app.py

import streamlit as st
import pandas as pd
from torch import cuda
from langchain_community.embeddings import HuggingFaceEmbeddings, HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores import Qdrant
from qdrant_client import QdrantClient
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CrossEncoderReranker
from langchain_community.cross_encoders import HuggingFaceCrossEncoder
from langchain_qdrant import FastEmbedSparse, RetrievalMode
from appStore.prep_data import process_giz_worldwide

# get the device to be used eithe gpu or cpu
device = 'cuda' if cuda.is_available() else 'cpu'


st.set_page_config(page_title="SEARCH IATI",layout='wide')
st.title("SEARCH IATI Database")
var=st.text_input("enter keyword")

def embed_chunks(chunks):
    """
    takes the chunks and does the hybrid embedding for the list of chunks
    """
    embeddings = HuggingFaceEmbeddings(
        model_kwargs = {'device': device},
        encode_kwargs = {'normalize_embeddings': True},
        model_name='BAAI/bge-m3'
    )
    #sparse_embeddings = FastEmbedSparse(model_name="Qdrant/bm25")
    # placeholder for collection
    print("starting embedding")
    qdrant_collections = {}
    qdrant_collections['all'] = Qdrant.from_documents(
                chunks,
                embeddings,
                path="/data/local_qdrant",
                collection_name='all',
            )
            
    print(qdrant_collections)
    print("vector embeddings done")
    
@st.cache_resource    
def get_local_qdrant(): 
    """once the local qdrant server is created this is used to make the connection to exisitng server"""

    qdrant_collections = {}
    embeddings = HuggingFaceEmbeddings(
        model_kwargs = {'device': device},
        encode_kwargs = {'normalize_embeddings': True},
        model_name='BAAI/bge-m3')
    client = QdrantClient(path="/data/local_qdrant") 
    print("Collections in local Qdrant:",client.get_collections())
    qdrant_collections['all'] = Qdrant(client=client, collection_name='all', embeddings=embeddings, )
    return qdrant_collections    

def get_context(vectorstore,query):
    # create metadata filter


    # getting context
    retriever = vectorstore.as_retriever(search_type="similarity_score_threshold", 
                                         search_kwargs={"score_threshold": 0.5, 
                                                        "k": 10,})
#    # re-ranking the retrieved results
#    model = HuggingFaceCrossEncoder(model_name=model_config.get('ranker','MODEL'))
#   compressor = CrossEncoderReranker(model=model, top_n=int(model_config.get('ranker','TOP_K')))
#    compression_retriever = ContextualCompressionRetriever(
#           base_compressor=compressor, base_retriever=retriever
#       )
    context_retrieved = retriever.invoke(query)
    print(f"retrieved paragraphs:{len(context_retrieved)}")

    return context_retrieved

# first we create the chunks for iati documents
chunks = process_giz_worldwide()
for i in range(5):
    print(chunks.loc[0,'chunks'])
#print("chunking done")

# once the chunks are done, we perform hybrid emebddings
#embed_chunks(chunks)

# vectorstores = get_local_qdrant() 
# vectorstore = vectorstores['all']
# button=st.button("search")
# results= get_context(vectorstore, f"find the relvant paragraphs for: {var}")
if button:
     st.write(f"Found {len(results)} results for query:{var}")

     for i in results: 
         st.subheader(str(i.metadata['id'])+":"+str(i.metadata['title_main']))
         st.caption(f"Status:{str(i.metadata['status'])}, Country:{str(i.metadata['country_name'])}")
         st.write(i.page_content)
         st.divider()