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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()
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