app.py

import gradio as gr
import chromadb
from sentence_transformers import CrossEncoder, SentenceTransformer
import json
from qdrant_client import QdrantClient

print("Setup client")
#chroma_client = chromadb.Client()
#collection = chroma_client.create_collection(
    #name="food_collection",
    #metadata={"hnsw:space": "cosine"} # l2 is the default
#)
client = QdrantClient(":memory:")

print("load data")
with open("test_json.json", "r") as f:
    payload = json.load(f)

def embedding_function(items_to_embed: list[str]):
    print("embedding")
    sentence_model = SentenceTransformer(
        "mixedbread-ai/mxbai-embed-large-v1"
    )
    embedded_items = sentence_model.encode(
        items_to_embed
    )
    print(len(embedded_items))
    print(type(embedded_items[0]))
    print(type(embedded_items[0][0]))
    embedded_list = [item.tolist() for item in embedded_items]
    print(len(embedded_list))
    print(type(embedded_list[0]))
    print(type(embedded_list[0][0]))
    return embedded_list


print('upserting')
print("printing item:")
embedding = embedding_function([item['doc'] for item in payload])
print(type(embedding))
client.add(
    collection_name="food",
    documents=[item['doc'] for item in payload],
    #embeddings=embedding,
    metadata=[{'payload':item} for item in payload],
    ids=[idx for idx, _ in enumerate(payload)]
    )

def search_chroma(query:str):
    results = client.query(
        #query_embeddings=embedding_function([query]),
        collection_name="food",
        query_text=query,
        limit=2
    )
    #print(results[0])
    #print(results[0].QueryResponse.metadata)
    #instructions = ['\n'.join(item.metadata['payload']['instructions']) for item in results]
    #text_only= [f"# Title:\n{item.metadata['payload']['title']}\n\n## Description:\n{item.metadata['payload']['doc']}\n\n ## Instructions:\n{instructions}" for item in results]
    text_only = []
    for item in results:
        instructions = "- "+'<br>- '.join(item.metadata['payload']['instructions'])
        markdown_text = f"# Title:\n{item.metadata['payload']['title']}\n\n## Description:\n{item.metadata['payload']['doc']}\n\n ## Instructions:\n{instructions}"
        text_only.append(markdown_text)
    print(text_only)
    return "\n".join(text_only)

def reranking_results(query: str, top_k_results: list[str]):
    # Load the model, here we use our base sized model
    rerank_model = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1")
    reranked_results = rerank_model.rank(query, top_k_results, return_documents=True)
    return reranked_results

def run_query(query_string: str):
    meal_string = search_chroma(query_string)
    return meal_string

with gr.Blocks() as meal_search:
    gr.Markdown("Start typing below and then click **Run** to see the output.")
    with gr.Row():
        inp = gr.Textbox(placeholder="What sort of meal are you after?")
        out = gr.Markdown()
    btn = gr.Button("Run")
    btn.click(
        fn=run_query,
        inputs=inp, 
        outputs=out
    )

meal_search.launch()