Add discord bot, github pipelines
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mudler
281
examples/discord/agent.py
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281
examples/discord/agent.py
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import openai
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#from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.embeddings import LocalAIEmbeddings
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import uuid
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import sys
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from queue import Queue
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import asyncio
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import threading
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from localagi import LocalAGI
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from loguru import logger
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from ascii_magic import AsciiArt
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from duckduckgo_search import DDGS
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from typing import Dict, List
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import os
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import discord
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import openai
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import urllib.request
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from datetime import datetime
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# these three lines swap the stdlib sqlite3 lib with the pysqlite3 package for chroma
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__import__('pysqlite3')
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import sys
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sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
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from langchain.vectorstores import Chroma
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from chromadb.config import Settings
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import json
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import os
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from io import StringIO
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FILE_NAME_FORMAT = '%Y_%m_%d_%H_%M_%S'
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EMBEDDINGS_MODEL = os.environ.get("EMBEDDINGS_MODEL", "all-MiniLM-L6-v2")
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EMBEDDINGS_API_BASE = os.environ.get("EMBEDDINGS_API_BASE", "http://api:8080")
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PERSISTENT_DIR = os.environ.get("PERSISTENT_DIR", "/data/")
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embeddings = LocalAIEmbeddings(model=EMBEDDINGS_MODEL,openai_api_base=EMBEDDINGS_API_BASE)
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chroma_client = Chroma(collection_name="memories", persist_directory="/data/db", embedding_function=embeddings)
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loop = None
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channel = None
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def call(thing):
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return asyncio.run_coroutine_threadsafe(thing,loop).result()
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def create_image(a, agent_actions={}, localagi=None):
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q = json.loads(a)
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logger.info(">>> creating image: ")
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logger.info(q["caption"])
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size=f"{q['width']}x{q['height']}"
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response = openai.Image.create(prompt=q["caption"], n=1, size=size)
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image_url = response["data"][0]["url"]
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image_name = download_image(image_url)
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image_path = f"{PERSISTENT_DIR}{image_name}"
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file = discord.File(image_path, filename=image_name)
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embed = discord.Embed(title="Generated image")
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embed.set_image(url=f"attachment://{image_name}")
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call(channel.send(file=file, content=f"Here is what I have generated", embed=embed))
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return f"Image created: {response['data'][0]['url']}"
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def download_image(url: str):
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file_name = f"{datetime.now().strftime(FILE_NAME_FORMAT)}.jpg"
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full_path = f"{PERSISTENT_DIR}{file_name}"
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urllib.request.urlretrieve(url, full_path)
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return file_name
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### Agent capabilities
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### These functions are called by the agent to perform actions
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###
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def save(memory, agent_actions={}, localagi=None):
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q = json.loads(memory)
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logger.info(">>> saving to memories: ")
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logger.info(q["content"])
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chroma_client.add_texts([q["content"]],[{"id": str(uuid.uuid4())}])
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chroma_client.persist()
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return f"The object was saved permanently to memory."
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def search_memory(query, agent_actions={}, localagi=None):
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q = json.loads(query)
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docs = chroma_client.similarity_search(q["reasoning"])
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text_res="Memories found in the database:\n"
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for doc in docs:
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text_res+="- "+doc.page_content+"\n"
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#if args.postprocess:
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# return post_process(text_res)
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#return text_res
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return localagi.post_process(text_res)
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# write file to disk with content
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def save_file(arg, agent_actions={}, localagi=None):
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arg = json.loads(arg)
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file = filename = arg["filename"]
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content = arg["content"]
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# create persistent dir if does not exist
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if not os.path.exists(PERSISTENT_DIR):
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os.makedirs(PERSISTENT_DIR)
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# write the file in the directory specified
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file = os.path.join(PERSISTENT_DIR, filename)
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# Check if the file already exists
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if os.path.exists(file):
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mode = 'a' # Append mode
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else:
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mode = 'w' # Write mode
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with open(file, mode) as f:
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f.write(content)
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file = discord.File(file, filename=filename)
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call(channel.send(file=file, content=f"Here is what I have generated"))
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return f"File {file} saved successfully."
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def ddg(query: str, num_results: int, backend: str = "api") -> List[Dict[str, str]]:
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"""Run query through DuckDuckGo and return metadata.
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Args:
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query: The query to search for.
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num_results: The number of results to return.
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Returns:
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A list of dictionaries with the following keys:
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snippet - The description of the result.
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title - The title of the result.
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link - The link to the result.
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"""
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ddgs = DDGS()
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try:
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results = ddgs.text(
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query,
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backend=backend,
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)
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if results is None:
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return [{"Result": "No good DuckDuckGo Search Result was found"}]
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def to_metadata(result: Dict) -> Dict[str, str]:
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if backend == "news":
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return {
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"date": result["date"],
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"title": result["title"],
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"snippet": result["body"],
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"source": result["source"],
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"link": result["url"],
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}
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return {
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"snippet": result["body"],
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"title": result["title"],
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"link": result["href"],
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}
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formatted_results = []
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for i, res in enumerate(results, 1):
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if res is not None:
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formatted_results.append(to_metadata(res))
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if len(formatted_results) == num_results:
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break
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except Exception as e:
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logger.error(e)
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return []
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return formatted_results
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## Search on duckduckgo
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def search_duckduckgo(a, agent_actions={}, localagi=None):
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a = json.loads(a)
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list=ddg(a["query"], 2)
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text_res=""
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for doc in list:
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text_res+=f"""{doc["link"]}: {doc["title"]} {doc["snippet"]}\n"""
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#if args.postprocess:
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# return post_process(text_res)
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return text_res
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#l = json.dumps(list)
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#return l
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### End Agent capabilities
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###
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### Agent action definitions
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agent_actions = {
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"create_image": {
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"function": create_image,
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"plannable": True,
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"description": 'If the user wants to generate an image, the assistant replies with "create_image", a detailed caption, the width and height of the image to generate.',
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"signature": {
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"name": "create_image",
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"parameters": {
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"type": "object",
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"properties": {
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"caption": {
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"type": "string",
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},
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"width": {
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"type": "number",
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},
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"height": {
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"type": "number",
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},
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},
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}
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},
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},
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"search_internet": {
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"function": search_duckduckgo,
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"plannable": True,
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"description": 'For searching the internet with a query, the assistant replies with the action "search_internet" and the query to search.',
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"signature": {
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"name": "search_internet",
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"description": """For searching internet.""",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "information to save"
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},
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},
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}
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},
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},
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"save_file": {
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"function": save_file,
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"plannable": True,
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"description": 'The assistant replies with the action "save_file", the filename and content to save for writing a file to disk permanently. This can be used to store the result of complex actions locally.',
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"signature": {
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"name": "save_file",
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"description": """For saving a file to disk with content.""",
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"parameters": {
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"type": "object",
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"properties": {
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"filename": {
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"type": "string",
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"description": "information to save"
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},
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"content": {
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"type": "string",
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"description": "information to save"
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},
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},
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}
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},
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},
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"save_memory": {
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"function": save,
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"plannable": True,
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"description": 'The assistant replies with the action "save_memory" and the string to remember or store an information that thinks it is relevant permanently.',
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"signature": {
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"name": "save_memory",
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"description": """Save or store informations into memory.""",
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"parameters": {
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"type": "object",
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"properties": {
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"content": {
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"type": "string",
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"description": "information to save"
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},
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},
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"required": ["content"]
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}
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},
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},
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"search_memory": {
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"function": search_memory,
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"plannable": True,
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"description": 'The assistant replies with the action "search_memory" for searching between its memories with a query term.',
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"signature": {
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"name": "search_memory",
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"description": """Search in memory""",
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"parameters": {
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"type": "object",
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"properties": {
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"reasoning": {
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"type": "string",
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"description": "reasoning behind the intent"
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},
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},
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"required": ["reasoning"]
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}
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},
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},
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}
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