init

.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
+*.psd filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.11
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+COPY ./pre-requirements.txt ~/app/pre-requirements.txt
+RUN pip install -r ~/app/pre-requirements.txt
+COPY ./requirements.txt ~/app/requirements.txt
+RUN pip install -r ~/app/requirements.txt
+COPY . .
+CMD ["chainlit", "run", "app.py", "--port", "7862"]

README.md

@@ -1,8 +1,8 @@
 ---
-title: Proj2
-emoji: 🏆
-colorFrom: purple
-colorTo: green
+title: Proj2rag
+emoji: 🐨
+colorFrom: blue
+colorTo: purple
 sdk: docker
 pinned: false
 license: apache-2.0

app.py

@@ -0,0 +1,109 @@
+from datasets import load_dataset
+from huggingface_hub import list_datasets
+from google.colab import userdata
+from langchain import OpenAI, LLMMathChain, SerpAPIWrapper
+from langchain.agents import initialize_agent, Tool, AgentExecutor
+from langchain_community.chat_models import ChatOpenAI
+import os
+import chainlit as cl
+import openai
+from google.colab import userdata
+from dotenv import load_dotenv
+from langchain_community.document_loaders import TextLoader
+from langchain_community.document_loaders.csv_loader import CSVLoader
+from langchain_community.vectorstores import FAISS
+from langchain.storage import LocalFileStore
+from langchain.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain.schema.runnable import RunnableMap
+from langchain.schema.output_parser import StrOutputParser
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.output_parsers import ResponseSchema, StructuredOutputParser
+import pandas as pd
+from langchain_openai import OpenAIEmbeddings
+import openai
+import asyncio
+from dotenv import dotenv_values
+
+# get keys
+my_secrets = dotenv_values("key.env")
+
+#load the csv
+loader = TextLoader('data.csv')
+documents = loader.load()
+
+#split using recursive text splitter
+text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=100,
+        length_function=len,
+        is_separator_regex=False,
+)
+
+docs = text_splitter.split_documents(documents)
+
+# create embeddings
+underlying_embeddings = OpenAIEmbeddings(model="text-embedding-ada-002",api_key=my_secrets["OPEN_API_KEY"])
+db = FAISS.from_documents(docs, underlying_embeddings)
+
+# Get the retriever for the Chat Model
+retriever = db.as_retriever(
+        search_kwargs={"k": 10}
+)
+
+
+@cl.on_chat_start
+def start():
+
+  # Create the prompt template make sure it doesn't return data not in rag
+  template = """
+      You're a helpful AI assistent tasked to answer the user's questions about movies.
+      You can only make conversations based on the provided context about movies. If a response cannot be formed strictly using the context, politely say you don’t have knowledge about that topic under new line character 'ANSWER:' tag which is prefixed with new line character.
+
+      Remember, you must return both an answer under 'ANSWER:' tag which is prefixed with new line character and citations in line separated format of answer and bulleted list of citiations under 'CITATIONS:' tag. A citation consists of a VERBATIM quote that \
+      justifies the answer and the ID of the quoted article.  Return a citation for every quote across all articles \
+      that justify the answer. Add a new line character after all citations. Use the following format for your final output:
+
+      new line character
+      ANSWER:
+
+      CITATIONS:
+      new line character
+
+      CONTEXT:
+      {context}
+
+      QUESTION: {question}
+
+      YOUR ANSWER:
+  """
+
+  prompt = ChatPromptTemplate.from_messages([("system", template)])
+
+  llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0, api_key=my_secrets["OPEN_API_KEY"])
+
+  # Define the chain
+  inputs = RunnableMap({
+      'context': lambda x: retriever.get_relevant_documents(x['question']),
+      'question': lambda x: x['question']
+  })
+
+  #create runnable chain
+  runnable_chain = (
+    inputs |
+    prompt |
+    llm |
+    StrOutputParser()
+  )
+  cl.user_session.set("runnable_chain", runnable_chain)
+
+
+@cl.on_message
+async def on_message(message: cl.Message):
+    runnable_chain = cl.user_session.get("runnable_chain")
+    msg = message.content
+
+    result = runnable_chain.invoke({"question": msg})
+
+    #print(str(result))
+    await cl.Message(content=result).send()

data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4adc33bd9fe74303c344be46e5916d65182fb218e248fe80452ab3f025b06c64
+size 2

key.env

@@ -0,0 +1,3 @@
+HF_TOKEN=
+NGROK_KEY=
+OPEN_API_KEY=

pre-requirements.txt

@@ -0,0 +1,2 @@
+pip>=23.2
+gradio_client==0.2.7

requirements.txt

@@ -0,0 +1,16 @@
+langchain
+chainlit
+langchain-openai
+openai
+chromadb
+tiktoken
+pymupdf
+datasets
+langchain_community
+chainlit
+pyngrok
+openai
+google-search-results
+optimum
+auto-gptq
+faiss-gpu