diff --git a/.env b/.env index 153f9d9..086fc74 100644 --- a/.env +++ b/.env @@ -4,4 +4,6 @@ # Prisma supports the native connection string format for PostgreSQL, MySQL, SQLite, SQL Server, MongoDB and CockroachDB. # See the documentation for all the connection string options: https://pris.ly/d/connection-strings -DATABASE_URL="postgresql://johndoe:randompassword@localhost:5432/mydb?schema=public" \ No newline at end of file +DATABASE_URL="postgresql://watches:watches@localhost:${FORWARD_DB_PORT:-5432}/watches?schema=public" +EMBEDDING_MODEL=all-minilm +CHAT_MODEL=phi3:mini diff --git a/.upsun/config.yaml b/.upsun/config.yaml index 8242e0c..2df9fcb 100644 --- a/.upsun/config.yaml +++ b/.upsun/config.yaml @@ -6,19 +6,46 @@ services: - vector applications: + ollama: + stack: + - "python@3.12" + - "ollama" + container_profile: BALANCED + relationships: + db: "postgresql:postgresql" + mounts: + "/.ollama": + source: "storage" + source_path: ".ollama" + web: + commands: + start: "OLLAMA_HOST=:$PORT ollama serve" + hooks: + build: | + set -eux + cd vectorize/ + pip install -r requirements.txt + deploy: | + set -eux + set -a + . /app/.env + export OLLAMA_HOST=:$PORT + ollama pull $EMBEDDING_MODEL + ollama pull $CHAT_MODEL + + cd vectorize/ + python vectorize.py + app: stack: - "nodejs@22" - - "python@3.12" relationships: db: "postgresql:postgresql" + ollama: "ollama:http" mounts: "/.npm": source: "storage" source_path: "npm" - "/.cache": - source: "storage" - source_path: "cache" web: commands: start: "npx next start -p $PORT" @@ -28,12 +55,6 @@ applications: npm install npx prisma generate npm run build - cd vectorize/ - pip install -r requirements.txt - post_deploy: | - set -eux - cd vectorize/ - python vectorize.py routes: "https://{default}/": diff --git a/README.md b/README.md index b1f9932..0adfd82 100644 --- a/README.md +++ b/README.md @@ -6,8 +6,8 @@ The data located in `vectorize/data` is a [dataset from Kaggle](https://www.kaggle.com/datasets/rkiattisak/luxury-watches-price-dataset/data). -This example uses 3 models from HuggingFace: -- `vectorize.py` uses the `sentence-transformers` and run `feature_extractor` with the `all-MiniLM-L6-v2` model locally (CPU). The model is downloaded (88MB) on the first run in the `.cache` folder. The embeddings are then stored in the postgres database. `vectorize.py` also migrates the database schema if it does not exists. +This example uses a local Ollama server: +- `vectorize.py` uses the `all-minilm` model run `feature_extractor` with the `all-MiniLM-L6-v2` model locally (CPU). The model is downloaded (88MB) on the first run in the `~/.cache/huggingface/` folder. The embeddings are then stored in the postgres database. `vectorize.py` also migrates the database schema if it does not exist. - When the user inputs a prompt, the `recommend` action will first run `featureExtraction` on the serverless HuggingFace inference API with the same model `all-MiniLM-L6-v2` to get the embedding. - The `recommend` action then query the database using the vector to get 5 similar watches. - The last step of the action is to query a text generation model on HuggingFace inference API to generate a proper text answer. We are using `mistralai/Mistral-7B-Instruct-v0.2` in this example. @@ -22,15 +22,17 @@ The `openai` branch does the same but relies on the OpenAI API for all the LLM a cd vectorize python3 -m venv venv source ./venv/bin/activate -pip install -r requirements.txt (or requirements-mac.txt) +pip install -r requirements.txt ``` ### Install dependencies and run services ``` -docker-compose up +export FORWARD_DB_PORT=8090 +docker compose up -d cd vectorize/ -DB_PATH=watches DB_HOST=127.0.0.1 DB_USERNAME=watches DB_PASSWORD=watches HUGGINGFACE_TOKEN=hf_****** python3 vectorize.py +source ../.env.local +DB_PORT=$FORWARD_DB_PORT DB_PATH=watches DB_HOST=127.0.0.1 DB_USERNAME=watches DB_PASSWORD=watches python3 vectorize.py cd ../ npm install npm rum dev @@ -40,8 +42,9 @@ npm rum dev ``` upsun project:create -upsun variable:create --name HUGGINGFACE_TOKEN --prefix env: --level project -upsun push +upsun variable:create --name env:EMBEDDING_MODEL --level project --visible-build=true --value=all-minilm +upsun variable:create --name env:CHAT_MODEL --level project --visible-build=true --value=tinyllama:1.1b-chat-v1-q2_K +upsun push --resources-init=manual ``` -The `vectorize.py` script is included in the `deploy` hook meaning that it will be triggered on every deploy. This is for demo purposes. You can run it manually instead to avoid delays in deployments. \ No newline at end of file +The `vectorize.py` script is included in the `deploy` hook meaning that it will be triggered on every deploy. This is for demo purposes. You can run it manually instead to avoid delays in deployments. diff --git a/docker/pgsql/postgres.Dockerfile b/docker/pgsql/postgres.Dockerfile index dbf9a63..38bc157 100644 --- a/docker/pgsql/postgres.Dockerfile +++ b/docker/pgsql/postgres.Dockerfile @@ -1,4 +1,4 @@ -FROM postgres:latest +FROM postgres:16 RUN apt-get update && apt-get install -y \ build-essential \ diff --git a/package-lock.json b/package-lock.json index c64181f..a57a7ab 100644 --- a/package-lock.json +++ b/package-lock.json @@ -8,8 +8,6 @@ "name": "watches-next", "version": "0.1.0", "dependencies": { - "@ai-sdk/openai": "^0.0.17", - "@huggingface/inference": "^2.7.0", "@prisma/client": "^5.14.0", "@radix-ui/react-slot": "^1.0.2", "ai": "^3.1.17", @@ -17,7 +15,7 @@ "clsx": "^2.1.1", "lucide-react": "^0.379.0", "next": "14.2.3", - "openai": "^4.47.2", + "ollama": "^0.5.2", "prisma": "^5.14.0", "react": "^18", "react-dom": "^18", @@ -38,69 +36,131 @@ "typescript": "^5" } }, - "node_modules/@ai-sdk/openai": { - "version": "0.0.17", - 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"node_modules/tr46": { - "version": "0.0.3", - "resolved": "https://registry.npmjs.org/tr46/-/tr46-0.0.3.tgz", - "integrity": "sha512-N3WMsuqV66lT30CrXNbEjx4GEwlow3v6rr4mCcv6prnfwhS01rkgyFdjPNBYd9br7LpXV1+Emh01fHnq2Gdgrw==" - }, "node_modules/trim-lines": { "version": "3.0.1", "resolved": "https://registry.npmjs.org/trim-lines/-/trim-lines-3.0.1.tgz", @@ -6479,9 +6335,9 @@ } }, "node_modules/tslib": { - "version": "2.6.2", - "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.6.2.tgz", - "integrity": "sha512-AEYxH93jGFPn/a2iVAwW87VuUIkR1FVUKB77NwMF7nBTDkDrrT/Hpt/IrCJ0QXhW27jTBDcf5ZY7w6RiqTMw2Q==" + "version": "2.6.3", + "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.6.3.tgz", + "integrity": "sha512-xNvxJEOUiWPGhUuUdQgAJPKOOJfGnIyKySOc09XkKsgdUV/3E2zvwZYdejjmRgPCgcym1juLH3226yA7sEFJKQ==" }, "node_modules/type-check": { "version": "0.4.0", @@ -6611,12 +6467,13 @@ "node_modules/undici-types": { "version": "5.26.5", "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz", - "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==" + "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==", + "dev": true }, "node_modules/unified": { - "version": "11.0.4", - "resolved": "https://registry.npmjs.org/unified/-/unified-11.0.4.tgz", - "integrity": "sha512-apMPnyLjAX+ty4OrNap7yumyVAMlKx5IWU2wlzzUdYJO9A8f1p9m/gywF/GM2ZDFcjQPrx59Mc90KwmxsoklxQ==", + "version": "11.0.5", + "resolved": "https://registry.npmjs.org/unified/-/unified-11.0.5.tgz", + "integrity": "sha512-xKvGhPWw3k84Qjh8bI3ZeJjqnyadK+GEFtazSfZv/rKeTkTjOJho6mFqh2SM96iIcZokxiOpg78GazTSg8+KHA==", "dependencies": { "@types/unist": "^3.0.0", "bail": "^2.0.0", @@ -6757,16 +6614,16 @@ } }, "node_modules/vue": { - "version": "3.4.27", - "resolved": "https://registry.npmjs.org/vue/-/vue-3.4.27.tgz", - "integrity": "sha512-8s/56uK6r01r1icG/aEOHqyMVxd1bkYcSe9j8HcKtr/xTOFWvnzIVTehNW+5Yt89f+DLBe4A569pnZLS5HzAMA==", + "version": "3.4.29", + "resolved": "https://registry.npmjs.org/vue/-/vue-3.4.29.tgz", + "integrity": "sha512-8QUYfRcYzNlYuzKPfge1UWC6nF9ym0lx7mpGVPJYNhddxEf3DD0+kU07NTL0sXuiT2HuJuKr/iEO8WvXvT0RSQ==", "peer": true, "dependencies": { - "@vue/compiler-dom": "3.4.27", - "@vue/compiler-sfc": "3.4.27", - "@vue/runtime-dom": "3.4.27", - "@vue/server-renderer": "3.4.27", - "@vue/shared": "3.4.27" + "@vue/compiler-dom": "3.4.29", + "@vue/compiler-sfc": "3.4.29", + "@vue/runtime-dom": "3.4.29", + "@vue/server-renderer": "3.4.29", + "@vue/shared": "3.4.29" }, "peerDependencies": { "typescript": "*" @@ -6777,27 +6634,10 @@ } } }, - "node_modules/web-streams-polyfill": { - "version": "3.3.3", - "resolved": "https://registry.npmjs.org/web-streams-polyfill/-/web-streams-polyfill-3.3.3.tgz", - "integrity": "sha512-d2JWLCivmZYTSIoge9MsgFCZrt571BikcWGYkjC1khllbTeDlGqZ2D8vD8E/lJa8WGWbb7Plm8/XJYV7IJHZZw==", - "engines": { - "node": ">= 8" - } - }, - "node_modules/webidl-conversions": { - "version": "3.0.1", - "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-3.0.1.tgz", - "integrity": "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ==" - }, - "node_modules/whatwg-url": { - "version": "5.0.0", - "resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-5.0.0.tgz", - "integrity": "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==", - "dependencies": { - "tr46": "~0.0.3", - "webidl-conversions": "^3.0.0" - } + "node_modules/whatwg-fetch": { + "version": "3.6.20", + "resolved": "https://registry.npmjs.org/whatwg-fetch/-/whatwg-fetch-3.6.20.tgz", + "integrity": "sha512-EqhiFU6daOA8kpjOWTL0olhVOF3i7OrFzSYiGsEMB8GcXS+RrzauAERX65xMeNWVqxA6HXH2m69Z9LaKKdisfg==" }, "node_modules/which": { "version": "2.0.2", @@ -6995,9 +6835,9 @@ "dev": true }, "node_modules/yaml": { - "version": "2.4.2", - "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.4.2.tgz", - "integrity": "sha512-B3VqDZ+JAg1nZpaEmWtTXUlBneoGx6CPM9b0TENK6aoSu5t73dItudwdgmi6tHlIZZId4dZ9skcAQ2UbcyAeVA==", + "version": "2.4.5", + "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.4.5.tgz", + "integrity": "sha512-aBx2bnqDzVOyNKfsysjA2ms5ZlnjSAW2eG3/L5G/CSujfjLJTJsEw1bGw8kCf04KodQWk1pxlGnZ56CRxiawmg==", "bin": { "yaml": "bin.mjs" }, diff --git a/package.json b/package.json index 79992c6..df5b363 100644 --- a/package.json +++ b/package.json @@ -9,8 +9,6 @@ "lint": "next lint" }, "dependencies": { - "@ai-sdk/openai": "^0.0.17", - "@huggingface/inference": "^2.7.0", "@prisma/client": "^5.14.0", "@radix-ui/react-slot": "^1.0.2", "ai": "^3.1.17", @@ -18,7 +16,7 @@ "clsx": "^2.1.1", "lucide-react": "^0.379.0", "next": "14.2.3", - "openai": "^4.47.2", + "ollama": "^0.5.2", "prisma": "^5.14.0", "react": "^18", "react-dom": "^18", diff --git a/public/next.svg b/public/next.svg deleted file mode 100644 index 5174b28..0000000 --- a/public/next.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/public/vercel.svg b/public/vercel.svg deleted file mode 100644 index d2f8422..0000000 --- a/public/vercel.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/src/app/actions.ts b/src/app/actions.ts index 350ac0d..1fcc902 100644 --- a/src/app/actions.ts +++ b/src/app/actions.ts @@ -1,16 +1,12 @@ "use server" -import prisma from '@/lib/prisma'; -import { extract, toMarkdown } from '@/lib/format'; -import { HfInference } from '@huggingface/inference'; -import { createStreamableValue } from 'ai/rsc'; +import prisma from '@/lib/prisma' +import { extract, toMarkdown } from '@/lib/format' +import { Ollama } from 'ollama' +import { createStreamableValue } from 'ai/rsc' export async function recommend(messages: Message[]) { - - const inference = new HfInference(process.env.HUGGINGFACE_TOKEN); - const embedding = await inference.featureExtraction({ - model: "sentence-transformers/all-MiniLM-L6-v2", - inputs: messages[messages.length - 1].content, - }); + const ollama = new Ollama({ host: (process.env.OLLAMA_SCHEME || 'http')+'://'+(process.env.OLLAMA_HOST || 'localhost')+':'+(process.env.OLLAMA_PORT || 11434) }) + const embedding = await ollama.embeddings({ model: process.env.EMBEDDING_MODEL || 'not-defined', prompt: messages[messages.length - 1].content }) // Query the database for similar watches const watches: Watch[] = await prisma.$queryRaw` @@ -29,34 +25,42 @@ export async function recommend(messages: Message[]) { complications, power_reserve, price_usd, - 1 - (embedding <=> ${embedding}::vector) as similarity + 1 - (embedding <=> ${embedding['embedding']}::vector) as similarity FROM watches ORDER BY similarity DESC LIMIT 5; ` - - const stream = createStreamableValue(''); - (async () => { + const stream = createStreamableValue('') + const message = { + 'role': 'user', + 'content': ` +You are an helpful AI that will recommend watches to the user. + +The watch selected is below with its attributes as a list. +Your response is a markdown document. +Write a paragraph of why this watch matches their request using the watch attributes. + +The recommended watch is: + +${toMarkdown(watches[0])}. + +Create a table of the following data titled as alternatives: + +${watches.map((watch: Watch) => extract(watch)).join('\n')} +`, + }; + (async () => { // Update the stream with all the watches[0] properties in a markdown table stream.append(toMarkdown(watches[0])) stream.append(`\n\n\n\n`) - for await (const chunk of inference.chatCompletionStream({ - model: "mistralai/Mistral-7B-Instruct-v0.2", - messages: [ - { - 'role': 'user', - 'content': `You are an helpful AI that will recommend watches to the user. The watch selected is below with its attributes as a list. Your response is a markdown document. Write a paragrah of why this watch matches their request using the watch attributes. The recommended watch is: ${toMarkdown(watches[0])}. Create a table of the following data titled as alternatives: ${watches.map((watch: Watch) => extract(watch)).join('\n')}` - } - ], - max_tokens: 500, - temperature: 0, - })) { - stream.update(chunk.choices[0].delta.content!); + const response = await ollama.chat({ model: process.env.CHAT_MODEL || 'not-defined', messages: [message], stream: true }) + for await (const part of response) { + stream.update(part.message.content) } - stream.done(); - })(); + stream.done() + })() - return { output: stream.value }; -} \ No newline at end of file + return { output: stream.value } +} diff --git a/vectorize/requirements-mac.txt b/vectorize/requirements-mac.txt deleted file mode 100644 index 34df3d1..0000000 --- a/vectorize/requirements-mac.txt +++ /dev/null @@ -1,35 +0,0 @@ -certifi==2024.2.2 -charset-normalizer==3.3.2 -filelock==3.14.0 -fsspec==2024.5.0 -huggingface-hub==0.23.2 -idna==3.7 -Jinja2==3.1.4 -joblib==1.4.2 -MarkupSafe==2.1.5 -mpmath==1.3.0 -networkx==3.3 -numpy==1.26.4 -packaging==24.0 -pandas==2.2.2 -pillow==10.3.0 -psycopg2-binary==2.9.9 -python-dateutil==2.9.0.post0 -pytz==2024.1 -PyYAML==6.0.1 -regex==2024.5.15 -requests==2.32.3 -safetensors==0.4.3 -scikit-learn==1.5.0 -scipy==1.13.1 -sentence-transformers==3.0.0 -six==1.16.0 -sympy==1.12.1 -threadpoolctl==3.5.0 -tokenizers==0.19.1 -torch==2.3.0 -tqdm==4.66.4 -transformers==4.41.2 -typing_extensions==4.12.0 -tzdata==2024.1 -urllib3==2.2.1 diff --git a/vectorize/requirements.txt b/vectorize/requirements.txt index dc866c0..bd9fc45 100644 --- a/vectorize/requirements.txt +++ b/vectorize/requirements.txt @@ -1,36 +1,2 @@ --f https://download.pytorch.org/whl/torch_stable.html -torch==2.3.0+cpu -certifi==2024.2.2 -charset-normalizer==3.3.2 -filelock==3.14.0 -fsspec==2024.5.0 -huggingface-hub==0.23.2 -idna==3.7 -Jinja2==3.1.4 -joblib==1.4.2 -MarkupSafe==2.1.5 -mpmath==1.3.0 -networkx==3.3 -numpy==1.26.4 -packaging==24.0 -pandas==2.2.2 -pillow==10.3.0 -psycopg2-binary==2.9.9 -python-dateutil==2.9.0.post0 -pytz==2024.1 -PyYAML==6.0.1 -regex==2024.5.15 -requests==2.32.3 -safetensors==0.4.3 -scikit-learn==1.5.0 -scipy==1.13.1 -sentence-transformers==3.0.0 -six==1.16.0 -sympy==1.12.1 -threadpoolctl==3.5.0 -tokenizers==0.19.1 -tqdm==4.66.4 -transformers==4.41.2 -typing_extensions==4.12.0 -tzdata==2024.1 -urllib3==2.2.1 +ollama==0.2.1 +psycopg2_binary==2.9.9 diff --git a/vectorize/vectorize.py b/vectorize/vectorize.py index 20abb17..444a5a6 100644 --- a/vectorize/vectorize.py +++ b/vectorize/vectorize.py @@ -1,17 +1,9 @@ -import pandas as pd -from sentence_transformers import SentenceTransformer +import ollama import psycopg2 import os from decimal import Decimal import re -from pprint import pprint - -def generate_embeddings(data): - # Generate embeddings for the extracted data - model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') - embeddings = model.encode(data) - # Return the embeddings - return embeddings +import csv def prepare_database(conn, cur): # cur.execute("CREATE EXTENSION IF NOT EXISTS vector") @@ -41,59 +33,44 @@ def prepare_database(conn, cur): def main(): conn = psycopg2.connect( - dbname=os.getenv('DB_PATH'), - user=os.getenv('DB_USERNAME'), - password=os.getenv('DB_PASSWORD'), - host=os.getenv('DB_HOST'), - port=os.getenv('DB_PORT') - ) - + dbname=os.getenv('DB_PATH'), + user=os.getenv('DB_USERNAME'), + password=os.getenv('DB_PASSWORD'), + host=os.getenv('DB_HOST'), + port=os.getenv('DB_PORT') + ) cur = conn.cursor() + prepare_database(conn, cur) + model = os.getenv('EMBEDDING_MODEL') - prepare_database(conn, cur); - - # Create an empty array to store the extracted data - extracted_data = [] + with open('data/watches-full.csv', 'r', encoding='utf-8-sig') as file: + reader = csv.DictReader(file) + for row in reader: + extract = [f"{column}: {value}" for column, value in row.items()] + cur.execute( + 'INSERT INTO watches ("brand", "model", "case_material", "strap_material", "movement_type", "water_resistance", "case_diameter_mm", "case_thickness_mm", "band_width_mm", "dial_color", "crystal_material", "complications", "power_reserve", "price_usd", "embedding") VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)', + ( + row['Brand'], + row['Model'], + row['Case Material'], + row['Strap Material'], + row['Movement Type'], + row['Water Resistance'], + Decimal(row['Case Diameter (mm)']), + Decimal(row['Case Thickness (mm)']), + Decimal(row['Band Width (mm)']), + row['Dial Color'], + row['Crystal Material'], + row['Complications'], + row['Power Reserve'], + int(re.sub("[^0-9.]", "", row['Price (USD)'])), + ollama.embeddings(model=model, prompt="\n".join(extract))['embedding'], + ) + ) - df = pd.read_csv('data/watches-full.csv') - # Loop over df and print each row with a : separator between columns - for index, row in df.iterrows(): - # Add a : separator between the column name and value - extract = [f"{column}: {value}" for column, value in row.items()] - # Join the columns into a single string - extract = "\r\n".join(extract) - # Append the extract to the extracted_data array - extracted_data.append(extract) - - # Generate embeddings for the extracted data - embeddings = generate_embeddings(extracted_data) - - # Store the watch and the embedding in pgsql - for index, row in df.iterrows(): - cur.execute( - 'INSERT INTO watches ("brand", "model", "case_material", "strap_material", "movement_type", "water_resistance", "case_diameter_mm", "case_thickness_mm", "band_width_mm", "dial_color", "crystal_material", "complications", "power_reserve", "price_usd", "embedding") VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)', - ( - row.loc['Brand'], - row.loc['Model'], - row.loc['Case Material'], - row.loc['Strap Material'], - row.loc['Movement Type'], - row.loc['Water Resistance'], - Decimal(row.loc['Case Diameter (mm)']), - Decimal(row.loc['Case Thickness (mm)']), - Decimal(row.loc['Band Width (mm)']), - row.loc['Dial Color'], - row.loc['Crystal Material'], - row.loc['Complications'], - row.loc['Power Reserve'], - int(re.sub("[^0-9.]", "", row.loc['Price (USD)'])), - embeddings[index].tolist()) - ) - conn.commit() - - # Close the cursor and connection + conn.commit() cur.close() conn.close() if __name__ == "__main__": - main() \ No newline at end of file + main()