Why text-davinci-003?

[vateseeb]

Hi,

first, thx for sharing this neat plugin.

I noticed that the text-davinci-003 model is currently being used for the agents in this project. However, I am curious about the decision behind this choice, especially since the gpt-3.5-turbo model is more cost-effective and allows for defining the AI's role separately.

Based on your experience, is the davinci model more effective in completing tasks? I would be interested in hearing your thoughts on this topic.

Thank you, Sebastian

[Doriandarko]

I actually had a version with chat api 3.5 turbo, but I didn't like how it was returning the tasks and executing them.

On Fri, Apr 14, 2023 at 5:46 PM Sebastian Vates @.***> wrote:

Hi,

first, thx for sharing this neat plugin.

I noticed that the text-davinci-003 model is currently being used for the agents in this project. However, I am curious about the decision behind this choice, especially since the gpt-3.5-turbo model is more cost-effective and allows for defining the AI's role separately.

Based on your experience, is the davinci model more effective in completing tasks? I would be interested in hearing your thoughts on this topic.

Thank you, Sebastian

— Reply to this email directly, view it on GitHub https://github.com/Doriandarko/BabyAGIChatGPT/issues/1, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAJQPTAQLN5RV4P4KKXRJ4DXBHAR5ANCNFSM6AAAAAAW657DEU . You are receiving this because you are subscribed to this thread.Message ID: @.***>

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---

Pietro Schirano Senior Product Designer | Brex http://brex.com

[cometbus]

from typing import Dict, List from quart import Quart, request, jsonify, send_file import quart_cors from collections import deque import openai import pinecone import aiohttp

app = Quart(name) quart_cors.cors(app, allow_origin="https://chat.openai.com")

Set API Keys

OPENAI_API_KEY = "" PINECONE_API_KEY = "" GOOGLE_API_KEY = "" CUSTOM_SEARCH_ENGINE_ID = ""

class BabyAGI:

def init(self, openai_api_key, pinecone_api_key, google_api_key, custom_search_engine_id, pinecone_environment="us-central1-gcp", table_name="test-table", first_task="Develop a task list"): self.openai_api_key = openai_api_key self.pinecone_api_key = pinecone_api_key self.google_api_key = google_api_key self.custom_search_engine_id = custom_search_engine_id self.pinecone_environment = pinecone_environment self.task_list = deque([]) self.objective = "" self.table_name = table_name self.first_task = first_task

openai.api_key = self.openai_api_key
pinecone.init(api_key=self.pinecone_api_key,
              environment=self.pinecone_environment)

def set_objective(self, objective): self.objective = objective

def add_task(self, task: Dict): if not self.task_list: task_id = 1 else: task_id = self.task_list[-1]["task_id"] + 1 task.update({"task_id": task_id}) self.task_list.append(task)

def get_ada_embedding(self, text): text = text.replace("\n", " ") return openai.Embedding.create( input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]

def task_creation_agent(self, objective: str, result: str, task_description: str, task_list: List[str]): messages = [ {"role": "system", "content": "You are a task creation AI that uses the result of an execution agent to create new tasks."}, {"role": "user", "content": f"Objective: {objective}\nResult: {result}\nTask description: {task_description}\nIncomplete tasks: {', '.join(task_list)}"}, ]

response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=messages
)

new_tasks_text = response.choices[0].message['content'].strip()
new_tasks = new_tasks_text.split('\n')
return [{"task_name": task_name} for task_name in new_tasks]

async def execution_agent(self, objective: str, task: str) -> str: context = await self.context_agent(index="test-table", query=objective, n=5) context_str = '\n'.join(context)

# Prepare messages for gpt-3.5-turbo
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": f"Objective: {objective}. Task: {task}."},
]
for snippet in context_str.split('\n'):
    messages.append({"role": "assistant", "content": snippet})

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=messages,
  max_tokens=2000,
  temperature=0.7,
)
return response['choices'][0]['message']['content'].strip()

async def context_agent(self, query: str, index: str, n: int):

Get search results

search_results = await self.search_internet(query, self.google_api_key,
                                            self.custom_search_engine_id)
search_snippets = [
  item["snippet"] for item in search_results.get("items", [])
]

query_embedding = self.get_ada_embedding(query)
index = pinecone.Index(index_name=index)
results = index.query(query_embedding, top_k=n, include_metadata=True)
sorted_results = sorted(results.matches,
                        key=lambda x: x.score,
                        reverse=True)
return search_snippets + [(str(item.metadata['task']))
                          for item in sorted_results]

async def search_internet(self, query, api_key, custom_search_engine_id): print("search_internet called with query:", query) base_url = "https://www.googleapis.com/customsearch/v1" params = {"key": api_key, "cx": custom_search_engine_id, "q": query} async with aiohttp.ClientSession() as session: async with session.get(base_url, params=params) as response: result = await response.json() print("Search results:", result) return result

def clear_task_list(self): self.task_list.clear() self.task_id_counter = 0

baby_agi = BabyAGI(OPENAI_API_KEY, PINECONE_API_KEY, GOOGLE_API_KEY, CUSTOM_SEARCH_ENGINE_ID, table_name="test-table", first_task="Develop a task list")

@app.route("/set_objective", methods=["POST"]) async def set_objective(): global baby_agi data = await request.json objective = data["objective"] baby_agi.clear_task_list( ) # Clear the task list and reset the task ID counter baby_agi.set_objective(objective) return jsonify({"status": "Objective set", "objective": objective})

@app.route("/add_task", methods=["POST"]) async def add_task(): global baby_agi data = await request.json task_name = data["task_name"] task = {"task_name": task_name} baby_agi.add_task(task) return jsonify({"status": "Task added"})

@app.route("/execute_next_task", methods=["POST"]) async def execute_next_task(): global baby_agi if not baby_agi.task_list: return jsonify({"status": "No tasks in the list"})

task = baby_agi.task_list.popleft() result = await baby_agi.execution_agent(baby_agi.objective, task["task_name"])

new_tasks = baby_agi.task_creation_agent( baby_agi.objective, result, task["task_name"], [t["task_name"] for t in baby_agi.task_list]) for new_task in new_tasks: baby_agi.add_task(new_task)

response = { "task_id": task["task_id"], "task_name": task["task_name"], "result": result, "new_tasks": new_tasks, }

return jsonify(response)

@app.route("/get_task_list", methods=["GET"]) async def get_task_list(): global baby_agi task_list = [task["task_name"] for task in baby_agi.task_list] return jsonify({"task_list": task_list})

@app.route("/openapi.yaml", methods=["GET"]) async def get_openapi_yaml(): return await send_file("openapi.yaml", mimetype="application/vnd.oai.openapi")

@app.route("/logo.png", methods=["GET"]) async def get_logo(): return await send_file("logo.png", mimetype="image/png")

@app.route("/.well-known/ai-plugin.json", methods=["GET"]) async def get_ai_plugin_json(): return await send_file("ai-plugin.json", mimetype="application/json")

@app.route("/test_google_api", methods=["GET"]) async def test_google_api(): global baby_agi query = request.args.get("query", "test") search_results = await baby_agi.search_internet( query, baby_agi.google_api_key, baby_agi.custom_search_engine_id) return jsonify(search_results)

if name == "main": app.run(debug=True, host="0.0.0.0", port=5001)

[vateseeb]

Thx for the reply and sharing the code!