From 7d2cd4681d564ee0b1d6254f87d705ef43545007 Mon Sep 17 00:00:00 2001 From: Hristo Date: Fri, 12 Apr 2024 17:28:31 -0400 Subject: [PATCH 1/4] snowflake block --- .../SNOWFLAKE/SNOWFLAKE.py | 120 ++ poetry.lock | 1068 ++++++++++++++++- pyproject.toml | 3 + 3 files changed, 1187 insertions(+), 4 deletions(-) create mode 100644 blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py diff --git a/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py b/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py new file mode 100644 index 000000000..c65fcde09 --- /dev/null +++ b/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py @@ -0,0 +1,120 @@ +from flojoy import flojoy, String +import snowflake.connector +from langchain_anthropic import ChatAnthropic +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.output_parsers import StrOutputParser +import os + + +@flojoy( + deps={ + "snowflake-connector-python": "3.8.1", + "langchain": "0.1.16", + "langchain-anthropic": "0.1.8", + } +) +def SNOWFLAKE( + user_prompt: String, + user: str, + password: str, + account: str, + warehouse: str, + database: str, + schema: str, + anthropic_api_key: str, +) -> String: + """ASK QUESTIONS ABOUT A DATABASE FROM SNOWFLAKE. + + Inputs + ------ + prompt : String + the question that will be answered + + Parameters + ---------- + user: str + the user name used to log in to the snowflake account + password: str + the password used to log in to the snowflake account + warehouse: str + the warehouse associated with the snowflake account to search for the database + database: str + the database name to get the data from + schema: str + the database schema containing the relevant tables to get the data from + anthropic_api_key: str + the Anthropic API key associated with your Anthropic account used to access Claude-3 + + Returns + ------- + String + the answer to the prompt + """ + # STEP 1: GET DATA FROM SNOWFLAKE + conn = snowflake.connector.connect( + user=user, + password=password, + account=account, + warehouse=warehouse, + database=database, + schema=schema, + ) + + print("Getting data from Snowflake DB", flush=True) + cursor = conn.cursor() + try: + # Fetch all tables in the specified schema + cursor.execute("SHOW TABLES") + tables = cursor.fetchall() + finally: + cursor.close() + + rows_str = [] + for table_data in tables: + table_name = table_data[1] # Depending on the format, might need adjustment + + cursor = conn.cursor() + try: + query = f"SELECT * FROM {table_name}" + cursor.execute(query) + + # Fetch and print rows + rows = cursor.fetchall() + for row in rows: + rows_str.append(str(row)) + + except Exception as e: + print(f"Error querying table {table_name}: {e}") + finally: + cursor.close() + + conn.close() + data_str = "\n".join(rows_str) + + # STEP 2: Feed into LLM + output_parser = StrOutputParser() + llm = ChatAnthropic( + api_key=anthropic_api_key, model="claude-3-opus-20240229", temperature=0 + ) + + prompt = ChatPromptTemplate.from_template( + """ + You are an expert data analyst. + + Use this data as context: + + {data} + + + Now, answer the following question: + + {input} + + + Begin! + """ + ) + + chain = prompt | llm | output_parser + print("Invoking LLM...", flush=True) + return String(s=chain.invoke({"data": data_str, "input": user_prompt.s})) diff --git a/poetry.lock b/poetry.lock index 5c7cc8fbc..442a2aa58 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,114 @@ # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +[[package]] +name = "aiohttp" +version = "3.9.4" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohttp-3.9.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:76d32588ef7e4a3f3adff1956a0ba96faabbdee58f2407c122dd45aa6e34f372"}, + {file = "aiohttp-3.9.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = 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171f0b96b..ea9d6918d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,9 @@ nimodinst = "^1.4.7" flojoy-cloud = "^0.1.3" bcrypt = "^4.1.2" tinymovr = "^1.6.5" +snowflake-connector-python = "3.8.1" +langchain = "0.1.16" +langchain-anthropic = "0.1.8" [tool.poetry.group.blocks.dependencies] scikit-image = "^0.22.0" From 90164ac6e797820405d4deb27a3d18e16e74a3de Mon Sep 17 00:00:00 2001 From: Hristo Date: Fri, 12 Apr 2024 17:36:33 -0400 Subject: [PATCH 2/4] add todo --- blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py | 1 + 1 file changed, 1 insertion(+) diff --git a/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py b/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py index c65fcde09..a7f47b7c1 100644 --- a/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py +++ b/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py @@ -51,6 +51,7 @@ def SNOWFLAKE( the answer to the prompt """ # STEP 1: GET DATA FROM SNOWFLAKE + # TODO: Have conn objects globally available sort of like hardware connections? conn = snowflake.connector.connect( user=user, password=password, From 87da039432c2264722a553bdc6d29f103adb6eac Mon Sep 17 00:00:00 2001 From: Hristo Date: Sun, 14 Apr 2024 10:06:42 -0400 Subject: [PATCH 3/4] working version --- .../SNOWFLAKE/SNOWFLAKE.py | 215 +++-- poetry.lock | 823 ++++++++++++------ pyproject.toml | 4 + 3 files changed, 671 insertions(+), 371 deletions(-) diff --git a/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py b/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py index a7f47b7c1..0d7b68252 100644 --- a/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py +++ b/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py @@ -1,18 +1,32 @@ -from flojoy import flojoy, String -import snowflake.connector -from langchain_anthropic import ChatAnthropic -from langchain_core.prompts import ChatPromptTemplate -from langchain_core.output_parsers import StrOutputParser -import os - - -@flojoy( - deps={ - "snowflake-connector-python": "3.8.1", - "langchain": "0.1.16", - "langchain-anthropic": "0.1.8", - } -) +from typing import TypedDict +from flojoy import flojoy, String, OrderedPair +import json +import ast +from langchain_community.utilities import SQLDatabase +from langchain_community.agent_toolkits.sql.base import create_sql_agent +from langchain_openai import ChatOpenAI +from langchain_core.tools import tool +from langchain.pydantic_v1 import BaseModel, Field + +MAX_RETRIES_FOR_STRUCTURED_OUTPUT = 3 # Number of retries + + +class SnowflakeOutput(TypedDict): + bar_plot_data: OrderedPair + text_answer: String + + +# @flojoy( +# deps={ +# "sqlalchemy": "1.4.51", # IMPORTANT: THIS IS THE ONLY VERSION THAT WORKS WITH SNOWFLAKE DB WRAPPER IN LANGCHAIN +# "snowflake-sqlalchemy": "1.5.1", # IMPORTANT: THIS IS THE ONLY VERSION THAT WORKS WITH SNOWFLAKE DB WRAPPER IN LANGCHAIN +# "snowflake-connector-python": "3.8.1", +# "langchain": "0.1.16", +# "langchain-openai": "0.1.3", +# "langchain-community": "0.0.32", +# } +# ) +@flojoy def SNOWFLAKE( user_prompt: String, user: str, @@ -21,8 +35,8 @@ def SNOWFLAKE( warehouse: str, database: str, schema: str, - anthropic_api_key: str, -) -> String: + openai_api_key: str, +) -> SnowflakeOutput: """ASK QUESTIONS ABOUT A DATABASE FROM SNOWFLAKE. Inputs @@ -42,80 +56,115 @@ def SNOWFLAKE( the database name to get the data from schema: str the database schema containing the relevant tables to get the data from - anthropic_api_key: str - the Anthropic API key associated with your Anthropic account used to access Claude-3 + openai_api_key: str + the OPENAI API key associated with your OPENAI account used to access Returns ------- - String - the answer to the prompt + OrderedPair + the data to plot """ - # STEP 1: GET DATA FROM SNOWFLAKE - # TODO: Have conn objects globally available sort of like hardware connections? - conn = snowflake.connector.connect( - user=user, - password=password, - account=account, - warehouse=warehouse, - database=database, - schema=schema, - ) - - print("Getting data from Snowflake DB", flush=True) - cursor = conn.cursor() - try: - # Fetch all tables in the specified schema - cursor.execute("SHOW TABLES") - tables = cursor.fetchall() - finally: - cursor.close() - - rows_str = [] - for table_data in tables: - table_name = table_data[1] # Depending on the format, might need adjustment - cursor = conn.cursor() - try: - query = f"SELECT * FROM {table_name}" - cursor.execute(query) - - # Fetch and print rows - rows = cursor.fetchall() - for row in rows: - rows_str.append(str(row)) - - except Exception as e: - print(f"Error querying table {table_name}: {e}") - finally: - cursor.close() - - conn.close() - data_str = "\n".join(rows_str) - - # STEP 2: Feed into LLM - output_parser = StrOutputParser() - llm = ChatAnthropic( - api_key=anthropic_api_key, model="claude-3-opus-20240229", temperature=0 + snowflake_url = f"snowflake://{user}:{password}@{account}/{database}/{schema}?warehouse={warehouse}" + + db = SQLDatabase.from_uri(snowflake_url) + + # define the schema for the final output + @tool + class Response(BaseModel): + """Finally, use this tool as response to return to the user.""" + + user_requested_plot: bool = Field( + "This field indicates whether the user requested something to be plotted in the initial field" + ) + x_axis_query: str = Field( + description="This is an SQL code snippet. This query will give the x-axis for the data to plot" + ) + y_axis_query: str = Field( + desciption="This is an SQL code snippet. This query will give the y-axis for the data to plot" + ) + answer_to_prompt: str = Field( + description="This field contains the answer to the user's initial prompt" + ) + + # use to validate Response, should match + class ResponseValidation(BaseModel): + user_requested_plot: bool + x_axis_query: str + y_axis_query: str + answer_to_prompt: str + + tools = [Response] + + llm = ChatOpenAI( + api_key=openai_api_key, + model="gpt-4-turbo-preview", + temperature=0, ) - prompt = ChatPromptTemplate.from_template( - """ - You are an expert data analyst. - - Use this data as context: - - {data} - - - Now, answer the following question: - - {input} - + prefix_template = f""" + You are a world expert data analyst. + DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database. + Always try to use the provided tools: {tools} + """ - Begin! + format_instructions = """ + Use the following format: + + Question: the input question you must answer + Thought: you should always think about what to do + Action: the action to take. Use a tool if you deem it necessary. The tools at your disposal are the following: [{tool_names}] + Action Input: the input to the action + Observation: the result of the action + ... (this Thought/Action/Action Input/Observation can repeat N times) + Thought: I now know the final answer + Final Answer: The JSON string corresponding to the input of the args of the Response tool """ + + agent_executor = create_sql_agent( + llm, + db=db, + prefix_template=prefix_template, + format_instructions=format_instructions, + agent_type="zero-shot-react-description", + verbose=True, + extra_tools=tools, ) - chain = prompt | llm | output_parser - print("Invoking LLM...", flush=True) - return String(s=chain.invoke({"data": data_str, "input": user_prompt.s})) + attempts = 0 + + while attempts < MAX_RETRIES_FOR_STRUCTURED_OUTPUT: + try: + answer = agent_executor.invoke({"input": user_prompt}) + + def remove_json_triple_quote_from(string: str): + if "```json" not in string: + return string + return string.split("```json")[1].split("```")[0] + + answer_obj = json.loads(remove_json_triple_quote_from(answer["output"])) + response = ResponseValidation.parse_obj(answer_obj) + x = ast.literal_eval(db.run(response.x_axis_query)) + y = ast.literal_eval(db.run(response.y_axis_query)) + print("__________") + print(type(x)) + print(x) + print("__________") + print(type(y)) + print(y) + x = list(map(lambda x: x[0], x)) + y = list(map(lambda y: y[0], y)) + print("+++++++++") + print(x) + print("+++++++++") + print(y) + ordered_pair = OrderedPair(x=x, y=y) + answer = String(s=response.answer_to_prompt) + return SnowflakeOutput(bar_plot_data=ordered_pair, text_answer=answer) + except Exception as e: # Catch the specific exception you expect + attempts += 1 + print(f"Attempt {attempts} failed with error: {e}") + if attempts == MAX_RETRIES_FOR_STRUCTURED_OUTPUT: + raise Exception( + "Agent failed to output properly multiple times, is the prompt correct?" + ) diff --git a/poetry.lock b/poetry.lock index 442a2aa58..84498761d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -320,20 +320,20 @@ aio = ["aiohttp (>=3.0)"] [[package]] name = "azure-identity" -version = "1.15.0" 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--git a/pyproject.toml b/pyproject.toml index ea9d6918d..1a031be4f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,7 +40,11 @@ bcrypt = "^4.1.2" tinymovr = "^1.6.5" snowflake-connector-python = "3.8.1" langchain = "0.1.16" +langchain-community = "0.0.32" langchain-anthropic = "0.1.8" +sqlalchemy = "1.4.51" +snowflake-sqlalchemy = "1.5.1" +langchain-openai = "0.1.3" [tool.poetry.group.blocks.dependencies] scikit-image = "^0.22.0" From 3c11260e7823201fdf5baba283b6533bbd156134 Mon Sep 17 00:00:00 2001 From: Hristo Date: Mon, 15 Apr 2024 10:50:32 -0400 Subject: [PATCH 4/4] remove useless print statements --- .../SNOWFLAKE/SNOWFLAKE.py | 26 +++---------------- 1 file changed, 3 insertions(+), 23 deletions(-) diff --git a/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py b/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py index 0d7b68252..e764bf62f 100644 --- a/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py +++ b/blocks/AI_ML/DATA_INTERPRETATION/SNOWFLAKE/SNOWFLAKE.py @@ -16,16 +16,6 @@ class SnowflakeOutput(TypedDict): text_answer: String -# @flojoy( -# deps={ -# "sqlalchemy": "1.4.51", # IMPORTANT: THIS IS THE ONLY VERSION THAT WORKS WITH SNOWFLAKE DB WRAPPER IN LANGCHAIN -# "snowflake-sqlalchemy": "1.5.1", # IMPORTANT: THIS IS THE ONLY VERSION THAT WORKS WITH SNOWFLAKE DB WRAPPER IN LANGCHAIN -# "snowflake-connector-python": "3.8.1", -# "langchain": "0.1.16", -# "langchain-openai": "0.1.3", -# "langchain-community": "0.0.32", -# } -# ) @flojoy def SNOWFLAKE( user_prompt: String, @@ -146,19 +136,9 @@ def remove_json_triple_quote_from(string: str): response = ResponseValidation.parse_obj(answer_obj) x = ast.literal_eval(db.run(response.x_axis_query)) y = ast.literal_eval(db.run(response.y_axis_query)) - print("__________") - print(type(x)) - print(x) - print("__________") - print(type(y)) - print(y) - x = list(map(lambda x: x[0], x)) - y = list(map(lambda y: y[0], y)) - print("+++++++++") - 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