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README.md

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+---
+title: {{Haystack RAG chainlit app}}
+emoji: {{emoji}}
+colorFrom: {{colorFrom}}
+colorTo: {{colorTo}}
+sdk: {{sdk}}
+sdk_version: {{sdkVersion}}
+app_file: app.py
+pinned: false
+---
+
+# 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
+```