Updated app.py with embeddings = FastEmbedEmbeddings and ChatGroq() instead of ChatOpenAI()

Updated app.py with embeddings = FastEmbedEmbeddings and ChatGroq() instead of ChatOpenAI()

New requirements.txt :
...

app.py

@@ -1,14 +1,18 @@
 import os
 from typing import List
 
-from langchain.embeddings.openai import OpenAIEmbeddings
+# from langchain.embeddings.openai import OpenAIEmbeddings # ORIGINAL
+from langchain_community.embeddings import FastEmbedEmbeddings # JB
+
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Chroma
 from langchain.chains import (
     ConversationalRetrievalChain,
 )
 from langchain.document_loaders import PyPDFLoader
-from langchain.chat_models import ChatOpenAI
+# from langchain.chat_models import ChatOpenAI # ORIGINAL
+from langchain_groq import ChatGroq # JB
+
 from langchain.prompts.chat import (
     ChatPromptTemplate,
     SystemMessagePromptTemplate,
@@ -20,6 +24,14 @@ from chainlit.types import AskFileResponse
 
 import chainlit as cl
 
+# JB
+from dotenv import load_dotenv
+import glob
+load_dotenv()  #
+groq_api_key = os.environ['GROQ_API_KEY']
+# groq_api_key = "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" # os.environ['GROQ_API_KEY']
+print"groq_api_key: ", groq_api_key)
+
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
 
 system_template = """Use the following pieces of context to answer the users question.
@@ -88,7 +100,8 @@ async def on_chat_start():
     metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
 
     # Create a Chroma vector store
-    embeddings = OpenAIEmbeddings()
+    # embeddings = OpenAIEmbeddings()  # ORIGINAL
+    embeddings = FastEmbedEmbeddings # JB
     docsearch = await cl.make_async(Chroma.from_texts)(
         texts, embeddings, metadatas=metadatas
     )
@@ -102,9 +115,16 @@ async def on_chat_start():
         return_messages=True,
     )
 
+
+
+    # JB
+    # llm = ChatGroq(temperature=0.2, groq_api_key=groq_api_key, model_name='mixtral-8x7b-32768')
+
+    
     # Create a chain that uses the Chroma vector store
     chain = ConversationalRetrievalChain.from_llm(
-        ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True),
+        # ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, streaming=True), # ORIGINAL
+        ChatGroq(temperature=0.2, groq_api_key=groq_api_key, model_name='mixtral-8x7b-32768', streaming=True), # JB
         chain_type="stuff",
         retriever=docsearch.as_retriever(),
         memory=memory,