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import os
os.environ["ANTHROPIC_API_KEY"] = "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"
import gradio as gr
from langchain_community.memory import ConversationBufferMemory
from langchain_community.chains import RetrievalQA
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_community.chat_models import ChatAnthropic
model_kwargs = {'trust_remote_code': True}
embeddings = HuggingFaceEmbeddings(model_name="jinaai/jina-embeddings-v2-base-en",
model_kwargs=model_kwargs)
llm = ChatAnthropic(model='claude-2',
temperature=0)
# Load the persisted Chroma database
persist_directory = os.path.expanduser('~/Electric_Machinerydb')
embeddings = embeddings
chroma_db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
retriever = chroma_db.as_retriever(search_kwargs={"k": 3}, return_source_documents=True)
# Initialize ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, input_key="query", output_key="result")
# Create a question-answering chain using the retriever
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True,
memory=memory,
output_key="result" # Specify the output key
)
# Gradio app
def ask_question(question, chat_history):
query = question.strip()
if query:
result = qa_chain({"query": query})
answer = result['result']
chat_history.append((query, answer))
metadata = show_metadata(chat_history)
return "", chat_history, metadata
else:
chat_history.append(("", "Please enter a question."))
return "", chat_history, ""
def show_metadata(chat_history):
if chat_history:
query, answer = chat_history[-1]
result = qa_chain({"query": query})
metadata = ""
for doc in result['source_documents']:
metadata += f"Page: {doc.metadata['page']}\n"
metadata += f"Source: {doc.metadata['source']}\n"
metadata += f"Content: {doc.page_content}\n"
metadata += "---\n"
return metadata
return ""
def clean_history():
memory.clear()
return [], "", ""
with gr.Blocks() as demo:
gr.Markdown("# Electric Machinery QA by Tamil")
with gr.Row():
with gr.Column(scale=2):
question = gr.Textbox(label="Question")
ask_btn = gr.Button("Ask")
clean_btn = gr.Button("Clean")
chatbot = gr.Chatbot(label="Conversation")
with gr.Column(scale=1):
gr.Markdown("## Metadata")
metadata_output = gr.Textbox(label="Source Information", lines=10)
ask_btn.click(ask_question, inputs=[question, chatbot], outputs=[question, chatbot, metadata_output])
clean_btn.click(clean_history, inputs=[], outputs=[chatbot, question, metadata_output])
demo.launch() |