Update app.py

app.py

@@ -14,10 +14,12 @@ from fastapi.responses import JSONResponse
 # Make sure you have these files in a 'utils' folder
 from utils.DocsLoader import load_and_chunk
 from utils.Schemas import RunRequest, RunResponse
-
+from concurrent.futures import ThreadPoolExecutor
+from langchain.vectorstores import FAISS
+from langchain.schema import Document
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_huggingface import HuggingFaceEmbeddings # Correct new import
-from langchain_chroma import Chroma
+# from langchain_chroma import Chroma
 from langchain_community.retrievers import BM25Retriever
 from langchain.retrievers import EnsembleRetriever, ContextualCompressionRetriever
 from langchain.retrievers.document_compressors import CrossEncoderReranker
@@ -145,10 +147,37 @@ async def run_hackrx(req: RunRequest):
     if not chunks:
         return JSONResponse({"error": "No documents could be processed."}, status_code=400)
 
+        
+    ####code for parallel####################################################################################
+
+    
+    docs = [Document(page_content=d.page_content) for d in chunks]
+
+    # Parallel embedding
+    def embed_batch(batch):
+        texts = [d.page_content for d in batch]
+        embs = ml_models['embedder'].embed_documents(texts)
+        for doc, emb in zip(batch, embs):
+            doc.embedding = emb
+        return batch
+
+    # Batch and run
+    batch_size = 64
+    batches = [docs[i:i+batch_size] for i in range(0, len(docs), batch_size)]
+    embedded_docs = []
+    with ThreadPoolExecutor() as executor:
+        for batch in executor.map(embed_batch, batches):
+            embedded_docs.extend(batch)
+
+    vectorstore = FAISS.from_documents(embedded_docs, embedding=None)
+    dense_retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
+    
+
     # Create retrievers using the pre-loaded models from our ml_models dictionary
     keyword_retriever = BM25Retriever.from_documents(chunks)
-    dense_retriever = Chroma.from_documents(documents=chunks, embedding=ml_models["embedder"]).as_retriever()
-    ensemble_retriever = EnsembleRetriever(retrievers=[keyword_retriever, dense_retriever], weights=[0.3, 0.7])
+    keyword_retriever.k = 4
+    # dense_retriever = Chroma.from_documents(documents=chunks, embedding=ml_models["embedder"]).as_retriever()
+    ensemble_retriever = EnsembleRetriever(retrievers=[keyword_retriever, dense_retriever], weights=[0.4, 0.65])
     
     compression_retriever = ContextualCompressionRetriever(
         base_retriever=ensemble_retriever, base_compressor=ml_models["reranker_compressor"]