pat/01-categorization-transactions

.dockerignore

@@ -1,5 +1,3 @@
 __pycache__
 storage
-data/*
-venv
-myvenv
+data/*

.env.example

@@ -43,11 +43,3 @@ APP_HOST=0.0.0.0
 
 # The port to start the backend app.
 APP_PORT=8000
-
-# Postgres database configuration
-POSTGRES_USER=postgres
-POSTGRES_PASSWORD=postgres
-POSTGRES_DB_NAME=postgres
-POSTGRES_DB_HOST=localhost
-POSTGRES_DB_PORT=5432
-SQLALCHEMY_DATABASE_URL=postgresql+asyncpg://${POSTGRES_USER}:${POSTGRES_PASSWORD}@${POSTGRES_DB_HOST}:${POSTGRES_DB_PORT}/${POSTGRES_DB_NAME}

.gitignore

@@ -1,5 +1,4 @@
 __pycache__
 storage
 .env
-venv
-myvenv
+data/*

.vscode/settings.json

@@ -1,11 +0,0 @@
-{
-    "jupyter.notebookFileRoot": "${workspaceFolder}",
-    "[python]": {
-        "editor.defaultFormatter": "ms-python.black-formatter",
-        "editor.formatOnSave": true,
-    },
-    "ms-python.black-formatter.args": [
-        "--line-length",
-        "119"
-    ]
-}

Dockerfile

@@ -25,13 +25,4 @@ COPY . .
 # Make port 8000 available to the world outside this container
 EXPOSE 8000
 
-ENV POSTGRES_USER codepath_project_owner
-ENV POSTGRES_PASSWORD 03EdiworgCJz
-ENV POSTGRES_DB_NAME codepath_project
-ENV POSTGRES_DB_HOST ep-icy-cloud-a5m4mcgo.us-east-2.aws.neon.tech
-ENV POSTGRES_DB_PORT 5432
-
-# Run migrations
-RUN alembic upgrade head
-
 CMD ["python", "main.py"]

Dockerfile.local.postgres

@@ -1,23 +0,0 @@
-# Pull the official PostgreSQL 16 image
-FROM postgres:16
-
-# Create a directory for the database files
-RUN mkdir -p /var/lib/postgresql/data
-
-# Change the ownership of the data directory
-RUN chown -R postgres:postgres /var/lib/postgresql/data
-
-# Copy the configuration files into the data directory
-# NOTE: the [f] suffix is to ensure that the COPY command will not fail if the files don't exist
-COPY ./pg_hba.con[f] /var/lib/postgresql/data/pg_hba.conf
-COPY ./postgresql.con[f] /var/lib/postgresql/data/postgresql.conf
-
-# Include environment variables for the PostgreSQL user and password
-ENV POSTGRES_USER=${POSTGRES_USER}
-ENV POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
-
-# Expose the default PostgreSQL port
-EXPOSE 5432
-
-# Start the PostgreSQL server
-CMD ["postgres", "-c", "config_file=/var/lib/postgresql/data/postgresql.conf"]

Makefile

@@ -1,14 +0,0 @@
-
-include .env
-
-build-postgres:
-	docker build -t codepath_project_postgres -f Dockerfile.local.postgres .
-
-run-postgres:
-	docker run -d -p 5432:5432 -e POSTGRES_PASSWORD=${POSTGRES_PASSWORD} -e POSTGRES_USER=${POSTGRES_USER} --name ${POSTGRES_DB_NAME} codepath_project_postgres
-
-run-migrations:
-	alembic upgrade head
-
-generate-migrations:
-	alembic revision --autogenerate -m "$(migration_title)"

README.md

@@ -69,36 +69,6 @@ The API allows CORS for all origins to simplify development. You can change this
 ENVIRONMENT=prod python main.py
 ```
 
-## Local Postgres database setup
-
-To setup a local postgres database, run:
-
-1. Build the docker image:
-
-```bash
-make build-postgres
-```
-
-2. Start the docker container:
-
-```bash
-make run-postgres
-```
-
-## Running Migrations
-
-To generate new migrations, run:
-
-```bash
-make generate-migrations migration_title="<name_for_migration>"
-```
-
-To locally verify your changes, run:
-
-```bash
-make run-migrations
-```
-
 ## Using Docker
 
 1. Build an image for the FastAPI app:

alembic.ini

@@ -1,116 +0,0 @@
-# A generic, single database configuration.
-
-[alembic]
-# path to migration scripts
-script_location = migration
-
-# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
-# Uncomment the line below if you want the files to be prepended with date and time
-# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
-# for all available tokens
-# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
-
-# sys.path path, will be prepended to sys.path if present.
-# defaults to the current working directory.
-prepend_sys_path = .
-
-# timezone to use when rendering the date within the migration file
-# as well as the filename.
-# If specified, requires the python>=3.9 or backports.zoneinfo library.
-# Any required deps can installed by adding `alembic[tz]` to the pip requirements
-# string value is passed to ZoneInfo()
-# leave blank for localtime
-# timezone =
-
-# max length of characters to apply to the
-# "slug" field
-# truncate_slug_length = 40
-
-# set to 'true' to run the environment during
-# the 'revision' command, regardless of autogenerate
-# revision_environment = false
-
-# set to 'true' to allow .pyc and .pyo files without
-# a source .py file to be detected as revisions in the
-# versions/ directory
-# sourceless = false
-
-# version location specification; This defaults
-# to migration/versions.  When using multiple version
-# directories, initial revisions must be specified with --version-path.
-# The path separator used here should be the separator specified by "version_path_separator" below.
-# version_locations = %(here)s/bar:%(here)s/bat:migration/versions
-
-# version path separator; As mentioned above, this is the character used to split
-# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
-# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
-# Valid values for version_path_separator are:
-#
-# version_path_separator = :
-# version_path_separator = ;
-# version_path_separator = space
-version_path_separator = os  # Use os.pathsep. Default configuration used for new projects.
-
-# set to 'true' to search source files recursively
-# in each "version_locations" directory
-# new in Alembic version 1.10
-# recursive_version_locations = false
-
-# the output encoding used when revision files
-# are written from script.py.mako
-# output_encoding = utf-8
-
-sqlalchemy.url = driver://user:pass@localhost/dbname
-
-
-[post_write_hooks]
-# post_write_hooks defines scripts or Python functions that are run
-# on newly generated revision scripts.  See the documentation for further
-# detail and examples
-
-# format using "black" - use the console_scripts runner, against the "black" entrypoint
-# hooks = black
-# black.type = console_scripts
-# black.entrypoint = black
-# black.options = -l 79 REVISION_SCRIPT_FILENAME
-
-# lint with attempts to fix using "ruff" - use the exec runner, execute a binary
-# hooks = ruff
-# ruff.type = exec
-# ruff.executable = %(here)s/.venv/bin/ruff
-# ruff.options = --fix REVISION_SCRIPT_FILENAME
-
-# Logging configuration
-[loggers]
-keys = root,sqlalchemy,alembic
-
-[handlers]
-keys = console
-
-[formatters]
-keys = generic
-
-[logger_root]
-level = WARN
-handlers = console
-qualname =
-
-[logger_sqlalchemy]
-level = WARN
-handlers =
-qualname = sqlalchemy.engine
-
-[logger_alembic]
-level = INFO
-handlers =
-qualname = alembic
-
-[handler_console]
-class = StreamHandler
-args = (sys.stderr,)
-level = NOTSET
-formatter = generic
-
-[formatter_generic]
-format = %(levelname)-5.5s [%(name)s] %(message)s
-datefmt = %H:%M:%S

app/api/routers/chat.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel
 from typing import List, Any, Optional, Dict, Tuple
 from fastapi import APIRouter, Depends, HTTPException, Request, status
 from llama_index.core.chat_engine.types import BaseChatEngine
@@ -19,8 +19,9 @@ class _Message(BaseModel):
 
 class _ChatData(BaseModel):
     messages: List[_Message]
-    model_config = ConfigDict(
-        json_schema_extra={
+
+    class Config:
+        json_schema_extra = {
             "example": {
                 "messages": [
                     {
@@ -30,7 +31,6 @@ class _ChatData(BaseModel):
                 ]
             }
         }
-    )
 
 
 class _SourceNodes(BaseModel):
@@ -121,7 +121,12 @@ async def chat(
         yield VercelStreamResponse.convert_data(
             {
                 "type": "sources",
-                "data": {"nodes": [_SourceNodes.from_source_node(node).dict() for node in response.source_nodes]},
+                "data": {
+                    "nodes": [
+                        _SourceNodes.from_source_node(node).dict()
+                        for node in response.source_nodes
+                    ]
+                },
             }
         )
 

app/api/routers/file_upload.py

@@ -1,43 +0,0 @@
-from typing import Annotated
-from fastapi import APIRouter, UploadFile, Depends
-from app.categorization.file_processing import process_file, save_results
-from app.schema.index import FileUploadCreate
-import asyncio
-import os
-import csv
-
-from app.engine.postgresdb import get_db_session
-from sqlalchemy.ext.asyncio import AsyncSession
-
-file_upload_router = r = APIRouter(prefix="/api/v1/file_upload", tags=["file_upload"])
-
-@r.post(
-    "/",
-    responses={
-        200: {"description": "File successfully uploaded"},
-        400: {"description": "Bad request"},
-        500: {"description": "Internal server error"},
-    },
-)
-async def create_file(input_file: UploadFile, db: AsyncSession = Depends(get_db_session)):
-    try:
-         # Create directory to store all uploaded .csv files
-        file_upload_directory_path = "data/tx_data/input"
-        if not os.path.exists(file_upload_directory_path):
-            os.makedirs(file_upload_directory_path)
-
-        # Write items of .csv filte to directory
-        with open(os.path.join(file_upload_directory_path, input_file.filename)) as output_file:
-            [output_file.write(" ".join(row)+'\n') for row in csv.reader(input_file)]
-        output_file.close()
-
-        # With the newly created file and it's path, process and save it for embedding
-        processed_file = process_file(os.path.realpath(input_file.filename))
-        result = await asyncio.gather(processed_file)
-        await save_results(db, result)
-
-    except Exception:
-        return {"message": "There was an error uploading this file. Ensure you have a .csv file with the following columns:"
-        "\n transaction_date, type, category, name_description, amount"}
-
-    return {"message": f"Successfully uploaded {input_file.filename}"}

app/api/routers/income_statement.py

@@ -1,64 +0,0 @@
-from typing import List
-from fastapi import APIRouter, Depends, HTTPException, status
-from sqlalchemy.ext.asyncio import AsyncSession
-from app.model.transaction import Transaction as TransactionModel
-from app.model.income_statement import IncomeStatement as IncomeStatementModel
-from app.schema.index import IncomeStatementCreateRequest, IncomeStatementResponse
-from app.engine.postgresdb import get_db_session
-from app.service.income_statement import call_llm_to_create_income_statement
-
-income_statement_router = r = APIRouter(prefix="/api/v1/income_statement", tags=["income_statement"])
-
-
-@r.post(
-    "/",
-    responses={
-        200: {"description": "New transaction created"},
-        400: {"description": "Bad request"},
-        500: {"description": "Internal server error"},
-    },
-)
-async def create_income_statement(payload: IncomeStatementCreateRequest, db: AsyncSession = Depends(get_db_session)) -> None:
-    try:
-        await call_llm_to_create_income_statement(payload, db)
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@r.get(
-    "/user/{user_id}",
-    response_model=List[IncomeStatementResponse],
-    responses={
-        200: {"description": "New user created"},
-        400: {"description": "Bad request"},
-        204: {"description": "No content"},
-        500: {"description": "Internal server error"},
-    },
-)
-async def get_income_statements(
-    user_id: int, db: AsyncSession = Depends(get_db_session)
-) -> List[IncomeStatementResponse]:
-    """
-    Retrieve all income statements.
-    """
-    result = await IncomeStatementModel.get_by_user(db, user_id)
-    if len(result) == 0:
-        raise HTTPException(status_code=status.HTTP_204_NO_CONTENT, detail="No income statements found for this user")
-    return result
-
-
-@r.get(
-    "/report/{report_id}",
-    response_model=IncomeStatementResponse,
-    responses={
-        200: {"description": "Income statement found"},
-        404: {"description": "Income statement not found"},
-        500: {"description": "Internal server error"},
-    },
-)
-async def get_income_statement(report_id: int, db: AsyncSession = Depends(get_db_session)) -> IncomeStatementResponse:
-    income_statement = await IncomeStatementModel.get(db, id=report_id)
-    if not income_statement:
-        raise HTTPException(status_code=404, detail="Income statement not found")
-    return income_statement

app/api/routers/transaction.py

@@ -1,30 +0,0 @@
-import os
-from typing import List
-from fastapi import APIRouter, Depends, HTTPException, status
-from sqlalchemy.ext.asyncio import AsyncSession
-from app.model.transaction import Transaction as TransactionModel
-from app.schema.index import TransactionResponse
-from app.engine.postgresdb import get_db_session
-
-transaction_router = r = APIRouter(prefix="/api/v1/transactions", tags=["transactions"])
-
-
-@r.get(
-    "/{user_id}",
-    response_model=List[TransactionResponse],
-    responses={
-        200: {"description": "New user created"},
-        400: {"description": "Bad request"},
-        204: {"description": "No content"},
-        500: {"description": "Internal server error"},
-    },
-)
-async def get_transactions(user_id: int, db: AsyncSession = Depends(get_db_session)):
-    """
-    Retrieve all transactions.
-    """
-    result = await TransactionModel.get_by_user(db, user_id)
-    all_rows = result.all()
-    if len(all_rows) == 0:
-        raise HTTPException(status_code=status.HTTP_204_NO_CONTENT, detail="No transactions found for this user")
-    return all_rows

app/api/routers/user.py

@@ -1,89 +0,0 @@
-import logging
-from fastapi import APIRouter, Depends, HTTPException
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.engine.postgresdb import get_db_session
-from app.schema.index import UserCreate, User as UserSchema, UserResponse, UserUpdate
-from app.model.user import User as UserModel
-
-
-user_router = r = APIRouter(prefix="/api/v1/users", tags=["users"])
-logger = logging.getLogger(__name__)
-
-
-@r.post(
-    "/",
-    response_model=UserResponse,
-    responses={
-        200: {"description": "New user created"},
-        400: {"description": "Bad request"},
-        409: {"description": "Conflict"},
-        500: {"description": "Internal server error"},
-    },
-)
-async def create_user(user: UserCreate, db: AsyncSession = Depends(get_db_session)):
-    try:
-        db_user = await UserModel.get(db, email=user.email)
-        if db_user and not db_user.is_deleted:
-            raise HTTPException(status_code=409, detail="User already exists")
-
-        await UserModel.create(db, **user.model_dump())
-        user = await UserModel.get(db, email=user.email)
-        return user
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@r.get(
-    "/{email}",
-    response_model=UserResponse,
-    responses={
-        200: {"description": "User found"},
-        404: {"description": "User not found"},
-        500: {"description": "Internal server error"},
-    },
-)
-async def get_user(email: str, db: AsyncSession = Depends(get_db_session)):
-    user = await UserModel.get(db, email=email)
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-    return user
-
-
-@r.put(
-    "/{email}",
-    response_model=UserResponse,
-    responses={
-        200: {"description": "User updated"},
-        404: {"description": "User not found"},
-        500: {"description": "Internal server error"},
-    },
-)
-async def update_user(email: str, user_payload: UserUpdate, db: AsyncSession = Depends(get_db_session)):
-    try:
-        user = await UserModel.get(db, email=email)
-        if not user:
-            raise HTTPException(status_code=404, detail="User not found")
-        await UserModel.update(db, id=user.id, **user_payload.model_dump())
-        user = await UserModel.get(db, email=email)
-        return user
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@r.delete(
-    "/{email}",
-    response_model=UserResponse,
-    responses={
-        200: {"description": "User deleted"},
-        404: {"description": "User not found"},
-        500: {"description": "Internal server error"},
-    },
-)
-async def delete_user(email: str, db: AsyncSession = Depends(get_db_session)):
-    user = await UserModel.get(db, email=email)
-    if not user:
-        raise HTTPException(status_code=404, detail="User not found")
-    await UserModel.delete(db, email=email)
-    user = await UserModel.get(db, email=email)
-    return user

app/categorization/categorizer.py

@@ -1,147 +0,0 @@
-# Standard library imports
-import re
-import ast
-import json
-import logging
-import os
-from typing import Any, List, Tuple, Optional, Dict, Union
-
-# Third-party library imports
-import numpy as np
-import pandas as pd
-import asyncio
-from rapidfuzz import process
-from tenacity import retry, wait_random_exponential, stop_after_attempt
-from pydantic import ValidationError
-
-# Local application/library specific imports
-from langchain_openai import ChatOpenAI
-from langchain.chains import LLMChain
-from langchain.output_parsers import PydanticOutputParser, OutputFixingParser
-from langchain.prompts import PromptTemplate
-import app.categorization.template as CATEGORY_TEMPLATE
-from app.categorization.config import CATEGORY_REFERENCE_OUTPUT_FILE, TX_PER_LLM_RUN
-
-
-def fuzzy_match_list_categorizer(
-    description: str,
-    descriptions: np.ndarray,
-    description_category_pairs: pd.DataFrame,
-    threshold: int = 75,
-) -> Optional[str]:
-    """Find the most similar transaction description and return its category.
-
-    This function uses fuzzy string matching to compare the input description
-    against a list of known descriptions. If a sufficient match is found,
-    the function returns the category associated with the matched description.
-
-    Args:
-        description (str): The transaction description to categorize.
-        descriptions (np.ndarray): Known descriptions to compare against.
-        description_category_pairs (pd.DataFrame): DataFrame mapping descriptions to categories.
-        threshold (int): Minimum similarity score to consider a match.
-
-    Returns:
-        str or None: Category of the matched description, or None if no match found.
-    """
-
-    # Fuzzy-match this description against the reference descriptions
-    match_results = process.extractOne(
-        description, descriptions, score_cutoff=threshold)
-
-    # If a match is found, return the category of the matched description
-    if match_results:
-        return description_category_pairs.at[match_results[2], 'category']
-
-    return None
-
-
-async def llm_list_categorizer(tx_list: pd.DataFrame) -> pd.DataFrame:
-    """Categorize a list of transactions using a language model.
-
-    This function uses a Language Model (LLM) to categorize a list of transaction descriptions.
-    It splits the input DataFrame into chunks and processes each chunk asynchronously to improve performance.
-
-    Args:
-        tx_list (pd.DataFrame): DataFrame containing the transaction descriptions to categorize.
-
-    Returns:
-        pd.DataFrame: DataFrame mapping transaction descriptions to their inferred categories.
-    """
-
-    # Initialize language model and prompt
-    openai_api_key = os.environ['OPENAI_API_KEY']
-    llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0125",
-                     api_key=openai_api_key)
-    prompt = PromptTemplate.from_template(template=CATEGORY_TEMPLATE)
-    chain = LLMChain(llm=llm, prompt=prompt)
-
-    # Iterate over the DataFrame in batches of TX_PER_LLM_RUN transactions
-    tasks = [llm_sublist_categorizer(tx_list.attrs['file_name'], chain=chain, tx_descriptions="\n".join(chunk['name/description']).strip())
-             for chunk in np.array_split(tx_list, tx_list.shape[0] // TX_PER_LLM_RUN + 1)]
-
-    # Gather results and extract (valid) outputs
-    # The results variable is a list of 'results', each 'result' being the output of a single LLM run
-    results = await asyncio.gather(*tasks)
-
-    # Extract valid results (each valid result is a list of description-category pairs)
-    valid_results = [result['output'] for result in results if result['valid']]
-
-    # Flatten the list of valid results to obtain a single list of description-category pairs
-    valid_outputs = [
-        output for valid_result in valid_results for output in valid_result]
-
-    # Return a DataFrame with the valid outputs
-    return pd.DataFrame(valid_outputs, columns=['name/description', 'category'])
-
-
-@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
-async def llm_sublist_categorizer(
-    file_name: str,
-    chain: LLMChain,
-    tx_descriptions: str,
-) -> Dict[str, Union[bool, List[Tuple[str, str]]]]:
-    """Categorize a batch of transactions using a language model.
-
-    This function takes a batch of transaction descriptions and passes them to a language model
-    for categorization. The function retries on failure, with an exponential backoff.
-
-    Args:
-        file_name (str): Name of the file the transaction descriptions were extracted from.
-        chain (LLMChain): Language model chain to use for categorization.
-        tx_descriptions (str): Concatenated transaction descriptions to categorize.
-
-    Returns:
-        dict: Dictionary containing a 'valid' flag and a list of categorized descriptions.
-    """
-
-    raw_result = await chain.arun(input_data=tx_descriptions)
-
-    logger = logging.getLogger(__name__)
-    result = {'valid': True, 'output': []}
-    try:
-        # Create a pattern to match a list Description-Category pairs (List[Tuple[str, str]])
-        pattern = r"\['([^']+)', '([^']+)'\]"
-
-        # Use it to extract all the correctly formatted pairs from the raw result
-        matches = re.findall(pattern, raw_result.replace("\\'", "'"))
-
-        # Loop over the matches, and try to parse them to ensure the content is valid
-        valid_outputs = []
-        for match in matches:
-            try:
-                parsed_pair = ast.literal_eval(str(list(match)))
-                valid_outputs.append(parsed_pair)
-            except Exception as e:
-                logger.log(logging.ERROR,
-                           f"Parsing Error: {e}\nMatch: {match}\n")
-                result['valid'] = False
-
-        result['output'] = valid_outputs
-
-    except Exception as e:
-        logging.log(
-            logging.ERROR, f"| File: {file_name} | Unexpected Error: {e}\nRaw Result: {raw_result}")
-        result['valid'] = False
-
-    return result

app/categorization/categorizer_list.py

@@ -1,70 +0,0 @@
-# Standard library imports
-import os
-from datetime import datetime
-
-# Third-party library imports
-import pandas as pd
-
-# Local application/library specific imports
-from app.categorization.config import CATEGORY_REFERENCE_OUTPUT_FILE
-from app.categorization.categorizer import llm_list_categorizer, fuzzy_match_list_categorizer
-
-
-async def categorize_list(tx_list: pd.DataFrame) -> pd.DataFrame:
-    """Asynchronously categorize a list of transactions.
-
-    This function categorizes a list of transactions using a combination of fuzzy matching
-    and a language model. It looks up new transaction descriptions in the reference file
-    (a combination of user input and previous executions) to minimize API calls.
-    Any uncategorized transactions are sent to the language model, and new description-category
-    pairs are added to the reference file.
-
-    Args:
-        tx_list (pd.DataFrame): The list of transactions to categorize.
-
-    Returns:
-        pd.DataFrame: The original DataFrame with an additional column for the category.
-    """
-
-    if os.path.exists(CATEGORY_REFERENCE_OUTPUT_FILE):
-        # Read description-category pairs from the reference file
-        description_category_pairs = pd.read_csv(
-            CATEGORY_REFERENCE_OUTPUT_FILE, header=None, names=['name/description', 'category']
-        )
-
-        # Extract only descriptions for faster matching
-        descriptions = description_category_pairs['name/description'].values
-
-        # Use fuzzy matching to find similar descriptions and assign the category
-        tx_list['category'] = tx_list['name/description'].apply(
-            fuzzy_match_list_categorizer,
-            args=(descriptions, description_category_pairs),
-        )
-
-    # Filter out uncategorized transactions, deduplicate, and sort by description
-    uncategorized_descriptions = (
-        tx_list[tx_list['category'].isnull()]
-        .drop_duplicates(subset=['name/description'])
-        .sort_values(by=['name/description'])
-    )
-
-    # Ask the language model to categorize the remaining descriptions
-    if not uncategorized_descriptions.empty:
-        categorized_descriptions = await llm_list_categorizer(
-            uncategorized_descriptions[['name/description', 'category']]
-        )
-
-        categorized_descriptions.dropna(inplace=True)
-
-        # Update the category for uncategorized transactions based on the language model results
-        if not categorized_descriptions.empty:
-            tx_list['category'] = tx_list['category'].fillna(
-                tx_list['name/description'].map(
-                    categorized_descriptions.set_index('name/description')['category']
-                )
-            )
-        
-        # Fill remaining NaN values in 'category' with 'Other'
-        tx_list['category'] = tx_list['category'].fillna('Other')
-
-    return tx_list

app/categorization/config.py

@@ -1,6 +0,0 @@
-# Data Folders
-RESULT_OUTPUT_FILE = 'data/tx_data/output/result_master_data.csv'
-CATEGORY_REFERENCE_OUTPUT_FILE = 'data/ref_data/category_ref_master_data.csv'
-
-# LLM CONFIG
-TX_PER_LLM_RUN = 10

app/categorization/file_processing.py

@@ -1,128 +0,0 @@
-import os
-import glob
-import shutil
-import logging
-import warnings
-from typing import Optional, Union, Dict, List
-from datetime import datetime
-
-import pandas as pd
-from dateparser import parse
-
-from app.categorization.categorizer_list import categorize_list
-from app.categorization.config import RESULT_OUTPUT_FILE, CATEGORY_REFERENCE_OUTPUT_FILE
-from app.model.transaction import Transaction
-from app.schema.index import TransactionCreate
-
-from sqlalchemy.ext.asyncio import AsyncSession
-
-
-# Read file and process it (e.g. categorize transactions)
-async def process_file(file_path: str) -> Dict[str, Union[str, pd.DataFrame]]:
-    """
-    Process the input file by reading, cleaning, standardizing, and categorizing the transactions.
-
-    Args:
-        file_path (str): Path to the input file.
-
-    Returns:
-        Dict[str, Union[str, pd.DataFrame]]: Dictionary containing the file name, processed output, and error information if any
-    """
-
-    file_name = os.path.basename(file_path)
-    result = {"file_name": file_name, "output": pd.DataFrame(), "error": ""}
-    try:
-        # Read file into standardized tx format: source, date, type, category, description, amount
-        tx_list = standardize_csv_file(file_path)
-
-        # Categorize transactions
-        result["output"] = await categorize_list(tx_list)
-        print(f"File processed sucessfully: {file_name}")
-
-    except Exception as e:
-        # Return an error indicator and exception info
-        logging.log(logging.ERROR, f"| File: {file_name} | Unexpected Error: {e}")
-        print(f"ERROR processing file {file_name}: {e}")
-        result["error"] = str(e)
-
-    return result
-
-
-def standardize_csv_file(file_path: str) -> pd.DataFrame:
-    """
-    Read and prepare the data from the input file.
-
-    Args:
-        file_path (str): Path to the input file.
-
-    Returns:
-        pd.DataFrame: Prepared transaction data.
-    """
-
-    tx_list = pd.read_csv(file_path, index_col=False)
-    tx_list.attrs["file_name"] = file_path
-    tx_list.columns = tx_list.columns.str.lower().str.strip()
-
-    # Standardize dates to YYYY/MM/DD format
-    tx_list["date"] = pd.to_datetime(tx_list["date"]).dt.strftime("%Y/%m/%d")
-
-    # Add source and reindex to desired tx format; category column is new and therefore empty
-    tx_list.loc[:, "source"] = os.path.basename(file_path)
-    tx_list = tx_list.reindex(columns=["date", "expense/income", "category", "name/description", "amount"])
-
-    return tx_list
-
-
-async def save_results(db: AsyncSession, results: List) -> None:
-    """
-    Merge all interim results in the input folder and write the merged results to the output file.
-
-    Args:
-        in_folder (str): Path to the input folder containing interim results.
-        out_file (str): Path to the output file.
-
-    Returns:
-        None
-    """
-
-    # Concatenate all (valid) results into a single DataFrame
-    # Print errors to console
-    ok_files = []
-    ko_files = []
-    error_messages = []
-
-    col_list = ["transaction_date", "type", "category", "name_description", "amount"]
-    tx_list = pd.DataFrame(columns=col_list)
-    for result in results:
-        if not result["error"]:
-            ok_files.append(result["file_name"])
-            result_df = result["output"]
-            result_df.columns = col_list
-            tx_list = pd.concat([tx_list, result_df], ignore_index=True)
-        else:
-            ko_files.append(result["file_name"])
-            error_messages.append(f"{result['file_name']}: {result['error']}")
-
-    # Save to database
-    # FIXME: get user_id from session
-    txn_list_to_save = [TransactionCreate(**row.to_dict(), user_id=1) for _, row in tx_list.iterrows()]
-    await Transaction.bulk_create(db, txn_list_to_save)
-
-    new_ref_data = tx_list[["name/description", "category"]]
-    if os.path.exists(CATEGORY_REFERENCE_OUTPUT_FILE):
-        # If it exists, add master file to interim results
-        old_ref_data = pd.read_csv(CATEGORY_REFERENCE_OUTPUT_FILE, names=["name/description", "category"], header=0)
-        new_ref_data = pd.concat([old_ref_data, new_ref_data], ignore_index=True)
-
-    # Drop duplicates, sort, and write to create new Master File
-    new_ref_data.drop_duplicates(subset=["name/description"]).sort_values(by=["name/description"]).to_csv(
-        CATEGORY_REFERENCE_OUTPUT_FILE, mode="w", index=False, header=True
-    )
-
-    # Summarize results
-    print(f"\nProcessed {len(results)} files: {len(ok_files)} successful, {len(ko_files)} with errors\n")
-    if len(ko_files):
-        print(f"Errors in the following files:")
-        for message in error_messages:
-            print(f"  {message}")
-        print("\n")

app/categorization/template.py

@@ -1,56 +0,0 @@
-CATEGORY_TEMPLATE = """
-    <context>
-    Your task is to create a list of [description, category] lists, where in each list item:
-    - the description is exactly the same you receive as input
-    - the category of the transaction (choose one from the list below based on the description)
-    </context>
-    
-    
-    <categories>
-    The following list contains the categories and associated keywords you often see in transaction descriptions:
-    -ATM: atm, cash, withdraw
-    -Auto: auto body
-    -Bars: bar, pubs, irish, brewery
-    -Beauty: body
-    -Cashback: cashback, reward, bonus, cash back
-    -Clothing: clothing, shoes, accessories
-    -Coffee Shops: coffee, cafe, tea, Starbucks, Dunkin
-    -Credit Card Payment: card payment, autopay
-    -Education: kindle, tuition
-    -Entertainment: event, show, movies, cinema, theater
-    -Fees: Fee
-    -Food: snack, Donalds, Burger King, KFC, Subway, Pizza, Domino, Taco Bell, Wendy, Chick-fil-A, Popeyes, Arby's, Chipotle
-    -Fuel: fuel, gas, petrol
-    -Gifts: donation, gift
-    -Groceries: groceries, supermarket, food, familia
-    -Gym: gym, fitness, yoga, pilates, crossfit
-    -Home: Ikea
-    -Housing: rent, mortgage
-    -Income: refund, deposit, paycheck
-    -Insurance: insurance
-    -Medical: medical, doctor, dentist, hospital, clinic
-    -Pets: vet, veterinary, pet, dog, cat
-    -Pharmacy: pharmacy, drugstore, cvs, walgreens, rite aid, duane
-    -Restaurants: restaurant, lunch, dinner
-    -Services: service, laundry, dry cleaning
-    -Shopping: shopping, amazon, walmart, target, safeway
-    -Streaming: Netflix, Spotify, Hulu, HBO
-    -Taxes: tax, irs
-    -Technology: technology, software, hardware, electronics
-    -Transportation: bus, train, subway, metro, airline, uber, lyft, taxi
-    -Travel: travel, holiday, trip, airbnb, kiwi, hotel, hostel, resort, kiwi, kayak, expedia, booking.com
-    -Transfer: payment from
-    -Utilities: electricity, water, gas, ting, verizon, comcast, sprint, t-mobile, at&t, mint
-    -Other: use this when very uncertain about the category
-    </categories>
-
-    <formatting_instructions>
-    Your output should be a valid list of lists (e.g. [[description1, category1], [description2, category2], ...]] parse-able by the command ast.literal_eval(output)
-    Don't include any kind of commentary, return carriages, spaces or other characters in the output
-    Fill all categories; if you don't know which one to choose, choose 'Other'
-    </formatting_instructions>
-    
-    
-    <financial_transactions>
-    {input_data}
-    </financial_transactions>"""

app/engine/postgresdb.py

@@ -1,81 +0,0 @@
-import logging
-import contextlib
-from typing import Any, AsyncIterator
-
-from sqlalchemy.ext.asyncio import (
-    AsyncConnection,
-    AsyncSession,
-    AsyncEngine,
-    async_sessionmaker,
-    create_async_engine,
-)
-from sqlalchemy.orm import declarative_base
-
-from config.index import config as env
-
-logger = logging.getLogger(__name__)
-
-Base = declarative_base()
-
-
-class PostgresDatabase:
-
-    def __init__(self):
-        self._engine: AsyncEngine | None = None
-        self._sessionmaker: async_sessionmaker | None = None
-
-    def init(self, host: str, engine_kwargs: dict[str, Any] = {}):
-        self._engine = create_async_engine(host, **engine_kwargs)
-        self._sessionmaker = async_sessionmaker(autocommit=False, bind=self._engine)
-
-    async def close(self):
-        if self._engine is None:
-            raise Exception("DatabaseSessionManager is not initialized")
-        await self._engine.dispose()
-
-        self._engine = None
-        self._sessionmaker = None
-
-    @contextlib.asynccontextmanager
-    async def connect(self) -> AsyncIterator[AsyncConnection]:
-        if self._engine is None:
-            raise Exception("DatabaseSessionManager is not initialized")
-
-        async with self._engine.begin() as connection:
-            try:
-                yield connection
-            except Exception:
-                await connection.rollback()
-                raise
-
-    @contextlib.asynccontextmanager
-    async def session(self) -> AsyncIterator[AsyncSession]:
-        if self._sessionmaker is None:
-            raise Exception("DatabaseSessionManager is not initialized")
-
-        session = self._sessionmaker()
-        try:
-            yield session
-        except Exception:
-            await session.rollback()
-            raise
-        finally:
-            await session.close()
-
-    def get_engine(self):
-        return self._engine
-
-    # Used for testing
-    async def create_all(self, connection: AsyncConnection):
-        await connection.run_sync(Base.metadata.create_all)
-
-    async def drop_all(self, connection: AsyncConnection):
-        await connection.run_sync(Base.metadata.drop_all)
-
-
-postgresdb = PostgresDatabase()
-
-
-async def get_db_session():
-    async with postgresdb.session() as session:
-        yield session

app/model/base.py

@@ -1,15 +0,0 @@
-from datetime import datetime, timezone
-from sqlalchemy import DateTime
-from sqlalchemy.orm import Mapped, mapped_column
-
-from app.engine.postgresdb import Base
-
-
-class BaseModel:
-    id: Mapped[int] = mapped_column(primary_key=True, index=True, autoincrement=True)
-    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=datetime.now(timezone.utc))
-    updated_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True),
-        default=datetime.now(timezone.utc),
-        onupdate=datetime.now(timezone.utc),
-    )

app/model/income_statement.py

@@ -1,46 +0,0 @@
-from datetime import datetime
-from typing import Any, Dict, List
-from sqlalchemy import ForeignKey
-from sqlalchemy.sql import expression as sql
-from sqlalchemy.orm import relationship, Mapped, mapped_column
-from sqlalchemy.ext.asyncio import AsyncSession
-from sqlalchemy.dialects.postgresql import JSON
-
-from app.model.base import BaseModel
-from app.engine.postgresdb import Base
-from app.schema.index import IncomeStatement as IncomeStatementSchema
-
-
-class IncomeStatement(Base, BaseModel):
-    __tablename__ = "income_statement"
-
-    date_from: Mapped[datetime]
-    date_to: Mapped[datetime]
-    income: Mapped[Dict] = mapped_column(JSON, nullable=True)
-    expenses: Mapped[Dict] = mapped_column(JSON, nullable=True)
-
-    user_id: Mapped[int] = mapped_column(ForeignKey("users.id"))
-    user = relationship("User", back_populates="income_statements")
-
-    def __str__(self) -> str:
-        return f"IncomeStatement(id={self.id}, user_id={self.user_id}, date_from={self.date_from}, date_to={self.date_to}, income={self.income}, expenses={self.expenses})"
-
-    @classmethod
-    async def create(cls: "type[IncomeStatement]", db: AsyncSession, **kwargs: IncomeStatementSchema) -> "IncomeStatement":
-        income_statement = cls(**kwargs)
-        db.add(income_statement)
-        await db.commit()
-        await db.refresh(income_statement)
-        return income_statement
-
-    @classmethod
-    async def get_by_user(cls: "type[IncomeStatement]", db: AsyncSession, user_id: int) -> "List[IncomeStatement]":
-        query = sql.select(cls).where(cls.user_id == user_id)
-        income_statements = await db.scalars(query)
-        return income_statements.all()
-
-    @classmethod
-    async def get(cls: "type[IncomeStatement]", db: AsyncSession, id: int) -> "IncomeStatement":
-        query = sql.select(cls).where(cls.id == id)
-        income_statement = await db.scalar(query)
-        return income_statement

app/model/transaction.py

@@ -1,62 +0,0 @@
-from datetime import datetime
-from typing import List
-from sqlalchemy import ForeignKey
-from sqlalchemy.orm import relationship, Mapped, mapped_column
-from sqlalchemy.sql import expression as sql
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.model.base import BaseModel
-from app.engine.postgresdb import Base
-from app.schema.index import TransactionCreate
-
-
-class Transaction(Base, BaseModel):
-    __tablename__ = "transactions"
-    transaction_date: Mapped[datetime]
-    category: Mapped[str]
-    name_description: Mapped[str]
-    amount: Mapped[float]
-    type: Mapped[str]
-
-    user_id = mapped_column(ForeignKey("users.id"))
-    user = relationship("User", back_populates="transactions")
-
-    def __str__(self) -> str:
-        return f"{self.transaction_date}, {self.category}, {self.name_description}, {self.amount}, {self.type}"
-
-    @classmethod
-    async def create(cls: "type[Transaction]", db: AsyncSession, **kwargs) -> "Transaction":
-        query = sql.insert(cls).values(**kwargs)
-        transactions = await db.execute(query)
-        transaction = transactions.first()
-        await db.commit()
-        return transaction
-
-    @classmethod
-    async def bulk_create(cls: "type[Transaction]", db: AsyncSession, transactions: List[TransactionCreate]) -> None:
-        values = [transaction.model_dump() for transaction in transactions]
-        query = sql.insert(cls).values(values)
-        await db.execute(query)
-        await db.commit()
-
-    @classmethod
-    async def update(cls: "type[Transaction]", db: AsyncSession, id: int, **kwargs) -> "Transaction":
-        query = sql.update(cls).where(cls.id == id).values(**kwargs).execution_options(synchronize_session="fetch")
-        transactions = await db.scalars(query)
-        transaction = transactions.first()
-        await db.commit()
-        return transaction
-
-    @classmethod
-    async def get_by_user(cls: "type[Transaction]", db: AsyncSession, user_id: int) -> "List[Transaction]":
-        query = sql.select(cls).where(cls.user_id == user_id)
-        transactions = await db.scalars(query)
-        return transactions
-
-    @classmethod
-    async def get_by_user_between_dates(
-        cls: "type[Transaction]", db: AsyncSession, user_id: int, start_date: datetime, end_date: datetime
-    ) -> "List[Transaction]":
-        query = sql.select(cls).where(cls.user_id == user_id).where(cls.transaction_date.between(start_date, end_date))
-        transactions = await db.scalars(query)
-        return transactions.all()

app/model/user.py

@@ -1,110 +0,0 @@
-import logging
-from sqlalchemy.orm import relationship, Mapped, mapped_column
-from sqlalchemy.sql import expression as sql
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.model.base import BaseModel
-from app.engine.postgresdb import Base
-from app.schema.index import UserCreate, UserUpdate
-
-logger = logging.getLogger(__name__)
-
-
-class User(Base, BaseModel):
-    __tablename__ = "users"
-
-    name: Mapped[str]
-    email: Mapped[str]
-    hashed_password: Mapped[str]
-    is_deleted: Mapped[bool] = mapped_column(default=False)
-
-    transactions = relationship("Transaction", back_populates="user")
-    income_statements = relationship("IncomeStatement", back_populates="user")
-
-    @classmethod
-    async def create(cls: "type[User]", db: AsyncSession, **kwargs: UserCreate) -> "User":
-        """
-        Creates a new user in the database.
-
-        Args:
-            cls (type[User]): The class object representing the User model.
-            db (AsyncSession): The asynchronous session object for interacting with the database.
-            **kwargs (UserCreate): The keyword arguments representing the user's attributes.
-
-        Returns:
-            User
-        """
-
-        print(f"Creating user: {kwargs}")
-        # query = sql.insert(cls).values(**kwargs)
-        user = cls(name=kwargs["name"], email=kwargs["email"], hashed_password=kwargs["hashed_password"])
-        db.add(user)
-        await db.commit()
-        await db.refresh(user)
-        return user
-
-    @classmethod
-    async def update(cls: "type[User]", db: AsyncSession, id: int, **kwargs: UserUpdate) -> "User":
-        """
-        Updates a user in the database with the given ID and keyword arguments.
-
-        Args:
-            cls (type[User]): The class object representing the User model.
-            db (AsyncSession): The asynchronous session object for interacting with the database.
-            id (int): The ID of the user to update.
-            **kwargs (UserUpdate): The keyword arguments representing the user's attributes to update.
-
-        Returns:
-            User
-        """
-
-        query = sql.update(cls).where(cls.id == id).values(**kwargs).execution_options(synchronize_session="fetch")
-        results = await db.execute(query)
-        user = results.fetchone()
-        await db.commit()
-        return user
-
-    @classmethod
-    async def get(cls: "type[User]", db: AsyncSession, email: str) -> "User":
-        """
-        Retrieves a user from the database based on their email.
-
-        Args:
-            cls (type[User]): The class object representing the User model.
-            db (AsyncSession): The asynchronous session object for interacting with the database.
-            email (str): The email of the user to retrieve.
-
-        Returns:
-            User
-        """
-
-        logging.info(f"Getting user: {email}")
-        query = sql.select(cls).where(cls.email == email)
-        logging.info(f"Query: {query}")
-        users = await db.scalars(query)
-        logging.info(f"Users: {users}")
-        return users.first()
-
-    @classmethod
-    async def delete(cls: "type[User]", db: AsyncSession, email: str) -> "User":
-        """
-        Deletes a user from the database based on their email.
-
-        Args:
-            cls (type[User]): The class object representing the User model.
-            db (AsyncSession): The asynchronous session object for interacting with the database.
-            email (str): The email of the user to delete.
-
-        Returns:
-            User
-        """
-        query = (
-            sql.update(cls)
-            .where(cls.email == email)
-            .values(is_deleted=True)
-            .execution_options(synchronize_session="fetch")
-        )
-        result = await db.execute(query)
-        user = result.fetchone()
-        await db.commit()
-        return user

app/schema/base.py

@@ -1,14 +0,0 @@
-from datetime import datetime
-from pydantic import BaseModel, ConfigDict
-
-
-class PydanticBaseModel(BaseModel):
-    pass
-
-
-class BaseModel(PydanticBaseModel):
-    model_config = ConfigDict(from_attributes=True)
-
-    id: int
-    created_at: datetime
-    updated_at: datetime

app/schema/index.py

@@ -1,85 +0,0 @@
-from enum import Enum
-from datetime import datetime
-from typing import Dict, List
-from typing_extensions import TypedDict
-
-from app.schema.base import BaseModel, PydanticBaseModel
-
-
-class TransactionType(str, Enum):
-    INCOME = "income"
-    EXPENSE = "expense"
-
-
-class UserCreate(PydanticBaseModel):
-    name: str
-    email: str
-    hashed_password: str
-
-
-class UserUpdate(PydanticBaseModel):
-    name: str
-    email: str
-    hashed_password: str
-
-
-class UserResponse(PydanticBaseModel):
-    id: int
-    name: str
-    email: str
-    is_deleted: bool
-
-
-class User(BaseModel):
-    name: str
-    email: str
-    hashed_password: str
-    is_deleted: bool = False
-    transactions: "List[Transaction]" = []
-
-
-class TransactionResponse(PydanticBaseModel):
-    transaction_date: datetime
-    category: str
-    name_description: str
-    amount: float
-    type: TransactionType
-
-
-class TransactionCreate(TransactionResponse):
-    user_id: int
-
-
-class Transaction(TransactionResponse):
-    user: User
-
-
-class FileUploadCreate(PydanticBaseModel):
-    source: str
-    date: datetime
-    category: str
-    name_description: str
-    amount: float
-    type: str
-
-
-class IncomeStatementCreateRequest(PydanticBaseModel):
-    user_id: int
-    date_from: datetime
-    date_to: datetime
-
-
-class IncomeStatementDetail(TypedDict):
-    total: float
-    category_totals: List[Dict[str, str | float]]
-
-class IncomeStatementLLMResponse(PydanticBaseModel):
-    income: IncomeStatementDetail
-    expenses: IncomeStatementDetail
-
-
-class IncomeStatement(IncomeStatementCreateRequest, IncomeStatementLLMResponse):
-    pass
-
-class IncomeStatementResponse(IncomeStatement):
-    id: int

app/service/income_statement.py

@@ -1,37 +0,0 @@
-import logging
-from app.schema.index import IncomeStatement, IncomeStatementCreateRequest
-from app.model.transaction import Transaction as TransactionModel
-from app.model.income_statement import IncomeStatement as IncomeStatementModel
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.service.llm import call_llm
-
-
-async def call_llm_to_create_income_statement(payload: IncomeStatementCreateRequest, db: AsyncSession) -> None:
-    transactions = await TransactionModel.get_by_user_between_dates(
-        db, payload.user_id, payload.date_from, payload.date_to
-    )
-
-    if not transactions:
-        print("No transactions found")
-        return
-
-    response = await call_llm(transactions)
-
-    income = response.dict()['income']
-    expenses = response.dict()['expenses']
-
-    try:
-        income_statement_create_payload = IncomeStatement(
-            user_id=payload.user_id,
-            date_from=payload.date_from,
-            date_to=payload.date_to,
-            income=income,
-            expenses=expenses,
-        )
-
-        income_statement = await IncomeStatementModel.create(db, **income_statement_create_payload.model_dump())
-        print(f"Income statement created: {income_statement}")
-    except Exception as e:
-        print(e)
-        raise e

app/service/llm.py

@@ -1,55 +0,0 @@
-from typing import List
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.runnables.base import RunnableSequence
-from langchain_openai import OpenAI
-from langchain.globals import set_llm_cache
-
-from app.model.transaction import Transaction
-from app.schema.index import IncomeStatementLLMResponse
-from config.index import config as env
-
-from langchain_core.output_parsers import PydanticOutputParser
-
-set_llm_cache(None)
-
-def income_statement_prompt () -> ChatPromptTemplate:
-    context_str = """
-        You are an accountant skilled at organizing transactions from multiple different bank
-        accounts and credit card statements to prepare an income statement.
-
-        Input data is in the below csv format:
-        transaction_date, category, name_description, amount, type\n
-        {input_data_csv}
-
-        Your task is to prepare an income statement. The output should be in the following format: {format_instructions}
-
-        """
-    prompt = ChatPromptTemplate.from_template(context_str)
-    return prompt
-
-async def call_llm(inputData: List[Transaction]) -> str:
-    input_data_csv = '\n'.join(str(x) for x in inputData)
-
-    output_parser = PydanticOutputParser(pydantic_object=IncomeStatementLLMResponse)
-
-    prompt = income_statement_prompt().partial(format_instructions=output_parser.get_format_instructions())
-
-    llm = OpenAI(name='Income Statement Generation Bot',
-                        api_key=env.OPENAI_API_KEY,
-                        # cache=True,
-                        temperature=0.7,
-                        verbose=True)
-
-    try:
-        runnable_chain = RunnableSequence(prompt, llm, output_parser)
-    except Exception as e:
-        print(f"runnable_chain error: {str(e)}")
-        raise e
-
-    try:
-        output_chunks = runnable_chain.invoke({"input_data_csv": input_data_csv})
-        return output_chunks
-
-    except Exception as e:
-        print(f"runnable_chain.invoke error: {str(e)}")
-        raise e

app/transactions_rag/categorize_transactions.ipynb

@@ -1,571 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defaulting to user installation because normal site-packages is not writeable\n",
-      "Requirement already satisfied: pandas in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (2.0.3)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from pandas) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from pandas) (2024.1)\n",
-      "Requirement already satisfied: tzdata>=2022.1 in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from pandas) (2024.1)\n",
-      "Requirement already satisfied: numpy>=1.20.3 in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from pandas) (1.24.4)\n",
-      "Requirement already satisfied: six>=1.5 in /Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas) (1.15.0)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Name / Description</th>\n",
-       "      <th>Expense/Income</th>\n",
-       "      <th>Amount</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2023-12-30</td>\n",
-       "      <td>Comcast Internet</td>\n",
-       "      <td>Expense</td>\n",
-       "      <td>9.96</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2023-12-30</td>\n",
-       "      <td>Lemonade Home Insurance</td>\n",
-       "      <td>Expense</td>\n",
-       "      <td>17.53</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2023-12-30</td>\n",
-       "      <td>Monthly Appartment Rent</td>\n",
-       "      <td>Expense</td>\n",
-       "      <td>2000.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2023-12-30</td>\n",
-       "      <td>Staples Office Supplies</td>\n",
-       "      <td>Expense</td>\n",
-       "      <td>12.46</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2023-12-29</td>\n",
-       "      <td>Selling Paintings</td>\n",
-       "      <td>Income</td>\n",
-       "      <td>13.63</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Date       Name / Description Expense/Income   Amount\n",
-       "0  2023-12-30         Comcast Internet        Expense     9.96\n",
-       "1  2023-12-30  Lemonade Home Insurance        Expense    17.53\n",
-       "2  2023-12-30  Monthly Appartment Rent        Expense  2000.00\n",
-       "3  2023-12-30  Staples Office Supplies        Expense    12.46\n",
-       "4  2023-12-29        Selling Paintings         Income    13.63"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Read the transactions csv\n",
-    "!pip3 install pandas\n",
-    "\n",
-    "import pandas as pd\n",
-    "df = pd.read_csv(\"transactions_2024.csv\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['Lemonade Home Insurance', 'Monthly Appartment Rent',\n",
-       "       'Staples Office Supplies', 'Selling Paintings', 'Spotify',\n",
-       "       'Target', 'IT Consulting', 'Phone', 'ML Consulting'], dtype=object)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get Unique transactions in the Name/Description column\n",
-    "unique_transactions = df[\"Name / Description\"].unique()\n",
-    "unique_transactions[1:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!python3 -m venv venv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!source venv/bin/activate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defaulting to user installation because normal site-packages is not writeable\n",
-      "Requirement already satisfied: setuptools in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (70.0.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip3 install --upgrade setuptools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Defaulting to user installation because normal site-packages is not writeable',\n",
-       " 'Requirement already satisfied: langchain in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from -r requirements.txt (line 1)) (0.2.1)',\n",
-       " 'Collecting langchain_openai (from -r requirements.txt (line 2))',\n",
-       " '  Downloading langchain_openai-0.1.7-py3-none-any.whl.metadata (2.5 kB)',\n",
-       " 'Collecting python-dotenv (from -r requirements.txt (line 3))',\n",
-       " '  Using cached python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)',\n",
-       " 'Collecting openai (from -r requirements.txt (line 4))',\n",
-       " '  Using cached openai-1.30.4-py3-none-any.whl.metadata (21 kB)',\n",
-       " 'Requirement already satisfied: tenacity in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from -r requirements.txt (line 5)) (8.3.0)',\n",
-       " 'Requirement already satisfied: rapidfuzz in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from -r requirements.txt (line 6)) (3.9.1)',\n",
-       " 'Requirement already satisfied: pydantic in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from -r requirements.txt (line 7)) (2.7.1)',\n",
-       " 'Requirement already satisfied: dateparser in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from -r requirements.txt (line 8)) (1.2.0)',\n",
-       " 'Requirement already satisfied: pandas in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from -r requirements.txt (line 9)) (2.0.3)',\n",
-       " 'Collecting path (from -r requirements.txt (line 10))',\n",
-       " '  Using cached path-16.14.0-py3-none-any.whl.metadata (6.3 kB)',\n",
-       " 'Collecting aiostream==0.5.2 (from -r requirements.txt (line 12))',\n",
-       " '  Using cached aiostream-0.5.2-py3-none-any.whl.metadata (9.9 kB)',\n",
-       " 'Collecting cachetools==5.3.3 (from -r requirements.txt (line 13))',\n",
-       " '  Using cached cachetools-5.3.3-py3-none-any.whl.metadata (5.3 kB)',\n",
-       " 'Collecting docx2txt==0.8 (from -r requirements.txt (line 14))',\n",
-       " '  Using cached docx2txt-0.8.tar.gz (2.8 kB)',\n",
-       " '  Preparing metadata (setup.py): started',\n",
-       " \"  Preparing metadata (setup.py): finished with status 'done'\",\n",
-       " 'Collecting fastapi==0.109.1 (from -r requirements.txt (line 15))',\n",
-       " '  Using cached fastapi-0.109.1-py3-none-any.whl.metadata (25 kB)',\n",
-       " 'Collecting llama-index-agent-openai==0.2.2 (from -r requirements.txt (line 16))',\n",
-       " '  Using cached llama_index_agent_openai-0.2.2-py3-none-any.whl.metadata (677 bytes)',\n",
-       " 'Collecting llama-index-core==0.10.28 (from -r requirements.txt (line 17))',\n",
-       " '  Using cached llama_index_core-0.10.28-py3-none-any.whl.metadata (3.6 kB)',\n",
-       " 'Collecting llama-index-vector-stores-pinecone==0.1.3 (from -r requirements.txt (line 18))',\n",
-       " '  Using cached llama_index_vector_stores_pinecone-0.1.3-py3-none-any.whl.metadata (725 bytes)',\n",
-       " 'Collecting llama-index==0.10.28 (from -r requirements.txt (line 19))',\n",
-       " '  Using cached llama_index-0.10.28-py3-none-any.whl.metadata (11 kB)',\n",
-       " 'Collecting python-dotenv (from -r requirements.txt (line 3))',\n",
-       " '  Using cached python_dotenv-1.0.0-py3-none-any.whl.metadata (21 kB)',\n",
-       " '\\x1b[31mERROR: Ignored the following versions that require a different python version: 0.0.10 Requires-Python >=3.9,<4.0; 0.0.11 Requires-Python >=3.9,<4.0; 0.0.12 Requires-Python >=3.9,<4.0; 0.0.13 Requires-Python >=3.9,<4.0; 0.0.14 Requires-Python >=3.9,<4.0; 0.0.15 Requires-Python >=3.9,<4.0; 0.0.16 Requires-Python >=3.9,<4.0; 0.0.17 Requires-Python >=3.9,<4.0; 0.0.18 Requires-Python >=3.9,<4.0; 0.0.19 Requires-Python >=3.9,<4.0; 0.0.20 Requires-Python >=3.9,<4.0; 0.0.21 Requires-Python >=3.9,<4.0; 0.0.22 Requires-Python >=3.9,<4.0; 0.0.23 Requires-Python >=3.9,<4.0; 0.0.24 Requires-Python >=3.9,<4.0; 0.0.25 Requires-Python >=3.9,<4.0; 0.14.3 Requires-Python >=3.9,<4; 0.14.4 Requires-Python <4,>=3.9; 0.14.5 Requires-Python <4,>=3.9; 0.15.0 Requires-Python <4,>=3.9; 0.15.1 Requires-Python <4,>=3.9; 0.15.10 Requires-Python <4,>=3.9; 0.15.11 Requires-Python <4,>=3.9; 0.15.12 Requires-Python <4,>=3.9; 0.15.13 Requires-Python <4,>=3.9; 0.15.2 Requires-Python <4,>=3.9; 0.15.3 Requires-Python <4,>=3.9; 0.15.4 Requires-Python <4,>=3.9; 0.15.5 Requires-Python <4,>=3.9; 0.15.6 Requires-Python <4,>=3.9; 0.15.7 Requires-Python <4,>=3.9; 0.15.8 Requires-Python <4,>=3.9; 0.15.9 Requires-Python <4,>=3.9; 0.16.0 Requires-Python <4,>=3.9; 0.16.1 Requires-Python <4,>=3.9; 0.16.2 Requires-Python <4,>=3.9; 0.16.3 Requires-Python <4,>=3.9; 0.16.4 Requires-Python <4,>=3.9; 0.16.5 Requires-Python <4,>=3.9; 0.16.6 Requires-Python <4,>=3.9; 0.16.7 Requires-Python <4,>=3.9; 0.16.8 Requires-Python <4,>=3.9; 0.16.9 Requires-Python <4,>=3.9; 0.17.0 Requires-Python <4,>=3.9; 0.17.1 Requires-Python <4,>=3.9; 0.17.2 Requires-Python <4,>=3.9; 0.17.3 Requires-Python <4,>=3.9; 0.17.4 Requires-Python <4,>=3.9; 0.17.5 Requires-Python <4,>=3.9; 0.17.6 Requires-Python <4,>=3.9; 0.17.7 Requires-Python <4,>=3.9; 0.18.0 Requires-Python <4,>=3.9; 0.18.1 Requires-Python <4,>=3.9; 0.18.2 Requires-Python <4,>=3.9; 0.19.0 Requires-Python <4,>=3.9; 0.20.0 Requires-Python <4,>=3.9; 0.21.0 Requires-Python <4,>=3.9\\x1b[0m\\x1b[31m',\n",
-       " '\\x1b[0m\\x1b[31mERROR: Could not find a version that satisfies the requirement traceloop-sdk==0.15.11 (from versions: 0.0.26, 0.0.27b0, 0.0.27, 0.0.28, 0.0.29, 0.0.31, 0.0.32, 0.0.33, 0.0.34, 0.0.35, 0.0.36, 0.0.37, 0.0.38, 0.0.39, 0.0.40, 0.0.41, 0.0.42, 0.0.43b0, 0.0.43b2, 0.0.43b3, 0.0.43b4, 0.0.43, 0.0.44b0, 0.0.44, 0.0.46, 0.0.47, 0.0.48, 0.0.49, 0.0.50, 0.0.51, 0.0.52, 0.0.53, 0.0.54, 0.0.55, 0.0.56a0, 0.0.56a1, 0.0.56a2, 0.0.56a3, 0.0.56a4, 0.0.56a5, 0.0.56a6, 0.0.56, 0.0.57, 0.0.58, 0.0.59, 0.0.60a0, 0.0.60a1, 0.0.60, 0.0.61a0, 0.0.61, 0.0.62, 0.0.63, 0.0.64a0, 0.0.64, 0.0.65, 0.0.66, 0.0.67, 0.0.68, 0.0.69, 0.0.70, 0.1.3, 0.1.5, 0.1.6, 0.1.7, 0.1.8, 0.1.9, 0.1.10, 0.1.11, 0.1.12, 0.2.0, 0.2.1, 0.3.0, 0.3.1, 0.3.2, 0.3.3, 0.3.4, 0.3.5, 0.3.6, 0.4.0, 0.4.1, 0.4.2, 0.5.0, 0.5.1, 0.5.2, 0.5.3, 0.6.0, 0.7.0, 0.8.0, 0.8.2, 0.9.0, 0.9.1, 0.9.2, 0.9.3, 0.9.4, 0.10.0, 0.10.1, 0.10.2, 0.10.3, 0.10.4, 0.10.5, 0.11.0, 0.11.1, 0.11.2, 0.11.3, 0.12.0, 0.12.1, 0.12.2, 0.12.3, 0.12.4, 0.12.5, 0.13.0, 0.13.1, 0.13.2, 0.13.3, 0.14.0, 0.14.1, 0.14.2)\\x1b[0m\\x1b[31m',\n",
-       " '\\x1b[0m\\x1b[31mERROR: No matching distribution found for traceloop-sdk==0.15.11\\x1b[0m\\x1b[31m',\n",
-       " '\\x1b[0m']"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "!!pip3 install -r requirements.txt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defaulting to user installation because normal site-packages is not writeable\n",
-      "Collecting dateparser\n",
-      "  Using cached dateparser-1.2.0-py2.py3-none-any.whl.metadata (28 kB)\n",
-      "Requirement already satisfied: python-dateutil in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from dateparser) (2.9.0.post0)\n",
-      "Requirement already satisfied: pytz in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from dateparser) (2024.1)\n",
-      "Collecting regex!=2019.02.19,!=2021.8.27 (from dateparser)\n",
-      "  Downloading regex-2024.5.15-cp38-cp38-macosx_10_9_x86_64.whl.metadata (40 kB)\n",
-      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 kB\u001b[0m \u001b[31m95.7 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
-      "\u001b[?25hCollecting tzlocal (from dateparser)\n",
-      "  Downloading tzlocal-5.2-py3-none-any.whl.metadata (7.8 kB)\n",
-      "Requirement already satisfied: six>=1.5 in /Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/site-packages (from python-dateutil->dateparser) (1.15.0)\n",
-      "Collecting backports.zoneinfo (from tzlocal->dateparser)\n",
-      "  Downloading backports.zoneinfo-0.2.1-cp38-cp38-macosx_10_14_x86_64.whl.metadata (4.7 kB)\n",
-      "Downloading dateparser-1.2.0-py2.py3-none-any.whl (294 kB)\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m510.0 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
-      "\u001b[?25hDownloading regex-2024.5.15-cp38-cp38-macosx_10_9_x86_64.whl (281 kB)\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m281.8/281.8 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
-      "\u001b[?25hDownloading tzlocal-5.2-py3-none-any.whl (17 kB)\n",
-      "Downloading backports.zoneinfo-0.2.1-cp38-cp38-macosx_10_14_x86_64.whl (35 kB)\n",
-      "Installing collected packages: regex, backports.zoneinfo, tzlocal, dateparser\n",
-      "\u001b[33m  WARNING: The script dateparser-download is installed in '/Users/patrickalexis/Library/Python/3.8/bin' which is not on PATH.\n",
-      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
-      "\u001b[0mSuccessfully installed backports.zoneinfo-0.2.1 dateparser-1.2.0 regex-2024.5.15 tzlocal-5.2\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip3 install dateparser"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defaulting to user installation because normal site-packages is not writeable\n",
-      "Collecting rapidfuzz\n",
-      "  Using cached rapidfuzz-3.9.1-cp38-cp38-macosx_10_9_x86_64.whl.metadata (11 kB)\n",
-      "Using cached rapidfuzz-3.9.1-cp38-cp38-macosx_10_9_x86_64.whl (2.1 MB)\n",
-      "Installing collected packages: rapidfuzz\n",
-      "Successfully installed rapidfuzz-3.9.1\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip3 install rapidfuzz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defaulting to user installation because normal site-packages is not writeable\n",
-      "Collecting dotenv\n",
-      "  Downloading dotenv-0.0.5.tar.gz (2.4 kB)\n",
-      "  Preparing metadata (setup.py) ... \u001b[?25lerror\n",
-      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
-      "  \n",
-      "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
-      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
-      "  \u001b[31m╰─>\u001b[0m \u001b[31m[76 lines of output]\u001b[0m\n",
-      "  \u001b[31m   \u001b[0m /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/setuptools/__init__.py:80: _DeprecatedInstaller: setuptools.installer and fetch_build_eggs are deprecated.\n",
-      "  \u001b[31m   \u001b[0m !!\n",
-      "  \u001b[31m   \u001b[0m \n",
-      "  \u001b[31m   \u001b[0m         ********************************************************************************\n",
-      "  \u001b[31m   \u001b[0m         Requirements should be satisfied by a PEP 517 installer.\n",
-      "  \u001b[31m   \u001b[0m         If you are using pip, you can try `pip install --use-pep517`.\n",
-      "  \u001b[31m   \u001b[0m         ********************************************************************************\n",
-      "  \u001b[31m   \u001b[0m \n",
-      "  \u001b[31m   \u001b[0m !!\n",
-      "  \u001b[31m   \u001b[0m   dist.fetch_build_eggs(dist.setup_requires)\n",
-      "  \u001b[31m   \u001b[0m   \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
-      "  \u001b[31m   \u001b[0m \n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m╰─>\u001b[0m \u001b[31m[16 lines of output]\u001b[0m\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m Traceback (most recent call last):\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m   File \"<string>\", line 2, in <module>\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m   File \"<pip-setuptools-caller>\", line 14, in <module>\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m   File \"/private/var/folders/g9/flbt409n1b7g0ry4b9ffdvnc0000gn/T/pip-wheel-66v7g1h6/distribute_6e0b47b8750a4aa8bfff62a3f867a97f/setuptools/__init__.py\", line 2, in <module>\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m     from setuptools.extension import Extension, Library\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m   File \"/private/var/folders/g9/flbt409n1b7g0ry4b9ffdvnc0000gn/T/pip-wheel-66v7g1h6/distribute_6e0b47b8750a4aa8bfff62a3f867a97f/setuptools/extension.py\", line 5, in <module>\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m     from setuptools.dist import _get_unpatched\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m   File \"/private/var/folders/g9/flbt409n1b7g0ry4b9ffdvnc0000gn/T/pip-wheel-66v7g1h6/distribute_6e0b47b8750a4aa8bfff62a3f867a97f/setuptools/dist.py\", line 7, in <module>\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m     from setuptools.command.install import install\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m   File \"/private/var/folders/g9/flbt409n1b7g0ry4b9ffdvnc0000gn/T/pip-wheel-66v7g1h6/distribute_6e0b47b8750a4aa8bfff62a3f867a97f/setuptools/command/__init__.py\", line 8, in <module>\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m     from setuptools.command import install_scripts\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m   File \"/private/var/folders/g9/flbt409n1b7g0ry4b9ffdvnc0000gn/T/pip-wheel-66v7g1h6/distribute_6e0b47b8750a4aa8bfff62a3f867a97f/setuptools/command/install_scripts.py\", line 3, in <module>\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m     from pkg_resources import Distribution, PathMetadata, ensure_directory\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m   File \"/private/var/folders/g9/flbt409n1b7g0ry4b9ffdvnc0000gn/T/pip-wheel-66v7g1h6/distribute_6e0b47b8750a4aa8bfff62a3f867a97f/pkg_resources.py\", line 1518, in <module>\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m     register_loader_type(importlib_bootstrap.SourceFileLoader, DefaultProvider)\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m AttributeError: module 'importlib._bootstrap' has no attribute 'SourceFileLoader'\n",
-      "  \u001b[31m   \u001b[0m   \u001b[31m   \u001b[0m \u001b[31m[end of output]\u001b[0m\n",
-      "  \u001b[31m   \u001b[0m \n",
-      "  \u001b[31m   \u001b[0m   \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
-      "  \u001b[31m   \u001b[0m \u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
-      "  \u001b[31m   \u001b[0m \n",
-      "  \u001b[31m   \u001b[0m \u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
-      "  \u001b[31m   \u001b[0m \u001b[31m╰─>\u001b[0m See above for output.\n",
-      "  \u001b[31m   \u001b[0m \n",
-      "  \u001b[31m   \u001b[0m \u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
-      "  \u001b[31m   \u001b[0m \u001b[1;36mhint\u001b[0m: See above for details.\n",
-      "  \u001b[31m   \u001b[0m Traceback (most recent call last):\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/setuptools/installer.py\", line 101, in _fetch_build_egg_no_warn\n",
-      "  \u001b[31m   \u001b[0m     subprocess.check_call(cmd)\n",
-      "  \u001b[31m   \u001b[0m   File \"/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/subprocess.py\", line 364, in check_call\n",
-      "  \u001b[31m   \u001b[0m     raise CalledProcessError(retcode, cmd)\n",
-      "  \u001b[31m   \u001b[0m subprocess.CalledProcessError: Command '['/Applications/Xcode.app/Contents/Developer/usr/bin/python3', '-m', 'pip', '--disable-pip-version-check', 'wheel', '--no-deps', '-w', '/var/folders/g9/flbt409n1b7g0ry4b9ffdvnc0000gn/T/tmpnw2yadh3', '--quiet', 'distribute']' returned non-zero exit status 1.\n",
-      "  \u001b[31m   \u001b[0m \n",
-      "  \u001b[31m   \u001b[0m The above exception was the direct cause of the following exception:\n",
-      "  \u001b[31m   \u001b[0m \n",
-      "  \u001b[31m   \u001b[0m Traceback (most recent call last):\n",
-      "  \u001b[31m   \u001b[0m   File \"<string>\", line 2, in <module>\n",
-      "  \u001b[31m   \u001b[0m   File \"<pip-setuptools-caller>\", line 34, in <module>\n",
-      "  \u001b[31m   \u001b[0m   File \"/private/var/folders/g9/flbt409n1b7g0ry4b9ffdvnc0000gn/T/pip-install-ac3_ml88/dotenv_7766945cfd7d4e62965375b30e9a0c21/setup.py\", line 13, in <module>\n",
-      "  \u001b[31m   \u001b[0m     setup(name='dotenv',\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/setuptools/__init__.py\", line 102, in setup\n",
-      "  \u001b[31m   \u001b[0m     _install_setup_requires(attrs)\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/setuptools/__init__.py\", line 75, in _install_setup_requires\n",
-      "  \u001b[31m   \u001b[0m     _fetch_build_eggs(dist)\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/setuptools/__init__.py\", line 80, in _fetch_build_eggs\n",
-      "  \u001b[31m   \u001b[0m     dist.fetch_build_eggs(dist.setup_requires)\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/setuptools/dist.py\", line 641, in fetch_build_eggs\n",
-      "  \u001b[31m   \u001b[0m     return _fetch_build_eggs(self, requires)\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/setuptools/installer.py\", line 38, in _fetch_build_eggs\n",
-      "  \u001b[31m   \u001b[0m     resolved_dists = pkg_resources.working_set.resolve(\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/pkg_resources/__init__.py\", line 787, in resolve\n",
-      "  \u001b[31m   \u001b[0m     dist = self._resolve_dist(\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/pkg_resources/__init__.py\", line 823, in _resolve_dist\n",
-      "  \u001b[31m   \u001b[0m     dist = best[req.key] = env.best_match(\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/pkg_resources/__init__.py\", line 1093, in best_match\n",
-      "  \u001b[31m   \u001b[0m     return self.obtain(req, installer)\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/pkg_resources/__init__.py\", line 1104, in obtain\n",
-      "  \u001b[31m   \u001b[0m     return installer(requirement) if installer else None\n",
-      "  \u001b[31m   \u001b[0m   File \"/Users/patrickalexis/Library/Python/3.8/lib/python/site-packages/setuptools/installer.py\", line 103, in _fetch_build_egg_no_warn\n",
-      "  \u001b[31m   \u001b[0m     raise DistutilsError(str(e)) from e\n",
-      "  \u001b[31m   \u001b[0m distutils.errors.DistutilsError: Command '['/Applications/Xcode.app/Contents/Developer/usr/bin/python3', '-m', 'pip', '--disable-pip-version-check', 'wheel', '--no-deps', '-w', '/var/folders/g9/flbt409n1b7g0ry4b9ffdvnc0000gn/T/tmpnw2yadh3', '--quiet', 'distribute']' returned non-zero exit status 1.\n",
-      "  \u001b[31m   \u001b[0m \u001b[31m[end of output]\u001b[0m\n",
-      "  \n",
-      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
-      "\u001b[?25h\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
-      "\n",
-      "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
-      "\u001b[31m╰─>\u001b[0m See above for output.\n",
-      "\n",
-      "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
-      "\u001b[1;36mhint\u001b[0m: See above for details.\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip3 install dotenv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create data folder\n",
-    "!mkdir -p data/tx_data/output\n",
-    "!mkdir -p data/ref_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Defaulting to user installation because normal site-packages is not writeable\n",
-      "Collecting openai\n",
-      "  Using cached openai-1.30.4-py3-none-any.whl.metadata (21 kB)\n",
-      "Collecting anyio<5,>=3.5.0 (from openai)\n",
-      "  Downloading anyio-4.4.0-py3-none-any.whl.metadata (4.6 kB)\n",
-      "Collecting distro<2,>=1.7.0 (from openai)\n",
-      "  Downloading distro-1.9.0-py3-none-any.whl.metadata (6.8 kB)\n",
-      "Collecting httpx<1,>=0.23.0 (from openai)\n",
-      "  Downloading httpx-0.27.0-py3-none-any.whl.metadata (7.2 kB)\n",
-      "Requirement already satisfied: pydantic<3,>=1.9.0 in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from openai) (2.7.1)\n",
-      "Collecting sniffio (from openai)\n",
-      "  Downloading sniffio-1.3.1-py3-none-any.whl.metadata (3.9 kB)\n",
-      "Requirement already satisfied: tqdm>4 in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from openai) (4.66.4)\n",
-      "Requirement already satisfied: typing-extensions<5,>=4.7 in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from openai) (4.12.0)\n",
-      "Requirement already satisfied: idna>=2.8 in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from anyio<5,>=3.5.0->openai) (3.7)\n",
-      "Collecting exceptiongroup>=1.0.2 (from anyio<5,>=3.5.0->openai)\n",
-      "  Downloading exceptiongroup-1.2.1-py3-none-any.whl.metadata (6.6 kB)\n",
-      "Requirement already satisfied: certifi in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from httpx<1,>=0.23.0->openai) (2024.2.2)\n",
-      "Collecting httpcore==1.* (from httpx<1,>=0.23.0->openai)\n",
-      "  Downloading httpcore-1.0.5-py3-none-any.whl.metadata (20 kB)\n",
-      "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx<1,>=0.23.0->openai)\n",
-      "  Downloading h11-0.14.0-py3-none-any.whl.metadata (8.2 kB)\n",
-      "Requirement already satisfied: annotated-types>=0.4.0 in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from pydantic<3,>=1.9.0->openai) (0.7.0)\n",
-      "Requirement already satisfied: pydantic-core==2.18.2 in /Users/patrickalexis/Library/Python/3.8/lib/python/site-packages (from pydantic<3,>=1.9.0->openai) (2.18.2)\n",
-      "Downloading openai-1.30.4-py3-none-any.whl (320 kB)\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.6/320.6 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n",
-      "\u001b[?25hDownloading anyio-4.4.0-py3-none-any.whl (86 kB)\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-      "\u001b[?25hDownloading distro-1.9.0-py3-none-any.whl (20 kB)\n",
-      "Downloading httpx-0.27.0-py3-none-any.whl (75 kB)\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-      "\u001b[?25hDownloading httpcore-1.0.5-py3-none-any.whl (77 kB)\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-      "\u001b[?25hDownloading sniffio-1.3.1-py3-none-any.whl (10 kB)\n",
-      "Downloading exceptiongroup-1.2.1-py3-none-any.whl (16 kB)\n",
-      "Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
-      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-      "\u001b[?25hInstalling collected packages: sniffio, h11, exceptiongroup, distro, httpcore, anyio, httpx, openai\n",
-      "\u001b[33m  WARNING: The script distro is installed in '/Users/patrickalexis/Library/Python/3.8/bin' which is not on PATH.\n",
-      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
-      "\u001b[0m\u001b[33m  WARNING: The script httpx is installed in '/Users/patrickalexis/Library/Python/3.8/bin' which is not on PATH.\n",
-      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
-      "\u001b[0m\u001b[33m  WARNING: The script openai is installed in '/Users/patrickalexis/Library/Python/3.8/bin' which is not on PATH.\n",
-      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
-      "\u001b[0mSuccessfully installed anyio-4.4.0 distro-1.9.0 exceptiongroup-1.2.1 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 openai-1.30.4 sniffio-1.3.1\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip3 install openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Processing file\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR:root:| File: transactions_2024.csv | Unexpected Error: expected str, got module\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ERROR processing file transactions_2024.csv: expected str, got module\n",
-      "\n",
-      "Processed 1 files: 0 successful, 1 with errors\n",
-      "\n",
-      "Errors in the following files:\n",
-      "  transactions_2024.csv: expected str, got module\n",
-      "\n",
-      "\n",
-      "[{'file_name': 'transactions_2024.csv', 'output': Empty DataFrame\n",
-      "Columns: []\n",
-      "Index: [], 'error': 'expected str, got module'}]\n",
-      "['name/description,category\\n']\n",
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Process the transactions csv and get the results from the categorization llm utility\n",
-    "from app.categorization.file_processing import process_file, save_results\n",
-    "from app.categorization.config import CATEGORY_REFERENCE_OUTPUT_FILE\n",
-    "# from dotenv import load_dotenv\n",
-    "import asyncio\n",
-    "import os.path\n",
-    "\n",
-    "# load_dotenv()\n",
-    "\n",
-    "# relative_dir = os.path.dirname(__file__)\n",
-    "# abs_file_path = os.path.join(relative_dir, CATEGORY_REFERENCE_OUTPUT_FILE)\n",
-    "\n",
-    "async def apply_categorization():\n",
-    "    processed_file = process_file(\"app/transactions_rag/transactions_2024.csv\")\n",
-    "\n",
-    "    print(\"\\nProcessing file\")\n",
-    "    result = await asyncio.gather(processed_file)\n",
-    "\n",
-    "    await save_results(result)\n",
-    "    print(result)\n",
-    "\n",
-    "    output_file = open(CATEGORY_REFERENCE_OUTPUT_FILE, \"r+\")\n",
-    "    print(output_file.readlines())\n",
-    "\n",
-    "\n",
-    "result = await apply_categorization()\n",
-    "print(result)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.8.9 64-bit",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.9"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

app/transactions_rag/transactions_2024.csv

@@ -1,29 +0,0 @@
-Date,Name / Description,Expense/Income,Amount
-2023-12-30,Comcast Internet,Expense,9.96
-2023-12-30,Lemonade Home Insurance,Expense,17.53
-2023-12-30,Monthly Appartment Rent,Expense,2000.0
-2023-12-30,Staples Office Supplies,Expense,12.46
-2023-12-29,Selling Paintings,Income,13.63
-2023-12-29,Spotify,Expense,12.19
-2023-12-23,Target,Expense,27.08
-2023-12-22,IT Consulting,Income,541.57
-2023-12-22,Phone,Expense,10.7
-2023-12-20,ML Consulting,Income,2641.93
-2023-12-19,Chipotle,Expense,18.9
-2023-12-18,Cold Brew Coffee,Expense,17.67
-2023-12-18,Gas,Expense,8.80
-2023-12-18,CA Property Tax,Expense,1670.34
-2022-11-26,Salary,Income,4000.36
-2022-11-26,Cellphone,Expense,19.27
-2022-11-26,Gym Membership,Expense,24.71
-2022-11-25,Wholefoods,Expense,17.35
-2022-11-24,Freelancing,Income,2409.55
-2022-11-19,Spotify,Expense,20.76
-2022-10-25,Blogging,Income,4044.27
-2022-10-24,Uber Taxi,Expense,18.9
-2022-10-23,Uber Taxi,Expense,27.54
-2022-10-22,Apple Services,Expense,41.25
-2022-10-21,Netflix,Expense,22.8
-2022-01-16,Amazon Lux,Expense,24.11
-2022-01-15,Burger King,Expense,30.08
-2022-01-14,Amazon,Expense,11.0

config/index.py

@@ -1,34 +0,0 @@
-import os
-from dotenv import load_dotenv
-
-load_dotenv()
-
-
-class Config:
-    DEBUG = os.getenv("DEBUG", False)
-    SQLALCHEMY_DATABASE_URI = os.getenv("SQLALCHEMY_DATABASE_URI")
-    SQLALCHEMY_TRACK_MODIFICATIONS = os.getenv("SQLALCHEMY_TRACK_MODIFICATIONS", False)
-    ENVIRONMENT = os.getenv("ENVIRONMENT", "dev")
-    APP_HOST = os.getenv("APP_HOST", "0.0.0.0")
-    APP_PORT = int(os.getenv("APP_PORT", "8000"))
-    PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
-    PINECONE_INDEX_NAME = os.getenv("PINECONE_INDEX_NAME")
-    PINECONE_ENVIRONMENT = os.getenv("PINECONE_ENVIRONMENT")
-    OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-    OPENAI_ORGANIZATION = os.getenv("OPENAI_ORGANIZATION")
-    MODEL = os.getenv("MODEL", "gpt-3.5-turbo")
-    EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "text-embedding-ada-002")
-    EMBEDDING_DIM = os.getenv("EMBEDDING_DIM", "1536")
-    CHUNK_SIZE = int(os.getenv("CHUNK_SIZE", "1024"))
-    CHUNK_OVERLAP = int(os.getenv("CHUNK_OVERLAP", "20"))
-    MAX_TOKENS = int(os.getenv("MAX_TOKENS", "4000"))
-
-    POSTGRES_USER = os.getenv("POSTGRES_USER")
-    POSTGRES_PASSWORD = os.getenv("POSTGRES_PASSWORD")
-    POSTGRES_DB_NAME = os.getenv("POSTGRES_DB_NAME")
-    POSTGRES_DB_HOST = os.getenv("POSTGRES_DB_HOST")
-    POSTGRES_DB_PORT = int(os.getenv("POSTGRES_DB_PORT", 5432))
-    SQLALCHEMY_DATABASE_URL = f"postgresql+asyncpg://{POSTGRES_USER}:{POSTGRES_PASSWORD}@{POSTGRES_DB_HOST}:{POSTGRES_DB_PORT}/{POSTGRES_DB_NAME}"
-
-
-config = Config

data/tx_data/input/transactions_2024.csv

@@ -1,28 +0,0 @@
-2023-12-30,Comcast Internet,Expense,9.96
-2023-12-30,Lemonade Home Insurance,Expense,17.53
-2023-12-30,Monthly Appartment Rent,Expense,2000.0
-2023-12-30,Staples Office Supplies,Expense,12.46
-2023-12-29,Selling Paintings,Income,13.63
-2023-12-29,Spotify,Expense,12.19
-2023-12-23,Target,Expense,27.08
-2023-12-22,IT Consulting,Income,541.57
-2023-12-22,Phone,Expense,10.7
-2023-12-20,ML Consulting,Income,2641.93
-2023-12-19,Chipotle,Expense,18.9
-2023-12-18,Cold Brew Coffee,Expense,17.67
-2023-12-18,Gas,Expense,8.80
-2023-12-18,CA Property Tax,Expense,1670.34
-2022-11-26,Salary,Income,4000.36
-2022-11-26,Cellphone,Expense,19.27
-2022-11-26,Gym Membership,Expense,24.71
-2022-11-25,Wholefoods,Expense,17.35
-2022-11-24,Freelancing,Income,2409.55
-2022-11-19,Spotify,Expense,20.76
-2022-10-25,Blogging,Income,4044.27
-2022-10-24,Uber Taxi,Expense,18.9
-2022-10-23,Uber Taxi,Expense,27.54
-2022-10-22,Apple Services,Expense,41.25
-2022-10-21,Netflix,Expense,22.8
-2022-01-16,Amazon Lux,Expense,24.11
-2022-01-15,Burger King,Expense,30.08
-2022-01-14,Amazon,Expense,11.0

main.py

@@ -1,74 +1,51 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import logging
 import os
 import uvicorn
-from contextlib import asynccontextmanager
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import RedirectResponse
 from app.api.routers.chat import chat_router
-from app.api.routers.user import user_router
-from app.api.routers.transaction import transaction_router
-from app.api.routers.file_upload import file_upload_router
-from app.api.routers.income_statement import income_statement_router
 from app.settings import init_settings
+from app.observability import init_observability
 from fastapi.staticfiles import StaticFiles
-from alembic.config import Config
-from alembic import command
-
 
-from app.engine.postgresdb import postgresdb
-from config.index import config as env
-
-logger = logging.getLogger("uvicorn")
 
+app = FastAPI()
 
 init_settings()
-# init_observability()
-
-
-def init_app(init_db: bool = True) -> FastAPI:
-    lifespan = None
+init_observability()
 
-    if init_db:
-        postgresdb.init(env.SQLALCHEMY_DATABASE_URL, {"echo": True, "future": True})
+environment = os.getenv("ENVIRONMENT", "dev")  # Default to 'development' if not set
 
-        @asynccontextmanager
-        async def lifespan(app: FastAPI):
-            yield
-            if postgresdb.get_engine() is not None:
-                await postgresdb.close()
+if environment == "dev":
+    logger = logging.getLogger("uvicorn")
+    logger.warning("Running in development mode - allowing CORS for all origins")
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
 
-    app = FastAPI(lifespan=lifespan)
+    # Redirect to documentation page when accessing base URL
+    @app.get("/")
+    async def redirect_to_docs():
+        return RedirectResponse(url="/docs")
 
-    if env.ENVIRONMENT == "dev":
-        logger.warning("Running in development mode - allowing CORS for all origins")
-        app.add_middleware(
-            CORSMiddleware,
-            allow_origins=["*"],
-            allow_credentials=True,
-            allow_methods=["*"],
-            allow_headers=["*"],
-        )
 
-    if os.path.exists("data"):
-        app.mount("/api/data", StaticFiles(directory="data"), name="static")
-    app.include_router(chat_router, prefix="/api/chat")
-    app.include_router(user_router)
-    app.include_router(transaction_router)
-    app.include_router(file_upload_router)
-    app.include_router(income_statement_router)
-
-    return app
-
-
-app = init_app()
-
-
-# Redirect to documentation page when accessing base URL
-@app.get("/")
-async def redirect_to_docs():
-    return RedirectResponse(url="/docs")
+if os.path.exists("data"):
+    app.mount("/api/data", StaticFiles(directory="data"), name="static")
+app.include_router(chat_router, prefix="/api/chat")
 
 
 if __name__ == "__main__":
-    uvicorn.run(app="main:app", host=env.APP_HOST, port=env.APP_PORT, reload=(env.ENVIRONMENT == "dev"))
+    app_host = os.getenv("APP_HOST", "0.0.0.0")
+    app_port = int(os.getenv("APP_PORT", "8000"))
+    reload = True if environment == "dev" else False
+
+    uvicorn.run(app="main:app", host=app_host, port=app_port, reload=reload)

migration/README

@@ -1 +0,0 @@
-Generic single-database configuration.

migration/env.py

@@ -1,91 +0,0 @@
-import asyncio
-from logging.config import fileConfig
-
-from app.engine.postgresdb import Base
-from sqlalchemy import Connection
-from sqlalchemy import pool
-from sqlalchemy.ext.asyncio import async_engine_from_config
-
-from config.index import config as env
-
-from alembic import context
-
-# this is the Alembic Config object, which provides
-# access to the values within the .ini file in use.
-config = context.config
-
-config.set_main_option("sqlalchemy.url", env.SQLALCHEMY_DATABASE_URL)
-
-# Interpret the config file for Python logging.
-# This line sets up loggers basically.
-if config.config_file_name is not None:
-    fileConfig(config.config_file_name)
-
-# Import all models so they're registered with SQLAlchemy.
-import app.model.user
-import app.model.transaction
-import app.model.income_statement
-
-# add your model's MetaData object here
-# for 'autogenerate' support
-# from myapp import mymodel
-# target_metadata = mymodel.Base.metadata
-target_metadata = Base.metadata
-
-# other values from the config, defined by the needs of env.py,
-# can be acquired:
-# my_important_option = config.get_main_option("my_important_option")
-# ... etc.
-
-
-def run_migrations_offline():
-    """Run migrations in 'offline' mode.
-    This configures the context with just a URL
-    and not an Engine, though an Engine is acceptable
-    here as well.  By skipping the Engine creation
-    we don't even need a DBAPI to be available.
-    Calls to context.execute() here emit the given string to the
-    script output.
-    """
-    url = config.get_main_option("sqlalchemy.url")
-    context.configure(
-        url=url,
-        target_metadata=target_metadata,
-        literal_binds=True,
-        dialect_opts={"paramstyle": "named"},
-    )
-
-    with context.begin_transaction():
-        context.run_migrations()
-
-
-def do_run_migrations(connection: Connection) -> None:
-    context.configure(connection=connection, target_metadata=target_metadata)
-
-    with context.begin_transaction():
-        context.run_migrations()
-
-
-async def run_migrations_online():
-    """Run migrations in 'online' mode.
-    In this scenario we need to create an Engine
-    and associate a connection with the context.
-    """
-    configuration = config.get_section(config.config_ini_section)
-    configuration["sqlalchemy.url"] = env.SQLALCHEMY_DATABASE_URL
-    connectable = async_engine_from_config(
-        configuration,
-        prefix="sqlalchemy.",
-        poolclass=pool.NullPool,
-    )
-
-    async with connectable.connect() as connection:
-        await connection.run_sync(do_run_migrations)
-
-    await connectable.dispose()
-
-
-if context.is_offline_mode():
-    run_migrations_offline()
-else:
-    asyncio.run(run_migrations_online())

migration/script.py.mako

@@ -1,26 +0,0 @@
-"""${message}
-
-Revision ID: ${up_revision}
-Revises: ${down_revision | comma,n}
-Create Date: ${create_date}
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-${imports if imports else ""}
-
-# revision identifiers, used by Alembic.
-revision: str = ${repr(up_revision)}
-down_revision: Union[str, None] = ${repr(down_revision)}
-branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
-depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
-
-
-def upgrade() -> None:
-    ${upgrades if upgrades else "pass"}
-
-
-def downgrade() -> None:
-    ${downgrades if downgrades else "pass"}

migration/versions/4e76691ab103_add_income_statement_table.py

@@ -1,43 +0,0 @@
-"""add_income_statement_table
-
-Revision ID: 4e76691ab103
-Revises: 7ea44cbc5b1f
-Create Date: 2024-06-03 23:59:03.717778
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision: str = '4e76691ab103'
-down_revision: Union[str, None] = '7ea44cbc5b1f'
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.create_table('income_statement',
-    sa.Column('income', postgresql.JSON(astext_type=sa.Text()), nullable=True),
-    sa.Column('expenses', postgresql.JSON(astext_type=sa.Text()), nullable=True),
-    sa.Column('date_from', sa.DateTime(), nullable=False),
-    sa.Column('date_to', sa.DateTime(), nullable=False),
-    sa.Column('user_id', sa.Integer(), nullable=False),
-    sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
-    sa.Column('created_at', sa.DateTime(timezone=True), nullable=False),
-    sa.Column('updated_at', sa.DateTime(timezone=True), nullable=False),
-    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index(op.f('ix_income_statement_id'), 'income_statement', ['id'], unique=False)
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_index(op.f('ix_income_statement_id'), table_name='income_statement')
-    op.drop_table('income_statement')
-    # ### end Alembic commands ###

migration/versions/7ea44cbc5b1f_default_timezone.py

@@ -1,69 +0,0 @@
-"""default_timezone
-
-Revision ID: 7ea44cbc5b1f
-Revises: 8feaedca36f9
-Create Date: 2024-06-02 14:36:01.552518
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision: str = "7ea44cbc5b1f"
-down_revision: Union[str, None] = "8feaedca36f9"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.alter_column(
-        "transactions",
-        "created_at",
-        existing_type=postgresql.TIMESTAMP(),
-        type_=sa.DateTime(timezone=True),
-        nullable=False,
-    )
-    op.alter_column(
-        "transactions",
-        "updated_at",
-        existing_type=postgresql.TIMESTAMP(),
-        type_=sa.DateTime(timezone=True),
-        nullable=False,
-    )
-    op.alter_column(
-        "users", "created_at", existing_type=postgresql.TIMESTAMP(), type_=sa.DateTime(timezone=True), nullable=False
-    )
-    op.alter_column(
-        "users", "updated_at", existing_type=postgresql.TIMESTAMP(), type_=sa.DateTime(timezone=True), nullable=False
-    )
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.alter_column(
-        "users", "updated_at", existing_type=sa.DateTime(timezone=True), type_=postgresql.TIMESTAMP(), nullable=True
-    )
-    op.alter_column(
-        "users", "created_at", existing_type=sa.DateTime(timezone=True), type_=postgresql.TIMESTAMP(), nullable=True
-    )
-    op.alter_column(
-        "transactions",
-        "updated_at",
-        existing_type=sa.DateTime(timezone=True),
-        type_=postgresql.TIMESTAMP(),
-        nullable=True,
-    )
-    op.alter_column(
-        "transactions",
-        "created_at",
-        existing_type=sa.DateTime(timezone=True),
-        type_=postgresql.TIMESTAMP(),
-        nullable=True,
-    )
-    # ### end Alembic commands ###

migration/versions/8feaedca36f9_users_datatype_updates.py

@@ -1,74 +0,0 @@
-"""Remove_Transactions_From_Users
-
-Revision ID: 8feaedca36f9
-Revises: cd515c44401d
-Create Date: 2024-06-02 01:26:54.731002
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision: str = '8feaedca36f9'
-down_revision: Union[str, None] = 'cd515c44401d'
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.alter_column('transactions', 'transaction_date',
-               existing_type=postgresql.TIMESTAMP(),
-               nullable=False)
-    op.alter_column('transactions', 'category',
-               existing_type=sa.VARCHAR(),
-               nullable=False)
-    op.alter_column('transactions', 'name_description',
-               existing_type=sa.VARCHAR(),
-               nullable=False)
-    op.alter_column('transactions', 'amount',
-               existing_type=sa.DOUBLE_PRECISION(precision=53),
-               nullable=False)
-    op.alter_column('transactions', 'type',
-               existing_type=sa.VARCHAR(),
-               nullable=False)
-    op.add_column('users', sa.Column('hashed_password', sa.String(), nullable=False))
-    op.add_column('users', sa.Column('is_deleted', sa.Boolean(), nullable=False))
-    op.alter_column('users', 'name',
-               existing_type=sa.VARCHAR(),
-               nullable=False)
-    op.alter_column('users', 'email',
-               existing_type=sa.VARCHAR(),
-               nullable=False)
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.alter_column('users', 'email',
-               existing_type=sa.VARCHAR(),
-               nullable=True)
-    op.alter_column('users', 'name',
-               existing_type=sa.VARCHAR(),
-               nullable=True)
-    op.drop_column('users', 'is_deleted')
-    op.drop_column('users', 'hashed_password')
-    op.alter_column('transactions', 'type',
-               existing_type=sa.VARCHAR(),
-               nullable=True)
-    op.alter_column('transactions', 'amount',
-               existing_type=sa.DOUBLE_PRECISION(precision=53),
-               nullable=True)
-    op.alter_column('transactions', 'name_description',
-               existing_type=sa.VARCHAR(),
-               nullable=True)
-    op.alter_column('transactions', 'category',
-               existing_type=sa.VARCHAR(),
-               nullable=True)
-    op.alter_column('transactions', 'transaction_date',
-               existing_type=postgresql.TIMESTAMP(),
-               nullable=True)
-    # ### end Alembic commands ###

migration/versions/cd515c44401d_add_users_transactions_tables.py

@@ -1,55 +0,0 @@
-"""Add users, transactions tables
-
-Revision ID: cd515c44401d
-Revises: 
-Create Date: 2024-06-01 01:32:42.500524
-
-"""
-from typing import Sequence, Union
-
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers, used by Alembic.
-revision: str = 'cd515c44401d'
-down_revision: Union[str, None] = None
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.create_table('users',
-    sa.Column('name', sa.String(), nullable=True),
-    sa.Column('email', sa.String(), nullable=True),
-    sa.Column('id', sa.Integer(), nullable=False),
-    sa.Column('created_at', sa.DateTime(), nullable=True),
-    sa.Column('updated_at', sa.DateTime(), nullable=True),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index(op.f('ix_users_id'), 'users', ['id'], unique=False)
-    op.create_table('transactions',
-    sa.Column('transaction_date', sa.DateTime(), nullable=True),
-    sa.Column('category', sa.String(), nullable=True),
-    sa.Column('name_description', sa.String(), nullable=True),
-    sa.Column('amount', sa.Float(), nullable=True),
-    sa.Column('type', sa.String(), nullable=True),
-    sa.Column('user_id', sa.Integer(), nullable=True),
-    sa.Column('id', sa.Integer(), nullable=False),
-    sa.Column('created_at', sa.DateTime(), nullable=True),
-    sa.Column('updated_at', sa.DateTime(), nullable=True),
-    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),
-    sa.PrimaryKeyConstraint('id')
-    )
-    op.create_index(op.f('ix_transactions_id'), 'transactions', ['id'], unique=False)
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_index(op.f('ix_transactions_id'), table_name='transactions')
-    op.drop_table('transactions')
-    op.drop_index(op.f('ix_users_id'), table_name='users')
-    op.drop_table('users')
-    # ### end Alembic commands ###

poetry.lock

@@ -123,25 +123,6 @@ files = [
 [package.dependencies]
 typing-extensions = "*"
 
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-version = "1.13.1"
-description = "A database migration tool for SQLAlchemy."
-optional = false
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-
-[package.extras]
-tz = ["backports.zoneinfo"]
-
 [[package]]
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-
-[[package]]
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-description = "reference implementation of PEP 3156"
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-python-versions = "*"
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-]
-
-[package.dependencies]
-async-timeout = {version = ">=4.0.3", markers = "python_version < \"3.12.0\""}
-
-[package.extras]
-docs = ["Sphinx (>=5.3.0,<5.4.0)", "sphinx-rtd-theme (>=1.2.2)", "sphinxcontrib-asyncio (>=0.3.0,<0.4.0)"]
-test = ["flake8 (>=6.1,<7.0)", "uvloop (>=0.15.3)"]
-
 [[package]]
 name = "attrs"
 version = "23.2.0"
@@ -480,28 +380,6 @@ files = [
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
-[[package]]
-name = "dateparser"
-version = "1.2.0"
-description = "Date parsing library designed to parse dates from HTML pages"
-optional = false
-python-versions = ">=3.7"
-files = [
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-
-[package.dependencies]
-python-dateutil = "*"
-pytz = "*"
-regex = "<2019.02.19 || >2019.02.19,<2021.8.27 || >2021.8.27"
-tzlocal = "*"
-
-[package.extras]
-calendars = ["convertdate", "hijri-converter"]
-fasttext = ["fasttext"]
-langdetect = ["langdetect"]
-
 [[package]]
 name = "deprecated"
 version = "1.2.14"
@@ -541,20 +419,6 @@ files = [
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-[[package]]
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-python-versions = ">=3.8"
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-
-[package.extras]
-testing = ["hatch", "pre-commit", "pytest", "tox"]
-
 [[package]]
 name = "fastapi"
 version = "0.109.2"
@@ -1039,17 +903,6 @@ files = [
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-description = "brain-dead simple config-ini parsing"
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-
 [[package]]
 name = "jinja2"
 version = "3.1.4"
@@ -1148,139 +1001,6 @@ files = [
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-[[package]]
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-version = "1.33"
-description = "Apply JSON-Patches (RFC 6902)"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
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-[package.dependencies]
-jsonpointer = ">=1.9"
-
-[[package]]
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-description = "Identify specific nodes in a JSON document (RFC 6901)"
-optional = false
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-[[package]]
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-description = "Building applications with LLMs through composability"
-optional = false
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 ]
 
 [package.dependencies]
@@ -3018,82 +2486,6 @@ docs = ["myst_parser", "sphinx", "sphinx_rtd_theme"]
 full = ["Pillow (>=8.0.0)", "PyCryptodome", "cryptography"]
 image = ["Pillow (>=8.0.0)"]
 
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-    {file = "rapidfuzz-3.9.2.tar.gz", hash = "sha256:c899d78709f8d4bd0059784fa27a9f6c53d04fc4aeaa21de7c0c8e34a7154e88"},
-]
-
-[package.extras]
-full = ["numpy"]
-
 [[package]]
 name = "regex"
 version = "2024.5.15"
@@ -3919,23 +3192,6 @@ files = [
     {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
-[[package]]
-name = "tzlocal"
-version = "5.2"
-description = "tzinfo object for the local timezone"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "tzlocal-5.2-py3-none-any.whl", hash = "sha256:49816ef2fe65ea8ac19d19aa7a1ae0551c834303d5014c6d5a62e4cbda8047b8"},
-    {file = "tzlocal-5.2.tar.gz", hash = "sha256:8d399205578f1a9342816409cc1e46a93ebd5755e39ea2d85334bea911bf0e6e"},
-]
-
-[package.dependencies]
-tzdata = {version = "*", markers = "platform_system == \"Windows\""}
-
-[package.extras]
-devenv = ["check-manifest", "pytest (>=4.3)", "pytest-cov", "pytest-mock (>=3.3)", "zest.releaser"]
-
 [[package]]
 name = "urllib3"
 version = "2.2.1"
@@ -4390,4 +3646,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11,<3.12"
-content-hash = "99d9141afef8d1d15a2e26cd82d141146ba630edd99b3adc82803f50c2682091"
+content-hash = "e1009d6d5a3112c225a6916b387f78d5e01bbda9edbd6ee1b78759540da5e72e"

pyproject.toml

@@ -3,7 +3,7 @@
 name = "app"
 version = "0.1.0"
 description = ""
-authors = [ "Praneeth Yerrapragada, Patrick Alexis" ]
+authors = [ "Marcus Schiesser <mail@marcusschiesser.de>" ]
 readme = "README.md"
 package-mode = false
 
@@ -19,42 +19,14 @@ llama-index = "0.10.28"
 llama-index-core = "0.10.28"
 llama-index-vector-stores-pinecone = "^0.1.7"
 traceloop-sdk = "^0.19.0"
-langchain = "^0.2.1"
-langchain-openai = "^0.1.8"
-openai = "^1.30.5"
-tenacity = "^8.3.0"
-rapidfuzz = "^3.9.2"
-pydantic = "^2.7.2"
-dateparser = "^1.2.0"
-pandas = "^2.2.2"
-path = "^16.14.0"
-pytest-xdist = "^3.6.1"
-pytest-asyncio = "^0.23.7"
-httpx = "^0.27.0"
-alembic = "^1.13.1"
-asyncpg = "^0.29.0"
-asyncio = "^3.4.3"
-pytest-postgresql = "^6.0.0"
-psycopg-binary = "^3.1.19"
-python-multipart = "^0.0.9"
 
 [tool.poetry.dependencies.uvicorn]
 extras = [ "standard" ]
 version = "^0.23.2"
 
-[tool.poetry.dependencies.sqlalchemy]
-extras = [ "asyncio" ]
-version = "^2.0.30"
-
 [tool.poetry.dependencies.llama-index-agent-openai]
 version = "0.2.2"
 
-[tool.black]
-line-length = 119
-
-[tool.pytest.ini_options]
-asyncio_mode = "auto"
-
 [build-system]
 requires = [ "poetry-core" ]
 build-backend = "poetry.core.masonry.api"

requirements.txt

@@ -0,0 +1,11 @@
+aiostream==0.5.2
+cachetools==5.3.3
+docx2txt==0.8
+fastapi==0.109.1
+llama-index-agent-openai==0.2.2
+llama-index-core==0.10.28
+llama-index-vector-stores-pinecone==0.1.3
+llama-index==0.10.28
+python-dotenv==1.0.0
+traceloop-sdk==0.15.11
+uvicorn==0.23.2

tests/conftest.py

@@ -1,83 +0,0 @@
-# Credits: https://github.com/ThomasAitken/demo-fastapi-async-sqlalchemy/blob/main/backend/app/conftest.py
-
-import asyncio
-from contextlib import ExitStack
-
-import pytest
-from alembic.config import Config
-from alembic.migration import MigrationContext
-from alembic.operations import Operations
-from alembic.script import ScriptDirectory
-from config.index import config as settings
-from app.engine.postgresdb import Base, get_db_session, postgresdb as sessionmanager
-from main import init_app
-from asyncpg import Connection
-from fastapi.testclient import TestClient
-from pytest_postgresql import factories
-from pytest_postgresql.factories.noprocess import postgresql_noproc
-from pytest_postgresql.janitor import DatabaseJanitor
-from sqlalchemy.testing.entities import ComparableEntity
-
-from config.index import config as env
-
-test_db = factories.postgresql_proc(dbname="test_db", port=5433)
-
-
-@pytest.fixture(autouse=True)
-def app():
-    with ExitStack():
-        # Don't initialize database connection.
-        # This is because we want to initialize the database connection manually, so that we can create the test database.
-        yield init_app(init_db=False)
-
-
-@pytest.fixture
-def client(app):
-    with TestClient(app) as c:
-        yield c
-
-
-@pytest.fixture(scope="session")
-def event_loop(request):
-    loop = asyncio.get_event_loop_policy().new_event_loop()
-    yield loop
-    loop.close()
-
-
-@pytest.fixture(scope="function", autouse=True)
-async def connection_test(test_db, event_loop):
-    pg_host = test_db.host
-    pg_port = test_db.port
-    pg_user = test_db.user
-    pg_db = test_db.dbname
-    pg_password = test_db.password
-
-    with DatabaseJanitor(user=pg_user, host=pg_host, port=pg_port, dbname=pg_db, version=test_db.version, password=pg_password):
-        connection_str = f"postgresql+psycopg://{pg_user}:@{pg_host}:{pg_port}/{pg_db}"
-        sessionmanager.init(connection_str,
-                            # {"echo": True, "future": True}
-                            )
-        yield
-        await sessionmanager.close()
-
-
-@pytest.fixture(scope="function", autouse=True)
-async def create_tables(connection_test):
-    async with sessionmanager.connect() as connection:
-        await sessionmanager.drop_all(connection)
-        await sessionmanager.create_all(connection)
-
-
-@pytest.fixture(scope="function", autouse=True)
-async def session_override(app, connection_test):
-    async def get_db_session_override():
-        async with sessionmanager.session() as session:
-            yield session
-
-    app.dependency_overrides[get_db_session] = get_db_session_override
-
-
-@pytest.fixture(scope="function", autouse=True)
-async def get_db_session_fixture():
-    async with sessionmanager.session() as session:
-        yield session

tests/pytest.ini

@@ -1,4 +0,0 @@
-[pytest]
-asyncio_mode = auto
-timeout = 60
-addopts = -vv --disable-warnings --durations=10 --durations-min=1.0

tests/test_income_statement.py

@@ -1,51 +0,0 @@
-from fastapi.testclient import TestClient
-import pytest
-
-from sqlalchemy.ext.asyncio import AsyncSession
-from app.model.transaction import Transaction
-from app.model.user import User
-from tests.utils import get_fake_transactions
-
-@pytest.mark.asyncio
-async def test_income_statement(client: TestClient, get_db_session_fixture: AsyncSession) -> None:
-    session_override = get_db_session_fixture
-
-    # 1. Create a user
-    user = await User.create(session_override, name="user", email="email", hashed_password="password")
-
-    # 2. Create a bunch of transactions
-    fake_transactions = get_fake_transactions(user.id)
-    await Transaction.bulk_create(session_override, fake_transactions)
-
-    # 3. Create an income statement
-    min_date = min(t.transaction_date for t in fake_transactions)
-    max_date = max(t.transaction_date for t in fake_transactions)
-
-    print(f"min_date: {min_date}, max_date: {max_date}")
-    response = client.post(
-        "/api/v1/income_statement",
-        json={
-            "user_id": 1,
-            "date_from": str(min_date),
-            "date_to": str(max_date),
-        },
-    )
-
-    assert response.status_code == 200
-
-    # 4. Verify that the income statement matches the transactions
-    response = client.get(f"/api/v1/income_statement/user/1")
-    print(response.json())
-    assert response.status_code == 200
-    assert response.json()[0].get("income")
-    assert response.json()[0].get("expenses")
-
-    report_id = response.json()[0].get("id")
-
-    # # 5. Verify that the income statement can be retrieved
-    if report_id is not None:
-        response = client.get(f"/api/v1/income_statement/report/{report_id}")
-        assert response.status_code == 200
-        assert response.json().get("income")
-        assert response.json().get("expenses")
-        assert response.json().get("id") == report_id

tests/test_transactions.py

@@ -1,25 +0,0 @@
-from fastapi.testclient import TestClient
-import pytest
-
-from app.model.transaction import Transaction
-from app.model.user import User
-from sqlalchemy.ext.asyncio import AsyncSession
-from tests.utils import get_fake_transactions
-
-
-@pytest.mark.asyncio
-async def test_transactions(client: TestClient, get_db_session_fixture: AsyncSession) -> None:
-
-    session_override = get_db_session_fixture
-
-    # 1. Create a user
-    user = await User.create(session_override, name="user", email="email", hashed_password="password")
-
-    # 2. Create a bunch of transactions
-    fake_transactions = get_fake_transactions(user.id)
-    await Transaction.bulk_create(session_override, fake_transactions)
-
-    # 3. Verify that the transactions are returned
-    response = client.get("/api/v1/transactions/1")
-    assert response.status_code == 200
-    assert len(response.json()) == 10