", request_body.id, request_body.new_answer)
+ return {"log": "Table response updated", "new_answer": request_body.new_answer, "status": "200"}
+ except Exception as ex:
+ return {"log": f"Table update failed: {ex}", "status": "500"}
+
+ @app.get("/chat/rag_table/retrieve")
+ async def retrieve_rag_table(
+ user: Union[None, UserInDB] = Depends(self.get_auth_dependency())
+ ):
+ try:
+ rows = component["component"].database.retrieve_all_question_answers()
+ modified_rows = []
+ for row in rows:
+ row_list = list(row) # Convert the tuple to a list
+ row_list[5] = row_list[5].split(QA_LIST_SEPARATOR)
+ row_list[6] = row_list[6].split(QA_LIST_SEPARATOR)
+ row_list[7] = row_list[7].split(QA_LIST_SEPARATOR)
+ modified_rows.append(row_list) # Append the modified list to the new list
+ return {"rows": modified_rows, "log": "RAG Table retrieved", "status": "200"}
+ except Exception as ex:
+ return {"log": f"Table retrieval failed: {ex}", "status": "500"}
+
def create_feedback_route(self, app: FastAPI, component: Dict[str, Any]):
"""
Create feedback routes for the application.
@@ -491,28 +563,20 @@ async def inference(
try:
print("[/retrieval]: model inference.....", request_body.prompt)
component["component"].retrieval_model.re_init(request_body.file_names)
- output = component[
- "component"
- ].retrieval_model.run_with_return_source_documents(
- {"query": request_body.prompt}
- )
- id = component["component"].database.insert_question_answer(
- request_body.prompt, output["result"]
- )
- print("output", output, output["result"])
+ output = component["component"].retrieval_model.run_with_return_source_documents({"query": request_body.prompt})
+ print('output', output, output["result"])
if output["source_documents"] == []:
- source = "N/A"
- source_content = "N/A"
+ source = ["N/A"]
+ source_content = ["N/A"]
else:
- source = output["source_documents"][0].metadata.get(
- "file_name", "No file name found"
- )
- source_content = (
- "1. "
- + output["source_documents"][0].page_content
- + "\n2. "
- + output["source_documents"][1].page_content
- )
+ source = []
+ source_content = []
+ for source_document in output["source_documents"]:
+ source.append(source_document.metadata.get('file_name', 'No file name found'))
+ source_content.append(source_document.page_content)
+ id = component["component"].database.insert_question_answer(
+ request_body.prompt, output["result"], request_body.file_names, source, source_content
+ )
return {
"id": id,
"log": "Inference complete",
diff --git a/pykoi/chat/__init__.py b/pykoi/chat/__init__.py
index cfdb23d..efbeb29 100644
--- a/pykoi/chat/__init__.py
+++ b/pykoi/chat/__init__.py
@@ -3,3 +3,4 @@
from pykoi.chat.llm.model_factory import ModelFactory
from pykoi.chat.db.qa_database import QuestionAnswerDatabase
from pykoi.chat.db.ranking_database import RankingDatabase
+from pykoi.chat.db.rag_database import RAGDatabase
\ No newline at end of file
diff --git a/pykoi/chat/db/constants.py b/pykoi/chat/db/constants.py
index 2c4e94d..432109e 100644
--- a/pykoi/chat/db/constants.py
+++ b/pykoi/chat/db/constants.py
@@ -25,3 +25,6 @@
RANKING_CSV_HEADER_UP_RANKING_ANSWER,
RANKING_CSV_HEADER_LOW_RANKING_ANSWER,
)
+
+# list separator
+QA_LIST_SEPARATOR = "||"
\ No newline at end of file
diff --git a/pykoi/chat/db/rag_database.py b/pykoi/chat/db/rag_database.py
new file mode 100644
index 0000000..52a82c7
--- /dev/null
+++ b/pykoi/chat/db/rag_database.py
@@ -0,0 +1,244 @@
+"""Question answer database module"""
+import csv
+import datetime
+import os
+import sqlite3
+import threading
+
+import pandas as pd
+
+from pykoi.chat.db.constants import QA_CSV_HEADER, QA_LIST_SEPARATOR
+
+
+class RAGDatabase:
+ """RAG Question Answer Database class"""
+
+ def __init__(
+ self,
+ db_file: str = os.path.join(os.getcwd(), "rag.db"),
+ debug: bool = False,
+ ):
+ """
+ Initializes a new instance of the RAGDatabase class.
+
+ Args:
+ db_file (str): The path to the SQLite database file.
+ debug (bool, optional): Whether to print debug messages. Defaults to False.
+ """
+ self._db_file = db_file
+ self._debug = debug
+ self._local = threading.local() # Thread-local storage
+ self._lock = threading.Lock() # Lock for concurrent write operations
+ self.create_table()
+
+ def get_connection(self):
+ """Returns the thread-local database connection"""
+ if not hasattr(self._local, "connection"):
+ self._local.connection = sqlite3.connect(self._db_file)
+ return self._local.connection
+
+ def get_cursor(self):
+ """Returns the thread-local database cursor"""
+ if not hasattr(self._local, "cursor"):
+ self._local.cursor = self.get_connection().cursor()
+ return self._local.cursor
+
+ def create_table(self):
+ """
+ Creates the question_answer table if it does not already exist in the database.
+ The table has 6 columns: id (primary key), question, answer, source, source_content, and vote_status.
+ vote_status is a text field that can only have the values 'up', 'down', or 'n/a'.
+ """
+ query = """
+ CREATE TABLE IF NOT EXISTS rag_question_answer (
+ id INTEGER PRIMARY KEY AUTOINCREMENT,
+ question TEXT,
+ answer TEXT,
+ edited_answer TEXT,
+ vote_status TEXT CHECK (vote_status IN ('up', 'down', 'n/a')),
+ rag_sources TEXT,
+ source TEXT,
+ source_content TEXT,
+ timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+ );
+ """
+ with self._lock:
+ cursor = self.get_cursor()
+ cursor.execute(query)
+ self.get_connection().commit()
+
+ if self._debug:
+ rows = self.retrieve_all_question_answers()
+ print("Table contents after creating table:")
+ self.print_table(rows)
+
+ def insert_question_answer(self, question: str, answer: str, rag_sources: list, source: list, source_content: list):
+ """
+ Inserts a new question-answer pair into the database with the given question and answer.
+ The vote_status field is set to 'n/a' by default.
+ Returns the ID of the newly inserted row.
+
+ Args:
+ question (str): The question to insert.
+ answer (str): The answer to insert.
+ rag_sources (list): The RAG sources of the question-answer pair.
+ source (list): The source(s) of the question-answer pair.
+ source_content (list): The source content(s) of the question-answer pair.
+
+ Returns:
+ int: The ID of the newly inserted row.
+ """
+ timestamp = datetime.datetime.now()
+ query = """
+ INSERT INTO rag_question_answer (question, answer, edited_answer, rag_sources, source, source_content, vote_status, timestamp)
+ VALUES (?, ?, '', ?, ?, ?, 'n/a', ?);
+ """
+ rag_sources = QA_LIST_SEPARATOR.join(rag_sources)
+ source = QA_LIST_SEPARATOR.join(source)
+ source_content = QA_LIST_SEPARATOR.join(source_content)
+ print("rag insert question answer", rag_sources)
+
+ with self._lock:
+ cursor = self.get_cursor()
+ cursor.execute(query, (question, answer, rag_sources, source, source_content, timestamp))
+ self.get_connection().commit()
+
+ if self._debug:
+ rows = self.retrieve_all_question_answers()
+ print("Table contents after inserting table:")
+ self.print_table(rows)
+
+ return cursor.lastrowid
+
+ def update_vote_status(self, id, vote_status):
+ """
+ Updates the vote status of a question-answer pair with the given ID.
+
+ Args:
+ id (int): The ID of the question-answer pair to update.
+ vote_status (str): The new vote status to set. Must be one of 'up', 'down', or 'n/a'.
+
+ Raises:
+ ValueError: If the question with the given ID does not exist.
+ """
+ query = """
+ UPDATE rag_question_answer
+ SET vote_status = ?
+ WHERE id = ?;
+ """
+ with self._lock:
+ cursor = self.get_cursor()
+ cursor.execute(query, (vote_status, id))
+ self.get_connection().commit()
+
+ if cursor.rowcount == 0:
+ raise ValueError(f"Question with ID {id} does not exist.")
+
+ if self._debug:
+ rows = self.retrieve_all_question_answers()
+ print("Table contents after updating table:")
+ self.print_table(rows)
+
+ def update_answer(self, id, new_answer):
+ """
+ Updates the edited_answer of a question-answer pair with the given ID.
+
+ Args:
+ id (int): The ID of the question-answer pair to update.
+ new_answer (str): The new modified response to set.
+
+ Raises:
+ ValueError: If the question with the given ID does not exist.
+ """
+ query = """
+ UPDATE rag_question_answer
+ SET edited_answer = ?
+ WHERE id = ?;
+ """
+ print('update_answer',new_answer)
+ with self._lock:
+ cursor = self.get_cursor()
+ cursor.execute(query, (new_answer, id))
+ self.get_connection().commit()
+
+ if cursor.rowcount == 0:
+ raise ValueError(f"Question with ID {id} does not exist.")
+
+ if self._debug:
+ rows = self.retrieve_all_question_answers()
+ print("Table contents after updating table:")
+ self.print_table(rows)
+
+ def retrieve_all_question_answers(self):
+ """
+ Retrieves all question-answer pairs from the database.
+
+ Returns:
+ list: A list of tuples representing the question-answer pairs.
+ """
+ query = """
+ SELECT * FROM rag_question_answer;
+ """
+ with self._lock:
+ cursor = self.get_cursor()
+ cursor.execute(query)
+ rows = cursor.fetchall()
+ return rows
+
+ def retrieve_all_question_answers_as_pandas(self):
+ """
+ Retrieves all question-answer pairs from the database as a pandas dataframe.
+
+ Returns:
+ DataFrame: A pandas dataframe.
+ """
+ rows = self.retrieve_all_question_answers()
+ rows_to_pd = pd.DataFrame(rows)
+ rows_to_pd.columns = QA_CSV_HEADER
+ return rows_to_pd
+
+ def close_connection(self):
+ """
+ Closes the connection to the database.
+ """
+ if hasattr(self._local, "cursor"):
+ self._local.cursor.close()
+ del self._local.cursor
+ if hasattr(self._local, "connection"):
+ self._local.connection.close()
+ del self._local.connection
+
+ def print_table(self, rows):
+ """
+ Prints the contents of the table in a formatted manner.
+
+ Args:
+ rows (list): A list of tuples where each tuple represents a row in the table.
+ Each tuple contains five elements: ID, Question, Answer, Timestamp, and Vote Status.
+ """
+ for row in rows:
+ print(
+ f"ID: {row[0]}, Question: {row[1]}, "
+ f"Answer: {row[2]}, Vote Status: {row[3]}, Timestamp: {row[4]}"
+ )
+
+ def save_to_csv(self, csv_file_name="question_answer_votes.csv"):
+ """
+ This method saves the contents of the question_answer table into a CSV file.
+
+ Args:
+ csv_file_name (str, optional): The name of the CSV file to which the data will be written.
+ Defaults to "question_answer_votes.csv".
+
+ The CSV file will have the following columns: ID, Question, Answer, Vote Status. Each row in the
+ CSV file corresponds to a row in the question_answer table.
+
+ This method first retrieves all question-answer pairs from the database by calling the
+ retrieve_all_question_answers method. It then writes this data to the CSV file.
+ """
+ my_sql_data = self.retrieve_all_question_answers()
+
+ with open(csv_file_name, "w", newline="") as file:
+ writer = csv.writer(file)
+ writer.writerow(QA_CSV_HEADER)
+ writer.writerows(my_sql_data)
diff --git a/pykoi/component/base.py b/pykoi/component/base.py
index 6f6e15e..44960f3 100644
--- a/pykoi/component/base.py
+++ b/pykoi/component/base.py
@@ -5,6 +5,7 @@
from pykoi.component.chatbot_database_factory import ChatbotDatabaseFactory
from pykoi.component.constants import FeedbackType
from pykoi.chat.db.qa_database import QuestionAnswerDatabase
+from pykoi.chat.db.rag_database import RAGDatabase
from pykoi.chat.db.ranking_database import RankingDatabase
from pykoi.chat.llm.abs_llm import AbsLlm
@@ -129,7 +130,7 @@ class Dashboard(Component):
database (str): The database to use for the dashboard.
"""
- def __init__(self, database: QuestionAnswerDatabase, **kwargs):
+ def __init__(self, database: any, **kwargs):
"""
Initialize a new instance of Dashboard.
diff --git a/pykoi/component/chatbot_database_factory.py b/pykoi/component/chatbot_database_factory.py
index 0c158e3..d774ba8 100644
--- a/pykoi/component/chatbot_database_factory.py
+++ b/pykoi/component/chatbot_database_factory.py
@@ -4,6 +4,7 @@
from pykoi.component.constants import FeedbackType
from pykoi.chat.db.qa_database import QuestionAnswerDatabase
from pykoi.chat.db.ranking_database import RankingDatabase
+from pykoi.chat.db.rag_database import RAGDatabase
class ChatbotDatabaseFactory:
@@ -20,13 +21,15 @@ def create(feedback: Union[str, FeedbackType]):
feedback (Union[str, FeedbackType]): The type of the chatbot.
Returns:
- Union[QuestionAnswerDatabase, RankingDatabase]: The created database.
+ Union[QuestionAnswerDatabase, RankingDatabase, RAGDatabase]: The created database.
"""
feedback = FeedbackType(feedback)
if feedback == FeedbackType.VOTE:
return QuestionAnswerDatabase()
elif feedback == FeedbackType.RANK:
return RankingDatabase()
+ elif feedback == FeedbackType.RAG:
+ return RAGDatabase()
else:
raise ValueError(
f"Invalid feedback name: {feedback}. "
diff --git a/pykoi/component/constants.py b/pykoi/component/constants.py
index 5d78872..f1f30ee 100644
--- a/pykoi/component/constants.py
+++ b/pykoi/component/constants.py
@@ -7,3 +7,4 @@ class FeedbackType(Enum):
VOTE = "vote"
RANK = "rank"
+ RAG = "rag"
diff --git a/pykoi/frontend/dist/assets/index-47e316ee.js b/pykoi/frontend/dist/assets/index-47e316ee.js
deleted file mode 100644
index 84a03c6..0000000
--- a/pykoi/frontend/dist/assets/index-47e316ee.js
+++ /dev/null
@@ -1,65 +0,0 @@
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