mirror of
https://github.com/hwchase17/langchain
synced 2024-11-13 19:10:52 +00:00
561 lines
20 KiB
Python
561 lines
20 KiB
Python
import os
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import re
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import warnings
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from abc import ABC, abstractmethod
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from typing import Any, Callable, Dict, List, Mapping, Optional
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import requests
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models import LLM
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from langchain_core.pydantic_v1 import (
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BaseModel,
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Extra,
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Field,
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PrivateAttr,
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root_validator,
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validator,
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)
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__all__ = ["Databricks"]
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class _DatabricksClientBase(BaseModel, ABC):
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"""A base JSON API client that talks to Databricks."""
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api_url: str
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api_token: str
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def request(self, method: str, url: str, request: Any) -> Any:
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headers = {"Authorization": f"Bearer {self.api_token}"}
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response = requests.request(
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method=method, url=url, headers=headers, json=request
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)
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# TODO: error handling and automatic retries
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if not response.ok:
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raise ValueError(f"HTTP {response.status_code} error: {response.text}")
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return response.json()
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def _get(self, url: str) -> Any:
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return self.request("GET", url, None)
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def _post(self, url: str, request: Any) -> Any:
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return self.request("POST", url, request)
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@abstractmethod
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def post(
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self, request: Any, transform_output_fn: Optional[Callable[..., str]] = None
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) -> Any:
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...
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@property
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def llm(self) -> bool:
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return False
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def _transform_completions(response: Dict[str, Any]) -> str:
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return response["choices"][0]["text"]
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def _transform_llama2_chat(response: Dict[str, Any]) -> str:
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return response["candidates"][0]["text"]
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def _transform_chat(response: Dict[str, Any]) -> str:
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return response["choices"][0]["message"]["content"]
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class _DatabricksServingEndpointClient(_DatabricksClientBase):
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"""An API client that talks to a Databricks serving endpoint."""
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host: str
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endpoint_name: str
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databricks_uri: str
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client: Any = None
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external_or_foundation: bool = False
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task: Optional[str] = None
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def __init__(self, **data: Any):
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super().__init__(**data)
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try:
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from mlflow.deployments import get_deploy_client
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self.client = get_deploy_client(self.databricks_uri)
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except ImportError as e:
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raise ImportError(
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"Failed to create the client. "
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"Please install mlflow with `pip install mlflow`."
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) from e
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endpoint = self.client.get_endpoint(self.endpoint_name)
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self.external_or_foundation = endpoint.get("endpoint_type", "").lower() in (
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"external_model",
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"foundation_model_api",
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)
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if self.task is None:
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self.task = endpoint.get("task")
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@property
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def llm(self) -> bool:
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return self.task in ("llm/v1/chat", "llm/v1/completions", "llama2/chat")
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@root_validator(pre=True)
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def set_api_url(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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if "api_url" not in values:
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host = values["host"]
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endpoint_name = values["endpoint_name"]
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api_url = f"https://{host}/serving-endpoints/{endpoint_name}/invocations"
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values["api_url"] = api_url
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return values
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def post(
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self, request: Any, transform_output_fn: Optional[Callable[..., str]] = None
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) -> Any:
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if self.external_or_foundation:
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resp = self.client.predict(endpoint=self.endpoint_name, inputs=request)
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if transform_output_fn:
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return transform_output_fn(resp)
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if self.task == "llm/v1/chat":
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return _transform_chat(resp)
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elif self.task == "llm/v1/completions":
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return _transform_completions(resp)
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return resp
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else:
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# See https://docs.databricks.com/machine-learning/model-serving/score-model-serving-endpoints.html
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wrapped_request = {"dataframe_records": [request]}
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response = self.client.predict(
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endpoint=self.endpoint_name, inputs=wrapped_request
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)
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preds = response["predictions"]
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# For a single-record query, the result is not a list.
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pred = preds[0] if isinstance(preds, list) else preds
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if self.task == "llama2/chat":
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return _transform_llama2_chat(pred)
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return transform_output_fn(pred) if transform_output_fn else pred
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class _DatabricksClusterDriverProxyClient(_DatabricksClientBase):
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"""An API client that talks to a Databricks cluster driver proxy app."""
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host: str
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cluster_id: str
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cluster_driver_port: str
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@root_validator(pre=True)
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def set_api_url(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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if "api_url" not in values:
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host = values["host"]
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cluster_id = values["cluster_id"]
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port = values["cluster_driver_port"]
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api_url = f"https://{host}/driver-proxy-api/o/0/{cluster_id}/{port}"
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values["api_url"] = api_url
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return values
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def post(
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self, request: Any, transform_output_fn: Optional[Callable[..., str]] = None
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) -> Any:
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resp = self._post(self.api_url, request)
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return transform_output_fn(resp) if transform_output_fn else resp
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def get_repl_context() -> Any:
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"""Get the notebook REPL context if running inside a Databricks notebook.
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Returns None otherwise.
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"""
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try:
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from dbruntime.databricks_repl_context import get_context
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return get_context()
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except ImportError:
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raise ImportError(
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"Cannot access dbruntime, not running inside a Databricks notebook."
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)
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def get_default_host() -> str:
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"""Get the default Databricks workspace hostname.
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Raises an error if the hostname cannot be automatically determined.
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"""
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host = os.getenv("DATABRICKS_HOST")
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if not host:
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try:
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host = get_repl_context().browserHostName
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if not host:
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raise ValueError("context doesn't contain browserHostName.")
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except Exception as e:
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raise ValueError(
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"host was not set and cannot be automatically inferred. Set "
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f"environment variable 'DATABRICKS_HOST'. Received error: {e}"
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)
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# TODO: support Databricks CLI profile
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host = host.lstrip("https://").lstrip("http://").rstrip("/")
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return host
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def get_default_api_token() -> str:
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"""Get the default Databricks personal access token.
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Raises an error if the token cannot be automatically determined.
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"""
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if api_token := os.getenv("DATABRICKS_TOKEN"):
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return api_token
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try:
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api_token = get_repl_context().apiToken
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if not api_token:
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raise ValueError("context doesn't contain apiToken.")
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except Exception as e:
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raise ValueError(
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"api_token was not set and cannot be automatically inferred. Set "
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f"environment variable 'DATABRICKS_TOKEN'. Received error: {e}"
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)
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# TODO: support Databricks CLI profile
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return api_token
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def _is_hex_string(data: str) -> bool:
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"""Checks if a data is a valid hexadecimal string using a regular expression."""
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if not isinstance(data, str):
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return False
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pattern = r"^[0-9a-fA-F]+$"
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return bool(re.match(pattern, data))
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def _load_pickled_fn_from_hex_string(
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data: str, allow_dangerous_deserialization: Optional[bool]
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) -> Callable:
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"""Loads a pickled function from a hexadecimal string."""
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if not allow_dangerous_deserialization:
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raise ValueError(
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"This code relies on the pickle module. "
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"You will need to set allow_dangerous_deserialization=True "
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"if you want to opt-in to allow deserialization of data using pickle."
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"Data can be compromised by a malicious actor if "
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"not handled properly to include "
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"a malicious payload that when deserialized with "
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"pickle can execute arbitrary code on your machine."
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)
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try:
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import cloudpickle
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except Exception as e:
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raise ValueError(f"Please install cloudpickle>=2.0.0. Error: {e}")
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try:
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return cloudpickle.loads(bytes.fromhex(data)) # ignore[pickle]: explicit-opt-in
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except Exception as e:
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raise ValueError(
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f"Failed to load the pickled function from a hexadecimal string. Error: {e}"
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)
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def _pickle_fn_to_hex_string(fn: Callable) -> str:
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"""Pickles a function and returns the hexadecimal string."""
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try:
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import cloudpickle
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except Exception as e:
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raise ValueError(f"Please install cloudpickle>=2.0.0. Error: {e}")
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try:
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return cloudpickle.dumps(fn).hex()
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except Exception as e:
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raise ValueError(f"Failed to pickle the function: {e}")
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class Databricks(LLM):
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"""Databricks serving endpoint or a cluster driver proxy app for LLM.
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It supports two endpoint types:
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* **Serving endpoint** (recommended for both production and development).
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We assume that an LLM was deployed to a serving endpoint.
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To wrap it as an LLM you must have "Can Query" permission to the endpoint.
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Set ``endpoint_name`` accordingly and do not set ``cluster_id`` and
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``cluster_driver_port``.
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If the underlying model is a model registered by MLflow, the expected model
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signature is:
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* inputs::
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[{"name": "prompt", "type": "string"},
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{"name": "stop", "type": "list[string]"}]
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* outputs: ``[{"type": "string"}]``
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If the underlying model is an external or foundation model, the response from the
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endpoint is automatically transformed to the expected format unless
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``transform_output_fn`` is provided.
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* **Cluster driver proxy app** (recommended for interactive development).
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One can load an LLM on a Databricks interactive cluster and start a local HTTP
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server on the driver node to serve the model at ``/`` using HTTP POST method
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with JSON input/output.
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Please use a port number between ``[3000, 8000]`` and let the server listen to
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the driver IP address or simply ``0.0.0.0`` instead of localhost only.
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To wrap it as an LLM you must have "Can Attach To" permission to the cluster.
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Set ``cluster_id`` and ``cluster_driver_port`` and do not set ``endpoint_name``.
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The expected server schema (using JSON schema) is:
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* inputs::
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{"type": "object",
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"properties": {
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"prompt": {"type": "string"},
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"stop": {"type": "array", "items": {"type": "string"}}},
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"required": ["prompt"]}`
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* outputs: ``{"type": "string"}``
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If the endpoint model signature is different or you want to set extra params,
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you can use `transform_input_fn` and `transform_output_fn` to apply necessary
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transformations before and after the query.
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"""
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host: str = Field(default_factory=get_default_host)
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"""Databricks workspace hostname.
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If not provided, the default value is determined by
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* the ``DATABRICKS_HOST`` environment variable if present, or
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* the hostname of the current Databricks workspace if running inside
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a Databricks notebook attached to an interactive cluster in "single user"
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or "no isolation shared" mode.
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"""
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api_token: str = Field(default_factory=get_default_api_token)
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"""Databricks personal access token.
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If not provided, the default value is determined by
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* the ``DATABRICKS_TOKEN`` environment variable if present, or
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* an automatically generated temporary token if running inside a Databricks
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notebook attached to an interactive cluster in "single user" or
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"no isolation shared" mode.
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"""
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endpoint_name: Optional[str] = None
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"""Name of the model serving endpoint.
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You must specify the endpoint name to connect to a model serving endpoint.
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You must not set both ``endpoint_name`` and ``cluster_id``.
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"""
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cluster_id: Optional[str] = None
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"""ID of the cluster if connecting to a cluster driver proxy app.
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If neither ``endpoint_name`` nor ``cluster_id`` is not provided and the code runs
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inside a Databricks notebook attached to an interactive cluster in "single user"
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or "no isolation shared" mode, the current cluster ID is used as default.
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You must not set both ``endpoint_name`` and ``cluster_id``.
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"""
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cluster_driver_port: Optional[str] = None
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"""The port number used by the HTTP server running on the cluster driver node.
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The server should listen on the driver IP address or simply ``0.0.0.0`` to connect.
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We recommend the server using a port number between ``[3000, 8000]``.
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"""
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model_kwargs: Optional[Dict[str, Any]] = None
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"""
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Deprecated. Please use ``extra_params`` instead. Extra parameters to pass to
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the endpoint.
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"""
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transform_input_fn: Optional[Callable] = None
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"""A function that transforms ``{prompt, stop, **kwargs}`` into a JSON-compatible
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request object that the endpoint accepts.
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For example, you can apply a prompt template to the input prompt.
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"""
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transform_output_fn: Optional[Callable[..., str]] = None
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"""A function that transforms the output from the endpoint to the generated text.
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"""
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databricks_uri: str = "databricks"
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"""The databricks URI. Only used when using a serving endpoint."""
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temperature: float = 0.0
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"""The sampling temperature."""
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n: int = 1
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"""The number of completion choices to generate."""
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stop: Optional[List[str]] = None
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"""The stop sequence."""
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max_tokens: Optional[int] = None
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"""The maximum number of tokens to generate."""
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extra_params: Dict[str, Any] = Field(default_factory=dict)
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"""Any extra parameters to pass to the endpoint."""
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task: Optional[str] = None
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"""The task of the endpoint. Only used when using a serving endpoint.
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If not provided, the task is automatically inferred from the endpoint.
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"""
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allow_dangerous_deserialization: bool = False
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"""Whether to allow dangerous deserialization of the data which
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involves loading data using pickle.
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If the data has been modified by a malicious actor, it can deliver a
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malicious payload that results in execution of arbitrary code on the target
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machine.
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"""
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_client: _DatabricksClientBase = PrivateAttr()
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class Config:
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extra = Extra.forbid
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underscore_attrs_are_private = True
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@property
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def _llm_params(self) -> Dict[str, Any]:
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params: Dict[str, Any] = {
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"temperature": self.temperature,
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"n": self.n,
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}
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if self.stop:
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params["stop"] = self.stop
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if self.max_tokens is not None:
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params["max_tokens"] = self.max_tokens
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return params
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@validator("cluster_id", always=True)
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def set_cluster_id(cls, v: Any, values: Dict[str, Any]) -> Optional[str]:
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if v and values["endpoint_name"]:
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raise ValueError("Cannot set both endpoint_name and cluster_id.")
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elif values["endpoint_name"]:
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return None
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elif v:
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return v
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else:
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try:
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if v := get_repl_context().clusterId:
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return v
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raise ValueError("Context doesn't contain clusterId.")
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except Exception as e:
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raise ValueError(
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"Neither endpoint_name nor cluster_id was set. "
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"And the cluster_id cannot be automatically determined. Received"
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f" error: {e}"
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)
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@validator("cluster_driver_port", always=True)
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def set_cluster_driver_port(cls, v: Any, values: Dict[str, Any]) -> Optional[str]:
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if v and values["endpoint_name"]:
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raise ValueError("Cannot set both endpoint_name and cluster_driver_port.")
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elif values["endpoint_name"]:
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return None
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elif v is None:
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raise ValueError(
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"Must set cluster_driver_port to connect to a cluster driver."
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)
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elif int(v) <= 0:
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raise ValueError(f"Invalid cluster_driver_port: {v}")
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else:
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return v
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@validator("model_kwargs", always=True)
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def set_model_kwargs(cls, v: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
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if v:
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assert "prompt" not in v, "model_kwargs must not contain key 'prompt'"
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assert "stop" not in v, "model_kwargs must not contain key 'stop'"
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return v
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def __init__(self, **data: Any):
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if "transform_input_fn" in data and _is_hex_string(data["transform_input_fn"]):
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data["transform_input_fn"] = _load_pickled_fn_from_hex_string(
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data=data["transform_input_fn"],
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allow_dangerous_deserialization=data.get(
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"allow_dangerous_deserialization"
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),
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)
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if "transform_output_fn" in data and _is_hex_string(
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data["transform_output_fn"]
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):
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data["transform_output_fn"] = _load_pickled_fn_from_hex_string(
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data=data["transform_output_fn"],
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allow_dangerous_deserialization=data.get(
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"allow_dangerous_deserialization"
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),
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)
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super().__init__(**data)
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if self.model_kwargs is not None and self.extra_params is not None:
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raise ValueError("Cannot set both extra_params and extra_params.")
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elif self.model_kwargs is not None:
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warnings.warn(
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"model_kwargs is deprecated. Please use extra_params instead.",
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DeprecationWarning,
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)
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if self.endpoint_name:
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self._client = _DatabricksServingEndpointClient(
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host=self.host,
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api_token=self.api_token,
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endpoint_name=self.endpoint_name,
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databricks_uri=self.databricks_uri,
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task=self.task,
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)
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elif self.cluster_id and self.cluster_driver_port:
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self._client = _DatabricksClusterDriverProxyClient( # type: ignore[call-arg]
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host=self.host,
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api_token=self.api_token,
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cluster_id=self.cluster_id,
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cluster_driver_port=self.cluster_driver_port,
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)
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else:
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raise ValueError(
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"Must specify either endpoint_name or cluster_id/cluster_driver_port."
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)
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Return default params."""
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return {
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"host": self.host,
|
|
# "api_token": self.api_token, # Never save the token
|
|
"endpoint_name": self.endpoint_name,
|
|
"cluster_id": self.cluster_id,
|
|
"cluster_driver_port": self.cluster_driver_port,
|
|
"databricks_uri": self.databricks_uri,
|
|
"model_kwargs": self.model_kwargs,
|
|
"temperature": self.temperature,
|
|
"n": self.n,
|
|
"stop": self.stop,
|
|
"max_tokens": self.max_tokens,
|
|
"extra_params": self.extra_params,
|
|
"task": self.task,
|
|
"transform_input_fn": None
|
|
if self.transform_input_fn is None
|
|
else _pickle_fn_to_hex_string(self.transform_input_fn),
|
|
"transform_output_fn": None
|
|
if self.transform_output_fn is None
|
|
else _pickle_fn_to_hex_string(self.transform_output_fn),
|
|
}
|
|
|
|
@property
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
return self._default_params
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of llm."""
|
|
return "databricks"
|
|
|
|
def _call(
|
|
self,
|
|
prompt: str,
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> str:
|
|
"""Queries the LLM endpoint with the given prompt and stop sequence."""
|
|
|
|
# TODO: support callbacks
|
|
|
|
request: Dict[str, Any] = {"prompt": prompt}
|
|
if self._client.llm:
|
|
request.update(self._llm_params)
|
|
request.update(self.model_kwargs or self.extra_params)
|
|
request.update(kwargs)
|
|
if stop:
|
|
request["stop"] = stop
|
|
|
|
if self.transform_input_fn:
|
|
request = self.transform_input_fn(**request)
|
|
|
|
return self._client.post(request, transform_output_fn=self.transform_output_fn)
|