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3a2c9d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | from app.utils.model_loader import ModelLoader
from app.ingestion.file_loader import load_documents_form_url
from app.ingestion.text_splitter import text_splitting
from app.reseasoning.query_parser import parsing_query
from app.reseasoning.descision_maker import evaluate_with_llm
from app.retrieval.retriever import retrieval_from_pinecone_vectoreStore
from app.schemas.request_models import QuerySpec,LogicResult, ClauseHit, HackRxRunRequest
from app.schemas.response_models import APIResponse
from fastapi import FastAPI, Header, HTTPException, Depends
from fastapi.middleware.cors import CORSMiddleware
from typing import Optional
from app.embedding.embeder import get_query_embedding
from app.embedding.vectore_store import create_vectore_store
from app.ingestion.file_loader import load_documents_form_url
from app.ingestion.text_splitter import text_splitting
app = FastAPI(title="RAG app")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
def verify_bearer_token(authorization: Optional[str] = Header(None)):
expected_token = "cc13b8bb7f4bc1570c8a39bda8c9d4c34b2be6b8abe1044c89abf49b28cee3f8"
if authorization is None or not authorization.startswith("Bearer "):
raise HTTPException(status_code=401, detail="Missing or invalid Authorization header")
token = authorization.split("Bearer ")[1]
if token != expected_token:
raise HTTPException(status_code=403, detail="Invalid token")
return True
@app.post("/api/v1/hackrx/run", response_model=APIResponse)
async def run_hackrx(request: HackRxRunRequest , authorized: bool = Depends(verify_bearer_token)):
## model initialisation
# LLM
model_loader = ModelLoader(model_loader= "gemini")
llm = model_loader.load_llm()
# Embedding model
model_loader = ModelLoader(model_provider="openai")
embedding_model = model_loader.load_llm()
print("LLMs are loaded!!")
answers = []
# file loading
document_url = request.documents
pdf_doc = load_documents_form_url(document_url)
print("file has been loaded")
## splitting into chunks
chunks = text_splitting(doc_content=pdf_doc)
print("Chunks have been splitted")
## creating a vectore store
index, namespace = create_vectore_store(text_chunks=chunks, embedding_model=embedding_model)
print("Index is created")
for question in request.questions:
#1. parsing query
parsed_query = parsing_query(query=question, llm = llm)
print("Query Parsed")
#2 emebed the query
query_embedding, expansions = get_query_embedding(query_spec = parsed_query, embedding_model=embedding_model)
print("Query Embedded")
# 3.Retrieve
top_hits = retrieval_from_pinecone_vectoreStore(pinecone_index=index, embeddings = query_embedding , top_k=3,namespace=namespace)
print("Documents retrieved!")
# 4. evaluate with llm
result = evaluate_with_llm(raw_query=question, top_clauses=top_hits, llm = llm)
print("Answer created!")
answers.append(result.answer)
print("Answers are appended!")
print(answers)
# index.delete(delete_all=True,namespace=namespace)
# print("index is deleted!!")
return APIResponse(answers=answers)
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