Source code for langchain_community.utilities.arcee

# This module contains utility classes and functions for interacting with Arcee API.
# For more information and updates, refer to the Arcee utils page:
# []

from enum import Enum
from typing import Any, Dict, List, Literal, Mapping, Optional, Union

import requests
from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator
from langchain_core.retrievers import Document

[docs]class ArceeRoute(str, Enum): """Routes available for the Arcee API as enumerator.""" generate = "models/generate" retrieve = "models/retrieve" model_training_status = "models/status/{id_or_name}"
[docs]class DALMFilterType(str, Enum): """Filter types available for a DALM retrieval as enumerator.""" fuzzy_search = "fuzzy_search" strict_search = "strict_search"
[docs]class DALMFilter(BaseModel): """Filters available for a DALM retrieval and generation. Arguments: field_name: The field to filter on. Can be 'document' or 'name' to filter on your document's raw text or title. Any other field will be presumed to be a metadata field you included when uploading your context data filter_type: Currently 'fuzzy_search' and 'strict_search' are supported. 'fuzzy_search' means a fuzzy search on the provided field is performed. The exact strict doesn't need to exist in the document for this to find a match. Very useful for scanning a document for some keyword terms. 'strict_search' means that the exact string must appear in the provided field. This is NOT an exact eq filter. ie a document with content "the happy dog crossed the street" will match on a strict_search of "dog" but won't match on "the dog". Python equivalent of `return search_string in full_string`. value: The actual value to search for in the context data/metadata """ field_name: str filter_type: DALMFilterType value: str _is_metadata: bool = False @root_validator() def set_meta(cls, values: Dict) -> Dict: """document and name are reserved arcee keys. Anything else is metadata""" values["_is_meta"] = values.get("field_name") not in ["document", "name"] return values
[docs]class ArceeDocumentSource(BaseModel): """Source of an Arcee document.""" document: str name: str id: str
[docs]class ArceeDocument(BaseModel): """Arcee document.""" index: str id: str score: float source: ArceeDocumentSource
[docs]class ArceeDocumentAdapter: """Adapter for Arcee documents"""
[docs] @classmethod def adapt(cls, arcee_document: ArceeDocument) -> Document: """Adapts an `ArceeDocument` to a langchain's `Document` object.""" return Document( page_content=arcee_document.source.document, metadata={ # arcee document; source metadata "name":, "source_id":, # arcee document metadata "index": arcee_document.index, "id":, "score": arcee_document.score, }, )
[docs]class ArceeWrapper: """Wrapper for Arcee API. For more details, see: """
[docs] def __init__( self, arcee_api_key: Union[str, SecretStr], arcee_api_url: str, arcee_api_version: str, model_kwargs: Optional[Dict[str, Any]], model_name: str, ): """Initialize ArceeWrapper. Arguments: arcee_api_key: API key for Arcee API. arcee_api_url: URL for Arcee API. arcee_api_version: Version of Arcee API. model_kwargs: Keyword arguments for Arcee API. model_name: Name of an Arcee model. """ if isinstance(arcee_api_key, str): arcee_api_key_ = SecretStr(arcee_api_key) else: arcee_api_key_ = arcee_api_key self.arcee_api_key: SecretStr = arcee_api_key_ self.model_kwargs = model_kwargs self.arcee_api_url = arcee_api_url self.arcee_api_version = arcee_api_version try: route = ArceeRoute.model_training_status.value.format(id_or_name=model_name) response = self._make_request("get", route) self.model_id = response.get("model_id") self.model_training_status = response.get("status") except Exception as e: raise ValueError( f"Error while validating model training status for '{model_name}': {e}" ) from e
[docs] def validate_model_training_status(self) -> None: if self.model_training_status != "training_complete": raise Exception( f"Model {self.model_id} is not ready. " "Please wait for training to complete." )
def _make_request( self, method: Literal["post", "get"], route: Union[ArceeRoute, str], body: Optional[Mapping[str, Any]] = None, params: Optional[dict] = None, headers: Optional[dict] = None, ) -> dict: """Make a request to the Arcee API Args: method: The HTTP method to use route: The route to call body: The body of the request params: The query params of the request headers: The headers of the request """ headers = self._make_request_headers(headers=headers) url = self._make_request_url(route=route) req_type = getattr(requests, method) response = req_type(url, json=body, params=params, headers=headers) if response.status_code not in (200, 201): raise Exception(f"Failed to make request. Response: {response.text}") return response.json() def _make_request_headers(self, headers: Optional[Dict] = None) -> Dict: headers = headers or {} if not isinstance(self.arcee_api_key, SecretStr): raise TypeError( f"arcee_api_key must be a SecretStr. Got {type(self.arcee_api_key)}" ) api_key = self.arcee_api_key.get_secret_value() internal_headers = { "X-Token": api_key, "Content-Type": "application/json", } headers.update(internal_headers) return headers def _make_request_url(self, route: Union[ArceeRoute, str]) -> str: return f"{self.arcee_api_url}/{self.arcee_api_version}/{route}" def _make_request_body_for_models( self, prompt: str, **kwargs: Mapping[str, Any] ) -> Mapping[str, Any]: """Make the request body for generate/retrieve models endpoint""" _model_kwargs = self.model_kwargs or {} _params = {**_model_kwargs, **kwargs} filters = [DALMFilter(**f) for f in _params.get("filters", [])] return dict( model_id=self.model_id, query=prompt, size=_params.get("size", 3), filters=filters, id=self.model_id, )
[docs] def generate( self, prompt: str, **kwargs: Any, ) -> str: """Generate text from Arcee DALM. Args: prompt: Prompt to generate text from. size: The max number of context results to retrieve. Defaults to 3. (Can be less if filters are provided). filters: Filters to apply to the context dataset. """ response = self._make_request( method="post", route=ArceeRoute.generate.value, body=self._make_request_body_for_models( prompt=prompt, **kwargs, ), ) return response["text"]
[docs] def retrieve( self, query: str, **kwargs: Any, ) -> List[Document]: """Retrieve {size} contexts with your retriever for a given query Args: query: Query to submit to the model size: The max number of context results to retrieve. Defaults to 3. (Can be less if filters are provided). filters: Filters to apply to the context dataset. """ response = self._make_request( method="post", route=ArceeRoute.retrieve.value, body=self._make_request_body_for_models( prompt=query, **kwargs, ), ) return [ ArceeDocumentAdapter.adapt(ArceeDocument(**doc)) for doc in response["results"] ]