Source code for langchain_community.llms.bittensor

import http.client
import json
import ssl
from typing import Any, List, Mapping, Optional

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM

[docs]class NIBittensorLLM(LLM): """NIBittensor LLMs NIBittensorLLM is created by Neural Internet (, powered by Bittensor, a decentralized network full of different AI models. To analyze API_KEYS and logs of your usage visit Example: .. code-block:: python from langchain_community.llms import NIBittensorLLM llm = NIBittensorLLM() """ system_prompt: Optional[str] """Provide system prompt that you want to supply it to model before every prompt""" top_responses: Optional[int] = 0 """Provide top_responses to get Top N miner responses on one request.May get delayed Don't use in Production""" @property def _llm_type(self) -> str: return "NIBittensorLLM" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """ Wrapper around the bittensor top miner models. Its built by Neural Internet. Call the Neural Internet's BTVEP Server and return the output. Parameters (optional): system_prompt(str): A system prompt defining how your model should respond. top_responses(int): Total top miner responses to retrieve from Bittensor protocol. Return: The generated response(s). Example: .. code-block:: python from langchain_community.llms import NIBittensorLLM llm = NIBittensorLLM(system_prompt="Act like you are programmer with \ 5+ years of experience.") """ # Creating HTTPS connection with SSL context = ssl.create_default_context() context.check_hostname = True conn = http.client.HTTPSConnection("", context=context) # Sanitizing User Input before passing to API. if isinstance(self.top_responses, int): top_n = min(100, self.top_responses) else: top_n = 0 default_prompt = "You are an assistant which is created by Neural Internet(NI) \ in decentralized network named as a Bittensor." if self.system_prompt is None: system_prompt = ( default_prompt + " Your task is to provide accurate response based on user prompt" ) else: system_prompt = default_prompt + str(self.system_prompt) # Retrieving API KEY to pass into header of each request conn.request("GET", "/admin/api-keys/") api_key_response = conn.getresponse() api_keys_data = ("utf-8").replace("\n", "").replace("\t", "") ) api_keys_json = json.loads(api_keys_data) api_key = api_keys_json[0]["api_key"] # Creating Header and getting top benchmark miner uids headers = { "Content-Type": "application/json", "Authorization": f"Bearer {api_key}", "Endpoint-Version": "2023-05-19", } conn.request("GET", "/top_miner_uids", headers=headers) miner_response = conn.getresponse() miner_data = ("utf-8").replace("\n", "").replace("\t", "") ) uids = json.loads(miner_data) # Condition for benchmark miner response if isinstance(uids, list) and uids and not top_n: for uid in uids: try: payload = json.dumps( { "uids": [uid], "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}, ], } ) conn.request("POST", "/chat", payload, headers) init_response = conn.getresponse() init_data = ( .decode("utf-8") .replace("\n", "") .replace("\t", "") ) init_json = json.loads(init_data) if "choices" not in init_json: continue reply = init_json["choices"][0]["message"]["content"] conn.close() return reply except Exception: continue # For top miner based on bittensor response try: payload = json.dumps( { "top_n": top_n, "messages": [ {"role": "system", "content": system_prompt}, {"role": "user", "content": prompt}, ], } ) conn.request("POST", "/chat", payload, headers) response = conn.getresponse() utf_string = ("utf-8").replace("\n", "").replace("\t", "") ) if top_n: conn.close() return utf_string json_resp = json.loads(utf_string) reply = json_resp["choices"][0]["message"]["content"] conn.close() return reply except Exception as e: conn.request("GET", f"/error_msg?e={e}&p={prompt}", headers=headers) return "Sorry I am unable to provide response now, Please try again later." @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return { "system_prompt": self.system_prompt, "top_responses": self.top_responses, }