Spaces:

zrile-95
/

AIE3-Demo

Runtime error

App Files Files Community

zrile-95 commited on Jun 23

Commit

87a129a

•

1 Parent(s): 4467421

Correction 1

Browse files

Files changed (4) hide show

Dockerfile +3 -0
README.md +1 -150
app.py +6 -6
chainlit.md +1 -1

Dockerfile CHANGED Viewed

@@ -5,6 +5,9 @@ ENV HOME=/home/user \
     PATH=/home/user/.local/bin:$PATH
 WORKDIR $HOME/app
 COPY --chown=user . $HOME/app
 COPY ./requirements.txt ~/app/requirements.txt
 RUN pip install -r requirements.txt
 COPY . .

     PATH=/home/user/.local/bin:$PATH
 WORKDIR $HOME/app
 COPY --chown=user . $HOME/app
+RUN mkdir -p $HOME/app/data
+RUN mkdir -p $HOME/app/data/vectorstore
+RUN chown -R user:user $HOME/app/data
 COPY ./requirements.txt ~/app/requirements.txt
 RUN pip install -r requirements.txt
 COPY . .

README.md CHANGED Viewed

@@ -1,150 +1 @@
-# Week 4: Tuesday
-In today's assignment, we'll be creating an Open Source LLM-powered LangChain RAG Application in Chainlit.
-There are 2 main sections to this assignment:
-## Build 🏗️
-### Build Task 1: Deploy LLM and Embedding Model to SageMaker Endpoint Through Hugging Face Inference Endpoints
-#### LLM Endpoint
-Select "Inference Endpoint" from the "Solutions" button in Hugging Face:
-![image](https://i.imgur.com/6KC9TCD.png)
-Create a "+ New Endpoint" from the Inference Endpoints dashboard.
-![image](https://i.imgur.com/G6Bq9KC.png)
-Select the `NousResearch/Meta-Llama-3-8B-Instruct` model repository and name your endpoint. Select N. Virginia as your region (`us-east-1`). Give your endpoint an appropriate name. Make sure to select *at least* a L4 GPU.
-![image](https://i.imgur.com/X3YlUbh.png)
-Select the following settings for your `Advanced Configuration`.
-![image](https://i.imgur.com/c0HQ7g1.png)
-Create a `Protected` endpoint.
-![image](https://i.imgur.com/Ak8kchZ.png)
-If you were successful, you should see the following screen:
-![image](https://i.imgur.com/IBYG3wm.png)
-#### Embedding Model Endpoint
-We'll be using `Snowflake/snowflake-arctic-embed-m` for our embedding model today.
-The process is the same as the LLM - but we'll make a few specific tweaks:
-Let's make sure our set-up reflects the following screenshots:
-![image](https://i.imgur.com/IHh8FnC.png)
-After which, make sure the advanced configuration is set like so:
-![image](https://i.imgur.com/bbcrhUj.png)
-> #### NOTE: PLEASE SHUTDOWN YOUR INSTANCES WHEN YOU HAVE COMPLETED THE ASSIGNMENT TO PREVENT UNESSECARY CHARGES.
-### Build Task 2: Create RAG Pipeline with LangChain
-Follow the [notebook](https://colab.research.google.com/drive/1v1FYmvKH4gsqcdZwIT9wvbQe0GUjrc9d?usp=sharing) to create a LangChain pipeline powered by Hugging Face endpoints!
-Once you're done - please move on to Build Task 3!
-### Build Task 3: Create a Chainlit Application
-1. Create a new empty Docker space through Hugging Face - with the following settings:
-![image](https://i.imgur.com/0YzyQX7.png)
-> NOTE: You may notice the application builds slowly (~15min.) with the default free-tier hardware. The process will be faster using the `CPU upgrade` Space Hardware - though it is not required.
-2. Clone the newly created space into a directory that is *NOT IN YOUR AI MAKERSPACE REPOSITORY* using the SSH option.
-> NOTE: You may need to ensure you've added your SSH key to Hugging Face, as well as GitHub. This should already be done.
-![image](https://i.imgur.com/5RyBdP5.png)
-3. Copy and Paste (`cp ...` or through UI) the contents of `Week 4/Day 1` into the newly cloned repository.
-> NOTE: Please keep the `README.md` that was cloned from your space and delete the class `README.md`.
-4. Using the `ls` command or the `tree` command verify that you have copied over:
- - `app.py`
- - `Dockerfile`
- - `data/paul_graham_essays.txt`
- - `chainlit.md`
- - `.gitignore`
- - `.env.sample`
- - `solution_app.py`
- - `requirements.txt`
- Here is an example as the `ls -al` CLI command:
- ![image](https://i.imgur.com/vazGYeb.png)
- 5. Work through the `app.py` file to migrate your LCEL LangChain RAG Chain from the Notebook to Chainlit!
- 6. Be sure to modify your `README.md` and `chainlit.md` as you see fit!
- > NOTE: If you get stuck, there is a working reference version in `solution_app.py`.
- 7. When you are done with local testing - push your changes to your space.
- 8. Make sure you add your `HF_LLM_ENDPOINT`, `HF_EMBED_ENDPOINT`, `HF_TOKEN` as "Secrets" in your Hugging Face Space.
-### Terminating Your Resources
-Please head to the settings of each endpoint and select `Delete Endpoint`. You will need to type the name of the endpoint to delete the resources.
-### Deliverables
-- Completed Notebook
-- Chainlit Application in a Hugging Face Space Powered by Hugging Face Endpoints
-- Screenshot of endpoint usage
-Example Screen Shot:
-![image](https://i.imgur.com/qfbcVpS.png)
-## Ship 🚢
-Create a Hugging Face Space powered by Hugging Face Endpoints!
-### Deliverables
-- A short Loom of the space, and a 1min. walkthrough of the application in full
-## Share 🚀
-Make a social media post about your final application!
-### Deliverables
-- Make a post on any social media platform about what you built!
-Here's a template to get you started:
-```
-🚀 Exciting News! 🚀
-I am thrilled to announce that I have just built and shipped a open-source LLM-powered Retrieval Augmented Generation Application with LangChain! 🎉🤖
-🔍 Three Key Takeaways:
-1️⃣
-2️⃣
-3️⃣
-Let's continue pushing the boundaries of what's possible in the world of AI and question-answering. Here's to many more innovations! 🚀
-Shout out to @AIMakerspace !
-#LangChain #QuestionAnswering #RetrievalAugmented #Innovation #AI #TechMilestone
-Feel free to reach out if you're curious or would like to collaborate on similar projects! 🤝🔥
-```
-> #### NOTE: PLEASE SHUTDOWN YOUR INSTANCES WHEN YOU HAVE COMPLETED THE ASSIGNMENT TO PREVENT UNESSECARY CHARGES.


1	+ Hello

app.py CHANGED Viewed

@@ -67,11 +67,11 @@ else:
     ### 4. INDEX FILES
     ### NOTE: REMEMBER TO BATCH THE DOCUMENTS WITH MAXIMUM BATCH SIZE = 32
-        for i in range(0, len(split_documents), 32):
-            if i == 0:
-                vectorstore = FAISS.from_documents(split_documents[i:i+32], hf_embeddings)
-                continue
-            vectorstore.add_documents(split_documents[i:i+32])
 hf_retriever = vectorstore.as_retriever()
@@ -148,7 +148,7 @@ async def main(message: cl.Message):
     """
     This function will be called every time a message is recieved from a session.
-    We will use the LCEL RAG chain to generate a response to the user query.
     The LCEL RAG chain is stored in the user session, and is unique to each user session - this is why we can access it here.
     """

     ### 4. INDEX FILES
     ### NOTE: REMEMBER TO BATCH THE DOCUMENTS WITH MAXIMUM BATCH SIZE = 32
+    for i in range(0, len(split_documents), 32):
+        if i == 0:
+            vectorstore = FAISS.from_documents(split_documents[i:i+32], hf_embeddings)
+            continue
+        vectorstore.add_documents(split_documents[i:i+32])
 hf_retriever = vectorstore.as_retriever()
     """
     This function will be called every time a message is recieved from a session.
+    We will use the LCEL RlAG chain to generate a response to the user query.
     The LCEL RAG chain is stored in the user session, and is unique to each user session - this is why we can access it here.
     """

chainlit.md CHANGED Viewed

	@@ -1 +1 @@
1	- # ~~FILL OUT YOUR CHAINLIT MD HERE WITH A DESCRIPTION OF YOUR APPLICATION~~


1	+ # Hello