Spaces:
Sleeping
Sleeping
Completed transactions answering query
Browse files- app/api/routers/transaction.py +15 -8
- app/api/routers/user.py +3 -0
- app/model/transaction.py +9 -0
- app/service/query_rag.py +142 -0
- app/service/transactions_query_rag.py +0 -116
- tests/utils.py +182 -22
app/api/routers/transaction.py
CHANGED
@@ -6,6 +6,10 @@ from app.model.transaction import Transaction as TransactionModel
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from app.schema.index import TransactionResponse
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from app.engine.postgresdb import get_db_session
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transaction_router = r = APIRouter(prefix="/api/v1/transactions", tags=["transactions"])
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@@ -59,11 +63,14 @@ async def answer_transactions_query(user_id: int, query: str, db: AsyncSession =
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"""
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Retrieve all transactions.
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"""
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from app.schema.index import TransactionResponse
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from app.engine.postgresdb import get_db_session
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from app.service.query_rag import answer_query, fetch_transaction_documents
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import pandas as pd
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transaction_router = r = APIRouter(prefix="/api/v1/transactions", tags=["transactions"])
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"""
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Retrieve all transactions.
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"""
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try:
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result = await TransactionModel.get_by_user(db, user_id)
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all_rows = result.all()
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if len(all_rows) == 0:
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raise HTTPException(status_code=500, detail="No transactions found for this user")
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document_splits = await fetch_transaction_documents(all_rows)
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answer = await answer_query(document_splits, query, user_id)
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return answer
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"answer_transactions_query error: {str(e)}")
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app/api/routers/user.py
CHANGED
@@ -28,14 +28,17 @@ logger = logging.getLogger(__name__)
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async def create_user(user: UserCreate, db: AsyncSession = Depends(get_db_session)):
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try:
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db_user = await UserModel.get(db, email=user.email)
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if db_user and not db_user.is_deleted:
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raise HTTPException(status_code=409, detail="User already exists")
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await UserModel.create(db, **user.model_dump())
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user = await UserModel.get(db, email=user.email)
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fake_transactions = get_fake_transactions(user.id)
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await TransactionModel.bulk_create(db, fake_transactions)
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return user
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async def create_user(user: UserCreate, db: AsyncSession = Depends(get_db_session)):
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try:
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db_user = await UserModel.get(db, email=user.email)
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print(f"db_user: {db_user}")
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if db_user and not db_user.is_deleted:
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raise HTTPException(status_code=409, detail="User already exists")
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await UserModel.create(db, **user.model_dump())
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user = await UserModel.get(db, email=user.email)
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print(f"user: {user}")
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fake_transactions = get_fake_transactions(user.id)
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await TransactionModel.bulk_create(db, fake_transactions)
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print(f"fake_transactions: {fake_transactions}")
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return user
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app/model/transaction.py
CHANGED
@@ -23,6 +23,15 @@ class Transaction(Base, BaseModel):
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def __str__(self) -> str:
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return f"{self.transaction_date}, {self.category}, {self.name_description}, {self.amount}, {self.type}"
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@classmethod
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async def create(cls: "type[Transaction]", db: AsyncSession, **kwargs) -> "Transaction":
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def __str__(self) -> str:
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return f"{self.transaction_date}, {self.category}, {self.name_description}, {self.amount}, {self.type}"
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def to_dict(self):
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return {
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'transaction_date': self.transaction_date,
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'category': self.category,
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'name_description': self.name_description,
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'amount': self.amount,
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'type': self.type
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}
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@classmethod
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async def create(cls: "type[Transaction]", db: AsyncSession, **kwargs) -> "Transaction":
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app/service/query_rag.py
ADDED
@@ -0,0 +1,142 @@
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from pinecone import Pinecone, ServerlessSpec
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from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
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from langchain_openai import OpenAIEmbeddings, ChatOpenAI
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from langchain_pinecone import PineconeVectorStore
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from langchain.vectorstores import Chroma
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from langchain.chains import RetrievalQA
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from langchain.prompts import PromptTemplate
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from langchain_core.documents import Document
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from fastapi import HTTPException
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from typing import List
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from app.model.transaction import Transaction
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import os
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async def fetch_transaction_documents(transactions: List[Transaction]) -> List[Document]:
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try:
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document = ''.join(str(row)+'\n' for row in transactions)
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page_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
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pages = page_splitter.split_text(document)
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=10)
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document_splits = text_splitter.create_documents(pages)
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return document_splits
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except Exception as e:
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raise HTTPException(status_code = 500, detail=f"fetch_transaction_documents error: {str(e)}")
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async def answer_query(document_splits: List[Document], query: str, user_id: int) -> str:
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"""Creates an embedding of the transactions table and then returns the answer for the given query.
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Args:
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df (pd.DataFrame): DataFrame containing the transactions that a user has entered
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query (str): The query the user will ask against said embedding
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Returns:
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str: Response to query
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"""
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try:
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openai_api_key = os.environ['OPENAI_API_KEY']
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embeddings = OpenAIEmbeddings(
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model="text-embedding-3-small",
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openai_api_key=openai_api_key
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)
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print(f"answer_transactions_query: \n document_splits: {type(document_splits)}\n")
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llm = ChatOpenAI(
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openai_api_key=openai_api_key,
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model_name="gpt-3.5-turbo",
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temperature=0.0
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)
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prompt_template = """
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Use the following pieces of context to answer the question at the end.
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If you don't know the answer, please think rationally answer from your own knowledge base
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{context}
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Question: {question}
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"""
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prompt = PromptTemplate(
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template=prompt_template, input_variables=["context", "question"]
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)
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chain_type_kwargs = {"prompt": prompt}
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vectorestore = Chroma.from_documents(documents=document_splits,embedding=embeddings)
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qa = RetrievalQA.from_chain_type(
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llm=llm,
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retriever=vectorestore.as_retriever(),
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chain_type_kwargs=chain_type_kwargs
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)
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print(f"answer_transactions_query: \n qa: {qa}\n")
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answer = qa({"query": query})
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print(f"answer_transactions_query: \n answer: {answer['result']}\n")
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result_split = answer['result'].split("\n")
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result = ''.join(item for item in result_split)
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return result
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except Exception as e:
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raise HTTPException(status_code = 500, detail=f"answer_query error: {str(e)}")
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# Note: Attempted to use Pinecone however it failed to learn from documents so settled for Chroma
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# batch_limit = 20
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# pinecone_api_key = os.environ['PINECONE_API_KEY']
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# namespace = "transactionsvector"
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# index_name = "transactionsrag"
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# pc = Pinecone(api_key=pinecone_api_key)
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# if index_name in pc.list_indexes().names():
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# pc.delete_index(index_name)
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# pc.create_index(
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# name=index_name,
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# dimension=1536,
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# metric="cosine",
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# spec=ServerlessSpec(
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# cloud="aws", region="us-east-1"
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# )
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# )
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# index = pc.Index(index_name)
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# transactions_search = PineconeVectorStore.from_documents(
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# documents=document_splits,
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# index_name=index_name,
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# embedding=embeddings,
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# namespace=namespace
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# )
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# print(f"answer_transactions_query: \n transactions_search: {transactions_search}\n")
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# for ids in index.list(namespace=namespace):
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# query_item = index.query(
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# id=ids[0],
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# namespace=namespace,
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# top_k=1,
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# include_values=True,
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# include_metadata=True
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# )
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# print(f"answer_transactions_query: \n query_item: {query_item}\n")
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# qa = RetrievalQA.from_chain_type(
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# llm=llm,
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# chain_type="stuff",
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# retriever=transactions_search.as_retriever(),
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# chain_type_kwargs=chain_type_kwargs
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# )
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app/service/transactions_query_rag.py
DELETED
@@ -1,116 +0,0 @@
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from pinecone import Pinecone, ServerlessSpec
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_openai import OpenAIEmbeddings
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from langchain_pinecone import PineconeVectorStore
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from langchain_openai import ChatOpenAI
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from langchain.chains import RetrievalQA
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from fastapi import HTTPException
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import os
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import pandas as pd
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from uuid import uuid4
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async def answer_query(df: pd.DataFrame, query: str) -> str:
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"""Creates an embedding of the transactions table and then returns the answer for the given query.
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Args:
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df (pd.DataFrame): DataFrame containing the transactions that a user has entered
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query (str): The query the user will ask against said embedding
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Returns:
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str: Response to query
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"""
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try:
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batch_limit = 100
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pinecone_api_key = os.environ['PINECONE_API_KEY']
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openai_api_key = os.environ['OPENAI_API_KEY']
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namespace = "transactionsvector"
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pc = Pinecone(api_key=pinecone_api_key)
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embeddings = OpenAIEmbeddings(
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model="text-embedding-3-small",
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openai_api_key=openai_api_key
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)
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-
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index_name = "transactions_rag"
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-
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if index_name in pc.list_indexes().names():
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pc.delete_index(index_name)
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-
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pc.create_index(
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name=index_name,
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dimension=1536,
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metric="cosine",
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spec=ServerlessSpec(
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cloud="aws",
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region="us-east-1"
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)
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)
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index = pc.Index(index_name)
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-
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texts = []
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all_texts = []
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metadatas = []
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-
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58 |
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text_splitter = RecursiveCharacterTextSplitter(
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chunk_size=1000,
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chunk_overlap=100
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)
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-
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for _, record in df.iterrows():
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content_texts = text_splitter.split_text(record['content'])
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-
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metadata = {
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'user_id': str(record['user_id'])
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}
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content_metadata = [{
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"chunk": j, "text": text, **metadata
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} for j, text in enumerate(content_texts)]
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-
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texts.extend(content_texts)
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all_texts.extend(content_texts)
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metadatas.extend(content_metadata)
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-
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# If we have reached the batch limit, then add the texts and reset
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78 |
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if len(texts) >= batch_limit:
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ids = [str(uuid4()) for _ in range(len(texts))]
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80 |
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embeds = embeddings.embed_documents(texts)
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index.upsert(vectors=zip(ids, embeds, metadatas))
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82 |
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texts = []
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metadatas = []
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-
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if len(texts) > 0:
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ids = [str(uuid4()) for _ in range(len(texts))]
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embeds = embeddings.embed_documents(texts)
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index.upsert(vectors=zip(ids, embeds, metadatas))
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89 |
-
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-
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transactions_search = PineconeVectorStore.from_documents(
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documents=all_texts,
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index_name=index_name,
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embedding=embeddings,
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namespace=namespace
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)
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98 |
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llm = ChatOpenAI(
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openai_api_key=openai_api_key,
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101 |
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model_name="gpt-3.5-turbo",
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temperature=0.0
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)
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qa = RetrievalQA.from_llm(
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llm=llm,
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retriever=transactions_search.as_retriever()
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)
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answer = qa.invoke(query)
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return answer
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113 |
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114 |
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except Exception as e:
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115 |
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raise HTTPException(status_code = 500, detail=f"fetch_pinecone_service error: {str(e)}")
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116 |
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tests/utils.py
CHANGED
@@ -8,80 +8,240 @@ def get_fake_transactions(user_id: int) -> List[TransactionCreate]:
|
|
8 |
TransactionCreate(
|
9 |
user_id=user_id,
|
10 |
transaction_date=datetime(2022, 1, 1),
|
11 |
-
category="
|
12 |
-
name_description="
|
13 |
-
amount=
|
14 |
type=TransactionType.EXPENSE,
|
15 |
),
|
16 |
TransactionCreate(
|
17 |
user_id=user_id,
|
18 |
transaction_date=datetime(2022, 1, 2),
|
19 |
-
category="
|
20 |
-
name_description="
|
21 |
-
amount=
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|
22 |
type=TransactionType.EXPENSE,
|
23 |
),
|
24 |
TransactionCreate(
|
25 |
user_id=user_id,
|
26 |
transaction_date=datetime(2022, 1, 3),
|
27 |
-
category="
|
28 |
-
name_description="
|
29 |
amount=3.0,
|
30 |
type=TransactionType.INCOME,
|
31 |
),
|
32 |
TransactionCreate(
|
33 |
user_id=user_id,
|
34 |
transaction_date=datetime(2022, 1, 4),
|
35 |
-
category="
|
36 |
-
name_description="
|
37 |
amount=4.0,
|
38 |
type=TransactionType.INCOME,
|
39 |
),
|
40 |
TransactionCreate(
|
41 |
user_id=user_id,
|
42 |
transaction_date=datetime(2022, 1, 5),
|
43 |
-
category="
|
44 |
-
name_description="
|
45 |
amount=5.0,
|
46 |
type=TransactionType.EXPENSE,
|
47 |
),
|
48 |
TransactionCreate(
|
49 |
user_id=user_id,
|
50 |
transaction_date=datetime(2022, 1, 6),
|
51 |
-
category="
|
52 |
-
name_description="
|
53 |
amount=6.0,
|
54 |
type=TransactionType.EXPENSE,
|
55 |
),
|
56 |
TransactionCreate(
|
57 |
user_id=user_id,
|
58 |
transaction_date=datetime(2022, 1, 7),
|
59 |
-
category="
|
60 |
-
name_description="
|
61 |
amount=7.0,
|
62 |
type=TransactionType.INCOME,
|
63 |
),
|
64 |
TransactionCreate(
|
65 |
user_id=user_id,
|
66 |
transaction_date=datetime(2022, 1, 8),
|
67 |
-
category="
|
68 |
-
name_description="
|
69 |
amount=8.0,
|
70 |
type=TransactionType.INCOME,
|
71 |
),
|
72 |
TransactionCreate(
|
73 |
user_id=user_id,
|
74 |
transaction_date=datetime(2022, 1, 9),
|
75 |
-
category="
|
76 |
-
name_description="
|
77 |
amount=9.0,
|
78 |
type=TransactionType.EXPENSE,
|
79 |
),
|
80 |
TransactionCreate(
|
81 |
user_id=user_id,
|
82 |
transaction_date=datetime(2022, 1, 10),
|
83 |
-
category="
|
84 |
-
name_description="
|
85 |
amount=10.0,
|
86 |
type=TransactionType.EXPENSE,
|
87 |
),
|
|
|
8 |
TransactionCreate(
|
9 |
user_id=user_id,
|
10 |
transaction_date=datetime(2022, 1, 1),
|
11 |
+
category="shopping",
|
12 |
+
name_description="Amazon",
|
13 |
+
amount=11.0,
|
14 |
type=TransactionType.EXPENSE,
|
15 |
),
|
16 |
TransactionCreate(
|
17 |
user_id=user_id,
|
18 |
transaction_date=datetime(2022, 1, 2),
|
19 |
+
category="Streaming",
|
20 |
+
name_description="Netflix",
|
21 |
+
amount=4.0,
|
22 |
+
type=TransactionType.EXPENSE,
|
23 |
+
),
|
24 |
+
TransactionCreate(
|
25 |
+
user_id=user_id,
|
26 |
+
transaction_date=datetime(2022, 1, 3),
|
27 |
+
category="Other",
|
28 |
+
name_description="Consulting",
|
29 |
+
amount=3.0,
|
30 |
+
type=TransactionType.INCOME,
|
31 |
+
),
|
32 |
+
TransactionCreate(
|
33 |
+
user_id=user_id,
|
34 |
+
transaction_date=datetime(2022, 1, 4),
|
35 |
+
category="Other",
|
36 |
+
name_description="Selling Paintings",
|
37 |
+
amount=4.0,
|
38 |
+
type=TransactionType.INCOME,
|
39 |
+
),
|
40 |
+
TransactionCreate(
|
41 |
+
user_id=user_id,
|
42 |
+
transaction_date=datetime(2022, 1, 5),
|
43 |
+
category="Gym",
|
44 |
+
name_description="Gym membership",
|
45 |
+
amount=5.0,
|
46 |
+
type=TransactionType.EXPENSE,
|
47 |
+
),
|
48 |
+
TransactionCreate(
|
49 |
+
user_id=user_id,
|
50 |
+
transaction_date=datetime(2022, 1, 6),
|
51 |
+
category="Transportation",
|
52 |
+
name_description="Uber Taxi",
|
53 |
+
amount=6.0,
|
54 |
+
type=TransactionType.EXPENSE,
|
55 |
+
),
|
56 |
+
TransactionCreate(
|
57 |
+
user_id=user_id,
|
58 |
+
transaction_date=datetime(2022, 1, 7),
|
59 |
+
category="Other",
|
60 |
+
name_description="Freelancing",
|
61 |
+
amount=7.0,
|
62 |
+
type=TransactionType.INCOME,
|
63 |
+
),
|
64 |
+
TransactionCreate(
|
65 |
+
user_id=user_id,
|
66 |
+
transaction_date=datetime(2022, 1, 8),
|
67 |
+
category="Income",
|
68 |
+
name_description="Salary",
|
69 |
+
amount=8.0,
|
70 |
+
type=TransactionType.INCOME,
|
71 |
+
),
|
72 |
+
TransactionCreate(
|
73 |
+
user_id=user_id,
|
74 |
+
transaction_date=datetime(2022, 1, 9),
|
75 |
+
category="Shopping",
|
76 |
+
name_description="Amazon Lux",
|
77 |
+
amount=9.0,
|
78 |
+
type=TransactionType.EXPENSE,
|
79 |
+
),
|
80 |
+
TransactionCreate(
|
81 |
+
user_id=user_id,
|
82 |
+
transaction_date=datetime(2022, 1, 10),
|
83 |
+
category="Taxes",
|
84 |
+
name_description="CA Property Tax",
|
85 |
+
amount=10.0,
|
86 |
+
type=TransactionType.EXPENSE,
|
87 |
+
),
|
88 |
+
TransactionCreate(
|
89 |
+
user_id=user_id,
|
90 |
+
transaction_date=datetime(2022, 1, 1),
|
91 |
+
category="shopping",
|
92 |
+
name_description="Amazon",
|
93 |
+
amount=11.0,
|
94 |
+
type=TransactionType.EXPENSE,
|
95 |
+
),
|
96 |
+
TransactionCreate(
|
97 |
+
user_id=user_id,
|
98 |
+
transaction_date=datetime(2022, 1, 2),
|
99 |
+
category="Streaming",
|
100 |
+
name_description="Netflix",
|
101 |
+
amount=4.0,
|
102 |
+
type=TransactionType.EXPENSE,
|
103 |
+
),
|
104 |
+
TransactionCreate(
|
105 |
+
user_id=user_id,
|
106 |
+
transaction_date=datetime(2022, 1, 3),
|
107 |
+
category="Other",
|
108 |
+
name_description="Consulting",
|
109 |
+
amount=3.0,
|
110 |
+
type=TransactionType.INCOME,
|
111 |
+
),
|
112 |
+
TransactionCreate(
|
113 |
+
user_id=user_id,
|
114 |
+
transaction_date=datetime(2022, 1, 4),
|
115 |
+
category="Other",
|
116 |
+
name_description="Selling Paintings",
|
117 |
+
amount=4.0,
|
118 |
+
type=TransactionType.INCOME,
|
119 |
+
),
|
120 |
+
TransactionCreate(
|
121 |
+
user_id=user_id,
|
122 |
+
transaction_date=datetime(2022, 1, 5),
|
123 |
+
category="Gym",
|
124 |
+
name_description="Gym membership",
|
125 |
+
amount=5.0,
|
126 |
+
type=TransactionType.EXPENSE,
|
127 |
+
),
|
128 |
+
TransactionCreate(
|
129 |
+
user_id=user_id,
|
130 |
+
transaction_date=datetime(2022, 1, 6),
|
131 |
+
category="Transportation",
|
132 |
+
name_description="Uber Taxi",
|
133 |
+
amount=6.0,
|
134 |
+
type=TransactionType.EXPENSE,
|
135 |
+
),
|
136 |
+
TransactionCreate(
|
137 |
+
user_id=user_id,
|
138 |
+
transaction_date=datetime(2022, 1, 7),
|
139 |
+
category="Other",
|
140 |
+
name_description="Freelancing",
|
141 |
+
amount=7.0,
|
142 |
+
type=TransactionType.INCOME,
|
143 |
+
),
|
144 |
+
TransactionCreate(
|
145 |
+
user_id=user_id,
|
146 |
+
transaction_date=datetime(2022, 1, 8),
|
147 |
+
category="Income",
|
148 |
+
name_description="Salary",
|
149 |
+
amount=8.0,
|
150 |
+
type=TransactionType.INCOME,
|
151 |
+
),
|
152 |
+
TransactionCreate(
|
153 |
+
user_id=user_id,
|
154 |
+
transaction_date=datetime(2022, 1, 9),
|
155 |
+
category="Shopping",
|
156 |
+
name_description="Amazon Lux",
|
157 |
+
amount=9.0,
|
158 |
+
type=TransactionType.EXPENSE,
|
159 |
+
),
|
160 |
+
TransactionCreate(
|
161 |
+
user_id=user_id,
|
162 |
+
transaction_date=datetime(2022, 1, 10),
|
163 |
+
category="Taxes",
|
164 |
+
name_description="CA Property Tax",
|
165 |
+
amount=10.0,
|
166 |
+
type=TransactionType.EXPENSE,
|
167 |
+
),
|
168 |
+
TransactionCreate(
|
169 |
+
user_id=user_id,
|
170 |
+
transaction_date=datetime(2022, 1, 1),
|
171 |
+
category="shopping",
|
172 |
+
name_description="Amazon",
|
173 |
+
amount=11.0,
|
174 |
+
type=TransactionType.EXPENSE,
|
175 |
+
),
|
176 |
+
TransactionCreate(
|
177 |
+
user_id=user_id,
|
178 |
+
transaction_date=datetime(2022, 1, 2),
|
179 |
+
category="Streaming",
|
180 |
+
name_description="Netflix",
|
181 |
+
amount=4.0,
|
182 |
type=TransactionType.EXPENSE,
|
183 |
),
|
184 |
TransactionCreate(
|
185 |
user_id=user_id,
|
186 |
transaction_date=datetime(2022, 1, 3),
|
187 |
+
category="Other",
|
188 |
+
name_description="Consulting",
|
189 |
amount=3.0,
|
190 |
type=TransactionType.INCOME,
|
191 |
),
|
192 |
TransactionCreate(
|
193 |
user_id=user_id,
|
194 |
transaction_date=datetime(2022, 1, 4),
|
195 |
+
category="Other",
|
196 |
+
name_description="Selling Paintings",
|
197 |
amount=4.0,
|
198 |
type=TransactionType.INCOME,
|
199 |
),
|
200 |
TransactionCreate(
|
201 |
user_id=user_id,
|
202 |
transaction_date=datetime(2022, 1, 5),
|
203 |
+
category="Gym",
|
204 |
+
name_description="Gym membership",
|
205 |
amount=5.0,
|
206 |
type=TransactionType.EXPENSE,
|
207 |
),
|
208 |
TransactionCreate(
|
209 |
user_id=user_id,
|
210 |
transaction_date=datetime(2022, 1, 6),
|
211 |
+
category="Transportation",
|
212 |
+
name_description="Uber Taxi",
|
213 |
amount=6.0,
|
214 |
type=TransactionType.EXPENSE,
|
215 |
),
|
216 |
TransactionCreate(
|
217 |
user_id=user_id,
|
218 |
transaction_date=datetime(2022, 1, 7),
|
219 |
+
category="Other",
|
220 |
+
name_description="Freelancing",
|
221 |
amount=7.0,
|
222 |
type=TransactionType.INCOME,
|
223 |
),
|
224 |
TransactionCreate(
|
225 |
user_id=user_id,
|
226 |
transaction_date=datetime(2022, 1, 8),
|
227 |
+
category="Income",
|
228 |
+
name_description="Salary",
|
229 |
amount=8.0,
|
230 |
type=TransactionType.INCOME,
|
231 |
),
|
232 |
TransactionCreate(
|
233 |
user_id=user_id,
|
234 |
transaction_date=datetime(2022, 1, 9),
|
235 |
+
category="Shopping",
|
236 |
+
name_description="Amazon Lux",
|
237 |
amount=9.0,
|
238 |
type=TransactionType.EXPENSE,
|
239 |
),
|
240 |
TransactionCreate(
|
241 |
user_id=user_id,
|
242 |
transaction_date=datetime(2022, 1, 10),
|
243 |
+
category="Taxes",
|
244 |
+
name_description="CA Property Tax",
|
245 |
amount=10.0,
|
246 |
type=TransactionType.EXPENSE,
|
247 |
),
|