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Add [Text Generation by Cloudflare Workers AI](https://developers.cloudflare.com/workers-ai/models/text-generation/). It's a new LLM integration. - Dependencies: N/A
128 lines
4.1 KiB
Python
128 lines
4.1 KiB
Python
import json
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import logging
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from typing import Any, Dict, Iterator, List, Optional
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import requests
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from langchain_core.outputs import GenerationChunk
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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logger = logging.getLogger(__name__)
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class CloudflareWorkersAI(LLM):
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"""Langchain LLM class to help to access Cloudflare Workers AI service.
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To use, you must provide an API token and
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account ID to access Cloudflare Workers AI, and
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pass it as a named parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain.llms.cloudflare_workersai import CloudflareWorkersAI
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my_account_id = "my_account_id"
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my_api_token = "my_secret_api_token"
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llm_model = "@cf/meta/llama-2-7b-chat-int8"
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cf_ai = CloudflareWorkersAI(
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account_id=my_account_id,
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api_token=my_api_token,
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model=llm_model
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)
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"""
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account_id: str
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api_token: str
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model: str = "@cf/meta/llama-2-7b-chat-int8"
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base_url: str = "https://api.cloudflare.com/client/v4/accounts"
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streaming: bool = False
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endpoint_url: str = ""
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def __init__(self, **kwargs: Any) -> None:
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"""Initialize the Cloudflare Workers AI class."""
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super().__init__(**kwargs)
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self.endpoint_url = f"{self.base_url}/{self.account_id}/ai/run/{self.model}"
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@property
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def _llm_type(self) -> str:
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"""Return type of LLM."""
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return "cloudflare"
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Default parameters"""
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return {}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Identifying parameters"""
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return {
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"account_id": self.account_id,
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"api_token": self.api_token,
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"model": self.model,
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"base_url": self.base_url,
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}
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def _call_api(self, prompt: str, params: Dict[str, Any]) -> requests.Response:
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"""Call Cloudflare Workers API"""
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headers = {"Authorization": f"Bearer {self.api_token}"}
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data = {"prompt": prompt, "stream": self.streaming, **params}
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response = requests.post(self.endpoint_url, headers=headers, json=data)
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return response
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def _process_response(self, response: requests.Response) -> str:
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"""Process API response"""
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if response.ok:
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data = response.json()
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return data["result"]["response"]
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else:
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raise ValueError(f"Request failed with status {response.status_code}")
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def _stream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[GenerationChunk]:
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"""Streaming prediction"""
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original_steaming: bool = self.streaming
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self.streaming = True
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_response_prefix_count = len("data: ")
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_response_stream_end = b"data: [DONE]"
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for chunk in self._call_api(prompt, kwargs).iter_lines():
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if chunk == _response_stream_end:
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break
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if len(chunk) > _response_prefix_count:
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try:
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data = json.loads(chunk[_response_prefix_count:])
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except Exception as e:
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logger.debug(chunk)
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raise e
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if data is not None and "response" in data:
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yield GenerationChunk(text=data["response"])
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if run_manager:
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run_manager.on_llm_new_token(data["response"])
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logger.debug("stream end")
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self.streaming = original_steaming
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Regular prediction"""
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if self.streaming:
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return "".join(
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[c.text for c in self._stream(prompt, stop, run_manager, **kwargs)]
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)
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else:
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response = self._call_api(prompt, kwargs)
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return self._process_response(response)
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