diff --git a/.env.sample b/.env.sample new file mode 100644 index 0000000..0b0a079 --- /dev/null +++ b/.env.sample @@ -0,0 +1,3 @@ +DATABASE_URL= +OPENAI_API_KEY= +BASE_DIR=./agent_results \ No newline at end of file diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..19719e8 --- /dev/null +++ b/.gitignore @@ -0,0 +1,51 @@ +.aider.ident.cache.v1 +.aider.tags.cache.v1 +.aider.chat.history.md +.aider.input.history + + +agent_results/ + +.env + +*.pyc + +# Packages +*.egg +!/tests/**/*.egg +/*.egg-info +/dist/* +build +_build +.cache +*.so + +# Installer logs +pip-log.txt + +# Unit test / coverage reports +.coverage +.pytest_cache + +.DS_Store +.idea/* +.python-version +.vscode/* + +/test.py +/test_*.* + +/setup.cfg +MANIFEST.in +/setup.py +/docs/site/* +/tests/fixtures/simple_project/setup.py +/tests/fixtures/project_with_extras/setup.py +.mypy_cache + +.venv +/releases/* +pip-wheel-metadata +/poetry.toml + +poetry/core/* \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..b332415 --- /dev/null +++ b/README.md @@ -0,0 +1,152 @@ +# Multi-Agent Postgres Data Analytics +*The way we interact with our data is changing.* + +![Multi-Agent Postgres Data Analytics](imgs/multi-agent-coding.png) + +# 💬 Read This First 💬 +> This repo is an **_experiment_** and **_learning tool_** for building multi-agent systems. +> +> It is **ONE** of **MANY** steps toward building fully autonomous, _agentic software_. +> +> It is **NOT** a framework, or library, or shortcut. +> +> It **IS** a **_stepping stone_** to help you internalize concepts, patterns and building blocks for your own multi-agent systems and applications. +> +> Code only tells a story at a moment in time. I highly recommend you watch the [video series](https://www.youtube.com/playlist?list=PLS_o2ayVCKvDzj2YxeFqMq9UbR1PkPEh0) to see the **how and the why** behind the structure of this experimental codebase. +> +> In the series we build this from scratch and dive deep into complexities, principles, patterns and ideas surrounding multi-agent software. The video order is linked below, mapping branches to videos. +> +> This repo will not be maintained or updated beyond the lifespan of the series. It is a snapshot in time of the code we built in the video series and is meant only to be a reference for you on your journey to building your own multi-agent systems, **_nothing more_**. +> +> When we complete the series will we freeze the codebase. We will then use it as a reference for experiments, products, and videos. + + +## 💻 Multi-Agent Postgres Data Analytics Tool 💻 +This is a multi-agent system that allows you to ask questions about your postgres database in natural language. + +The codebase is powered by GPT-4, Assistance API, AutoGen, Postgres, and Guidance. + +It's the first of many multi-agent applications that utilize LLMs (large language models) to enable reasoning and decision making with reduced need for explicit rules or logic. + +## 💻 Setup 💻 +- **Read the codebase first**. Remember, this is an experiment and learning tool. It's not meant to be a framework or library. +- Run `git branch -a` to view all branches. Each branch is a video in the series. + - `git checkout ` you want to view. +- `poetry install` +- `cp .env.sample .env` +- Fill out `.env` with your postgres url and openai api key +- Run a prompt against your database + - `poetry run start --prompt ""` + - Start with something simple to get a feel for it and then build up to more complex questions. + +## 🛠️ Core Tech Stack 🛠️ +- [OpenAI](https://openai.com/) - GPT-4, GPT-4 Turbo, Assistance API +- [AutoGen](https://microsoft.github.io/autogen/) - Multi-Agent Framework +- [Postgres](https://www.postgresql.org/) - Database +- [Guidance](https://github.com/guidance-ai/guidance) - Structured LLM Responses +- [Aider](https://aider.chat/) - AI Pair Programming +- [Poetry](https://python-poetry.org/) - Package Manager +- [Python ^3.10](https://www.python.org/downloads/release/python-3100/) - Programming Language + +## 🔵 Multi-Agent Patterns & Terminology 🔵 +Throughout the codebase we built up several existing and new patterns and terminology you've likely seen in some shape or form. Here's a quick overview of the most important ones. +- **Agent** - An agent is LLM powered tool with a single purpose that can be assigned a function and/or prompt. +- **Multi-Agent Team** - A collection of agents that exchange messages and work together to accomplish a goal. +- **Conversations** - The exchange of messages between a multi-agent team. +- **Conversation Flows** - The way agents communicate with each other. How you're agents communicate completely changes the way your application works. The conversation flow dictates which agent speaks, the order in which they speak, who they speak to and what they say. +- **Orchestrator** - Manages a single agent team, their conversations and their output. Orchestrators contain different types of conversation flows. +- **Instruments** - Instruments are the tools agents can use. Think of it like a front-end store. It contains state and functions that both agents and orchestrators can utilize throughout the lifecycle of the application. Agents and Orchestrators can consume and manipulate the state of instruments although typically, only agents update state. +- **Decision Agents** - Agents that respond with concrete decisions which can dictate the flow of your applications. To build complex agentic systems you need agents to have the ability to make concrete decisions that then drive the flow of your application. +- **Structured vs Unstructured Agents** - Structured agents are agents that respond with structured data. Unstructured agents are agents that respond with unstructured data. Structured agents are typically decision agents. + +## 📺 Video Series - Learn By Watching 📺 + +### [Part 1 - Prompt Engineering an ENTIRE codebase: Postgres Data Analytics Al Agent](https://youtu.be/jmDMusirPKA) +Branch: [v1-prompt-engineering-an-entire-codebase](https://github.com/disler/multi-agent-postgres-data-analytics/tree/v1-prompt-engineering-an-entire-codebase) + +Video: [https://youtu.be/jmDMusirPKA](https://youtu.be/jmDMusirPKA) + + + +### [Part 2 - One Prompt is NOT enough: Using AutoGen to code a Multi-Agent Postgres AI Tool](https://youtu.be/JjVvYDPVrAQ) +Branch: [v2-using-autogen-to-build-our-multi-agent-tool](https://github.com/disler/multi-agent-postgres-data-analytics/tree/v2-using-autogen-to-build-our-multi-agent-tool) + +Video: [https://youtu.be/JjVvYDPVrAQ](https://youtu.be/JjVvYDPVrAQ) + + + +### [Part 3 - Make AutoGen Consistent: CONTROL your LLM agents for ACCURATE Postgres Al Data Analytics](https://youtu.be/4o8tymMQ5GM) +Branch: [v3-make-autogen-consistent-control-your-llm](https://github.com/disler/multi-agent-postgres-data-analytics/tree/v3-make-autogen-consistent-control-your-llm) + +Video: [https://youtu.be/4o8tymMQ5GM](https://youtu.be/4o8tymMQ5GM) + + + +### [Part 4 - AutoGen Token Tactics: FIRING AI Agents, USELESS Vector Embeddings, GPT-4 Memory Tricks](https://youtu.be/CKo-czvxFkY) +Branch: [v4-autogen-token-tactics-firing-ai-agents](https://github.com/disler/multi-agent-postgres-data-analytics/tree/v4-autogen-token-tactics-firing-ai-agents) + +Video: [https://youtu.be/CKo-czvxFkY](https://youtu.be/CKo-czvxFkY) + + + +### [Part 5 - AutoGen SPYWARE: Coding Systems for SUCCESSFUL AI Agents (Postgres Data Analytics)](https://youtu.be/UA6IVMDPuC8) +Branch: [v5-autogen-spyware-coding-systems-for-successful-ai](https://github.com/disler/multi-agent-postgres-data-analytics/tree/v5-autogen-spyware-coding-systems-for-successful-ai) + +Video: [https://youtu.be/UA6IVMDPuC8](https://youtu.be/UA6IVMDPuC8) + + + +### [Part 6 - Using AUTOGEN & GUIDANCE to code LLM Control Flow & JSON Agents (No Prompt Engineering)](https://youtu.be/XGCWyfA3rgQ) +Branch: [v6-control-flow-and-structured-response](https://github.com/disler/multi-agent-postgres-data-analytics/tree/v6-control-flow-and-structured-response) + +Video: [https://youtu.be/XGCWyfA3rgQ](https://youtu.be/XGCWyfA3rgQ) + + + +### [Part 7 - OpenAI Macro & Micro Strategy: Master Assistants API, Threads, Messages, and Runs](https://youtu.be/KwcrjP3vuy0) +Branch: [v7-turbo4-assistants-threads-messages](https://github.com/disler/multi-agent-postgres-data-analytics/tree/v7-turbo4-assistants-threads-messages) + +Video: [https://youtu.be/KwcrjP3vuy0](https://youtu.be/KwcrjP3vuy0) + + + +### [Part 8 - UNRELEASED](#) + +### [Part 9 - UNRELEASED](#) + +### [Part 10 - UNRELEASED](#) + +--- + +# 🧠 Major Learnings Throughout the Series 🧠 + +## 💡 Why are multi-agent applications important? +- They're important because they allows us to create a more accurate model of the world. +- We become orchestrators enabling less engineering level and more product level work. +- They enable reasoning and decision making in a way that is more human like than ever before. +- We can build systems that make decisions as we would while operating alongside us. +- We can solve problems that previously required a dedicated hire or an entire team to solve. + +## ✅ Multi-Agent Systems: The Good +- Can assign functions & prompts to specific agents, enabling specialization yielding better results. +- Agents can reflect on results to provide feedback thus improving the results. +- Can role play real organizational structures, existing and new. +- Ecosystem is evolving rapidly. New tools and frameworks are being built every day. +- Upside potential is ridiculously massive. We're talking asymmetric ROI, max [leverage](https://www.navalmanack.com/almanack-of-naval-ravikant/find-a-position-of-leverage), [superlinear](http://www.paulgraham.com/superlinear.html) upside. The more agentic build blocks you have the more powerful your engineering and product potential becomes. +- Multi-agent engineering is probably the most important thing happening in software right now (2023-2024). +- The road to agentic software is clear. Solve small problems, create reusable building blocks, and then combine them to solve bigger problems. +- GPT-4 can support multi-agent systems without a doubt. It is the best model by light-years and drives incredible reasoning readily available at your fingertips. + +## ❌ Multi-Agent Systems: The Bad +- It's an art to get the roles and the function of your agent right. How many do you need? What are they? How do you know? +- Can get expensive in testing and scales with # of agents. The more agents the more expensive each query is. +- Can be difficult to debug why a multi-agent system is not working as expected due to the non-deterministic nature of LLMs. +- Memory management is a major issue. The context window is forcing a lot of weird, intricate code to manage memory. +- Too much noise and hype in the AI Agent ecosystem. Lot's of clickbait hype with little follow through value. Hard to find good resources. +- Very few are engineers are publicly building multi-agent systems. Most are toy examples or ripping from example codebases. +- OpenAI is inadvertently killing startups with every new release. Risky to commit to building LLM powered applications. +- At the current price, we cannot run a fully agentic system that runs 24/7 or even for an hour on GPT-4 without burning thousands per day. The price must come down WITHOUT sacrificing quality (looking at you open source models). +- It's tricky to know when to write explicit code vs prompt engineer vs build a multi-agent team. 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b/postgres_da_ai_agent/agents/agent_config.py new file mode 100644 index 0000000..0d80b75 --- /dev/null +++ b/postgres_da_ai_agent/agents/agent_config.py @@ -0,0 +1,128 @@ +import os +from postgres_da_ai_agent.modules.db import PostgresManager +from postgres_da_ai_agent.modules import llm +from postgres_da_ai_agent.modules import orchestrator +from postgres_da_ai_agent.modules import file +import dotenv +import argparse +import autogen + + +# build the gpt_configuration object +# Base Configuration +base_config = { + "use_cache": False, + "temperature": 0, + "config_list": autogen.config_list_from_models(["gpt-4"]), + "request_timeout": 120, +} + +# Configuration with "run_sql" +run_sql_config = { + **base_config, # Inherit base configuration + "functions": [ + { + "name": "run_sql", + "description": "Run a SQL query against the postgres database", + "parameters": { + "type": "object", + "properties": { + "sql": { + "type": "string", + "description": "The SQL query to run", + } + }, + "required": ["sql"], + }, + } + ], +} + +# Configuration with "write_file" +write_file_config = { + **base_config, # Inherit base configuration + "functions": [ + { + "name": "write_file", + "description": "Write a file to the filesystem", + "parameters": { + "type": "object", + "properties": { + "fname": { + "type": "string", + "description": "The name of the file to write", + }, + "content": { + "type": "string", + "description": "The content of the file to write", + }, + }, + "required": ["fname", "content"], + }, + } + ], +} + +# Configuration with "write_json_file" +write_json_file_config = { + **base_config, # Inherit base configuration + "functions": [ + { + "name": "write_json_file", + "description": "Write a json file to the filesystem", + "parameters": { + "type": "object", + "properties": { + "fname": { + "type": "string", + "description": "The name of the file to write", + }, + "json_str": { + "type": "string", + "description": "The content of the file to write", + }, + }, + "required": ["fname", "json_str"], + }, + } + ], +} + +write_yaml_file_config = { + **base_config, # Inherit base configuration + "functions": [ + { + "name": "write_yml_file", + "description": "Write a yml file to the filesystem", + "parameters": { + "type": "object", + "properties": { + "fname": { + "type": "string", + "description": "The name of the file to write", + }, + "json_str": { + "type": "string", + "description": "The json content of the file to write", + }, + }, + "required": ["fname", "json_str"], + }, + } + ], +} + + +def create_func_map(name: str, func: callable): + return { + name: func, + } + + +def build_function_map_run_sql(db: PostgresManager): + return create_func_map("run_sql", db.run_sql) + + +function_map_write_file = create_func_map("write_file", file.write_file) +function_map_write_json_file = create_func_map("write_json_file", file.write_json_file) +function_map_write_yaml_file = create_func_map("write_yml_file", file.write_yml_file) diff --git a/postgres_da_ai_agent/agents/agents.py b/postgres_da_ai_agent/agents/agents.py new file mode 100644 index 0000000..3d6df0a --- /dev/null +++ b/postgres_da_ai_agent/agents/agents.py @@ -0,0 +1,156 @@ +import os +from postgres_da_ai_agent.agents.instruments import PostgresAgentInstruments +from postgres_da_ai_agent.modules.db import PostgresManager +from postgres_da_ai_agent.modules import llm +from postgres_da_ai_agent.modules import orchestrator +from postgres_da_ai_agent.modules import file +from postgres_da_ai_agent.agents import agent_config +import dotenv +import argparse +import autogen + +# ------------ PROMPTS ------------ + + +# create our terminate msg function +def is_termination_msg(content): + have_content = content.get("content", None) is not None + if have_content and "APPROVED" in content["content"]: + return True + return False + + +COMPLETION_PROMPT = "If everything looks good, respond with APPROVED" + +USER_PROXY_PROMPT = "A human admin. Interact with the Product Manager to discuss the plan. Plan execution needs to be approved by this admin." +DATA_ENGINEER_PROMPT = "A Data Engineer. Generate the initial SQL based on the requirements provided. Send it to the Sr Data Analyst to be executed. " +SR_DATA_ANALYST_PROMPT = "Sr Data Analyst. You run the SQL query using the run_sql function, send the raw response to the data viz team. You use the run_sql function exclusively." +PRODUCT_MANAGER_PROMPT = ( + "Product Manager. Validate the response to make sure it's correct" + + COMPLETION_PROMPT +) + +TEXT_REPORT_ANALYST_PROMPT = "Text File Report Analyst. You exclusively use the write_file function on a summarized report." +JSON_REPORT_ANALYST_PROMPT = "Json Report Analyst. You exclusively use the write_json_file function on the report." +YML_REPORT_ANALYST_PROMPT = "Yaml Report Analyst. You exclusively use the write_yml_file function on the report." + + +# ------------ AGENTS ------------ +def build_data_eng_team(instruments: PostgresAgentInstruments): + # create a set of agents with specific roles + # admin user proxy agent - takes in the prompt and manages the group chat + user_proxy = autogen.UserProxyAgent( + name="Admin", + system_message=USER_PROXY_PROMPT, + code_execution_config=False, + human_input_mode="NEVER", + ) + + # data engineer agent - generates the sql query + data_engineer = autogen.AssistantAgent( + name="Engineer", + llm_config=agent_config.base_config, + system_message=DATA_ENGINEER_PROMPT, + code_execution_config=False, + human_input_mode="NEVER", + ) + + sr_data_analyst = autogen.AssistantAgent( + name="Sr_Data_Analyst", + llm_config=agent_config.run_sql_config, + system_message=SR_DATA_ANALYST_PROMPT, + code_execution_config=False, + human_input_mode="NEVER", + function_map={ + "run_sql": instruments.run_sql, + }, + ) + + # product manager - validate the response to make sure it's correct + product_manager = autogen.AssistantAgent( + name="Product_Manager", + llm_config=agent_config.base_config, + system_message=PRODUCT_MANAGER_PROMPT, + code_execution_config=False, + human_input_mode="NEVER", + ) + + return [ + user_proxy, + data_engineer, + sr_data_analyst, + ] + + +def build_data_viz_team(instruments: PostgresAgentInstruments): + # admin user proxy agent - takes in the prompt and manages the group chat + user_proxy = autogen.UserProxyAgent( + name="Admin", + system_message=USER_PROXY_PROMPT, + code_execution_config=False, + human_input_mode="NEVER", + ) + + # text report analyst - writes a summary report of the results and saves them to a local text file + text_report_analyst = autogen.AssistantAgent( + name="Text_Report_Analyst", + llm_config=agent_config.write_file_config, + system_message=TEXT_REPORT_ANALYST_PROMPT, + human_input_mode="NEVER", + function_map={ + "write_file": instruments.write_file, + }, + ) + + # json report analyst - writes a summary report of the results and saves them to a local json file + json_report_analyst = autogen.AssistantAgent( + name="Json_Report_Analyst", + llm_config=agent_config.write_json_file_config, + system_message=JSON_REPORT_ANALYST_PROMPT, + human_input_mode="NEVER", + function_map={ + "write_json_file": instruments.write_json_file, + }, + ) + + yaml_report_analyst = autogen.AssistantAgent( + name="Yml_Report_Analyst", + llm_config=agent_config.write_yaml_file_config, + system_message=YML_REPORT_ANALYST_PROMPT, + human_input_mode="NEVER", + function_map={ + "write_yml_file": instruments.write_yml_file, + }, + ) + + return [ + user_proxy, + text_report_analyst, + json_report_analyst, + yaml_report_analyst, + ] + + +# ------------ ORCHESTRATION ------------ + + +def build_team_orchestrator( + team: str, + agent_instruments: PostgresAgentInstruments, + validate_results: callable = None, +) -> orchestrator.Orchestrator: + if team == "data_eng": + return orchestrator.Orchestrator( + name="Postgres Data Analytics Multi-Agent ::: Data Engineering Team", + agents=build_data_eng_team(agent_instruments), + instruments=agent_instruments, + validate_results_func=validate_results, + ) + elif team == "data_viz": + return orchestrator.Orchestrator( + name="Postgres Data Analytics Multi-Agent ::: Data Viz Team", + agents=build_data_viz_team(agent_instruments), + validate_results_func=validate_results, + ) + + raise Exception("Unknown team: " + team) diff --git a/postgres_da_ai_agent/agents/instruments.py b/postgres_da_ai_agent/agents/instruments.py new file mode 100644 index 0000000..0679787 --- /dev/null +++ b/postgres_da_ai_agent/agents/instruments.py @@ -0,0 +1,156 @@ +from postgres_da_ai_agent.modules.db import PostgresManager +from postgres_da_ai_agent.modules import file +import os + +BASE_DIR = os.environ.get("BASE_DIR", "./agent_results") + + +class AgentInstruments: + """ + Base class for multli-agent instruments that are tools, state, and functions that an agent can use across the lifecycle of conversations + """ + + def __init__(self) -> None: + self.session_id = None + self.messages = [] + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, traceback): + pass + + def sync_messages(self, messages: list): + """ + Syncs messages with the orchestrator + """ + raise NotImplementedError + + @property + def root_dir(self): + return os.path.join(BASE_DIR, self.session_id) + + @property + def agent_chat_file(self): + return os.path.join(self.root_dir, "agent_chats.json") + + +class PostgresAgentInstruments(AgentInstruments): + """ + Unified Toolset for the Postgres Data Analytics Multi-Agent System + + Advantages: + - All agents have access to the same state and functions + - Gives agent functions awareness of changing context + - Clear and concise capabilities for agents + - Clean database connection management + + Guidelines: + - Agent Functions should not call other agent functions directly + - Instead Agent Functions should call external lower level modules + - Prefer 1 to 1 mapping of agents and their functions (controverisal) + - The state lifecycle lives between all agent orchestrations + """ + + def __init__(self, db_url: str, session_id: str) -> None: + super().__init__() + + self.db_url = db_url + self.db = None + self.session_id = session_id + self.messages = [] + self.complete_keyword = "APPROVED" + + self.innovation_index = 0 + + def __enter__(self): + self.reset_files() + self.db = PostgresManager() + self.db.connect_with_url(self.db_url) + return self, self.db + + def __exit__(self, exc_type, exc_val, exc_tb): + self.db.close() + + def sync_messages(self, messages: list): + self.messages = messages + + def reset_files(self): + """ + Clear everything in the root_dir + """ + + # if it does not exist create it + if not os.path.exists(self.root_dir): + os.makedirs(self.root_dir) + + for fname in os.listdir(self.root_dir): + os.remove(os.path.join(self.root_dir, fname)) + + def get_file_path(self, fname: str): + return os.path.join(self.root_dir, fname) + + # -------------------------- Agent Properties -------------------------- # + + @property + def run_sql_results_file(self): + return self.get_file_path("run_sql_results.json") + + # -------------------------- Agent Functions -------------------------- # + + def run_sql(self, sql: str) -> str: + """ + Run a SQL query against the postgres database + """ + results_as_json = self.db.run_sql(sql) + + fname = self.run_sql_results_file + + # dump these results to a file + with open(fname, "w") as f: + f.write(results_as_json) + + return "Successfully delivered results to json file" + + def validate_run_sql(self): + """ + validate that the run_sql results file exists and has content + """ + fname = self.run_sql_results_file + + with open(fname, "r") as f: + content = f.read() + + if not content: + return False + + return True + + def write_file(self, content: str): + fname = self.get_file_path(f"write_file.txt") + return file.write_file(fname, content) + + def write_json_file(self, json_str: str): + fname = self.get_file_path(f"write_json_file.json") + return file.write_json_file(fname, json_str) + + def write_yml_file(self, json_str: str): + fname = self.get_file_path(f"write_yml_file.yml") + return file.write_yml_file(fname, json_str) + + def write_innovation_file(self, content: str): + fname = self.get_file_path(f"{self.innovation_index}_innovation_file.txt") + file.write_file(fname, content) + self.innovation_index += 1 + return f"Successfully wrote innovation file. You can check my work." + + def validate_innovation_files(self): + # loop from 0 to innovation_index and verify file exists with content + for i in range(self.innovation_index): + fname = self.get_file_path(f"{i}_innovation_file.txt") + with open(fname, "r") as f: + content = f.read() + if not content: + return False + + return True diff --git a/postgres_da_ai_agent/example.py b/postgres_da_ai_agent/example.py new file mode 100644 index 0000000..7bad8e6 --- /dev/null +++ b/postgres_da_ai_agent/example.py @@ -0,0 +1,51 @@ +# config_list_gpt4 = ["gpt-4"] + +# gpt4_config = { +# "seed": 42, # change the seed for different trials +# "temperature": 0, +# "config_list": config_list_gpt4, +# "request_timeout": 120, +# } +# user_proxy = autogen.UserProxyAgent( +# name="Admin", +# system_message="A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin.", +# code_execution_config=False, +# ) +# engineer = autogen.AssistantAgent( +# name="Engineer", +# llm_config=gpt4_config, +# system_message="""Engineer. You follow an approved plan. You write python/shell code to solve tasks. Wrap the code in a code block that specifies the script type. The user can't modify your code. So do not suggest incomplete code which requires others to modify. Don't use a code block if it's not intended to be executed by the executor. +# Don't include multiple code blocks in one response. Do not ask others to copy and paste the result. Check the execution result returned by the executor. +# If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try. +# """, +# ) +# scientist = autogen.AssistantAgent( +# name="Scientist", +# llm_config=gpt4_config, +# system_message="""Scientist. You follow an approved plan. You are able to categorize papers after seeing their abstracts printed. You don't write code.""", +# ) +# planner = autogen.AssistantAgent( +# name="Planner", +# system_message="""Planner. Suggest a plan. Revise the plan based on feedback from admin and critic, until admin approval. +# The plan may involve an engineer who can write code and a scientist who doesn't write code. +# Explain the plan first. Be clear which step is performed by an engineer, and which step is performed by a scientist. +# """, +# llm_config=gpt4_config, +# ) +# executor = autogen.UserProxyAgent( +# name="Executor", +# system_message="Executor. Execute the code written by the engineer and report the result.", +# human_input_mode="NEVER", +# code_execution_config={"last_n_messages": 3, "work_dir": "paper"}, +# ) +# critic = autogen.AssistantAgent( +# name="Critic", +# system_message="Critic. Double check plan, claims, code from other agents and provide feedback. Check whether the plan includes adding verifiable info such as source URL.", +# llm_config=gpt4_config, +# ) +# groupchat = autogen.GroupChat( +# agents=[user_proxy, engineer, scientist, planner, executor, critic], +# messages=[], +# max_round=50, +# ) +# manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=gpt4_config) diff --git a/postgres_da_ai_agent/main.py b/postgres_da_ai_agent/main.py new file mode 100644 index 0000000..bf72aa2 --- /dev/null +++ b/postgres_da_ai_agent/main.py @@ -0,0 +1,92 @@ +import os +from postgres_da_ai_agent.agents.instruments import PostgresAgentInstruments +from postgres_da_ai_agent.modules.db import PostgresManager +from postgres_da_ai_agent.modules import llm +from postgres_da_ai_agent.modules import orchestrator +from postgres_da_ai_agent.modules import rand +from postgres_da_ai_agent.modules import file +from postgres_da_ai_agent.modules import embeddings +from postgres_da_ai_agent.agents import agents +import dotenv +import argparse +import autogen + +from postgres_da_ai_agent.types import ConversationResult + +dotenv.load_dotenv() + +assert os.environ.get("DATABASE_URL"), "POSTGRES_CONNECTION_URL not found in .env file" +assert os.environ.get( + "OPENAI_API_KEY" +), "POSTGRES_CONNECTION_URL not found in .env file" + +DB_URL = os.environ.get("DATABASE_URL") +OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY") + +POSTGRES_TABLE_DEFINITIONS_CAP_REF = "TABLE_DEFINITIONS" + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--prompt", help="The prompt for the AI") + args = parser.parse_args() + + if not args.prompt: + print("Please provide a prompt") + return + + raw_prompt = args.prompt + + prompt = f"Fulfill this database query: {raw_prompt}. " + + session_id = rand.generate_session_id(raw_prompt) + + with PostgresAgentInstruments(DB_URL, session_id) as (agent_instruments, db): + map_table_name_to_table_def = db.get_table_definition_map_for_embeddings() + + database_embedder = embeddings.DatabaseEmbedder() + + for name, table_def in map_table_name_to_table_def.items(): + database_embedder.add_table(name, table_def) + + similar_tables = database_embedder.get_similar_tables(raw_prompt, n=5) + + table_definitions = database_embedder.get_table_definitions_from_names( + similar_tables + ) + + prompt = llm.add_cap_ref( + prompt, + f"Use these {POSTGRES_TABLE_DEFINITIONS_CAP_REF} to satisfy the database query.", + POSTGRES_TABLE_DEFINITIONS_CAP_REF, + table_definitions, + ) + + data_eng_orchestrator = agents.build_team_orchestrator( + "data_eng", + agent_instruments, + validate_results=agent_instruments.validate_run_sql, + ) + + data_eng_conversation_result: ConversationResult = ( + data_eng_orchestrator.sequential_conversation(prompt) + ) + + match data_eng_conversation_result: + case ConversationResult( + success=True, cost=data_eng_cost, tokens=data_eng_tokens + ): + print( + f"✅ Orchestrator was successful. Team: {data_eng_orchestrator.name}" + ) + print( + f"💰📊🤖 {data_eng_orchestrator.name} Cost: {data_eng_cost}, tokens: {data_eng_tokens}" + ) + case _: + print( + f"❌ Orchestrator failed. Team: {data_eng_orchestrator.name} Failed" + ) + + +if __name__ == "__main__": + main() diff --git a/postgres_da_ai_agent/modules/db.py b/postgres_da_ai_agent/modules/db.py new file mode 100644 index 0000000..2512679 --- /dev/null +++ b/postgres_da_ai_agent/modules/db.py @@ -0,0 +1,154 @@ +from datetime import datetime +import json +import psycopg2 +from psycopg2.sql import SQL, Identifier + + +class PostgresManager: + def __init__(self): + self.conn = None + self.cur = None + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + if self.cur: + self.cur.close() + if self.conn: + self.conn.close() + + def connect_with_url(self, url): + self.conn = psycopg2.connect(url) + self.cur = self.conn.cursor() + + def upsert(self, table_name, _dict): + columns = _dict.keys() + values = [SQL("%s")] * len(columns) + upsert_stmt = SQL( + "INSERT INTO {} ({}) VALUES ({}) ON CONFLICT (id) DO UPDATE SET {}" + ).format( + Identifier(table_name), + SQL(", ").join(map(Identifier, columns)), + SQL(", ").join(values), + SQL(", ").join( + [ + SQL("{} = EXCLUDED.{}").format(Identifier(k), Identifier(k)) + for k in columns + ] + ), + ) + self.cur.execute(upsert_stmt, list(_dict.values())) + self.conn.commit() + + def delete(self, table_name, _id): + delete_stmt = SQL("DELETE FROM {} WHERE id = %s").format(Identifier(table_name)) + self.cur.execute(delete_stmt, (_id,)) + self.conn.commit() + + def get(self, table_name, _id): + select_stmt = SQL("SELECT * FROM {} WHERE id = %s").format( + Identifier(table_name) + ) + self.cur.execute(select_stmt, (_id,)) + return self.cur.fetchone() + + def get_all(self, table_name): + select_all_stmt = SQL("SELECT * FROM {}").format(Identifier(table_name)) + self.cur.execute(select_all_stmt) + return self.cur.fetchall() + + # def run_sql(self, sql): + # self.cur.execute(sql) + # return self.cur.fetchall() + + def run_sql(self, sql) -> str: + self.cur.execute(sql) + columns = [desc[0] for desc in self.cur.description] + res = self.cur.fetchall() + + list_of_dicts = [dict(zip(columns, row)) for row in res] + + json_result = json.dumps(list_of_dicts, indent=4, default=self.datetime_handler) + + # dump these results to a file + with open("results.json", "w") as f: + f.write(json_result) + + return json_result + + def datetime_handler(self, obj): + """ + Handle datetime objects when serializing to JSON. + """ + if isinstance(obj, datetime): + return obj.isoformat() + return str(obj) # or just return the object unchanged, or another default value + + def get_table_definition(self, table_name): + get_def_stmt = """ + SELECT pg_class.relname as tablename, + pg_attribute.attnum, + pg_attribute.attname, + format_type(atttypid, atttypmod) + FROM pg_class + JOIN pg_namespace ON pg_namespace.oid = pg_class.relnamespace + JOIN pg_attribute ON pg_attribute.attrelid = pg_class.oid + WHERE pg_attribute.attnum > 0 + AND pg_class.relname = %s + AND pg_namespace.nspname = 'public' -- Assuming you're interested in public schema + """ + self.cur.execute(get_def_stmt, (table_name,)) + rows = self.cur.fetchall() + create_table_stmt = "CREATE TABLE {} (\n".format(table_name) + for row in rows: + create_table_stmt += "{} {},\n".format(row[2], row[3]) + create_table_stmt = create_table_stmt.rstrip(",\n") + "\n);" + return create_table_stmt + + def get_all_table_names(self): + get_all_tables_stmt = ( + "SELECT tablename FROM pg_tables WHERE schemaname = 'public';" + ) + self.cur.execute(get_all_tables_stmt) + return [row[0] for row in self.cur.fetchall()] + + def get_table_definitions_for_prompt(self): + table_names = self.get_all_table_names() + definitions = [] + for table_name in table_names: + definitions.append(self.get_table_definition(table_name)) + return "\n\n".join(definitions) + + def get_table_definitions_for_prompt_MOCK(self): + return """CREATE TABLE users ( +id integer, +created timestamp without time zone, +updated timestamp without time zone, +authed boolean, +plan text, +name text, +email text +); + +CREATE TABLE jobs ( +id integer, +created timestamp without time zone, +updated timestamp without time zone, +parentuserid integer, +status text, +totaldurationms bigint + );""" + + def get_table_definition_map_for_embeddings(self): + table_names = self.get_all_table_names() + definitions = {} + for table_name in table_names: + definitions[table_name] = self.get_table_definition(table_name) + return definitions + + def close(self): + if self.cur: + self.cur.close() + if self.conn: + self.conn.close() diff --git a/postgres_da_ai_agent/modules/embeddings.py b/postgres_da_ai_agent/modules/embeddings.py new file mode 100644 index 0000000..67cbff0 --- /dev/null +++ b/postgres_da_ai_agent/modules/embeddings.py @@ -0,0 +1,76 @@ +from sklearn.metrics.pairwise import cosine_similarity +from transformers import BertTokenizer, BertModel + + +class DatabaseEmbedder: + def __init__(self): + self.tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") + self.model = BertModel.from_pretrained("bert-base-uncased") + self.map_name_to_embeddings = {} + self.map_name_to_table_def = {} + + def add_table(self, table_name: str, text_representation: str): + self.map_name_to_embeddings[table_name] = self.compute_embeddings( + text_representation + ) + + self.map_name_to_table_def[table_name] = text_representation + + def compute_embeddings(self, text): + inputs = self.tokenizer( + text, return_tensors="pt", truncation=True, padding=True, max_length=512 + ) + outputs = self.model(**inputs) + return outputs["pooler_output"].detach().numpy() + + def get_similar_tables_via_embeddings(self, query, n=3): + """ + Given a query, find the top 'n' tables that are most similar to it. + + Args: + - query (str): The user's natural language query. + - n (int, optional): Number of top tables to return. Defaults to 3. + + Returns: + - list: Top 'n' table names ranked by their similarity to the query. + """ + # Compute the embedding for the user's query + query_embedding = self.compute_embeddings(query) + # Calculate cosine similarity between the query and all tables + similarities = { + table: cosine_similarity(query_embedding, emb)[0][0] + for table, emb in self.map_name_to_embeddings.items() + } + # Rank tables based on their similarity scores and return top 'n' + return sorted(similarities, key=similarities.get, reverse=True)[:n] + + def get_similar_table_names_via_word_match(self, query: str): + """ + if any word in our query is a table name, add the table to a list + """ + + tables = [] + + for table_name in self.map_name_to_table_def.keys(): + if table_name.lower() in query.lower(): + tables.append(table_name) + + return tables + + def get_similar_tables(self, query: str, n=3): + """ + combines results from get_similar_tables_via_embeddings and get_similar_table_names_via_word_match + """ + + similar_tables_via_embeddings = self.get_similar_tables_via_embeddings(query, n) + similar_tables_via_word_match = self.get_similar_table_names_via_word_match( + query + ) + + return similar_tables_via_embeddings + similar_tables_via_word_match + + def get_table_definitions_from_names(self, table_names: list) -> list: + table_defs = [ + self.map_name_to_table_def[table_name] for table_name in table_names + ] + return "\n\n".join(table_defs) diff --git a/postgres_da_ai_agent/modules/file.py b/postgres_da_ai_agent/modules/file.py new file mode 100644 index 0000000..bf1f4e3 --- /dev/null +++ b/postgres_da_ai_agent/modules/file.py @@ -0,0 +1,40 @@ +import json + +import yaml + + +def write_file(fname, content): + with open(fname, "w") as f: + f.write(content) + + +def write_json_file(fname, json_str: str): + # convert ' to " + json_str = json_str.replace("'", '"') + + # Convert the string to a Python object + data = json.loads(json_str) + + # Write the Python object to the file as JSON + with open(fname, "w") as f: + json.dump(data, f, indent=4) + + +def write_yml_file(fname, json_str: str): + print("write_yml_file() ", json_str) + + # Try to replace single quotes with double quotes for JSON + cleaned_json_str = json_str.replace("'", '"') + + print("cleaned_json_str() ", cleaned_json_str) + + # Safely convert the JSON string to a Python object + try: + data = json.loads(cleaned_json_str) + except json.JSONDecodeError as e: + print(f"Error decoding JSON: {e}") + return + + # Write the Python object to the file as YAML + with open(fname, "w") as f: + yaml.dump(data, f) diff --git a/postgres_da_ai_agent/modules/llm.py b/postgres_da_ai_agent/modules/llm.py new file mode 100644 index 0000000..f9e0386 --- /dev/null +++ b/postgres_da_ai_agent/modules/llm.py @@ -0,0 +1,107 @@ +""" +Purpose: + Interact with the OpenAI API. + Provide supporting prompt engineering functions. +""" + +import sys +from dotenv import load_dotenv +import os +from typing import Any, Dict +import openai +import tiktoken + +# load .env file +load_dotenv() + +assert os.environ.get("OPENAI_API_KEY") + +# get openai api key +openai.api_key = os.environ.get("OPENAI_API_KEY") + +# ------------------ helpers ------------------ + + +def safe_get(data, dot_chained_keys): + """ + {'a': {'b': [{'c': 1}]}} + safe_get(data, 'a.b.0.c') -> 1 + """ + keys = dot_chained_keys.split(".") + for key in keys: + try: + if isinstance(data, list): + data = data[int(key)] + else: + data = data[key] + except (KeyError, TypeError, IndexError): + return None + return data + + +def response_parser(response: Dict[str, Any]): + return safe_get(response, "choices.0.message.content") + + +# ------------------ content generators ------------------ + + +def prompt(prompt: str, model: str = "gpt-4") -> str: + # validate the openai api key - if it's not valid, raise an error + if not openai.api_key: + sys.exit( + """ +ERORR: OpenAI API key not found. Please export your key to OPENAI_API_KEY +Example bash command: + export OPENAI_API_KEY= + """ + ) + + response = openai.ChatCompletion.create( + model=model, + messages=[ + { + "role": "user", + "content": prompt, + } + ], + ) + + return response_parser(response) + + +def add_cap_ref( + prompt: str, prompt_suffix: str, cap_ref: str, cap_ref_content: str +) -> str: + """ + Attaches a capitalized reference to the prompt. + Example + prompt = 'Refactor this code.' + prompt_suffix = 'Make it more readable using this EXAMPLE.' + cap_ref = 'EXAMPLE' + cap_ref_content = 'def foo():\n return True' + returns 'Refactor this code. Make it more readable using this EXAMPLE.\n\nEXAMPLE\n\ndef foo():\n return True' + """ + + new_prompt = f"""{prompt} {prompt_suffix}\n\n{cap_ref}\n\n{cap_ref_content}""" + + return new_prompt + + +def count_tokens(text: str): + enc = tiktoken.get_encoding("cl100k_base") + return len(enc.encode(text)) + + +def estimate_price_and_tokens(text): + # round up to the output tokens + COST_PER_1k_TOKENS = 0.06 + + tokens = count_tokens(text) + + estimated_cost = (tokens / 1000) * COST_PER_1k_TOKENS + + # round + estimated_cost = round(estimated_cost, 2) + + return estimated_cost, tokens diff --git a/postgres_da_ai_agent/modules/orchestrator.py b/postgres_da_ai_agent/modules/orchestrator.py new file mode 100644 index 0000000..c31fc24 --- /dev/null +++ b/postgres_da_ai_agent/modules/orchestrator.py @@ -0,0 +1,280 @@ +import dataclasses +import json +from typing import List, Optional, Tuple +import autogen +from postgres_da_ai_agent.agents.instruments import AgentInstruments +from postgres_da_ai_agent.modules import llm +from postgres_da_ai_agent.types import Chat, ConversationResult + + +class Orchestrator: + def __init__( + self, + name: str, + agents: List[autogen.ConversableAgent], + instruments: AgentInstruments, + validate_results_func: callable = None, + ): + self.name = name + self.agents = agents + self.messages = [] + self.complete_keyword = "APPROVED" + self.error_keyword = "ERROR" + self.instruments = instruments + self.chats: List[Chat] = [] + self.validate_results_func: callable = validate_results_func + + if len(self.agents) < 2: + raise Exception("Orchestrator needs at least two agents") + + @property + def total_agents(self): + return len(self.agents) + + @property + def last_message_is_dict(self): + return isinstance(self.messages[-1], dict) + + @property + def last_message_is_string(self): + return isinstance(self.messages[-1], str) + + @property + def last_message_is_func_call(self): + return self.last_message_is_dict and self.latest_message.get( + "function_call", None + ) + + @property + def last_message_is_content(self): + return self.last_message_is_dict and self.latest_message.get("content", None) + + @property + def latest_message(self) -> Optional[str]: + if not self.messages: + return None + return self.messages[-1] + + @property + def last_message_always_string(self): + if not self.messages: + return "" + if self.last_message_is_content: + return self.latest_message.get("content", "") + return str(self.messages[-1]) + + def send_message( + self, + from_agent: autogen.ConversableAgent, + to_agent: autogen.ConversableAgent, + message: str, + ): + from_agent.send(message, to_agent) + + self.chats.append( + Chat( + from_name=from_agent.name, + to_name=to_agent.name, + message=str(message), + ) + ) + + def get_message_as_str(self): + messages_as_str = "" + + for message in self.messages: + if message is None: + continue + + if isinstance(message, dict): + content_from_dict = message.get("content", None) + func_call_from_dict = message.get("function_call", None) + content = content_from_dict or func_call_from_dict + if not content: + continue + messages_as_str += str(content) + else: + messages_as_str += str(message) + + return messages_as_str + + def get_cost_and_tokens(self): + return llm.estimate_price_and_tokens(self.get_message_as_str()) + + def add_message(self, message: str): + self.messages.append(message) + + def has_functions(self, agent: autogen.ConversableAgent): + return len(agent._function_map) > 0 + + def basic_chat( + self, + agent_a: autogen.ConversableAgent, + agent_b: autogen.ConversableAgent, + message: str, + ): + print(f"basic_chat(): {agent_a.name} -> {agent_b.name}") + + self.send_message(agent_a, agent_b, message) + + reply = agent_b.generate_reply(sender=agent_a) + + self.add_message(reply) + + print(f"basic_chat(): replied with:", reply) + + def memory_chat( + self, + agent_a: autogen.ConversableAgent, + agent_b: autogen.ConversableAgent, + message: str, + ): + print(f"memory_chat() '{agent_a.name}' --> '{agent_b.name}'") + + self.send_message(agent_a, agent_b, message) + + reply = agent_b.generate_reply(sender=agent_a) + + self.send_message(agent_b, agent_b, message) + + self.add_message(reply) + + def function_chat( + self, + agent_a: autogen.ConversableAgent, + agent_b: autogen.ConversableAgent, + message: str, + ): + print(f"function_call(): {agent_a.name} -> {agent_b.name}") + + self.basic_chat(agent_a, agent_a, message) + + assert self.last_message_is_content + + self.basic_chat(agent_a, agent_b, self.latest_message) + + def self_function_chat(self, agent: autogen.ConversableAgent, message: str): + print(f"self_function_chat(): {agent.name} -> {agent.name}") + + self.send_message(agent, agent, message) + + reply = agent.generate_reply(sender=agent) + + self.send_message(agent, agent, message) + + self.add_message(reply) + + print(f"self_function_chat(): replied with:", reply) + + def spy_on_agents(self, append_to_file: bool = True): + conversations = [] + + for chat in self.chats: + conversations.append(dataclasses.asdict(chat)) + + if append_to_file: + file_name = self.instruments.agent_chat_file + with open(file_name, "w") as f: + f.write(json.dumps(conversations, indent=4)) + + def sequential_conversation(self, prompt: str) -> ConversationResult: + """ + Runs a sequential conversation between agents + + For example + "Agent A" -> "Agent B" -> "Agent C" -> "Agent D" -> "Agent E" + """ + + print(f"\n\n--------- {self.name} Orchestrator Starting ---------\n\n") + + self.add_message(prompt) + + for idx, agent in enumerate(self.agents): + agent_a = self.agents[idx] + agent_b = self.agents[idx + 1] + + print( + f"\n\n--------- Running iteration {idx} with (agent_a: {agent_a.name}, agent_b: {agent_b.name}) ---------\n\n" + ) + + # agent_a -> chat -> agent_b + if self.last_message_is_string: + self.basic_chat(agent_a, agent_b, self.latest_message) + + # agent_a -> func() -> agent_b + if self.last_message_is_func_call and self.has_functions(agent_a): + self.function_chat(agent_a, agent_b, self.latest_message) + + self.spy_on_agents() + + if idx == self.total_agents - 2: + if self.has_functions(agent_b): + # agent_b -> func() -> agent_b + self.self_function_chat(agent_b, self.latest_message) + + print(f"-------- Orchestrator Complete --------\n\n") + + was_successful = self.validate_results_func() + + self.spy_on_agents() + + cost, tokens = self.get_cost_and_tokens() + + return ConversationResult( + success=was_successful, + messages=self.messages, + cost=cost, + tokens=tokens, + last_message_str=self.last_message_always_string, + ) + + def broadcast_conversation(self, prompt: str) -> ConversationResult: + """ + Broadcast a message from agent_a to all agents. + + For example + "Agent A" -> "Agent B" + "Agent A" -> "Agent C" + "Agent A" -> "Agent D" + "Agent A" -> "Agent E" + """ + + print(f"\n\n--------- {self.name} Orchestrator Starting ---------\n\n") + + self.add_message(prompt) + + broadcast_agent = self.agents[0] + + for idx, agent_iterate in enumerate(self.agents[1:]): + print( + f"\n\n--------- Running iteration {idx} with (agent_broadcast: {broadcast_agent.name}, agent_iteration: {agent_iterate.name}) ---------\n\n" + ) + + # agent_a -> chat -> agent_b + if self.last_message_is_string: + self.memory_chat(broadcast_agent, agent_iterate, prompt) + + # agent_b -> func() -> agent_b + if self.last_message_is_func_call and self.has_functions(agent_iterate): + self.function_chat(agent_iterate, agent_iterate, self.latest_message) + + self.spy_on_agents() + + print(f"-------- Orchestrator Complete --------\n\n") + + was_successful = self.validate_results_func() + + if was_successful: + print(f"✅ Orchestrator was successful") + else: + print(f"❌ Orchestrator failed") + + cost, tokens = self.get_cost_and_tokens() + + return ConversationResult( + success=was_successful, + messages=self.messages, + cost=cost, + tokens=tokens, + last_message_str=self.last_message_always_string, + ) diff --git a/postgres_da_ai_agent/modules/rand.py b/postgres_da_ai_agent/modules/rand.py new file mode 100644 index 0000000..e6cb5a7 --- /dev/null +++ b/postgres_da_ai_agent/modules/rand.py @@ -0,0 +1,26 @@ +from datetime import datetime +import uuid + + +def generate_session_id(raw_prompt: str): + """ + "get jobs with 'Completed' or 'Started' status" + + -> + + "get_jobs_with_Completed_or_Started_status__12_22_22" + """ + + now = datetime.now() + hours = now.hour + minutes = now.minute + seconds = now.second + + short_time_mm_ss = f"{hours:02}_{minutes:02}_{seconds:02}" + + lower_case = raw_prompt.lower() + no_spaces = lower_case.replace(" ", "_") + no_quotes = no_spaces.replace("'", "") + shorter = no_quotes[:30] + with_uuid = shorter + "__" + short_time_mm_ss + return with_uuid diff --git a/postgres_da_ai_agent/types.py b/postgres_da_ai_agent/types.py new file mode 100644 index 0000000..2286141 --- /dev/null +++ b/postgres_da_ai_agent/types.py @@ -0,0 +1,18 @@ +from dataclasses import dataclass +from typing import List + + +@dataclass +class Chat: + from_name: str + to_name: str + message: str + + +@dataclass +class ConversationResult: + success: bool + messages: List[Chat] + cost: float + tokens: int + last_message_str: str diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..7476ad7 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,27 @@ +[tool.poetry] +name = "postgres-da-ai-agent" +version = "0.1.0" +description = "" +authors = ["qwukker "] +readme = "README.md" +packages = [{include = "postgres_da_ai_agent"}] + +[tool.poetry.dependencies] +python = "^3.10" +openai = "^0.28.1" +psycopg2-binary = "^2.9.8" +argparse = "^1.4.0" +python-dotenv = "^1.0.0" +pyautogen = "^0.1.7" +transformers = "^4.34.1" +torch = "^2.1.0" +scikit-learn = "^1.3.1" +tiktoken = "^0.5.1" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry.scripts] +start = "postgres_da_ai_agent.main:main"