feat(Instructor): introduce Instructor Hub with tutorials, examples, and new CLI

docs/hub/index.md

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+# Instructor Hub
+
+Welcome to instructor hub, the goal of this project is to provide a set of tutorials and examples to help you get started, and allow you to pull in the code you need to get started with `instructor`
+
+Make sure you're using the latest version of `instructor` by running:
+
+```bash
+pip install -U instructor
+```
+
+## Contributing
+
+We welcome contributions to the instructor hub, if you have a tutorial or example you'd like to add, please open a pull request in `docs/hub` and we'll review it.
+
+1. The code must be in a single file
+2. Make sure that its referenced in the `mkdocs.yml`
+3. Make sure that the code is unit tested.
+
+### Using pytest_examples
+
+By running the following command you can run the tests and update the examples. This ensures that the examples are always up to date.
+Linted correctly and that the examples are working, make sure to include a `if __name__ == "__main__":` block in your code and add some asserts to ensure that the code is working.
+
+```bash
+poetry run pytest tests/openai/docs/test_hub.py --update-examples
+```
+
+## CLI Usage
+
+Instructor hub comes with a command line interface (CLI) that allows you to view and interact with the tutorials and examples and allows you to pull in the code you need to get started with the API.
+
+### List Cookbooks
+
+By running `instructor hub list` you can see all the available tutorials and examples. By clickony (doc) you can see the full tutorial back on this website.
+
+```bash
+$ instructor hub list --sort
+```
+
+| hub_id | slug                          | title                         | n_downloads |
+| ------ | ----------------------------- | ----------------------------- | ----------- |
+| 2      | multiple_classification (doc) | Multiple Classification Model | 24          |
+| 1      | single_classification (doc)   | Single Classification Model   | 2           |
+
+### Searching for Cookbooks
+
+You can search for a tutorial by running `instructor hub list -q <QUERY>`. This will return a list of tutorials that match the query.
+
+```bash
+$ instructor hub list -q multi
+```
+
+| hub_id | slug                          | title                         | n_downloads |
+| ------ | ----------------------------- | ----------------------------- | ----------- |
+| 2      | multiple_classification (doc) | Multiple Classification Model | 24          |
+
+### Reading a Cookbook
+
+To read a tutorial, you can run `instructor hub pull --id <hub_id> --page` to see the full tutorial in the terminal. You can use `j,k` to scroll up and down, and `q` to quit. You can also run it without `--page` to print the tutorial to the terminal.
+
+```bash
+$ instructor hub pull --id 2 --page
+```
+
+### Pulling in Code
+
+You can pull in the code with `--py --output=<filename>` to save the code to a file, or you cal also run it without `--output` to print the code to the terminal.
+
+```bash
+$ instructor hub pull --id 2 --py --output=run.py
+$ instructor hub pull --id 2 --py > run.py
+```
+
+You can run the code instantly if you `|` it to `python`:
+
+```bash
+$ instructor hub pull --id 2 --py | python
+```
+
+## Call for Contributions
+
+We're looking for a bunch more hub examples, if you have a tutorial or example you'd like to add, please open a pull request in `docs/hub` and we'll review it.
+
+- [ ] Converting the cookbooks to the new format
+- [ ] Validator examples
+- [ ] Data extraction examples
+- [ ] Streaming examples (Iterable and Partial)
+- [ ] Batch Parsing examples
+- [ ] Open Examples, together, anyscale, ollama, llama-cpp, etc
+- [ ] Query Expansion examples
+- [ ] Batch Data Processing examples
+- [ ] Batch Data Processing examples with Cache

docs/hub/multiple_classification.md

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+For multi-label classification, we introduce a new enum class and a different Pydantic model to handle multiple labels.
+
+```python
+import openai
+import instructor
+
+from typing import List, Literal
+from pydantic import BaseModel, Field
+
+# Apply the patch to the OpenAI client
+# enables response_model keyword
+client = instructor.patch(openai.OpenAI())
+
+LABELS = Literal["ACCOUNT", "BILLING", "GENERAL_QUERY"]
+
+
+class MultiClassPrediction(BaseModel):
+    labels: List[LABELS] = Field(
+        ...,
+        description="Only select the labels that apply to the support ticket.",
+    )
+
+
+def multi_classify(data: str) -> MultiClassPrediction:
+    return client.chat.completions.create(
+        model="gpt-4-turbo-preview",  # gpt-3.5-turbo fails
+        response_model=MultiClassPrediction,
+        messages=[
+            {
+                "role": "system",
+                "content": f"You are a support agent at a tech company. Only select the labels that apply to the support ticket.",
+            },
+            {
+                "role": "user",
+                "content": f"Classify the following support ticket: {data}",
+            },
+        ],
+    )  # type: ignore
+
+
+if __name__ == "__main__":
+    ticket = "My account is locked and I can't access my billing info."
+    prediction = multi_classify(ticket)
+    assert {"ACCOUNT", "BILLING"} == {label for label in prediction.labels}
+    print("input:", ticket)
+    #> input: My account is locked and I can't access my billing info.
+    print("labels:", LABELS)
+    #> labels: typing.Literal['ACCOUNT', 'BILLING', 'GENERAL_QUERY']
+    print("prediction:", prediction)
+    #> prediction: labels=['ACCOUNT', 'BILLING']
+```

docs/hub/single_classification.md

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+# Single-Label Classification
+
+This example demonstrates how to perform single-label classification using the OpenAI API. The example uses the `gpt-3.5-turbo` model to classify text as either `SPAM` or `NOT_SPAM`.
+
+```python
+from pydantic import BaseModel, Field
+from typing import Literal
+from openai import OpenAI
+import instructor
+
+# Apply the patch to the OpenAI client
+# enables response_model keyword
+client = instructor.patch(OpenAI())
+
+
+class ClassificationResponse(BaseModel):
+    label: Literal["SPAM", "NOT_SPAM"] = Field(
+        ...,
+        description="The predicted class label.",
+    )
+
+
+def classify(data: str) -> ClassificationResponse:
+    """Perform single-label classification on the input text."""
+    return client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        response_model=ClassificationResponse,
+        messages=[
+            {
+                "role": "user",
+                "content": f"Classify the following text: {data}",
+            },
+        ],
+    )
+
+
+if __name__ == "__main__":
+    for text, label in [
+        ("Hey Jason! You're awesome", "NOT_SPAM"),
+        ("I am a nigerian prince and I need your help.", "SPAM"),
+    ]:
+        prediction = classify(text)
+        assert prediction.label == label
+        print(f"Text: {text}, Predicted Label: {prediction.label}")
+        #> Text: Hey Jason! You're awesome, Predicted Label: NOT_SPAM
+        #> Text: I am a nigerian prince and I need your help., Predicted Label: SPAM
+```

instructor-hub-proxy/.editorconfig

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+# http://editorconfig.org
+root = true
+
+[*]
+indent_style = tab
+tab_width = 2
+end_of_line = lf
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+
+[*.yml]
+indent_style = space

instructor-hub-proxy/.gitignore

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+# Logs
+
+logs
+_.log
+npm-debug.log_
+yarn-debug.log*
+yarn-error.log*
+lerna-debug.log*
+.pnpm-debug.log*
+
+# Diagnostic reports (https://nodejs.org/api/report.html)
+
+report.[0-9]_.[0-9]_.[0-9]_.[0-9]_.json
+
+# Runtime data
+
+pids
+_.pid
+_.seed
+\*.pid.lock
+
+# Directory for instrumented libs generated by jscoverage/JSCover
+
+lib-cov
+
+# Coverage directory used by tools like istanbul
+
+coverage
+\*.lcov
+
+# nyc test coverage
+
+.nyc_output
+
+# Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files)
+
+.grunt
+
+# Bower dependency directory (https://bower.io/)
+
+bower_components
+
+# node-waf configuration
+
+.lock-wscript
+
+# Compiled binary addons (https://nodejs.org/api/addons.html)
+
+build/Release
+
+# Dependency directories
+
+node_modules/
+jspm_packages/
+
+# Snowpack dependency directory (https://snowpack.dev/)
+
+web_modules/
+
+# TypeScript cache
+
+\*.tsbuildinfo
+
+# Optional npm cache directory
+
+.npm
+
+# Optional eslint cache
+
+.eslintcache
+
+# Optional stylelint cache
+
+.stylelintcache
+
+# Microbundle cache
+
+.rpt2_cache/
+.rts2_cache_cjs/
+.rts2_cache_es/
+.rts2_cache_umd/
+
+# Optional REPL history
+
+.node_repl_history
+
+# Output of 'npm pack'
+
+\*.tgz
+
+# Yarn Integrity file
+
+.yarn-integrity
+
+# dotenv environment variable files
+
+.env
+.env.development.local
+.env.test.local
+.env.production.local
+.env.local
+
+# parcel-bundler cache (https://parceljs.org/)
+
+.cache
+.parcel-cache
+
+# Next.js build output
+
+.next
+out
+
+# Nuxt.js build / generate output
+
+.nuxt
+dist
+
+# Gatsby files
+
+.cache/
+
+# Comment in the public line in if your project uses Gatsby and not Next.js
+
+# https://nextjs.org/blog/next-9-1#public-directory-support
+
+# public
+
+# vuepress build output
+
+.vuepress/dist
+
+# vuepress v2.x temp and cache directory
+
+.temp
+.cache
+
+# Docusaurus cache and generated files
+
+.docusaurus
+
+# Serverless directories
+
+.serverless/
+
+# FuseBox cache
+
+.fusebox/
+
+# DynamoDB Local files
+
+.dynamodb/
+
+# TernJS port file
+
+.tern-port
+
+# Stores VSCode versions used for testing VSCode extensions
+
+.vscode-test
+
+# yarn v2
+
+.yarn/cache
+.yarn/unplugged
+.yarn/build-state.yml
+.yarn/install-state.gz
+.pnp.\*
+
+# wrangler project
+
+.dev.vars
+.wrangler/

instructor-hub-proxy/.prettierrc

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+{
+	"printWidth": 140,
+	"singleQuote": true,
+	"semi": true,
+	"useTabs": true
+}

instructor-hub-proxy/create.sql

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+CREATE TABLE hub_analytics (
+    id SERIAL PRIMARY KEY,
+    event_type VARCHAR(255) NOT NULL,
+    user_agent VARCHAR(255) NOT NULL,
+    request_ip VARCHAR(100) NOT NULL,
+    request_time TIMESTAMP WITH TIME ZONE NOT NULL,
+    branch VARCHAR(255) NOT NULL,
+    slug VARCHAR(255) NOT NULL
+);
+