diff --git a/techniques/sentiment-analysis/README.md b/techniques/sentiment-analysis/README.md new file mode 100644 index 0000000..f8544bb --- /dev/null +++ b/techniques/sentiment-analysis/README.md @@ -0,0 +1,198 @@ +# Sentiment Analysis + +
+How to run this example +
+ +```bash +# Set your API key as an environment variable. +export SUBSTRATE_API_KEY=ENTER_YOUR_KEY + +# Run the TypeScript example + +# If using tsx: +cd typescript # Navigate to the typescript example +npm install # Install dependencies +npx tsx example.ts # Run the example + +# If using Deno: +cd typescript +deno run example.ts + +# Run the Python example + +# If using Poetry: +cd python # Navigate to the python example +poetry install # Install dependencies and build the example +poetry run main # Run the example + +# If using Rye: +# Update pyproject.toml to switch to Rye. +cd python +rye sync +rye run main +``` + +
+ +![hero](hero.png) + +Sentiment analysis is used to extract and quantify feelings from text. It's used in many contexts such +as customer support, customer reviews, recommendations, and more. + +In this example, we'll use LLMs via Substrate to build a simple sentiment analysis pipeline that will +output scores for how positive, negative, or neutral some given text is and use this to determine an +overall classification. + +The first thing we're going to do is to is use an LLM to analyze some content and generate a report by +asking the LLM to write out it's interpretation of the text and score the text. This can be done with +the `ComputeText` node and some prompt engineering. + +In TypeScript this could look like this: + +```typescript +const message = "what a great product, NOT!"; + +const assess = new ComputeText({ + prompt: ` + Your job is to assess a piece of CONTENT on the underlying emotional sentiment it communicates. + Please follow the RULES of the assignment and be mindful of the TIPS. + + === RULES + Analyze the content and report on POSITIVE sentiment if there is any, NEUTRAL sentiment if there is any, and NEGATIVE sentiment if there is any. + Record your rationale for your conclusions. + + Once you have done so, please assign a score for each: + POSITIVE (0-100): ___ + NEUTRAL (0-100): ___ + NEGATIVE (0-100): ___ + + === TIPS + * Sometimes content may seem positive when it is negative, for example when using sarcasm or negation + * When the sentiment is mixed, do your best to represent that across the scores + + === CONTENT + ${message} + `, + temperature: 0.2, +}) +``` + +And similarly in Python it could look like this: + +```python +message = "what a great product, NOT!" + +assess = ComputeText( + prompt=f""" + Your job is to assess a piece of CONTENT on the underlying emotional sentiment it communicates. + Please follow the RULES of the assignment and be mindful of the TIPS. + + === RULES + Analyze the content and report on POSITIVE sentiment if there is any, NEUTRAL sentiment if there is any, and NEGATIVE sentiment if there is any. + Record your rationale for your conclusions. + + Once you have done so, please assign a score for each: + POSITIVE (0-100): ___ + NEUTRAL (0-100): ___ + NEGATIVE (0-100): ___ + + === TIPS + * Sometimes content may seem positive when it is negative, for example when using sarcasm or negation + * When the sentiment is mixed, do your best to represent that across the scores + + === CONTENT + ${message}""", + temperature=0.2, +) +``` + +The output of this node will be a `string` of the report, but next we can use a `ComputeJSON` node to extract +structured data from this report to be used in a real system. + +In TypeScript the next step could look like this: + +```typescript +const extract = new ComputeJSON({ + prompt: sb.interpolate` + Your job is to extract the relevant details of the sentiment analysis REPORT. + Please do so as accurately as possible and include a short summary of the rationale. + + === REPORT + ${assess.future.text} + `, + json_schema: { + type: "object", + properties: { + positive_score: { type: "integer", minimum: 0, maximum: 100 }, + neutral_score: { type: "integer", minimum: 0, maximum: 100 }, + negative_score: { type: "integer", minimum: 0, maximum: 100 }, + sentiment: { type: "string", enum: ["positive", "neutral", "negative"] }, + summary: { type: "string" }, + } + } +}) +``` + +Or in Python, like this: + +```python +extract = ComputeJSON( + prompt=sb.format( + """ + Your job is to extract the relevant details of the sentiment analysis REPORT. + Please do so as accurately as possible and include a short summary of the rationale. + + === REPORT + ${report} + """, + report=assess.future.text, + ), + json_schema={ + "type": "object", + "properties": { + "positive_score": {"type": "integer", "minimum": 0, "maximum": 100}, + "neutral_score": {"type": "integer", "minimum": 0, "maximum": 100}, + "negative_score": {"type": "integer", "minimum": 0, "maximum": 100}, + "sentiment": {"type": "string", "enum": ["positive", "neutral", "negative"]}, + "summary": {"type": "string"}, + }, + }, +) +``` + +We're using a JSON Schema here to structure the outputs exactly how we want them and can ensure the fields +are constrained as we like. Note here that we're passing in the output of the previous node using the _future_ +provided by the `assess` node we created. This way we can run the entire workflow with a single API request. + +Finally we can run this graph like so in TypeScript: + +```typescript +const res = await substrate.run(extract); +console.log(res.get(extract).json_object); +``` + +Or in Python: + +```python +res = substrate.run(extract) +print(res.get(extract).json_object) +``` + +The final result will look like the following: + +```json +{ + "positive_score": 0, + "neutral_score": 0, + "negative_score": 100, + "sentiment": "negative", + "summary": "The sentiment is primarily negative due to the use of sarcasm and negation in the phrase 'NOT!' at the end of the sentence." +} +``` + +You can build on this example further if you like. For example, perhaps there are other dimensions you would like to assess for or +perhaps there are more details you would like the LLM to pay closer attention too. You could even run the same pipeline multiple +times on the same data to collect some aggregated assessment of several passes. + +We hope this example is helpful for you and that you enjoy working with Substrate as much as we do! diff --git a/techniques/sentiment-analysis/diagram.d2 b/techniques/sentiment-analysis/diagram.d2 new file mode 100644 index 0000000..43037e9 --- /dev/null +++ b/techniques/sentiment-analysis/diagram.d2 @@ -0,0 +1,43 @@ +direction: right +classes: { + substrate: { + label: "substrate" + style: { + font: mono + font-color: gray + font-size: 20 + stroke: gray + stroke-dash: 1 + fill: "transparent" + border-radius: 16 + } + } + node: { + style: { + font: mono + font-size: 24 + stroke-width: 2 + fill: transparent + stroke: gray + border-radius: 16 + stroke-dash: 1 + 3d: true + } + } + edge: { + style: { + stroke: "#000" + stroke-dash: 2 + } + } +} + +substrate.class: substrate +substrate.a.class: node +substrate.b.class: node +substrate.c.class: node +substrate.a.label: heuristic +substrate.b.label: symbolic +substrate.c.label: computation +substrate.a->substrate.c { class: edge } +substrate.b->substrate.c { class: edge } diff --git a/techniques/sentiment-analysis/diagram.svg b/techniques/sentiment-analysis/diagram.svg new file mode 100644 index 0000000..290d401 --- /dev/null +++ b/techniques/sentiment-analysis/diagram.svg @@ -0,0 +1,111 @@ +substrate + +heuristic + +symbolic + +computation + + + + + + diff --git a/techniques/sentiment-analysis/hero.png b/techniques/sentiment-analysis/hero.png new file mode 100644 index 0000000..3327492 Binary files /dev/null and b/techniques/sentiment-analysis/hero.png differ diff --git a/techniques/sentiment-analysis/python/.gitignore b/techniques/sentiment-analysis/python/.gitignore new file mode 100644 index 0000000..ae8554d --- /dev/null +++ b/techniques/sentiment-analysis/python/.gitignore @@ -0,0 +1,10 @@ +# python generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# venv +.venv diff --git a/techniques/sentiment-analysis/python/README.md b/techniques/sentiment-analysis/python/README.md new file mode 100644 index 0000000..8a1841b --- /dev/null +++ b/techniques/sentiment-analysis/python/README.md @@ -0,0 +1,26 @@ +# Substrate Example Templates in Python + +This is a Substrate example template written in Python. To run this example, + +```bash +# Set your API key as an environment variable. +# Get one here https://www.substrate.run/dashboard/keys if this is your first time. +export SUBSTRATE_API_KEY= + +# Navigate to the python example directory. +cd python +``` + +To run the example with Poetry (default), run the following. + +```bash +poetry install +poetry run main +``` + +To run the example with Rye, comment out the Poetry sections and uncomment the Rye sections in `pyproject.toml` and run the following. + +```bash +rye sync +rye run main +``` diff --git a/techniques/sentiment-analysis/python/poetry.lock b/techniques/sentiment-analysis/python/poetry.lock new file mode 100644 index 0000000..0f5d04f --- /dev/null +++ b/techniques/sentiment-analysis/python/poetry.lock @@ -0,0 +1,805 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with 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b/techniques/sentiment-analysis/python/poetry.toml @@ -0,0 +1,2 @@ +[virtualenvs] +in-project = true diff --git a/techniques/sentiment-analysis/python/pyproject.toml b/techniques/sentiment-analysis/python/pyproject.toml new file mode 100644 index 0000000..18d6a28 --- /dev/null +++ b/techniques/sentiment-analysis/python/pyproject.toml @@ -0,0 +1,68 @@ +[project] +name = "template" +version = "0.1.0" +description = "" +authors = [{ name = "Hanwen Wu", email = "hwwu@substrate.run" }] +readme = "README.md" + +requires-python = ">= 3.9" +dependencies = ["substrate"] + +[project.scripts] +main = "python.example:main" + +[tool.ruff] +line-length = 120 +output-format = "grouped" +target-version = "py310" + +[tool.pyright] +venvPath = "." +venv = ".venv" + +############################## +# Uncomment for Poetry support +############################## + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry] +name = "template" +version = "0.1.0" +description = "" +authors = ["Hanwen Wu "] +readme = "README.md" + +packages = [{ include = "python", from = "src" }] + +[tool.poetry.dependencies] +python = "^3.9" +substrate = "220240617.*" + +[tool.poetry.group.dev.dependencies] +ruff = "^0.6.1" +marimo = "^0.8.0" + +[tool.poetry.scripts] +main = "python.example:main" + + +########################### +# Uncomment for Rye support +########################### + +# [build-system] +# requires = ["hatchling"] +# build-backend = "hatchling.build" + +# [tool.rye] +# managed = true +# dev-dependencies = ["ruff>=0.6.1", "marimo>=0.8.0"] + +# [tool.hatch.metadata] +# allow-direct-references = true + +# [tool.hatch.build.targets.wheel] +# packages = ["src/python"] diff --git a/techniques/sentiment-analysis/python/src/python/__init__.py b/techniques/sentiment-analysis/python/src/python/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/techniques/sentiment-analysis/python/src/python/example.py b/techniques/sentiment-analysis/python/src/python/example.py new file mode 100644 index 0000000..6c1805a --- /dev/null +++ b/techniques/sentiment-analysis/python/src/python/example.py @@ -0,0 +1,80 @@ +import os +import time + +from substrate import ComputeJSON, ComputeText, Substrate, sb + + +def main(): + api_key = os.environ.get("SUBSTRATE_API_KEY") or "YOUR_API_KEY" + substrate = Substrate(api_key=api_key) + + message = "what a great product, NOT!" + + assess = ComputeText( + prompt=f""" + Your job is to assess a piece of CONTENT on the underlying emotional sentiment it communicates. + Please follow the RULES of the assignment and be mindful of the TIPS. + + === RULES + Analyze the content and report on POSITIVE sentiment if there is any, NEUTRAL sentiment if there is any, and NEGATIVE sentiment if there is any. + Record your rationale for your conclusions. + + Once you have done so, please assign a score for each: + POSITIVE (0-100): ___ + NEUTRAL (0-100): ___ + NEGATIVE (0-100): ___ + + === TIPS + * Sometimes content may seem positive when it is negative, for example when using sarcasm or negation + * When the sentiment is mixed, do your best to represent that across the scores + + === CONTENT + ${message}""", + temperature=0.2, + ) + + extract = ComputeJSON( + prompt=sb.format( + """ + Your job is to extract the relevant details of the sentiment analysis REPORT. + Please do so as accurately as possible and include a short summary of the rationale. + + === REPORT + ${report} + """, + report=assess.future.text, + ), + json_schema={ + "type": "object", + "properties": { + "positive_score": {"type": "integer", "minimum": 0, "maximum": 100}, + "neutral_score": {"type": "integer", "minimum": 0, "maximum": 100}, + "negative_score": {"type": "integer", "minimum": 0, "maximum": 100}, + "sentiment": {"type": "string", "enum": ["positive", "neutral", "negative"]}, + "summary": {"type": "string"}, + }, + }, + ) + + start = log_start() + res = substrate.run(extract) + log_end(start) + + print(res.get(extract).json_object) + + +def log_start(): + start = time.time() + print("\033[94m֍ \033[34mRunning...\033[0m") + return start + + +def log_end(start): + end = time.time() + duration = end - start + seconds = f"{duration:.2f}" + print(f"\033[92m֍ \033[32mCompleted in {seconds}s\033[0m") + + +if __name__ == "__main__": + main() diff --git a/techniques/sentiment-analysis/typescript/README.md b/techniques/sentiment-analysis/typescript/README.md new file mode 100644 index 0000000..22230b4 --- /dev/null +++ b/techniques/sentiment-analysis/typescript/README.md @@ -0,0 +1,28 @@ +# Substrate Example Templates in Typescript + +This is a Substrate example template written in Typescript. To run this example, + +```bash +# Set your API key as an environment variable. +# Get one here https://www.substrate.run/dashboard/keys if this is your first time. +export SUBSTRATE_API_KEY= + +# Navigate to the python example directory. +cd typescript +``` + +To run the example with tsx (default), run the following. + +```bash +npx tsx ./example.ts + +# Or you can use the package.json scripts +npm run start +``` + +To run the example with Deno, uncomment the Deno sections in `example.ts` and +run the following. + +```bash +deno run ./example.ts +``` diff --git a/techniques/sentiment-analysis/typescript/example.ts b/techniques/sentiment-analysis/typescript/example.ts new file mode 100755 index 0000000..61b4d67 --- /dev/null +++ b/techniques/sentiment-analysis/typescript/example.ts @@ -0,0 +1,76 @@ +import { ComputeText, ComputeJSON, Substrate, sb } from "substrate"; + +// Uncomment the following line if using Deno +// import process from "node:process"; + +async function main() { + const apiKey = process.env["SUBSTRATE_API_KEY"] || "YOUR_API_KEY"; + const substrate = new Substrate({ apiKey: apiKey }); + + const message = "what a great product, NOT!"; + + const assess = new ComputeText({ + prompt: ` + Your job is to assess a piece of CONTENT on the underlying emotional sentiment it communicates. + Please follow the RULES of the assignment and be mindful of the TIPS. + + === RULES + Analyze the content and report on POSITIVE sentiment if there is any, NEUTRAL sentiment if there is any, and NEGATIVE sentiment if there is any. + Record your rationale for your conclusions. + + Once you have done so, please assign a score for each: + POSITIVE (0-100): ___ + NEUTRAL (0-100): ___ + NEGATIVE (0-100): ___ + + === TIPS + * Sometimes content may seem positive when it is negative, for example when using sarcasm or negation + * When the sentiment is mixed, do your best to represent that across the scores + + === CONTENT + ${message} + `, + temperature: 0.2, + }) + + const extract = new ComputeJSON({ + prompt: sb.interpolate` + Your job is to extract the relevant details of the sentiment analysis REPORT. + Please do so as accurately as possible and include a short summary of the rationale. + + === REPORT + ${assess.future.text} + `, + json_schema: { + type: "object", + properties: { + positive_score: { type: "integer", minimum: 0, maximum: 100 }, + neutral_score: { type: "integer", minimum: 0, maximum: 100 }, + negative_score: { type: "integer", minimum: 0, maximum: 100 }, + sentiment: { type: "string", enum: ["positive", "neutral", "negative"] }, + summary: { type: "string" }, + } + } + }) + + const start = logStart(); + const res = await substrate.run(extract); + logEnd(start); + + console.log(res.get(extract).json_object); +} + +main(); + +function logStart(): number { + const start = Date.now(); + console.log("\x1b[94m֍ \x1b[34m Running... \x1b[0m"); + return start; +} + +function logEnd(start: number) { + const end = Date.now(); + const duration = end - start; + const seconds = (duration / 1000).toFixed(2); + console.log(`\x1b[92m֍ \x1b[32m Completed in ${seconds}s\x1b[0m`); +} diff --git a/techniques/sentiment-analysis/typescript/package-lock.json b/techniques/sentiment-analysis/typescript/package-lock.json new file mode 100644 index 0000000..cc44abe --- /dev/null +++ b/techniques/sentiment-analysis/typescript/package-lock.json @@ -0,0 +1,166 @@ +{ + "name": "substrate-example", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "substrate-example", + "version": "1.0.0", + "dependencies": { + "substrate": "^120240617" + }, + "devDependencies": { + "@types/node": "^22", + "typescript": "^5" + } + }, + "node_modules/@types/node": { + "version": "22.5.0", + "resolved": "https://registry.npmjs.org/@types/node/-/node-22.5.0.tgz", + "integrity": "sha512-DkFrJOe+rfdHTqqMg0bSNlGlQ85hSoh2TPzZyhHsXnMtligRWpxUySiyw8FY14ITt24HVCiQPWxS3KO/QlGmWg==", + "dependencies": { + "undici-types": 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100644 index 0000000..46cac12 --- /dev/null +++ b/techniques/sentiment-analysis/typescript/package.json @@ -0,0 +1,18 @@ +{ + "name": "substrate-example", + "version": "1.0.0", + "private": "true", + "main": "example.ts", + "type": "module", + "scripts": { + "start": "npx tsx example.ts", + "typecheck": "tsc --noEmit" + }, + "dependencies": { + "substrate": "^120240617" + }, + "devDependencies": { + "@types/node": "^22", + "typescript": "^5" + } +} diff --git a/techniques/sentiment-analysis/typescript/tsconfig.json b/techniques/sentiment-analysis/typescript/tsconfig.json new file mode 100644 index 0000000..2f3b370 --- /dev/null +++ b/techniques/sentiment-analysis/typescript/tsconfig.json @@ -0,0 +1,20 @@ +{ + "compilerOptions": { + "moduleDetection": "force", + "module": "Preserve", + "resolveJsonModule": true, + "allowJs": true, + "esModuleInterop": true, + "skipLibCheck": true, + "isolatedModules": true, + "strict": true, + "noEmit": true, + "moduleResolution": "node", + "jsx": 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