461 lines
15 KiB
Python
Executable File
461 lines
15 KiB
Python
Executable File
import requests
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import json
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import yaml
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from typing_extensions import Annotated, List
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from autogen.coding.func_with_reqs import with_requirements
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# 读取YAML配置文件
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def load_agent_configs(config_path):
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with open(config_path, 'r') as file:
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return yaml.safe_load(file)
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# Define the termination message function
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def termination_msg(x):
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return isinstance(x, dict) and "TERMINATE" == str(x.get("content", ""))[-9:].upper()
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@with_requirements(python_packages=["requests"], global_imports=["requests"])
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def retrieval_from_knowledge_base(
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query: str,
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topk: int
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) -> str:
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"""
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Retrieval for knowledge from the knowledge base based on the specified query and returns the topk results.
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Parameters:
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query (str): The query for knowledge retrieval.
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topk (int): The number of top results to return, default is 3.
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Returns:
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str: The result of the knowledge retrieval in JSON format.
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"""
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url = 'http://127.0.0.1:7080/v1/chat-messages'
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headers = {
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'Authorization': f'Bearer app-uJgo3TQKcS1O9PMCDHko71Fp',
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'Content-Type': 'application/json'
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}
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data = {
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"inputs": {"topK": topk},
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"query": query,
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"response_mode": "blocking",
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"user": "tangger",
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"files": []
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}
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response = requests.post(url, headers=headers, json=data)
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if response.status_code == 524:
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print("Server is not responding. Please try again later. Maybe GPU was down in the container.")
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return None
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try:
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result = response.json()
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except ValueError:
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return [{"error": "Response is not in JSON format"}]
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useful_results = []
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try:
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answer = eval(result.get("answer", "[]"))
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for item in answer:
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metadata = item.get("metadata", {})
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useful_info = {
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"id": metadata.get("document_id"),
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"title": item.get("title"),
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"content": item.get("content"),
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"metadata": None,
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"embedding": None,
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"score": metadata.get("score")
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}
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useful_results.append(useful_info)
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except Exception as e:
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return [{"error": f"Error processing result: {e}", "status": "TERMINATE"}]
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if useful_results == []:
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useful_results = "NULL"
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return str(useful_results)
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@with_requirements(python_packages=["graphrag", "graphrag_api"], global_imports=["graphrag", "graphrag_api"])
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def retrieval_from_graphrag(query: str,) -> str:
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from graphrag_api.search import SearchRunner
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search_runner = SearchRunner(root_dir="/home/ubuntu/workplace/LYT/llm-agent/psk-graphrag")
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result = search_runner.remove_sources(search_runner.run_local_search(query=query))
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return result
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@with_requirements(python_packages=["requests"], global_imports=["requests", "urllib.parse"])
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def web_searcher(query: str):
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from urllib.parse import quote
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encoded_query = quote(query)
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url = 'https://s.jina.ai/' + encoded_query
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headers = {
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'Authorization': 'Bearer jina_8fc99db105ed48d7ab6a76ecd117e8e9GkNoWHY3tbmIMYSwQ6GAgzmr6c7z',
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'X-Return-Format': 'markdown'
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}
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response = requests.get(url, headers=headers)
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# print(response.text)
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return str(response.text)
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@with_requirements(python_packages=["requests", "mp-api"], global_imports=["requests", "mp-api"])
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def mp_database(api_key: str, query: str):
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"""
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Query the Materials Project database using an API key and a material ID.
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Parameters:
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api_key (str): The API key used to access the Materials Project database.
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query (str): The material ID to query in the database.
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Returns:
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list: A list of document objects related to the material ID query.
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"""
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from mp_api.client import MPRester
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import requests.exceptions
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# Validate API key and query parameters
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if not isinstance(api_key, str) or not api_key.strip():
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raise ValueError("The API key must be a non-empty string.")
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if not isinstance(query, str) or not query.strip():
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raise ValueError("The query must be a non-empty string.")
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try:
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# Accessing the MPRester with provided API key
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with MPRester(api_key) as mpr:
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# Search for the material based on the query
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docs = mpr.materials.summary.search(
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material_ids=[query]
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)
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# Check if any documents are returned
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if not docs:
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return f"No results found for the given material ID: {query}"
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return docs
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except requests.exceptions.HTTPError as http_err:
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return f"HTTP error occurred: {http_err}"
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except requests.exceptions.ConnectionError as conn_err:
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return f"Connection error occurred: {conn_err}"
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except requests.exceptions.Timeout as timeout_err:
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return f"Request timed out: {timeout_err}"
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except requests.exceptions.RequestException as req_err:
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return f"An error occurred while making the request: {req_err}"
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except Exception as e:
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return f"An unexpected error occurred: {e}"
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@with_requirements(python_packages=["requests"], global_imports=["requests"])
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def oqdm_database(query: str):
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"""
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Query the OQMD (Open Quantum Materials Database) to retrieve information about a material composition.
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Parameters:
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query (str): The material composition to query in the database.
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Returns:
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dict: A dictionary containing information related to the queried material composition.
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"""
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import requests
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# Validate query parameter
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if not isinstance(query, str) or not query.strip():
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raise ValueError("The query must be a non-empty string.")
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url = f"https://oqmd.org/materials/composition/{query}"
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try:
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# Make the request to the OQMD API
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response = requests.get(url)
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response.raise_for_status()
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# Parse the response
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data = response.json()
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# Check if data is available
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if not data:
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return f"No results found for the given material composition: {query}"
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return data
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except requests.exceptions.HTTPError as http_err:
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return f"HTTP error occurred: {http_err}"
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except requests.exceptions.ConnectionError as conn_err:
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return f"Connection error occurred: {conn_err}"
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except requests.exceptions.Timeout as timeout_err:
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return f"Request timed out: {timeout_err}"
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except requests.exceptions.RequestException as req_err:
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return f"An error occurred while making the request: {req_err}"
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except Exception as e:
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return f"An unexpected error occurred: {e}"
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@with_requirements(python_packages=["requests"], global_imports=["requests"])
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def aflow_database(query: list[str]):
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"""
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Query the AFLOW database to retrieve information about materials based on a list of species.
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Parameters:
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query (list[str]): A list of chemical species to query in the AFLOW database.
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Returns:
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dict: A dictionary containing information related to the queried species.
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"""
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import requests
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# Validate query parameter
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if not isinstance(query, list) or not query:
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raise ValueError("The query must be a non-empty list of species.")
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if not all(isinstance(species, str) and species.strip() for species in query):
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raise ValueError("All species in the query must be non-empty strings.")
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query_str = ",".join(query)
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url = f"https://aflowlib.duke.edu/search/ui/API/aflux/?species({query_str}),$catalog(ICSD),$paging(1,1000)"
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try:
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# Make the request to the AFLOW API
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response = requests.get(url)
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response.raise_for_status()
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# Parse the response
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data = response.json()
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# Check if data is available
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if not data:
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return f"No results found for the given species: {query_str}"
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return data
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except requests.exceptions.HTTPError as http_err:
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return f"HTTP error occurred: {http_err}"
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except requests.exceptions.ConnectionError as conn_err:
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return f"Connection error occurred: {conn_err}"
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except requests.exceptions.Timeout as timeout_err:
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return f"Request timed out: {timeout_err}"
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except requests.exceptions.RequestException as req_err:
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return f"An error occurred while making the request: {req_err}"
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except Exception as e:
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return f"An unexpected error occurred: {e}"
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@with_requirements(python_packages=["requests"], global_imports=["socket, json, re"])
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def send_instruction_to_robot(json_data: str):
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import socket
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import json
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import re
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# 去掉可能存在的 ```json 和 ``` 标记
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json_data_cleaned = re.sub(r'```json|```', '', json_data).strip()
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try:
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# 尝试解析清理后的JSON数据
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data = json.loads(json_data_cleaned)
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except json.JSONDecodeError as e:
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print(f"JSON解析错误: {e}")
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return
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# 创建UDP套接字
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sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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# 目标地址和端口
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server_address = ('172.20.103.79', 10000)
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try:
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# 序列化为JSON字符串并编码为字节
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json_bytes = json.dumps(data).encode('utf-8')
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# 发送数据
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sock.sendto(json_bytes, server_address)
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print("指令发送成功")
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except Exception as e:
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print(f"发送数据时发生错误: {e}")
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finally:
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# 关闭套接字
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sock.close()
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@with_requirements(python_packages=["pandas"], global_imports=["pandas", "os", "glob"])
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def get_uv_latest_file():
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import os
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import glob
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# UV数据缓存文件夹路径 (请将此路径修改为实际的文件夹路径)
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current_folder = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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folder_path = os.path.join(current_folder, 'data/UV/')
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# 查找文件夹中的所有 .wls 文件
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uv_files = sorted(glob.glob(os.path.join(folder_path, '*.[Tt][Xx][Tt]')))
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if not uv_files:
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res = f"ERROR: 缓存文件夹{current_folder}中没有找到任何UV文件"
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return res
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# 找到最新修改的文件
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latest_file = uv_files[-1]
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res = f"找到最新的UV数据文件: {latest_file}"
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return res
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@with_requirements(python_packages=["pandas"], global_imports=["pandas", "os", "glob"])
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def get_max_uv_wavelength_from_txt(latest_file_path: str):
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import pandas as pd
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import os
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# 文件检查
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if not os.path.isfile(latest_file_path):
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res = "ERROR: 指定的文件不存在"
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return res
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# 打开并读取最新文件
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with open(latest_file_path, 'r') as file:
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lines = file.readlines()
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# 找到数据开始的行号
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data_start_index = -1
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for i, line in enumerate(lines):
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if "Wavelength Scan Data Record" in line:
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data_start_index = i + 2 # 数据从该行的下两行开始
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break
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if data_start_index == -1:
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res = "ERROR: 无法找到数据记录部分"
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return res
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# 解析数据并构建表格
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data = []
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for line in lines[data_start_index:]:
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parts = line.split()
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if len(parts) == 7: # 保证每行有7列数据
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no, wavelength, abs_value, trans, energy, energy_100, energy_0 = parts
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try:
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data.append({
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'No': int(no),
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'Wavelength(nm)': float(wavelength),
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'Abs': float(abs_value),
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'Trans(%T)': float(trans),
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'Energy': float(energy),
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'Energy(100%T)': float(energy_100),
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'Energy(0%T)': float(energy_0)
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})
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except ValueError:
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print(f"跳过无法解析的行: {line}")
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if not data:
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res = "ERROR: 未解析到任何有效数据"
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return res
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# 构建DataFrame
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df = pd.DataFrame(data)
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# 找到Abs值最大的行
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max_abs_row = df.loc[df['Abs'].idxmax()]
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# 获取最大Abs值对应的波长
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max_abs_wavelength = max_abs_row['Wavelength(nm)']
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res = f"本次实验的UV波长为: {max_abs_wavelength} nm"
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print(res)
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return res
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@with_requirements(python_packages=["pandas"], global_imports=["pandas", "os", "glob"])
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def get_pl_latest_file():
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import os
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import glob
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# PL数据缓存文件夹路径 (请将此路径修改为实际的文件夹路径)
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current_folder = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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folder_path = os.path.join(current_folder, 'data/PL/')
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# 查找文件夹中的所有 .txt 或 .TXT 文件
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pl_files = sorted(glob.glob(os.path.join(folder_path, '*.[Tt][Xx][Tt]')))
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if not pl_files:
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res = f"ERROR: 缓存文件夹{current_folder}中没有找到任何PL文件"
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return res
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# 找到最新修改的文件
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latest_file = pl_files[-1]
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res = f"找到最新的PL数据文件: {latest_file}"
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# print(res)
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return res
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@with_requirements(python_packages=["pandas"], global_imports=["pandas", "os", "glob"])
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def get_max_pl_peak_from_txt(latest_file_path: str):
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import pandas as pd
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import os
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# 文件检查
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if not os.path.isfile(latest_file_path):
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res = "ERROR: 指定的文件不存在"
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return res
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# 打开并读取最新文件
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with open(latest_file_path, 'r') as file:
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lines = file.readlines()
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# 找到 'Data Points' 开始的行号
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data_start_index = -1
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for i, line in enumerate(lines):
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if "Data Points" in line:
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data_start_index = i + 1 # 数据从该行的下一行开始
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break
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if data_start_index == -1:
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res = "ERROR: 无法找到数据记录部分"
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return res
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# 解析nm和Data数据
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data = []
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for line in lines[data_start_index:]:
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parts = line.split()
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if len(parts) == 2: # 每行应该有2列数据，nm 和 Data
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try:
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nm = float(parts[0])
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data_value = float(parts[1])
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data.append({'nm': nm, 'Data': data_value})
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except ValueError:
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print(f"跳过无法解析的行: {line}")
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if not data:
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res = "ERROR: 未解析到任何有效数据"
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return res
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# 构建DataFrame
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df = pd.DataFrame(data)
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# 找到Data值最大的行
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max_data_row = df.loc[df['Data'].idxmax()]
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# 获取最大Data值对应的nm
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max_data_nm = max_data_row['nm']
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res = f"本次实验的PL峰位为: {max_data_nm} nm"
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print(res)
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return res
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if __name__ == "__main__":
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# res = web_searcher("how to Synthesis CsPbBr3 nanocubes at room temperature?")
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# print(res)
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# res = retrieval_from_knowledge_base("how to Synthesis CsPbBr3 nanocubes at room temperature?", 3)
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# print(res)
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# res = get_max_uv_wavelength_from_txt()
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# print(res)
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# res = get_max_pl_peak_from_txt()
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# print(res)
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# res = retrieval_from_graphrag("how to Synthesis CsPbBr3 nanocubes at room temperature?")
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# print(res)
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pass
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