IMDB chat bot works locally with 'chainlit run app.py'

Added imdb.csv, index.faiss, and index.pkl to gitlfs due to size

Removing larges files so that they're exclusively tracked by git lfs

.gitattributes

@@ -33,3 +33,6 @@ 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
+imdb.csv filter=lfs diff=lfs merge=lfs -text
+data/index.faiss filter=lfs diff=lfs merge=lfs -text
+data/index.pkl filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,5 @@
+__pycache__/
+.chainlit
+.files
+
+.DS_Store

app.py

@@ -1,11 +1,26 @@
+import os
 import chainlit as cl
 
+import build_db as db
+import chat_bot as bot
+
+
+@cl.on_chat_start
+async def onStart():
+    global embedding_model
+    (embedder, embedding_model) = db.create_embedder(openai_api_key)
+
+    global vector_store
+    vector_store = db.load_vector_store(embedder)
 
 @cl.on_message
-async def main(message: cl.Message):
-    # Your custom logic goes here...
+async def onMessage(message: cl.Message):
+    response = bot.query_movie_critic(message.content, vector_store, openai_api_key)
+
+    msg = cl.Message(content="")
+    async for chunk in response:
+        await msg.stream_token(f"{chunk}")
 
-    # Send a response back to the user
-    await cl.Message(
-        content=f"Received: {message.content}",
-    ).send()
+openai_api_key = os.getenv("openai_api_key")
+embedding_model = None
+vector_store = None

build_db.py

@@ -0,0 +1,114 @@
+from datasets import load_dataset
+import pandas as pd
+
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings
+from langchain.embeddings import CacheBackedEmbeddings
+from langchain.storage import LocalFileStore
+
+from pathlib import Path
+from functools import reduce
+import os
+
+def build_db(openai_api_key):
+    data = load_imdb_data()
+
+    (embedder, embedding_model) = create_embedder(openai_api_key)
+    build_vectore_store(data, embedder)
+    vector_store = load_vector_store(embedder)
+
+    run_test_query(embedding_model, vector_store)
+
+def load_imdb_data():
+  print("Loading IMDB dataset")
+  dataset = load_dataset("ShubhamChoksi/IMDB_Movies")
+  dataset_dict = dataset
+  dataset_dict["train"].to_csv('data/imdb.csv')
+  print("")
+
+  print("Creating dataframe")
+  movies_dataframe = pd.read_csv('data/imdb.csv')
+  print(movies_dataframe.head())
+  print("")
+
+  print("Loading data from CSV")
+  loader = CSVLoader(file_path='data/imdb.csv')
+  data = loader.load()
+  print("Done loading data...")
+  print("Length: " + str(len(data))) # ensure we have actually loaded data into a format LangChain can recognize
+  print("Data list type: " + str(type(data)))
+  print("Data type: " + str(type(data[0])))
+  print(data[0])
+  print("")
+
+  print("Calculating total length of data")
+  add_length = lambda sum, doc: len(doc.page_content) + sum
+  total_length = reduce(add_length, data, 0)
+  print("Total number of characters in dataset: " + str(total_length))
+  print("Total divided by 1,000: " + str(total_length / 1000))
+  print("")
+
+  return data
+
+def create_embedder(openai_api_key):
+  embedding_model = OpenAIEmbeddings(openai_api_key=openai_api_key)
+
+  # Create the embedding store file if it doesn't already exist.
+  storeFile = str(Path.cwd() / 'data/embedding-store')
+  # storeFilePath = Path(storeFile)
+  # if not storeFilePath.exists():
+  #   storeFilePath.touch()
+  
+  # Create the embedder, using a local file store as the backing store.
+  store = LocalFileStore(storeFile)
+  embedder = CacheBackedEmbeddings.from_bytes_store(
+    embedding_model,
+    store
+  )
+
+  return (embedder, embedding_model)
+
+def build_vectore_store(data, embedder):
+  text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=1000,
+    chunk_overlap=100,
+  )
+  chunked_documents = text_splitter.split_documents(data)
+  len(chunked_documents) # ensure we have actually split the data into chunks
+
+  print("Trying to load vector store from file...")
+  vector_store = None
+  try:
+    os.makedirs("data/week2-movies", exist_ok=True)
+    vector_store = FAISS.load_local("data/week2-movies", embedder, allow_dangerous_deserialization=True)
+  except Exception as e:
+      vector_store = None
+
+  if vector_store is None:
+    print("No local vector store found - computing a new one...")
+    vector_store = FAISS.from_documents(data, embedder)
+    print("Done computing new vectore store. Saving to local file.")
+    vector_store.save_local("data/week2-movies")
+  else:
+    print("Found vector store in local file. Using that.")
+  print("")
+
+def load_vector_store(embedder):
+   vector_store = FAISS.load_local("data/week2-movies", embedder, allow_dangerous_deserialization=True)
+   return vector_store
+
+def run_test_query(embedding_model, vector_store):
+  print("Verifying that we can query the vectore dB...")
+  query = "I have a need. A need for speed."
+  embedded_query = embedding_model.embed_query(query)
+  similar_documents = vector_store.similarity_search_by_vector(embedded_query)
+  for page in similar_documents:
+    print(str(page.page_content))
+  print("")
+
+
+if __name__ == "__main__":
+  openai_api_key = os.getenv("openai_api_key")
+  build_db(openai_api_key)

chainlit.md

@@ -1,14 +1,2 @@
-# 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.
+# IMDB Movie Critic
+Welcome to the IMDB movie critic. Ask the critic about types of movies and recommendations.

chat_bot.py

@@ -0,0 +1,34 @@
+from langchain_core.runnables.base import RunnableSequence
+from langchain_core.runnables.passthrough import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+
+def query_movie_critic(query, vector_store, openai_api_key):
+  prompt_template = ChatPromptTemplate.from_messages(
+      [
+          ("system", """
+          You are a movie critic AI bot. Your name is Roger Ebert.\n
+          \n
+          Movie descriptions:\n
+          {imdb}
+          """),
+          ("human", "{user_input}"),
+      ]
+  )
+
+  retriever = vector_store.as_retriever()
+
+  chat_model = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0, api_key=openai_api_key)
+
+  parser = StrOutputParser()
+
+  runnable_chain = (
+      {"imdb": retriever, "user_input": RunnablePassthrough()} |
+      prompt_template | 
+      chat_model | 
+      parser
+  )
+
+  output_stream = runnable_chain.astream(query)
+  return output_stream

data/embedding-store/00018b96-f3aa-5234-8a0d-88e0e63720d0

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