physics bot

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+physics_bot/faiss_index/index.faiss filter=lfs diff=lfs merge=lfs -text
+physics_bot/faiss_index/index.pkl filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/physics_bot
+
+RUN chown -R user:user $HOME/physics_bot    
+
+ADD  physics_bot $HOME/physics_bot
+
+CMD ["chainlit", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

physics_bot/chainlit.md

@@ -0,0 +1,14 @@
+# Welcome to Chainlit! 🚀🤖
+
+Hi there, Developer! 👋 We're excited to have you on board. Chainlit is a powerful tool designed to help you prototype, debug and share applications built on top of LLMs.
+
+## Useful Links 🔗
+
+- **Documentation:** Get started with our comprehensive [Chainlit Documentation](https://docs.chainlit.io) 📚
+- **Discord Community:** Join our friendly [Chainlit Discord](https://discord.gg/k73SQ3FyUh) to ask questions, share your projects, and connect with other developers! 💬
+
+We can't wait to see what you create with Chainlit! Happy coding! 💻😊
+
+## Welcome screen
+
+To modify the welcome screen, edit the `chainlit.md` file at the root of your project. If you do not want a welcome screen, just leave this file empty.

physics_bot/faiss_index/index.faiss

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7a1cc5ce370f0500a4dca5086dd4f140ac3d3788f0a6b3821d6c0cc10d75b87
+size 1105965

physics_bot/faiss_index/index.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c3f3fc8c3a888d691da14142d54c5599fe112c9ee4dcaa427d6fad10c922f35
+size 87480

physics_bot/physics_app.py

@@ -0,0 +1,56 @@
+from langchain_community.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema import StrOutputParser
+from langchain.schema.runnable import Runnable
+from langchain.schema.runnable.config import RunnableConfig
+import chainlit as cl
+from langchain_community.embeddings import OpenAIEmbeddings
+from langchain_core.prompts import PromptTemplate
+from langchain_community.vectorstores import FAISS
+
+
+@cl.on_chat_start
+async def on_chat_start():
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
+    vectorstore = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 1})
+
+    model = ChatOpenAI(streaming=True)
+
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are an 8th grader physics teacher. "
+                "You are looking to create notes for the chapter, "
+                "Explain the concept in a separate paragraph with bullet points and examples."
+                "Keep it concise and to the point. "
+                "Example output format"
+                "Title: What is Motion?"
+                "Motion is a change in position over time."
+                "To be able to describe an object or individual’s motion, you need to be able to accurately describe its position. "
+                "In order to describe an object or individual’s position, you need to know the distance, direction and a reference point. A reference point can be used to describe an object or individual’s position in comparison to another location. "
+                "Example1: “The ball rolled towards the red car.” The red car would be the reference point."
+                "Example2: “The mall is two miles north of my house.” Your house would be the reference point.",
+            ),
+            ("human", "{question}"),
+        ]
+    )
+
+    runnable = retriever| prompt | model | StrOutputParser()
+    cl.user_session.set("runnable", runnable)
+
+
+@cl.on_message
+async def on_message(message: cl.Message):
+    runnable = cl.user_session.get("runnable")  # type: Runnable
+
+    msg = cl.Message(content="")
+
+    async for chunk in runnable.astream(
+        message.content,
+        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
+    ):
+        await msg.stream_token(chunk)
+
+    await msg.send()

requirements.txt

@@ -0,0 +1,6 @@
+langchain==0.2.0
+langchain-community==0.2.0
+langchain-core==0.2.0
+langchain-openai==0.1.7
+langchain-text-splitters==0.2.0
+tiktoken==0.7.0