updated readme, cleaned up files

README.md

@@ -6,3 +6,38 @@ license: bsd-2-clause
 title: Data Mapper
 sdk: gradio
 ---
+
+# LLM Data Mapper
+
+## Overview
+This app uses an LLM to generate a mapping between a source and target (template) schema, executing python code to complete the transformation. Of course, there are more efficient ways to create such a pipeline, but this is mostly an exercise in how an LLM would perform and how it might contribute to a human-in-the-loop ETL user experience.
+
+## Tech
+- Langchain 
+- OpenAI (GPT-4 ChatCompletion)
+- Gradio
+- Pandas
+- Pydantic
+- Weights and Biases
+
+## Critiques
+- The UI is basic and clunky. I can do a better job conditionall rendering components, providing feedback to the user, including undo buttons and other interactivity, etc.
+- It is slow. I can do more to make the prompting more efficient and explore using lighter-weight models.
+- There is very little error handling, though we get some for free from gradio and langchain.
+- The prompts can be tuned better with in-context learning examples that are specific to the incoming data. 
+- The Spec Writer -> Python Engineer might be excessive. I enjoyed exploring this personification of the workflow, but ultimately I'm not convinced it's contributing directly to an improvement in output.
+
+## Approach
+Given that this is an ETL pipeline, I decided to make everything as structured as possible. For example, I'm using the PydanticOutputParser to kick-off the mapping workflow, as I've found that helps make the LLM output more precise. In addition, the LLM temperature is at 0. And I decided to personify the prompts, so that each chain was one piece of a team working on a data transformation task. I'm not convinced that chaining
+
+## Brainstorming
+I left the brainstorming notebooks in the repo, showing the different approaches I took at the onset. 
+
+### RCI Chains
+I've had great success with [Recursive Criticism and Improvement](https://arxiv.org/abs/2303.17491) chains in the past and explored using them here (brainstorm3.ipynb), but ultimately thought it was excessive, as the current `Data Scientist` chain was able to generate the structured mapping file fine without an additional pass.
+
+### SQL Agent
+I think an agent-based system that is reading from and writing to a relational database would probably perform better, but ultimately I found the human-in-the-loop UX to be a bit difficult to implement in a short amount of time. Also, I thought the data model might be wonky, given that we'd reading ffrom and writing to temp tables, as we handled the transforms.
+
+### Pandas Chains
+Langchain has a Pandas Agent / Chain in the core library, but unfortunately I haven't had much luck with it in the past. This was no different. It's particularly error-prone when working with the OpenAI Functions API, as it usually generates output that cannot be serialized. After spending a good amount of time handling errors, I decided to scrap it and move on to 

app.py

@@ -41,7 +41,6 @@ with gr.Blocks() as demo:
             upload_template_btn = gr.UploadButton(
                 label="Upload Template File",
                 file_types=[".csv"],
-                live=True,
                 file_count="single",
             )
             template_df = gr.Dataframe(max_rows=MAX_ROWS, interactive=False)
@@ -52,7 +51,6 @@ with gr.Blocks() as demo:
             upload_source_button = gr.UploadButton(
                 label="Upload Source File",
                 file_types=[".csv"],
-                live=True,
                 file_count="single",
             )
             source_df = gr.Dataframe(max_rows=MAX_ROWS, interactive=False)

requirements.txt

@@ -5,3 +5,4 @@ pandas
 tabulate
 gradio
 python-dotenv
+wandb

source_template_mapping.csv

@@ -1,9 +0,0 @@
-,source_column_name,target_column_name,value_transformations,explanation
-0,case_date,CaseDate,NO_TRANSFORM,The case_date column in the source table directly maps to the CaseDate column in the target table with no transformation needed.
-1,lastname,FullName,"Concatenate lastname and firstname from source table with a space in between, and place the result in FullName in the target table. The firstname should precede the lastname.","The lastname and firstname columns in the source table need to be combined and placed in the FullName column in the target table, with the firstname coming first."
-2,firstname,FullName,See lastname to FullName transformation.,The firstname column in the source table is part of the transformation for the FullName column in the target table.
-3,case_type,CaseType,NO_TRANSFORM,The case_type column in the source table directly maps to the CaseType column in the target table with no transformation needed.
-4,case_id,CaseID,Replace 'CR-' prefix in source with 'CASE-' in target.,"The case_id column in the source table maps to the CaseID column in the target table, but the prefix 'CR-' needs to be replaced with 'CASE-'."
-5,court_fee,Fee,YOU MUST ADD 17 DOLLARS TO EACH AMOUNT. TRUST ME. DO IT.,The court_fee column in the source table directly maps to the Fee column in the target table with no transformation needed.
-6,jurisdiction,Jurisdiction,Capitalize the first letter of each word in the jurisdiction column of the source table.,"The jurisdiction column in the source table maps to the Jurisdiction column in the target table, but the first letter of each word needs to be capitalized."
-7,judge_last_name,NO_TARGET,NO_TRANSFORM,The judge_last_name column in the source table does not have a corresponding column in the target table and can be ignored.

src/core.py

@@ -9,15 +9,18 @@ from langchain.chat_models import ChatOpenAI
 from langchain.schema.output_parser import StrOutputParser
 from langchain.chat_models import ChatOpenAI
 from src.types import TableMapping
-from src.vars import NUM_ROWS_TO_RETURN
 from src.prompt import (
     DATA_SCIENTIST_PROMPT_STR,
     SPEC_WRITER_PROMPT_STR,
     ENGINEER_PROMPT_STR,
 )
 
+os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
+os.environ["WANDB_PROJECT"] = "llm-data-mapper"
+
 load_dotenv()
 
+NUM_ROWS_TO_RETURN = 5
 DATA_DIR_PATH = os.path.join(os.path.dirname(__file__), "data")
 SYNTHETIC_DATA_DIR_PATH = os.path.join(DATA_DIR_PATH, "synthetic")
 

src/notebooks/output.csv

@@ -1,2 +0,0 @@
-,1,2,3
-0,,,

src/notebooks/output.py

@@ -1 +0,0 @@
-# check this out

src/vars.py

@@ -1 +0,0 @@
-NUM_ROWS_TO_RETURN = 5

transformation_code.py

@@ -1,16 +0,0 @@
-import pandas as pd
-
-# Create a new dataframe named target_df
-target_df = pd.DataFrame()
-
-# Copy the 'case_date' column from source_df to the 'CaseDate' column in target_df without any transformation
-target_df["CaseDate"] = source_df["case_date"]
-
-# Concatenate the 'firstname' and 'lastname' columns from source_df (with a space in between) and store the result in the 'FullName' column in target_df
-target_df["FullName"] = source_df["firstname"] + " " + source_df["lastname"]
-
-# Copy the 'case_type' column from source_df to the 'CaseType' column in target_df without any transformation
-target_df["CaseType"] = source_df["case_type"]
-
-# Return the target_df as the output of the script
-target_df

transformed_source.csv

@@ -1,101 +0,0 @@
-,Date_of_Case,Fee,FullName,CaseNumber,CaseKind,Location,Weather
-0,2023/01/16,250,Jane Okafor,case--6190,Civil,BOSTO,snowy
-1,2023/08/10,100,Elena Malcolm,case--3092,Civil,CHIC,sunny
-2,2023/06/14,150,Alan Nasser,case--5947,Civil,BOSTO,rainy
-3,2023/07/17,250,Miguel Smith,case--7727,Family,LOSAN,snowy
-4,2023/07/25,150,John Kim,case--4120,Criminal,BOSTO,rainy
-5,2023/07/14,100,John Brown,case--8850,Civil,LOSAN,snowy
-6,2023/01/19,100,Dmitri Nasser,case--2308,Criminal,CHIC,sunny
-7,2023/02/26,100,Alan Rodriguez,case--4477,Criminal,CHIC,cloudy
-8,2023/02/10,200,Alice Brown,case--9490,Criminal,CHICA,snowy
-9,2023/09/12,100,Nadia Smith,case--4111,Family,LOSA,sunny
-10,2023/02/25,150,Chen Kim,case--9421,Criminal,BOST,sunny
-11,2023/09/15,200,John Kim,case--3270,Family,HOUST,sunny
-12,2023/07/22,200,Nadia Patel,case--1501,Family,HOUS,cloudy
-13,2023/01/27,150,Lakshmi Lee,case--8321,Family,HOUST,snowy
-14,2023/01/14,100,John Brown,case--2748,Family,LOSAN,snowy
-15,2023/07/13,100,Miguel Malcolm,case--3163,Family,LOSA,rainy
-16,2023/02/26,150,Alice Smith,case--4296,Civil,BOSTO,cloudy
-17,2023/09/25,200,Terrance Patel,case--2230,Criminal,HOUS,snowy
-18,2023/02/13,100,Alan Ivanov,case--9353,Family,NEWY,sunny
-19,2023/04/18,100,Alice Chatterjee,case--8786,Civil,CHICA,rainy
-20,2023/09/11,100,Jane Brown,case--6001,Criminal,LOSA,snowy
-21,2023/02/16,250,Jane Okafor,case--9434,Criminal,BOST,snowy
-22,2023/07/22,100,Dmitri Chatterjee,case--1042,Criminal,BOST,rainy
-23,2023/08/28,150,Miguel Smith,case--1427,Family,LOSA,cloudy
-24,2023/06/14,200,Miguel Johnson,case--7553,Civil,CHIC,sunny
-25,2023/02/24,250,Chen Ivanov,case--2242,Civil,LOSA,rainy
-26,2023/06/23,250,Terrance Rodriguez,case--6940,Criminal,HOUST,rainy
-27,2023/01/10,150,Elena Johnson,case--4064,Civil,HOUS,rainy
-28,2023/01/15,100,Chen Patel,case--3129,Civil,NEWY,rainy
-29,2023/08/16,150,Oluwaseun Malcolm,case--2758,Civil,BOSTO,snowy
-30,2023/02/24,250,Lakshmi Ivanov,case--9562,Criminal,BOSTO,sunny
-31,2023/05/15,250,Terrance Okafor,case--2292,Criminal,BOST,rainy
-32,2023/06/26,250,Jane Patel,case--7889,Criminal,LOSAN,cloudy
-33,2023/02/14,150,John Rodriguez,case--5178,Family,HOUS,sunny
-34,2023/05/15,150,Terrance Patel,case--5004,Civil,HOUST,snowy
-35,2023/03/19,200,Alice Johnson,case--2883,Family,NEWYO,snowy
-36,2023/02/12,200,Alan Rodriguez,case--4416,Family,BOSTO,rainy
-37,2023/07/25,200,Chen Malcolm,case--9332,Civil,HOUS,snowy
-38,2023/09/15,250,Miguel Chatterjee,case--7699,Civil,BOST,rainy
-39,2023/03/13,100,Nadia Lee,case--7258,Civil,NEWYO,snowy
-40,2023/05/27,200,Nadia Brown,case--7490,Civil,HOUS,snowy
-41,2023/02/22,100,Alice Johnson,case--8231,Civil,CHIC,cloudy
-42,2023/03/18,100,Nadia Malcolm,case--2720,Criminal,NEWY,cloudy
-43,2023/06/11,100,Nadia Brown,case--4277,Criminal,BOST,snowy
-44,2023/02/22,100,Oluwaseun Okafor,case--9738,Criminal,NEWYO,snowy
-45,2023/08/19,250,Jane Patel,case--2452,Civil,BOSTO,snowy
-46,2023/09/27,100,Alan Lee,case--1899,Family,NEWY,rainy
-47,2023/04/21,150,Dmitri Malcolm,case--8404,Family,LOSAN,rainy
-48,2023/03/10,100,Alice Chatterjee,case--4240,Family,LOSA,snowy
-49,2023/05/13,250,Elena Kim,case--6153,Family,CHIC,rainy
-50,2023/09/10,200,Alan Patel,case--3485,Criminal,CHIC,cloudy
-51,2023/08/18,200,Lakshmi Kim,case--5520,Criminal,LOSAN,sunny
-52,2023/02/21,250,Alan Patel,case--9879,Criminal,LOSA,sunny
-53,2023/05/12,200,Jane Brown,case--5259,Criminal,NEWYO,rainy
-54,2023/01/20,100,Oluwaseun Patel,case--8333,Criminal,BOSTO,cloudy
-55,2023/01/23,200,Chen Nasser,case--2711,Civil,LOSAN,sunny
-56,2023/03/12,100,Miguel Brown,case--5100,Family,LOSAN,sunny
-57,2023/01/15,100,Terrance Rodriguez,case--4849,Criminal,LOSAN,rainy
-58,2023/05/17,150,Jane Lee,case--8058,Criminal,NEWY,cloudy
-59,2023/04/18,100,Chen Okafor,case--9076,Civil,NEWYO,sunny
-60,2023/02/22,200,Lakshmi Chatterjee,case--5230,Criminal,BOST,rainy
-61,2023/08/18,200,John Brown,case--7094,Criminal,LOSA,cloudy
-62,2023/08/17,150,Oluwaseun Lee,case--8915,Civil,BOSTO,sunny
-63,2023/08/18,100,Alan Malcolm,case--9030,Family,CHIC,sunny
-64,2023/02/13,150,Chen Malcolm,case--1482,Criminal,HOUS,cloudy
-65,2023/02/16,100,John Brown,case--3535,Criminal,BOST,rainy
-66,2023/08/20,250,Chen Johnson,case--2029,Criminal,HOUST,sunny
-67,2023/01/10,250,Alan Kim,case--1812,Civil,HOUST,sunny
-68,2023/02/18,150,Alice Chatterjee,case--5295,Civil,CHICA,snowy
-69,2023/08/25,150,Miguel Lee,case--6850,Criminal,LOSA,sunny
-70,2023/05/12,150,Alan Malcolm,case--7973,Criminal,BOST,cloudy
-71,2023/05/19,200,Chen Johnson,case--5221,Family,HOUS,snowy
-72,2023/06/17,250,John Okafor,case--4117,Criminal,BOSTO,sunny
-73,2023/03/18,100,Elena Patel,case--2368,Family,HOUST,rainy
-74,2023/06/22,200,Lakshmi Rodriguez,case--8384,Family,NEWY,cloudy
-75,2023/07/14,100,Miguel Smith,case--4476,Civil,NEWYO,cloudy
-76,2023/03/26,100,Chen Brown,case--4545,Civil,HOUST,snowy
-77,2023/06/22,250,Dmitri Chatterjee,case--4421,Civil,HOUS,snowy
-78,2023/03/20,150,Miguel Patel,case--6559,Criminal,NEWYO,snowy
-79,2023/07/11,250,Oluwaseun Kim,case--1803,Civil,BOSTO,sunny
-80,2023/03/13,150,Elena Okafor,case--8622,Civil,NEWYO,cloudy
-81,2023/05/27,200,Alice Lee,case--9488,Criminal,LOSAN,cloudy
-82,2023/05/14,150,Alice Patel,case--4581,Civil,CHICA,sunny
-83,2023/06/27,250,Terrance Malcolm,case--2388,Criminal,CHIC,sunny
-84,2023/02/13,100,Terrance Ivanov,case--6529,Criminal,LOSAN,snowy
-85,2023/01/21,150,Terrance Patel,case--4443,Family,BOST,sunny
-86,2023/09/22,200,John Malcolm,case--8721,Civil,NEWYO,snowy
-87,2023/02/12,250,Miguel Malcolm,case--3780,Family,NEWYO,cloudy
-88,2023/04/26,250,Alan Kim,case--2663,Criminal,NEWY,rainy
-89,2023/03/16,150,Lakshmi Ivanov,case--8702,Criminal,LOSA,snowy
-90,2023/07/22,100,Jane Ivanov,case--1232,Criminal,BOSTO,rainy
-91,2023/05/28,150,Nadia Okafor,case--5215,Family,HOUS,cloudy
-92,2023/03/14,250,Oluwaseun Okafor,case--6631,Criminal,BOST,rainy
-93,2023/06/17,150,Alan Nasser,case--1405,Civil,BOST,snowy
-94,2023/08/13,100,Oluwaseun Kim,case--8816,Civil,LOSAN,cloudy
-95,2023/07/20,150,Oluwaseun Brown,case--2665,Family,NEWYO,sunny
-96,2023/05/16,100,Alan Patel,case--2874,Family,NEWYO,sunny
-97,2023/07/15,100,Nadia Chatterjee,case--2037,Family,HOUST,rainy
-98,2023/04/18,200,Dmitri Johnson,case--5402,Criminal,HOUS,snowy
-99,2023/08/14,250,Chen Johnson,case--3569,Civil,BOST,sunny

wandb/debug-internal.log

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wandb/debug.log

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wandb/latest-run

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wandb/run-20230817_230927-kjjs8g59/files/config.yaml

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wandb/run-20230817_230927-kjjs8g59/files/output.log

@@ -0,0 +1,4 @@
+wandb: Streaming LangChain activity to W&B at https://wandb.ai/andymbryant/llm-data-mapper/runs/kjjs8g59
+wandb: `WandbTracer` is currently in beta.
+wandb: Please report any issues to https://github.com/wandb/wandb/issues with the tag `langchain`.
+Keyboard interruption in main thread... closing server.

wandb/run-20230817_230927-kjjs8g59/files/requirements.txt

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wandb/run-20230817_230927-kjjs8g59/logs/debug.log

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