Source code for langchain_community.retrievers.thirdai_neuraldb

from __future__ import annotations

import importlib
import os
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union

from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import Extra, SecretStr, root_validator
from langchain_core.retrievers import BaseRetriever
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env


[docs]class NeuralDBRetriever(BaseRetriever): """Document retriever that uses ThirdAI's NeuralDB.""" thirdai_key: SecretStr """ThirdAI API Key""" db: Any = None #: :meta private: """NeuralDB instance""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid underscore_attrs_are_private = True @staticmethod def _verify_thirdai_library(thirdai_key: Optional[str] = None) -> None: try: from thirdai import licensing importlib.util.find_spec("thirdai.neural_db") licensing.activate(thirdai_key or os.getenv("THIRDAI_KEY")) except ImportError: raise ImportError( "Could not import thirdai python package and neuraldb dependencies. " "Please install it with `pip install thirdai[neural_db]`." )
[docs] @classmethod def from_scratch( cls, thirdai_key: Optional[str] = None, **model_kwargs: dict, ) -> NeuralDBRetriever: """ Create a NeuralDBRetriever from scratch. To use, set the ``THIRDAI_KEY`` environment variable with your ThirdAI API key, or pass ``thirdai_key`` as a named parameter. Example: .. code-block:: python from langchain_community.retrievers import NeuralDBRetriever retriever = NeuralDBRetriever.from_scratch( thirdai_key="your-thirdai-key", ) retriever.insert([ "/path/to/doc.pdf", "/path/to/doc.docx", "/path/to/doc.csv", ]) documents = retriever.invoke("AI-driven music therapy") """ NeuralDBRetriever._verify_thirdai_library(thirdai_key) from thirdai import neural_db as ndb return cls(thirdai_key=thirdai_key, db=ndb.NeuralDB(**model_kwargs)) # type: ignore[arg-type]
[docs] @classmethod def from_checkpoint( cls, checkpoint: Union[str, Path], thirdai_key: Optional[str] = None, ) -> NeuralDBRetriever: """ Create a NeuralDBRetriever with a base model from a saved checkpoint To use, set the ``THIRDAI_KEY`` environment variable with your ThirdAI API key, or pass ``thirdai_key`` as a named parameter. Example: .. code-block:: python from langchain_community.retrievers import NeuralDBRetriever retriever = NeuralDBRetriever.from_checkpoint( checkpoint="/path/to/checkpoint.ndb", thirdai_key="your-thirdai-key", ) retriever.insert([ "/path/to/doc.pdf", "/path/to/doc.docx", "/path/to/doc.csv", ]) documents = retriever.invoke("AI-driven music therapy") """ NeuralDBRetriever._verify_thirdai_library(thirdai_key) from thirdai import neural_db as ndb return cls(thirdai_key=thirdai_key, db=ndb.NeuralDB.from_checkpoint(checkpoint)) # type: ignore[arg-type]
@root_validator() def validate_environments(cls, values: Dict) -> Dict: """Validate ThirdAI environment variables.""" values["thirdai_key"] = convert_to_secret_str( get_from_dict_or_env( values, "thirdai_key", "THIRDAI_KEY", ) ) return values
[docs] def insert( self, sources: List[Any], train: bool = True, fast_mode: bool = True, **kwargs: dict, ) -> None: """Inserts files / document sources into the retriever. Args: train: When True this means that the underlying model in the NeuralDB will undergo unsupervised pretraining on the inserted files. Defaults to True. fast_mode: Much faster insertion with a slight drop in performance. Defaults to True. """ sources = self._preprocess_sources(sources) self.db.insert( sources=sources, train=train, fast_approximation=fast_mode, **kwargs, )
def _preprocess_sources(self, sources: list) -> list: """Checks if the provided sources are string paths. If they are, convert to NeuralDB document objects. Args: sources: list of either string paths to PDF, DOCX or CSV files, or NeuralDB document objects. """ from thirdai import neural_db as ndb if not sources: return sources preprocessed_sources = [] for doc in sources: if not isinstance(doc, str): preprocessed_sources.append(doc) else: if doc.lower().endswith(".pdf"): preprocessed_sources.append(ndb.PDF(doc)) elif doc.lower().endswith(".docx"): preprocessed_sources.append(ndb.DOCX(doc)) elif doc.lower().endswith(".csv"): preprocessed_sources.append(ndb.CSV(doc)) else: raise RuntimeError( f"Could not automatically load {doc}. Only files " "with .pdf, .docx, or .csv extensions can be loaded " "automatically. For other formats, please use the " "appropriate document object from the ThirdAI library." ) return preprocessed_sources
[docs] def upvote(self, query: str, document_id: int) -> None: """The retriever upweights the score of a document for a specific query. This is useful for fine-tuning the retriever to user behavior. Args: query: text to associate with `document_id` document_id: id of the document to associate query with. """ self.db.text_to_result(query, document_id)
[docs] def upvote_batch(self, query_id_pairs: List[Tuple[str, int]]) -> None: """Given a batch of (query, document id) pairs, the retriever upweights the scores of the document for the corresponding queries. This is useful for fine-tuning the retriever to user behavior. Args: query_id_pairs: list of (query, document id) pairs. For each pair in this list, the model will upweight the document id for the query. """ self.db.text_to_result_batch(query_id_pairs)
[docs] def associate(self, source: str, target: str) -> None: """The retriever associates a source phrase with a target phrase. When the retriever sees the source phrase, it will also consider results that are relevant to the target phrase. Args: source: text to associate to `target`. target: text to associate `source` to. """ self.db.associate(source, target)
[docs] def associate_batch(self, text_pairs: List[Tuple[str, str]]) -> None: """Given a batch of (source, target) pairs, the retriever associates each source phrase with the corresponding target phrase. Args: text_pairs: list of (source, target) text pairs. For each pair in this list, the source will be associated with the target. """ self.db.associate_batch(text_pairs)
def _get_relevant_documents( self, query: str, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any ) -> List[Document]: """Retrieve {top_k} contexts with your retriever for a given query Args: query: Query to submit to the model top_k: The max number of context results to retrieve. Defaults to 10. """ try: if "top_k" not in kwargs: kwargs["top_k"] = 10 references = self.db.search(query=query, **kwargs) return [ Document( page_content=ref.text, metadata={ "id": ref.id, "upvote_ids": ref.upvote_ids, "source": ref.source, "metadata": ref.metadata, "score": ref.score, "context": ref.context(1), }, ) for ref in references ] except Exception as e: raise ValueError(f"Error while retrieving documents: {e}") from e
[docs] def save(self, path: str) -> None: """Saves a NeuralDB instance to disk. Can be loaded into memory by calling NeuralDB.from_checkpoint(path) Args: path: path on disk to save the NeuralDB instance to. """ self.db.save(path)