Organize examples

examples/chainlit/basic-app/Dockerfile

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+FROM python:3.11
+
+COPY app.py app.py
+RUN pip install chainlit
+COPY chainlit.md chainlit.md
+
+
+ENTRYPOINT ["chainlit", "run", "app.py", "--host=0.0.0.0", "--port=80", "--headless"]

examples/chainlit/basic-app/README.md

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+# Basic app
+
+A basic Chainlit application to get started.

examples/chainlit/basic-app/app.py

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+import chainlit as cl
+
+
+@cl.on_message  # this function will be called every time a user inputs a message in the UI
+async def main(message: str):
+    # this is an intermediate step
+    await cl.Message(author="Tool 1", content=f"Response from tool1", indent=1).send()
+
+    # send back the final answer
+    await cl.Message(content=f"This is the final answer").send()

examples/chainlit/basic-app/chainlit.md

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+This app is hosted in [Ploomber Cloud!](https://ploomber.io/)

examples/fastapi/ai-helper/Dockerfile

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+FROM python:3.11
+
+COPY app.py app.py
+COPY pipelinehelper.py pipelinehelper.py
+COPY data.csv data.csv
+COPY .env .env
+COPY requirements.txt requirements.txt
+RUN pip install torch==2.1.1 torchvision==0.16.1 --index-url https://download.pytorch.org/whl/cpu
+RUN pip install -r requirements.txt
+
+ENTRYPOINT ["uvicorn", "app:app", "--host=0.0.0.0", "--port=80"]

examples/fastapi/ai-helper/README.md

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+# Orders LLM
+
+An LLM-based app to ask questions about an orders database.
+
+## How it works
+
+This API uses the [Haystack](https://haystack.deepset.ai/overview/intro) framework to build a 
+Retrieval Augmented Generation (RAG) pipeline that can answer questions about orders. 
+The pipeline is initialized with a document store that contains information about orders.
+The pipeline uses the document store to answer questions about orders.
+
+The API is built with [FastAPI](https://fastapi.tiangolo.com/), a modern, fast (high-performance),
+web framework for building APIs with Python 3.6+ based on standard Python type hints.
+
+It is deployed on [Ploomber Cloud](https://ploomber.io/), a platform for building and deploying
+Python data and AI-based apps.
+
+## Sample data
+
+Data source: UCI Machine Learning Repository "Online Retail" dataset
+
+Link: https://archive.ics.uci.edu/dataset/352/online+retail
+
+| invoiceno	| stockcode	| description                       | quantity  |invoicedate     |unitprice  | customerid| country         |
+|-----------|-----------|-----------------------------------|-----------|----------------|-----------|----------|-----------------|
+|536365	    |85123A	    |WHITE HANGING HEART T-LIGHT HOLDER	| 6         | 12/1/2010 8:26 | 2.55	     | 17850	| United Kingdom  |
+|536367     |84879      |ASSORTED COLOUR BIRD ORNAMENT      | 32        | 12/1/2010 8:34 | 1.69      | 13047    | United Kingdom  |
+|536381     |47580      |TEA TIME DES TEA COSY              | 2         | 12/1/2010 9:41 | 2.55      | 15311    | United Kingdom  |
+|536858     |22326      |ROUND SNACK BOXES SET OF4 WOODLAND | 30        | 12/3/2010 10:36| 2.95      | 13520    | Switzerland     |
+|537463     |22961      |JAM MAKING SET PRINTED             | 12        | 12/7/2010 10:08| 1.45      | 12681    | France          |
+
+## Sample questions
+
+* What is the total cost for order with invoice number 537463? 
+* What were the items in order 536365?
+* How many items were in order 536858?
+* Can I still cancel order with invoice number 536365? 
+
+Order numbers you can try: '536365', '536366', '536367', '536368', '536369', '536370'
+Orders that contain information on cancellations: 'C536379', 'C536383', 'C536391'
+
+## Using the curl command
+
+For example, for the question `What were the items in order 536365?` you can run
+the following curl command:
+
+```bash
+curl -X 'POST'
+    'https://calm-violet-6179.ploomberapp.io/query'
+    -H 'accept: application/json'
+    -H 'Content-Type: application/json'
+    -d '{
+    "query": "What were the items in order 536365?"
+    }'
+```
+This yields a JSON object with an answer key:
+
+```bash
+{
+    "answer": "The items in order 536365 were:
+                1. WHITE METAL LANTERN
+                2. SET 7 BABUSHKA NESTING BOXES
+                3. WHITE HANGING HEART T-LIGHT HOLDER
+                4. CREAM CUPID HEARTS COAT HANGER
+                5. RED WOOLLY HOTTIE WHITE HEART.
+                6. GLASS STAR FROSTED T-LIGHT HOLDER
+                7. KNITTED UNION FLAG HOT WATER BOTTLE"
+    }
+``` 
+
+Similarly, for `What is the total cost for order with invoice number 537463?` you can run
+
+```bash
+curl -X 'POST'
+    'https://calm-violet-6179.ploomberapp.io/query'
+    -H 'accept: application/json'
+    -H 'Content-Type: application/json'
+    -d '{
+    "query": "What is the total cost for order with invoice number 537463?"
+    }'
+```
+
+This yields a JSON object with an answer key:
+
+```bash
+{
+  "answer": "The total cost for order with invoice number 537463 is calculated as follows:
+  - STRAWBERRY LUNCH BOX WITH CUTLERY: 6 * 2.55 = 15.3
+  - LUNCH BOX WITH CUTLERY RETROSPOT: 6 * 2.55 = 15.3
+  - POSTAGE: 4 * 18.0 = 72.0
+  - DOORMAT RESPECTABLE HOUSE: 2 * 7.95 = 15.9
+  - TABLECLOTH RED APPLES DESIGN: 4 * 8.5 = 34.0
+  - GUMBALL COAT RACK: 36 * 2.1 = 75.6
+  - WOODLAND STICKERS: 12 * 0.85 = 10.2
+  - IVORY DINER WALL CLOCK: 2 * 8.5 = 17.0
+  - RED RETROSPOT CUP: 16 * 0.85 = 13.6
+  - WOODLAND CHARLOTTE BAG: 10 * 0.85 = 8.5
+
+  Adding all these up, the total cost for order with invoice number 537463 is 15.3 + 15.3 + 72.0 + 15.9 + 34.0 + 75.6 + 10.2 + 17.0 + 13.6 + 8.5 = 277.4."
+}
+```
+
+## Key components in this application
+
+1. `pipelinehelper.py`: This file contains the code for the pipeline. It uses the Haystack framework to build a RAG pipeline that can answer questions about orders. The pipeline is initialized with a document store that contains information about orders. The pipeline uses the document store to answer questions about orders.
+
+2. `app.py`: This file contains the code for the API. It uses FastAPI to build an API that can answer questions about orders. The API is initialized with a pipeline that can answer questions about orders.
+
+3. `Dockerfile`: This file contains the instructions for building a Docker image for this application.
+
+4. `requirements.txt`: This file contains the Python dependencies for this application.
+
+
+## Deploying this application on Ploomber Cloud
+
+Ensure you have an account and an API key. Refer to the [documentation on creating an account](https://docs.cloud.ploomber.io/en/latest/quickstart/signup.html) and [documentation on generating an API key](https://docs.cloud.ploomber.io/en/latest/quickstart/apikey.html) for more details.
+
+### 1. Install Ploomber Cloud's CLI
+
+```bash
+pip install ploomber-cloud
+```
+
+### 2. Connect your API key
+
+```bash
+ploomber-cloud key YOURKEY
+```
+
+### 3. Initialize your app
+
+```bash
+ploomber-cloud init
+```
+
+### 4. Deploy your app
+
+```bash
+ploomber-cloud deploy
+```

examples/fastapi/ai-helper/app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import os
+from dotenv import load_dotenv
+import uvicorn
+
+from pipelinehelper import (read_and_clean_csv, 
+                            generate_haystack_documents, 
+                            populate_document_store, 
+                            initialize_pipeline)
+
+description = """
+This is an LLM-based API with a focus on helping you answer questions about orders. 🚀
+
+## How it works
+
+This API uses the  [Haystack](https://haystack.deepset.ai/overview/intro) framework to build a 
+Retrieval Augmented Generation (RAG) pipeline that can answer questions about orders. 
+The pipeline is initialized with a document store that contains information about orders.
+The pipeline uses the document store to answer questions about orders.
+
+The API is built with [FastAPI](https://fastapi.tiangolo.com/), a modern, fast (high-performance),
+web framework for building APIs with Python 3.6+ based on standard Python type hints.
+
+It is deployed on [Ploomber Cloud](https://ploomber.io/), a platform for building and deploying
+Python data and AI-based apps.
+
+## Sample data
+
+Data source: UCI Machine Learning Repository "Online Retail" dataset
+
+Link: https://archive.ics.uci.edu/dataset/352/online+retail
+
+| invoiceno	| stockcode	| description                       | quantity  |invoicedate     |unitprice  | customerid| country         |
+|-----------|-----------|-----------------------------------|-----------|----------------|-----------|----------|-----------------|
+|536365	    |85123A	    |WHITE HANGING HEART T-LIGHT HOLDER	| 6         | 12/1/2010 8:26 | 2.55	     | 17850	| United Kingdom  |
+|536367     |84879      |ASSORTED COLOUR BIRD ORNAMENT      | 32        | 12/1/2010 8:34 | 1.69      | 13047    | United Kingdom  |
+|536381     |47580      |TEA TIME DES TEA COSY              | 2         | 12/1/2010 9:41 | 2.55      | 15311    | United Kingdom  |
+|536858     |22326      |ROUND SNACK BOXES SET OF4 WOODLAND | 30        | 12/3/2010 10:36| 2.95      | 13520    | Switzerland     |
+|537463     |22961      |JAM MAKING SET PRINTED             | 12        | 12/7/2010 10:08| 1.45      | 12681    | France          |
+
+## Sample questions
+
+* What is the total cost for order with invoice number 537463? 
+* What were the items in order 536365?
+* How many items were in order 536858?
+* Can I still cancel order with invoice number 536365? 
+
+Order numbers you can try: '536365', '536366', '536367', '536368', '536369', '536370'
+Orders that contain information on cancellations: 'C536379', 'C536383', 'C536391'
+
+## Using the curl command
+
+For example, for the question `What were the items in order 536365?` you can run
+the following curl command:
+
+```bash
+curl -X 'POST'
+    'https://calm-violet-6179.ploomberapp.io/query'
+    -H 'accept: application/json'
+    -H 'Content-Type: application/json'
+    -d '{
+    "query": "What were the items in order 536365?"
+    }'
+```
+This yields a JSON object with an answer key:
+
+```bash
+{
+    "answer": "The items in order 536365 were:
+                1. WHITE METAL LANTERN
+                2. SET 7 BABUSHKA NESTING BOXES
+                3. WHITE HANGING HEART T-LIGHT HOLDER
+                4. CREAM CUPID HEARTS COAT HANGER
+                5. RED WOOLLY HOTTIE WHITE HEART.
+                6. GLASS STAR FROSTED T-LIGHT HOLDER
+                7. KNITTED UNION FLAG HOT WATER BOTTLE"
+    }
+``` 
+
+Similarly, for `What is the total cost for order with invoice number 537463?` you can run
+
+```bash
+curl -X 'POST'
+    'https://calm-violet-6179.ploomberapp.io/query'
+    -H 'accept: application/json'
+    -H 'Content-Type: application/json'
+    -d '{
+    "query": "What is the total cost for order with invoice number 537463?"
+    }'
+```
+
+This yields a JSON object with an answer key:
+
+```bash
+{
+  "answer": "The total cost for order with invoice number 537463 is calculated as follows:
+  - STRAWBERRY LUNCH BOX WITH CUTLERY: 6 * 2.55 = 15.3
+  - LUNCH BOX WITH CUTLERY RETROSPOT: 6 * 2.55 = 15.3
+  - POSTAGE: 4 * 18.0 = 72.0
+  - DOORMAT RESPECTABLE HOUSE: 2 * 7.95 = 15.9
+  - TABLECLOTH RED APPLES DESIGN: 4 * 8.5 = 34.0
+  - GUMBALL COAT RACK: 36 * 2.1 = 75.6
+  - WOODLAND STICKERS: 12 * 0.85 = 10.2
+  - IVORY DINER WALL CLOCK: 2 * 8.5 = 17.0
+  - RED RETROSPOT CUP: 16 * 0.85 = 13.6
+  - WOODLAND CHARLOTTE BAG: 10 * 0.85 = 8.5
+
+  Adding all these up, the total cost for order with invoice number 537463 is 15.3 + 15.3 + 72.0 + 15.9 + 34.0 + 75.6 + 10.2 + 17.0 + 13.6 + 8.5 = 277.4."
+}
+```
+
+"""
+
+# Load environment variables
+load_dotenv(".env")
+
+# Initialize FastAPI app
+app = FastAPI(description=description)
+
+# Define Pydantic models
+class QueryModel(BaseModel):
+    query: str
+
+class ResponseModel(BaseModel):
+    answer: str
+
+# Load and prepare the pipeline
+openai_key = os.getenv("OPENAI_KEY")
+df_dict = read_and_clean_csv("data.csv")
+haystack_documents = generate_haystack_documents(df_dict=df_dict)
+document_store = populate_document_store(haystack_documents=haystack_documents)
+prediction_pipeline = initialize_pipeline(document_store=document_store, openai_key=openai_key)
+
+@app.post("/query", response_model=ResponseModel)
+async def query_sales_data(query_model: QueryModel):
+    try:
+        query = query_model.query
+        result = prediction_pipeline.run(data={"retriever": {"query": query}, 
+                                               "prompt_builder": {"query": query},
+                                               })
+        return {"answer": result['generator']['replies'][0]}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/")
+async def read_root():
+    return {"message": "Haystack AI Sales Query API"}
+
+if __name__ == "__main__":
+
+    uvicorn.run(app, host="0.0.0.0", port=80)