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README.md

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-# Welcome! 
-
-This chatbot uses RAG to answer questions about the Seven Wonders of the Ancient World. 
-
-Here are sample questions you can ask it:
-
-1. What is the Great Pyramid of Giza?
-2. What is the Hanging Gardens of Babylon?
-3. What is the Temple of Artemis at Ephesus?
-4. What is the Statue of Zeus at Olympia?
-5. What is the Mausoleum at Halicarnassus?
-6. Where is Gardens of Babylon?
-7. Why did people build Great Pyramid of Giza?
-8. What does Rhodes Statue look like?
-9. Why did people visit the Temple of Artemis?
-10. What is the importance of Colossus of Rhodes?
-11. What happened to the Tomb of Mausolus?
-12. How did Colossus of Rhodes collapse?
-
-## How is it built?
-
-### Poetry package management
-
-This project uses [Poetry](https://python-poetry.org/) for package management.
-
-It uses [this `pyproject.toml` file](pyproject.toml)
-
-To install dependencies:
-
-```bash
-pip install poetry
-poetry install
-```
-
-### Data source: 
-
-The data is from the [Seven Wonders dataset][1] on Hugging Face. https://huggingface.co/datasets/bilgeyucel/seven-wonders
-
-### Method
-
-The chatbots retrieval mechanism is developed using Retrieval Augmented Generation (RAG) with [Haystack](https://haystack.deepset.ai/tutorials/22_pipeline_with_promptnode) and its user interface is built with [Chainlit](https://docs.chainlit.io/overview). It is using OpenAI GPT-3.5-turbo. 
-
-### Pipeline steps (Haystack) - check the full script here: [src/app.py](src/app.py)
-
-1. Initialize in-memory Document store
-
-```python
-# Initialize Haystack's QA system
-document_store = InMemoryDocumentStore(use_bm25=True)
-```
-2. Load dataset from HF
-
-```python
-dataset = load_dataset("bilgeyucel/seven-wonders", split="train")
-```
-
-3. Transform documents and load into document store
-
-```python
-document_store.write_documents(dataset)
-```
-4. Initialize a RAG prompt
-
-```
-rag_prompt = PromptTemplate(
-    prompt="""Synthesize a brief answer from the following text for the given question.
-                             Provide a clear and concise response that summarizes the key points and information presented in the text.
-                             Your answer should be in your own words and be no longer than 50 words.
-                             \n\n Related text: {join(documents)} \n\n Question: {query} \n\n Answer:""",
-    output_parser=AnswerParser(),
-)
-
-```
-
-5. Set the nodes using GPT-3.5-turbo
-
-```python
- Set up nodes
-retriever = BM25Retriever(document_store=document_store, top_k=2)
-pn = PromptNode("gpt-3.5-turbo", 
-                api_key=MY_API_KEY, 
-                model_kwargs={"stream":False},
-                default_prompt_template=rag_prompt)
-
-```
-
-6. Build the pipeline
-
-```python
-# Set up pipeline
-pipe = Pipeline()
-pipe.add_node(component=retriever, name="retriever", inputs=["Query"])
-pipe.add_node(component=pn, name="prompt_node", inputs=["retriever"])
-```
-
-### Connecting the pipeline to Chainlit
-
-```python
-
-@cl.on_message
-async def main(message: str):
-    # Use the pipeline to get a response
-    output = pipe.run(query=message)
-
-    # Create a Chainlit message with the response
-    response = output['answers'][0].answer
-    msg = cl.Message(content=response)
-
-    # Send the message to the user
-    await msg.send()
-```
-
-### Run application
-
-``` bash
-poetry run chainlit run src/app.py --port 7860
-```