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multiple: langchain 0.2 in master (#21191)
0.2rc migrations - [x] Move memory - [x] Move remaining retrievers - [x] graph_qa chains - [x] some dependency from evaluation code potentially on math utils - [x] Move openapi chain from `langchain.chains.api.openapi` to `langchain_community.chains.openapi` - [x] Migrate `langchain.chains.ernie_functions` to `langchain_community.chains.ernie_functions` - [x] migrate `langchain/chains/llm_requests.py` to `langchain_community.chains.llm_requests` - [x] Moving `langchain_community.cross_enoders.base:BaseCrossEncoder` -> `langchain_community.retrievers.document_compressors.cross_encoder:BaseCrossEncoder` (namespace not ideal, but it needs to be moved to `langchain` to avoid circular deps) - [x] unit tests langchain -- add pytest.mark.community to some unit tests that will stay in langchain - [x] unit tests community -- move unit tests that depend on community to community - [x] mv integration tests that depend on community to community - [x] mypy checks Other todo - [x] Make deprecation warnings not noisy (need to use warn deprecated and check that things are implemented properly) - [x] Update deprecation messages with timeline for code removal (likely we actually won't be removing things until 0.4 release) -- will give people more time to transition their code. - [ ] Add information to deprecation warning to show users how to migrate their code base using langchain-cli - [ ] Remove any unnecessary requirements in langchain (e.g., is SQLALchemy required?) --------- Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Eugene Yurtsev
GitHub
229
libs/community/langchain_community/chains/openapi/chain.py
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229
libs/community/langchain_community/chains/openapi/chain.py
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"""Chain that makes API calls and summarizes the responses to answer a question."""
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from __future__ import annotations
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import json
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from typing import Any, Dict, List, NamedTuple, Optional, cast
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from langchain.chains.api.openapi.requests_chain import APIRequesterChain
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from langchain.chains.api.openapi.response_chain import APIResponderChain
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain_core.callbacks import CallbackManagerForChainRun, Callbacks
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.pydantic_v1 import BaseModel, Field
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from requests import Response
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from langchain_community.tools.openapi.utils.api_models import APIOperation
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from langchain_community.utilities.requests import Requests
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class _ParamMapping(NamedTuple):
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"""Mapping from parameter name to parameter value."""
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query_params: List[str]
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body_params: List[str]
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path_params: List[str]
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class OpenAPIEndpointChain(Chain, BaseModel):
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"""Chain interacts with an OpenAPI endpoint using natural language."""
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api_request_chain: LLMChain
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api_response_chain: Optional[LLMChain]
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api_operation: APIOperation
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requests: Requests = Field(exclude=True, default_factory=Requests)
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param_mapping: _ParamMapping = Field(alias="param_mapping")
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return_intermediate_steps: bool = False
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instructions_key: str = "instructions" #: :meta private:
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output_key: str = "output" #: :meta private:
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max_text_length: Optional[int] = Field(ge=0) #: :meta private:
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@property
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def input_keys(self) -> List[str]:
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"""Expect input key.
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:meta private:
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"""
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return [self.instructions_key]
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@property
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def output_keys(self) -> List[str]:
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"""Expect output key.
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:meta private:
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"""
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if not self.return_intermediate_steps:
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return [self.output_key]
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else:
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return [self.output_key, "intermediate_steps"]
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def _construct_path(self, args: Dict[str, str]) -> str:
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"""Construct the path from the deserialized input."""
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path = self.api_operation.base_url + self.api_operation.path
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for param in self.param_mapping.path_params:
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path = path.replace(f"{{{param}}}", str(args.pop(param, "")))
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return path
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def _extract_query_params(self, args: Dict[str, str]) -> Dict[str, str]:
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"""Extract the query params from the deserialized input."""
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query_params = {}
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for param in self.param_mapping.query_params:
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if param in args:
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query_params[param] = args.pop(param)
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return query_params
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def _extract_body_params(self, args: Dict[str, str]) -> Optional[Dict[str, str]]:
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"""Extract the request body params from the deserialized input."""
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body_params = None
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if self.param_mapping.body_params:
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body_params = {}
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for param in self.param_mapping.body_params:
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if param in args:
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body_params[param] = args.pop(param)
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return body_params
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def deserialize_json_input(self, serialized_args: str) -> dict:
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"""Use the serialized typescript dictionary.
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Resolve the path, query params dict, and optional requestBody dict.
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"""
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args: dict = json.loads(serialized_args)
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path = self._construct_path(args)
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body_params = self._extract_body_params(args)
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query_params = self._extract_query_params(args)
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return {
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"url": path,
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"data": body_params,
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"params": query_params,
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}
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def _get_output(self, output: str, intermediate_steps: dict) -> dict:
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"""Return the output from the API call."""
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if self.return_intermediate_steps:
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return {
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self.output_key: output,
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"intermediate_steps": intermediate_steps,
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}
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else:
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return {self.output_key: output}
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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intermediate_steps = {}
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instructions = inputs[self.instructions_key]
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instructions = instructions[: self.max_text_length]
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_api_arguments = self.api_request_chain.predict_and_parse(
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instructions=instructions, callbacks=_run_manager.get_child()
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)
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api_arguments = cast(str, _api_arguments)
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intermediate_steps["request_args"] = api_arguments
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_run_manager.on_text(
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api_arguments, color="green", end="\n", verbose=self.verbose
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)
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if api_arguments.startswith("ERROR"):
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return self._get_output(api_arguments, intermediate_steps)
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elif api_arguments.startswith("MESSAGE:"):
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return self._get_output(
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api_arguments[len("MESSAGE:") :], intermediate_steps
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)
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try:
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request_args = self.deserialize_json_input(api_arguments)
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method = getattr(self.requests, self.api_operation.method.value)
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api_response: Response = method(**request_args)
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if api_response.status_code != 200:
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method_str = str(self.api_operation.method.value)
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response_text = (
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f"{api_response.status_code}: {api_response.reason}"
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+ f"\nFor {method_str.upper()} {request_args['url']}\n"
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+ f"Called with args: {request_args['params']}"
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)
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else:
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response_text = api_response.text
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except Exception as e:
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response_text = f"Error with message {str(e)}"
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response_text = response_text[: self.max_text_length]
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intermediate_steps["response_text"] = response_text
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_run_manager.on_text(
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response_text, color="blue", end="\n", verbose=self.verbose
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)
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if self.api_response_chain is not None:
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_answer = self.api_response_chain.predict_and_parse(
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response=response_text,
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instructions=instructions,
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callbacks=_run_manager.get_child(),
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)
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answer = cast(str, _answer)
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_run_manager.on_text(answer, color="yellow", end="\n", verbose=self.verbose)
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return self._get_output(answer, intermediate_steps)
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else:
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return self._get_output(response_text, intermediate_steps)
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@classmethod
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def from_url_and_method(
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cls,
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spec_url: str,
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path: str,
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method: str,
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llm: BaseLanguageModel,
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requests: Optional[Requests] = None,
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return_intermediate_steps: bool = False,
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**kwargs: Any,
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# TODO: Handle async
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) -> "OpenAPIEndpointChain":
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"""Create an OpenAPIEndpoint from a spec at the specified url."""
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operation = APIOperation.from_openapi_url(spec_url, path, method)
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return cls.from_api_operation(
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operation,
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requests=requests,
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llm=llm,
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return_intermediate_steps=return_intermediate_steps,
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**kwargs,
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)
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@classmethod
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def from_api_operation(
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cls,
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operation: APIOperation,
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llm: BaseLanguageModel,
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requests: Optional[Requests] = None,
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verbose: bool = False,
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return_intermediate_steps: bool = False,
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raw_response: bool = False,
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callbacks: Callbacks = None,
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**kwargs: Any,
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# TODO: Handle async
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) -> "OpenAPIEndpointChain":
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"""Create an OpenAPIEndpointChain from an operation and a spec."""
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param_mapping = _ParamMapping(
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query_params=operation.query_params,
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body_params=operation.body_params,
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path_params=operation.path_params,
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)
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requests_chain = APIRequesterChain.from_llm_and_typescript(
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llm,
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typescript_definition=operation.to_typescript(),
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verbose=verbose,
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callbacks=callbacks,
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)
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if raw_response:
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response_chain = None
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else:
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response_chain = APIResponderChain.from_llm(
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llm, verbose=verbose, callbacks=callbacks
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)
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_requests = requests or Requests()
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return cls(
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api_request_chain=requests_chain,
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api_response_chain=response_chain,
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api_operation=operation,
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requests=_requests,
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param_mapping=param_mapping,
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verbose=verbose,
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return_intermediate_steps=return_intermediate_steps,
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callbacks=callbacks,
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**kwargs,
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)
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