clean up git messages

.gitattributes

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README.md

@@ -1,118 +1,11 @@
 ---
-title: DeployPythonicRAG
-emoji: 📉
-colorFrom: blue
-colorTo: purple
+title: AI Insightsbot
+emoji: 🐠
+colorFrom: indigo
+colorTo: gray
 sdk: docker
 pinned: false
 license: apache-2.0
 ---
 
-# Deploying Pythonic Chat With Your Text File Application
-
-In today's breakout rooms, we will be following the processed that you saw during the challenge - for reference, the instructions for that are available [here](https://github.com/AI-Maker-Space/Beyond-ChatGPT/tree/main).
-
-Today, we will repeat the same process - but powered by our Pythonic RAG implementation we created last week. 
-
-You'll notice a few differences in the `app.py` logic - as well as a few changes to the `aimakerspace` package to get things working smoothly with Chainlit.
-
-## Reference Diagram (It's Busy, but it works)
-
-![image](https://i.imgur.com/IaEVZG2.png)
-
-## Deploying the Application to Hugging Face Space
-
-Due to the way the repository is created - it should be straightforward to deploy this to a Hugging Face Space!
-
-> NOTE: If you wish to go through the local deployments using `chainlit run app.py` and Docker - please feel free to do so!
-
-<details>
-    <summary>Creating a Hugging Face Space</summary>
-
-1.  Navigate to the `Spaces` tab.
-
-![image](https://i.imgur.com/aSMlX2T.png)
-
-2. Click on `Create new Space`
-
-![image](https://i.imgur.com/YaSSy5p.png)
-
-3. Create the Space by providing values in the form. Make sure you've selected "Docker" as your Space SDK.
-
-![image](https://i.imgur.com/6h9CgH6.png)
-
-</details>
-
-<details>
-    <summary>Adding this Repository to the Newly Created Space</summary>
-
-1. Collect the SSH address from the newly created Space. 
-
-![image](https://i.imgur.com/Oag0m8E.png)
-
-> NOTE: The address is the component that starts with `[email protected]:spaces/`.
-
-2. Use the command:
-
-```bash
-git remote add hf HF_SPACE_SSH_ADDRESS_HERE
-```
-
-3. Use the command:
-
-```bash
-git pull hf main --no-rebase --allow-unrelated-histories -X ours
-```
-
-4. Use the command: 
-
-```bash 
-git add .
-```
-
-5. Use the command:
-
-```bash
-git commit -m "Deploying Pythonic RAG"
-```
-
-6. Use the command: 
-
-```bash
-git push hf main
-```
-
-7. The Space should automatically build as soon as the push is completed!
-
-> NOTE: The build will fail before you complete the following steps!
-
-</details>
-
-<details>
-    <summary>Adding OpenAI Secrets to the Space</summary>
-
-1. Navigate to your Space settings.
-
-![image](https://i.imgur.com/zh0a2By.png)
-
-2. Navigate to `Variables and secrets` on the Settings page and click `New secret`: 
-
-![image](https://i.imgur.com/g2KlZdz.png)
-
-3. In the `Name` field - input `OPENAI_API_KEY` in the `Value (private)` field, put your OpenAI API Key.
-
-![image](https://i.imgur.com/eFcZ8U3.png)
-
-4. The Space will begin rebuilding!
-
-</details>
-
-## 🎉
-
-You just deployed Pythonic RAG!
-
-Try uploading a text file and asking some questions!
-
-## 🚧CHALLENGE MODE 🚧
-
-For more of a challenge, please reference [Building a Chainlit App](./BuildingAChainlitApp.md)!
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -63,22 +63,31 @@ rag_documents_2 = PyMuPDFLoader(file_path=DATA_LINK2).load()
 chunked_rag_documents = chunk_documents(rag_documents_1, CHUNK_SIZE, CHUNK_OVERLAP) + \
                         chunk_documents(rag_documents_2, CHUNK_SIZE, CHUNK_OVERLAP)
 
-embeddings = OpenAIEmbeddings(model=EMBEDDING_MODEL)
-retriever = build_retriever(chunked_rag_documents, embeddings, COLLECTION_NAME)
 
-rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
-qa_llm = ChatOpenAI(model=QA_MODEL)
+@cl.on_chat_start
+async def on_chat_start():
+    embeddings = OpenAIEmbeddings(model=EMBEDDING_MODEL)
+    retriever = build_retriever(chunked_rag_documents, embeddings, COLLECTION_NAME)
+
+    rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
+    qa_llm = ChatOpenAI(model=QA_MODEL)
+
+    rag_chain = (
+        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
+        | rag_prompt | qa_llm | StrOutputParser()
+    )
+
+    cl.user_session.set("chain", rag_chain)
+    
 
-rag_chain = (
-    {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
-    | rag_prompt | llm | StrOutputParser()
-)
 
 # Chainlit app
 @cl.on_message
-async def main(message: str):
-    response = rag_chain.invoke({"question": message})
+async def main(message):
+    chain = cl.user_session.get("chain")
+    result = chain.invoke({"question" : message.content})
+
     await cl.Message(
-        content=response["response"],  # Extract the response from the chain
+        content=result,  # Extract the response from the chain
         author="AI"
     ).send()