396 lines
13 KiB
Python
396 lines
13 KiB
Python
import openai
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#from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.embeddings import LocalAIEmbeddings
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from langchain.document_loaders import (
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SitemapLoader,
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# GitHubIssuesLoader,
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# GitLoader,
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)
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import uuid
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import sys
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from app.env import *
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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 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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from langchain.text_splitter import RecursiveCharacterTextSplitter
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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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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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if not os.environ.get("PYSQL_HACK", "false") == "false":
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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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if MILVUS_HOST == "":
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from langchain.vectorstores import Chroma
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else:
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from langchain.vectorstores import Milvus
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embeddings = LocalAIEmbeddings(model=EMBEDDINGS_MODEL,openai_api_base=EMBEDDINGS_API_BASE)
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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 ingest(a, agent_actions={}, localagi=None):
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q = json.loads(a)
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chunk_size = MEMORY_CHUNK_SIZE
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chunk_overlap = MEMORY_CHUNK_OVERLAP
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print(">>> ingesting: ")
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print(q)
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documents = []
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sitemap_loader = SitemapLoader(web_path=q["url"])
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
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documents.extend(sitemap_loader.load())
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texts = text_splitter.split_documents(documents)
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if MILVUS_HOST == "":
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db = Chroma.from_documents(texts,embeddings,collection_name=MEMORY_COLLECTION, persist_directory=PERSISTENT_DIR)
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db.persist()
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db = None
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else:
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Milvus.from_documents(texts,embeddings,collection_name=MEMORY_COLLECTION, connection_args={"host": MILVUS_HOST, "port": MILVUS_PORT})
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return f"Documents ingested"
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# def create_image(a, agent_actions={}, localagi=None):
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# """
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# Create an image based on a description using OpenAI's API.
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# Args:
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# a (str): A JSON string containing the description, width, and height for the image to be created.
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# agent_actions (dict, optional): A dictionary of agent actions. Defaults to {}.
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# localagi (LocalAGI, optional): An instance of the LocalAGI class. Defaults to None.
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# Returns:
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# str: A string containing the URL of the created image.
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# """
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# q = json.loads(a)
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# print(">>> creating image: ")
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# print(q["description"])
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# size=f"{q['width']}x{q['height']}"
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# response = openai.Image.create(prompt=q["description"], 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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print(">>> saving to memories: ")
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print(q["content"])
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if MILVUS_HOST == "":
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chroma_client = Chroma(collection_name=MEMORY_COLLECTION,embedding_function=embeddings, persist_directory=PERSISTENT_DIR)
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else:
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chroma_client = Milvus(collection_name=MEMORY_COLLECTION,embedding_function=embeddings, connection_args={"host": MILVUS_HOST, "port": MILVUS_PORT})
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chroma_client.add_texts([q["content"]],[{"id": str(uuid.uuid4())}])
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if MILVUS_HOST == "":
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chroma_client.persist()
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chroma_client = None
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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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if MILVUS_HOST == "":
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chroma_client = Chroma(collection_name=MEMORY_COLLECTION,embedding_function=embeddings, persist_directory=PERSISTENT_DIR)
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else:
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chroma_client = Milvus(collection_name=MEMORY_COLLECTION,embedding_function=embeddings, connection_args={"host": MILVUS_HOST, "port": MILVUS_PORT})
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#docs = chroma_client.search(q["keywords"], "mmr")
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retriever = chroma_client.as_retriever(search_type=MEMORY_SEARCH_TYPE, search_kwargs={"k": MEMORY_RESULTS})
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docs = retriever.get_relevant_documents(q["keywords"])
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text_res="Memories found in the database:\n"
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sources = set() # To store unique sources
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# Collect unique sources
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for document in docs:
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if "source" in document.metadata:
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sources.add(document.metadata["source"])
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for doc in docs:
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# drop newlines from page_content
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content = doc.page_content.replace("\n", " ")
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content = " ".join(content.split())
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text_res+="- "+content+"\n"
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# Print the relevant sources used for the answer
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for source in sources:
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if source.startswith("http"):
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text_res += "" + source + "\n"
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chroma_client = None
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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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print(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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print("Found")
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print(text_res)
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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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# "generate_picture": {
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# "function": create_image,
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# "plannable": True,
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# "description": 'For creating a picture, the assistant replies with "generate_picture" and a detailed description, enhancing it with as much detail as possible.',
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# "signature": {
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# "name": "generate_picture",
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# "parameters": {
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# "type": "object",
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# "properties": {
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# "description": {
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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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"ingest": {
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"function": ingest,
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"plannable": True,
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"description": 'The assistant replies with the action "ingest" when there is an url to a sitemap to ingest memories from.',
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"signature": {
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"name": "ingest",
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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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"url": {
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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": ["url"]
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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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"keywords": {
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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": ["keywords"]
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}
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},
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},
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}
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def localagi(q):
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localagi = LocalAGI(
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agent_actions=agent_actions,
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llm_model=LLM_MODEL,
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tts_model=VOICE_MODEL,
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tts_api_base=TTS_API_BASE,
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functions_model=FUNCTIONS_MODEL,
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api_base=LOCALAI_API_BASE,
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stablediffusion_api_base=IMAGE_API_BASE,
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stablediffusion_model=STABLEDIFFUSION_MODEL,
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)
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conversation_history = []
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conversation_history=localagi.evaluate(
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q,
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conversation_history,
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critic=False,
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re_evaluate=False,
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# Enable to lower context usage but increases LLM calls
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postprocess=False,
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subtaskContext=True,
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)
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return conversation_history[-1]["content"] |