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import gradio as gr | |
import os | |
from langchain_community.document_loaders import JSONLoader | |
from langchain_community.vectorstores import Qdrant | |
from langchain_community.embeddings import HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings | |
from sentence_transformers.cross_encoder import CrossEncoder | |
from groq import Groq | |
client = Groq( | |
api_key=os.environ.get("GROQ_API"), | |
) | |
# loading data | |
json_path = "format_food.json" | |
json_path = "llama70b_food_dump.json" | |
def metadata_func(record: dict, metadata: dict) -> dict: | |
metadata["title"] = record.get("title") | |
metadata["cuisine"] = record.get("cuisine") | |
metadata["time"] = record.get("time") | |
metadata["instructions"] = record.get("instructions") | |
return metadata | |
def reranking_results(query, top_k_results, rerank_model): | |
# Load the model, here we use our base sized model | |
top_results_formatted = [f"{item.metadata['title']}, {item.page_content}" for item in top_k_results] | |
reranked_results = rerank_model.rank(query, top_results_formatted, return_documents=True) | |
return reranked_results | |
loader = JSONLoader( | |
file_path=json_path, | |
jq_schema='.dishes[].dish', | |
text_content=False, | |
content_key='doc', | |
metadata_func=metadata_func | |
) | |
data = loader.load() | |
# Models | |
# model_name = "Snowflake/snowflake-arctic-embed-xs" | |
# rerank_model = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1") | |
# Embedding | |
# model_kwargs = {"device": "cpu"} | |
# encode_kwargs = {"normalize_embeddings": True} | |
# hf_embedding = HuggingFaceEmbeddings( | |
# model_name=model_name, | |
# encode_kwargs=encode_kwargs, | |
# model_kwargs=model_kwargs, | |
# show_progress=True | |
# ) | |
model_name = "BAAI/bge-small-en" | |
model_kwargs = {"device": "cpu"} | |
encode_kwargs = {"normalize_embeddings": True} | |
hf_embedding = HuggingFaceBgeEmbeddings( | |
model_name=model_name, | |
model_kwargs=model_kwargs, | |
encode_kwargs=encode_kwargs | |
) | |
qdrant = Qdrant.from_documents( | |
data, | |
hf_embedding, | |
location=":memory:", # Local mode with in-memory storage only | |
collection_name="my_documents", | |
) | |
def format_to_markdown(response_list): | |
response_list[0] = "- " + response_list[0] | |
temp_string = "\n- ".join(response_list) | |
return temp_string | |
def run_query(query: str, groq: bool): | |
print("Running Query") | |
answer = qdrant.similarity_search(query=query, k=10) | |
title_and_description = f"# Best Choice:\nA {answer[0].metadata['title']}: {answer[0].page_content}" | |
instructions = format_to_markdown(answer[0].metadata['instructions']) | |
recipe = f"# Standard Method\n## Cooking time:\n{answer[0].metadata['time']}\n\n## Recipe:\n{instructions}" | |
print("Returning query") | |
if groq: | |
chat_completion = client.chat.completions.create( | |
messages=[ | |
{ | |
"role": "user", | |
"content": f"please write a more detailed recipe for the following recipe:\n{recipe}\n\n please return it in the same format.", | |
} | |
], | |
model="Llama3-70b-8192", | |
) | |
groq_update = "# Groq Update\n"+chat_completion.choices[0].message.content | |
else: | |
groq_update = "# Groq Update \nPlease select the tick box if you need more information." | |
return title_and_description, recipe, groq_update | |
with gr.Blocks() as demo: | |
gr.Markdown("Start typing below and then click **Run** to see the output.") | |
inp = gr.Textbox(placeholder="What sort of meal are you after?") | |
groq_button = gr.Checkbox(value=False, label="Use Llama for a better recipe?") | |
title_output = gr.Markdown(label="Title and description") | |
instructions_output = gr.Markdown(label="Recipe") | |
updated_recipe = gr.Markdown(label="Updated Recipe") | |
btn = gr.Button("Run") | |
btn.click(fn=run_query, inputs=[inp, groq_button], outputs=[title_output, instructions_output, updated_recipe]) | |
demo.launch() |