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import gradio as gr
import hnswlib
import pandas as pd
from sentence_transformers import SentenceTransformer, CrossEncoder
import os
from together import Together
from dotenv import load_dotenv
from cryptography.fernet import Fernet
import gzip
import io
os.environ["TOKENIZERS_PARALLELISM"] = "false"
load_dotenv()
client = Together(api_key=os.environ.get("API_KEY"))
#read data
fernet = Fernet(os.environ.get("KEY2").encode("utf-8"))
with gzip.open("corpus.gz",'rb') as f:
bytes_enc = f.read()
pq_bytes = fernet.decrypt(bytes_enc)
pq_file = io.BytesIO(pq_bytes)
corpus = pd.read_parquet(pq_file)
biencoder = SentenceTransformer("intfloat/multilingual-e5-small", device="cpu")
embedding_size = biencoder.get_sentence_embedding_dimension()
crossencoder = CrossEncoder("KennethTM/MiniLM-L6-danish-reranker", device="cpu")
index = hnswlib.Index(space = 'cosine', dim = embedding_size)
index.load_index("corpus.index")
index.set_ef(40)
state = {}
source_label = {"wiki": "Wikipedia", "lex": "lex.dk", "mfkn": "MFKN", "dce": "DCE"}
def format_markdown(results):
result_template = '### {idx}. [{title}]({url}) ({source}):\n"{text}"'
result_join = "\n\n".join([result_template.format(idx=i+1, source=source_label[source], title=title, url=url, text=text)
for i, (title, source, url, text) in enumerate(zip(results["title"], results["source"], results["url"], results["text_chunks"]))])
results_formatted = f"## Referencer:\n\n{result_join}"
return(results_formatted)
def format_context(results):
result_template = "Kilde {idx}:\n{text}"
result_join = "\n\n".join([result_template.format(idx=i+1, text=text) for i, text in enumerate(results["text_chunks"])])
return(result_join)
def search(query, top_k):
query_embedding = biencoder.encode(query, prompt = "query: ")
biencoder_hits = int(top_k)*2
ids, _ = index.knn_query(query_embedding, k = biencoder_hits)
ids = ids[0]
results = corpus.iloc[ids].copy()
results["scores"] = crossencoder.predict([(query, i) for i in results["text_chunks"]])
results = results.sort_values("scores", ascending=False)
results = results[:int(top_k)]
results_markdown = format_markdown(results)
results_context = format_context(results)
state["context"] = results_context
state["query"] = query
return(results_markdown)
def search_summary():
context = state["context"]
query = state["query"]
prompt = [{"role": "system", "content": "Svar på spørgsmålet. Anvend kilderne i konteksten hvis de kan bruges til besvarelsen. Besvar kun på dansk."},
{"role": "user", "content": f"Kontekst:\n{context}\n\nSpørgsmål:\n{query}"}]
stream = client.chat.completions.create(
model="meta-llama/Llama-3-8b-chat-hf",
messages=prompt,
stream=True,
max_tokens=1024
)
partial_message = ""
for chunk in stream:
partial_message += chunk.choices[0].delta.content or ""
yield partial_message
with gr.Blocks() as demo:
gr.Markdown("# Natur og miljø BOT")
gr.Markdown("Dette er en simpel spørgsmål-svar applikation indenfor Danmarks natur og miljø. Svar genereres af en sprogmodel (LLAMA-3-8B) og anvender relevante referencer i en stor samling af dokumenter. Dette er blandt andet artikler fra [Wikipedia](https://da.wikipedia.org/wiki/Forside), rapporter fra [DCE - Nationalt Center for Miljø og Energi](https://dce.au.dk/udgivelser), [lex.dk - Den Store Danske](https://denstoredanske.lex.dk/) samt sager fra [Miljø og fødevareklagenævnet](https://mfkn.naevneneshus.dk).")
with gr.Row():
textbox = gr.Textbox(placeholder="Søg...", lines=1, scale=8, label="Spørgsmål")
num = gr.Number(5, label="Referencer", scale=1, minimum=1, maximum=10)
btn = gr.Button("Søg!", size="sm", scale=2)
with gr.Row():
summary = gr.Textbox(interactive=False, lines=10, label="Svar")
with gr.Row():
results = gr.Markdown()
gr.Markdown("*Applikation lavet af Kenneth Thorø Martinsen (email: [email protected])*")
btn.click(fn=search, inputs=[textbox, num], outputs=results).then(search_summary, inputs=None, outputs=summary)
textbox.submit(fn=search, inputs=[textbox, num], outputs=results).then(search_summary, inputs=None, outputs=summary)
demo.queue().launch() |