initialize project

app.py

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+import os
+import sys
+import time
+
+import boto3
+
+from langchain_aws import BedrockLLM
+from langchain.embeddings import BedrockEmbeddings
+from langchain.vectorstores import FAISS
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+import gradio as gr
+
+module_path = ".."
+sys.path.append(os.path.abspath(module_path))
+bedrock_client = boto3.client('bedrock-runtime',region_name=os.environ.get("AWS_DEFAULT_REGION", "us-west-2"))
+
+modelId = 'meta.llama3-1-70b-instruct-v1:0'
+
+llm = BedrockLLM(
+    model_id=modelId,
+    client=bedrock_client
+)
+
+br_embeddings = BedrockEmbeddings(model_id="cohere.embed-multilingual-v3", client=bedrock_client)
+
+db = FAISS.load_local('faiss_index', embeddings=br_embeddings, allow_dangerous_deserialization=True)
+retriever = db.as_retriever(k=5)
+
+
+
+prompt = ChatPromptTemplate.from_messages([
+    ('system',
+     "Answer the questions witht the provided context. Do not include based on the context or based on the documents in your answer."
+     "Please say you do not know if you do not know or cannot find the information needed."
+     "\n Question: {question} \nContext: {context}"),
+    ('user', "{question}")
+])
+
+
+chat_history = []
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+rag_chain = (
+    {"context": retriever | format_docs, "question": RunnablePassthrough()}
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+response = rag_chain.invoke("Who are the board of directors in KCE company?")
+
+
+def chat_gen(message, history):
+    response = rag_chain.invoke(message)
+    partial_message = ""
+    for token in response:
+        partial_message = partial_message + token
+        time.sleep(0.05)
+        yield partial_message
+
+
+initial_msg = "Hello! I am KCE assistant. You can ask me anything about KCE. I am happy to assist you."
+chatbot = gr.Chatbot(value = [[None, initial_msg]])
+demo = gr.ChatInterface(chat_gen, chatbot=chatbot).queue()
+
+try:
+  demo.launch(debug=True, share=False, show_api=False)
+  demo.close()
+except Exception as e:
+  demo.close()
+  print(e)
+  raise e

requirements.txt

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+langchain
+langchain-aws
+langchain_community
+faiss-cpu
+boto3