import asyncio import multiprocessing import re import warnings from concurrent.futures import ProcessPoolExecutor from enum import Enum from functools import partial, reduce from hashlib import sha256 from itertools import repeat from pathlib import Path from typing import Any, Generator, List, Optional, Sequence, Union from fsspec import AbstractFileSystem from llama_index.legacy.bridge.pydantic import BaseModel, Field from llama_index.legacy.embeddings.utils import resolve_embed_model from llama_index.legacy.ingestion.cache import DEFAULT_CACHE_NAME, IngestionCache from llama_index.legacy.node_parser import SentenceSplitter from llama_index.legacy.readers.base import ReaderConfig from llama_index.legacy.schema import ( BaseNode, Document, MetadataMode, TransformComponent, ) from llama_index.legacy.service_context import ServiceContext from llama_index.legacy.storage.docstore import BaseDocumentStore, SimpleDocumentStore from llama_index.legacy.storage.storage_context import DOCSTORE_FNAME from llama_index.legacy.utils import concat_dirs from llama_index.legacy.vector_stores.types import BasePydanticVectorStore def remove_unstable_values(s: str) -> str: """Remove unstable key/value pairs. Examples include: - <__main__.Test object at 0x7fb9f3793f50> - """ pattern = r"<[\w\s_\. ]+ at 0x[a-z0-9]+>" return re.sub(pattern, "", s) def get_transformation_hash( nodes: List[BaseNode], transformation: TransformComponent ) -> str: """Get the hash of a transformation.""" nodes_str = "".join( [str(node.get_content(metadata_mode=MetadataMode.ALL)) for node in nodes] ) transformation_dict = transformation.to_dict() transform_string = remove_unstable_values(str(transformation_dict)) return sha256((nodes_str + transform_string).encode("utf-8")).hexdigest() def run_transformations( nodes: List[BaseNode], transformations: Sequence[TransformComponent], in_place: bool = True, cache: Optional[IngestionCache] = None, cache_collection: Optional[str] = None, **kwargs: Any, ) -> List[BaseNode]: """Run a series of transformations on a set of nodes. Args: nodes: The nodes to transform. transformations: The transformations to apply to the nodes. Returns: The transformed nodes. """ if not in_place: nodes = list(nodes) for transform in transformations: if cache is not None: hash = get_transformation_hash(nodes, transform) cached_nodes = cache.get(hash, collection=cache_collection) if cached_nodes is not None: nodes = cached_nodes else: nodes = transform(nodes, **kwargs) cache.put(hash, nodes, collection=cache_collection) else: nodes = transform(nodes, **kwargs) return nodes async def arun_transformations( nodes: List[BaseNode], transformations: Sequence[TransformComponent], in_place: bool = True, cache: Optional[IngestionCache] = None, cache_collection: Optional[str] = None, **kwargs: Any, ) -> List[BaseNode]: """Run a series of transformations on a set of nodes. Args: nodes: The nodes to transform. transformations: The transformations to apply to the nodes. Returns: The transformed nodes. """ if not in_place: nodes = list(nodes) for transform in transformations: if cache is not None: hash = get_transformation_hash(nodes, transform) cached_nodes = cache.get(hash, collection=cache_collection) if cached_nodes is not None: nodes = cached_nodes else: nodes = await transform.acall(nodes, **kwargs) cache.put(hash, nodes, collection=cache_collection) else: nodes = await transform.acall(nodes, **kwargs) return nodes def arun_transformations_wrapper( nodes: List[BaseNode], transformations: Sequence[TransformComponent], in_place: bool = True, cache: Optional[IngestionCache] = None, cache_collection: Optional[str] = None, **kwargs: Any, ) -> List[BaseNode]: """Wrapper for async run_transformation. To be used in loop.run_in_executor within a ProcessPoolExecutor. """ loop = asyncio.new_event_loop() nodes = loop.run_until_complete( arun_transformations( nodes=nodes, transformations=transformations, in_place=in_place, cache=cache, cache_collection=cache_collection, **kwargs, ) ) loop.close() return nodes class DocstoreStrategy(str, Enum): """Document de-duplication strategy.""" UPSERTS = "upserts" DUPLICATES_ONLY = "duplicates_only" UPSERTS_AND_DELETE = "upserts_and_delete" class IngestionPipeline(BaseModel): """An ingestion pipeline that can be applied to data.""" transformations: List[TransformComponent] = Field( description="Transformations to apply to the data" ) documents: Optional[Sequence[Document]] = Field(description="Documents to ingest") reader: Optional[ReaderConfig] = Field(description="Reader to use to read the data") vector_store: Optional[BasePydanticVectorStore] = Field( description="Vector store to use to store the data" ) cache: IngestionCache = Field( default_factory=IngestionCache, description="Cache to use to store the data", ) docstore: Optional[BaseDocumentStore] = Field( default=None, description="Document store to use for de-duping with a vector store.", ) docstore_strategy: DocstoreStrategy = Field( default=DocstoreStrategy.UPSERTS, description="Document de-dup strategy." ) disable_cache: bool = Field(default=False, description="Disable the cache") class Config: arbitrary_types_allowed = True def __init__( self, transformations: Optional[List[TransformComponent]] = None, reader: Optional[ReaderConfig] = None, documents: Optional[Sequence[Document]] = None, vector_store: Optional[BasePydanticVectorStore] = None, cache: Optional[IngestionCache] = None, docstore: Optional[BaseDocumentStore] = None, docstore_strategy: DocstoreStrategy = DocstoreStrategy.UPSERTS, disable_cache: bool = False, ) -> None: if transformations is None: transformations = self._get_default_transformations() super().__init__( transformations=transformations, reader=reader, documents=documents, vector_store=vector_store, cache=cache or IngestionCache(), docstore=docstore, docstore_strategy=docstore_strategy, disable_cache=disable_cache, ) @classmethod def from_service_context( cls, service_context: ServiceContext, reader: Optional[ReaderConfig] = None, documents: Optional[Sequence[Document]] = None, vector_store: Optional[BasePydanticVectorStore] = None, cache: Optional[IngestionCache] = None, docstore: Optional[BaseDocumentStore] = None, disable_cache: bool = False, ) -> "IngestionPipeline": transformations = [ *service_context.transformations, service_context.embed_model, ] return cls( transformations=transformations, reader=reader, documents=documents, vector_store=vector_store, cache=cache, docstore=docstore, disable_cache=disable_cache, ) def persist( self, persist_dir: str = "./pipeline_storage", fs: Optional[AbstractFileSystem] = None, cache_name: str = DEFAULT_CACHE_NAME, docstore_name: str = DOCSTORE_FNAME, ) -> None: """Persist the pipeline to disk.""" if fs is not None: persist_dir = str(persist_dir) # NOTE: doesn't support Windows here docstore_path = concat_dirs(persist_dir, docstore_name) cache_path = concat_dirs(persist_dir, cache_name) else: persist_path = Path(persist_dir) docstore_path = str(persist_path / docstore_name) cache_path = str(persist_path / cache_name) self.cache.persist(cache_path, fs=fs) if self.docstore is not None: self.docstore.persist(docstore_path, fs=fs) def load( self, persist_dir: str = "./pipeline_storage", fs: Optional[AbstractFileSystem] = None, cache_name: str = DEFAULT_CACHE_NAME, docstore_name: str = DOCSTORE_FNAME, ) -> None: """Load the pipeline from disk.""" if fs is not None: self.cache = IngestionCache.from_persist_path( concat_dirs(persist_dir, cache_name), fs=fs ) self.docstore = SimpleDocumentStore.from_persist_path( concat_dirs(persist_dir, docstore_name), fs=fs ) else: self.cache = IngestionCache.from_persist_path( str(Path(persist_dir) / cache_name) ) self.docstore = SimpleDocumentStore.from_persist_path( str(Path(persist_dir) / docstore_name) ) def _get_default_transformations(self) -> List[TransformComponent]: return [ SentenceSplitter(), resolve_embed_model("default"), ] def _prepare_inputs( self, documents: Optional[List[Document]], nodes: Optional[List[BaseNode]] ) -> List[Document]: input_nodes: List[BaseNode] = [] if documents is not None: input_nodes += documents if nodes is not None: input_nodes += nodes if self.documents is not None: input_nodes += self.documents if self.reader is not None: input_nodes += self.reader.read() return input_nodes def _handle_duplicates( self, nodes: List[BaseNode], store_doc_text: bool = True, ) -> List[BaseNode]: """Handle docstore duplicates by checking all hashes.""" assert self.docstore is not None existing_hashes = self.docstore.get_all_document_hashes() current_hashes = [] nodes_to_run = [] for node in nodes: if node.hash not in existing_hashes and node.hash not in current_hashes: self.docstore.set_document_hash(node.id_, node.hash) nodes_to_run.append(node) current_hashes.append(node.hash) self.docstore.add_documents(nodes_to_run, store_text=store_doc_text) return nodes_to_run def _handle_upserts( self, nodes: List[BaseNode], store_doc_text: bool = True, ) -> List[BaseNode]: """Handle docstore upserts by checking hashes and ids.""" assert self.docstore is not None existing_doc_ids_before = set(self.docstore.get_all_document_hashes().values()) doc_ids_from_nodes = set() deduped_nodes_to_run = {} for node in nodes: ref_doc_id = node.ref_doc_id if node.ref_doc_id else node.id_ doc_ids_from_nodes.add(ref_doc_id) existing_hash = self.docstore.get_document_hash(ref_doc_id) if not existing_hash: # document doesn't exist, so add it self.docstore.set_document_hash(ref_doc_id, node.hash) deduped_nodes_to_run[ref_doc_id] = node elif existing_hash and existing_hash != node.hash: self.docstore.delete_ref_doc(ref_doc_id, raise_error=False) if self.vector_store is not None: self.vector_store.delete(ref_doc_id) self.docstore.set_document_hash(ref_doc_id, node.hash) deduped_nodes_to_run[ref_doc_id] = node else: continue # document exists and is unchanged, so skip it if self.docstore_strategy == DocstoreStrategy.UPSERTS_AND_DELETE: # Identify missing docs and delete them from docstore and vector store doc_ids_to_delete = existing_doc_ids_before - doc_ids_from_nodes for ref_doc_id in doc_ids_to_delete: self.docstore.delete_document(ref_doc_id) if self.vector_store is not None: self.vector_store.delete(ref_doc_id) nodes_to_run = list(deduped_nodes_to_run.values()) self.docstore.add_documents(nodes_to_run, store_text=store_doc_text) return nodes_to_run @staticmethod def _node_batcher( num_batches: int, nodes: Union[List[BaseNode], List[Document]] ) -> Generator[Union[List[BaseNode], List[Document]], Any, Any]: """Yield successive n-sized chunks from lst.""" batch_size = max(1, int(len(nodes) / num_batches)) for i in range(0, len(nodes), batch_size): yield nodes[i : i + batch_size] def run( self, show_progress: bool = False, documents: Optional[List[Document]] = None, nodes: Optional[List[BaseNode]] = None, cache_collection: Optional[str] = None, in_place: bool = True, store_doc_text: bool = True, num_workers: Optional[int] = None, **kwargs: Any, ) -> Sequence[BaseNode]: """ Args: show_progress (bool, optional): Shows execution progress bar(s). Defaults to False. documents (Optional[List[Document]], optional): Set of documents to be transformed. Defaults to None. nodes (Optional[List[BaseNode]], optional): Set of nodes to be transformed. Defaults to None. cache_collection (Optional[str], optional): Cache for transformations. Defaults to None. in_place (bool, optional): Whether transformations creates a new list for transformed nodes or modifies the array passed to `run_transformations`. Defaults to True. num_workers (Optional[int], optional): The number of parallel processes to use. If set to None, then sequential compute is used. Defaults to None. Returns: Sequence[BaseNode]: The set of transformed Nodes/Documents """ input_nodes = self._prepare_inputs(documents, nodes) # check if we need to dedup if self.docstore is not None and self.vector_store is not None: if self.docstore_strategy in ( DocstoreStrategy.UPSERTS, DocstoreStrategy.UPSERTS_AND_DELETE, ): nodes_to_run = self._handle_upserts( input_nodes, store_doc_text=store_doc_text ) elif self.docstore_strategy == DocstoreStrategy.DUPLICATES_ONLY: nodes_to_run = self._handle_duplicates( input_nodes, store_doc_text=store_doc_text ) else: raise ValueError(f"Invalid docstore strategy: {self.docstore_strategy}") elif self.docstore is not None and self.vector_store is None: if self.docstore_strategy == DocstoreStrategy.UPSERTS: print( "Docstore strategy set to upserts, but no vector store. " "Switching to duplicates_only strategy." ) self.docstore_strategy = DocstoreStrategy.DUPLICATES_ONLY elif self.docstore_strategy == DocstoreStrategy.UPSERTS_AND_DELETE: print( "Docstore strategy set to upserts and delete, but no vector store. " "Switching to duplicates_only strategy." ) self.docstore_strategy = DocstoreStrategy.DUPLICATES_ONLY nodes_to_run = self._handle_duplicates( input_nodes, store_doc_text=store_doc_text ) else: nodes_to_run = input_nodes if num_workers and num_workers > 1: num_cpus = multiprocessing.cpu_count() if num_workers > num_cpus: warnings.warn( "Specified num_workers exceed number of CPUs in the system. " "Setting `num_workers` down to the maximum CPU count." ) num_workers = num_cpus with multiprocessing.get_context("spawn").Pool(num_workers) as p: node_batches = self._node_batcher( num_batches=num_workers, nodes=nodes_to_run ) nodes_parallel = p.starmap( run_transformations, zip( node_batches, repeat(self.transformations), repeat(in_place), repeat(self.cache if not self.disable_cache else None), repeat(cache_collection), ), ) nodes = reduce(lambda x, y: x + y, nodes_parallel, []) else: nodes = run_transformations( nodes_to_run, self.transformations, show_progress=show_progress, cache=self.cache if not self.disable_cache else None, cache_collection=cache_collection, in_place=in_place, **kwargs, ) if self.vector_store is not None: self.vector_store.add([n for n in nodes if n.embedding is not None]) return nodes # ------ async methods ------ async def _ahandle_duplicates( self, nodes: List[BaseNode], store_doc_text: bool = True, ) -> List[BaseNode]: """Handle docstore duplicates by checking all hashes.""" assert self.docstore is not None existing_hashes = await self.docstore.aget_all_document_hashes() current_hashes = [] nodes_to_run = [] for node in nodes: if node.hash not in existing_hashes and node.hash not in current_hashes: await self.docstore.aset_document_hash(node.id_, node.hash) nodes_to_run.append(node) current_hashes.append(node.hash) await self.docstore.async_add_documents(nodes_to_run, store_text=store_doc_text) return nodes_to_run async def _ahandle_upserts( self, nodes: List[BaseNode], store_doc_text: bool = True, ) -> List[BaseNode]: """Handle docstore upserts by checking hashes and ids.""" assert self.docstore is not None existing_doc_ids_before = set( (await self.docstore.aget_all_document_hashes()).values() ) doc_ids_from_nodes = set() deduped_nodes_to_run = {} for node in nodes: ref_doc_id = node.ref_doc_id if node.ref_doc_id else node.id_ doc_ids_from_nodes.add(ref_doc_id) existing_hash = await self.docstore.aget_document_hash(ref_doc_id) if not existing_hash: # document doesn't exist, so add it await self.docstore.aset_document_hash(ref_doc_id, node.hash) deduped_nodes_to_run[ref_doc_id] = node elif existing_hash and existing_hash != node.hash: await self.docstore.adelete_ref_doc(ref_doc_id, raise_error=False) if self.vector_store is not None: await self.vector_store.adelete(ref_doc_id) await self.docstore.aset_document_hash(ref_doc_id, node.hash) deduped_nodes_to_run[ref_doc_id] = node else: continue # document exists and is unchanged, so skip it if self.docstore_strategy == DocstoreStrategy.UPSERTS_AND_DELETE: # Identify missing docs and delete them from docstore and vector store doc_ids_to_delete = existing_doc_ids_before - doc_ids_from_nodes for ref_doc_id in doc_ids_to_delete: await self.docstore.adelete_document(ref_doc_id) if self.vector_store is not None: await self.vector_store.adelete(ref_doc_id) nodes_to_run = list(deduped_nodes_to_run.values()) await self.docstore.async_add_documents(nodes_to_run, store_text=store_doc_text) return nodes_to_run async def arun( self, show_progress: bool = False, documents: Optional[List[Document]] = None, nodes: Optional[List[BaseNode]] = None, cache_collection: Optional[str] = None, in_place: bool = True, store_doc_text: bool = True, num_workers: Optional[int] = None, **kwargs: Any, ) -> Sequence[BaseNode]: input_nodes = self._prepare_inputs(documents, nodes) # check if we need to dedup if self.docstore is not None and self.vector_store is not None: if self.docstore_strategy in ( DocstoreStrategy.UPSERTS, DocstoreStrategy.UPSERTS_AND_DELETE, ): nodes_to_run = await self._ahandle_upserts( input_nodes, store_doc_text=store_doc_text ) elif self.docstore_strategy == DocstoreStrategy.DUPLICATES_ONLY: nodes_to_run = await self._ahandle_duplicates( input_nodes, store_doc_text=store_doc_text ) else: raise ValueError(f"Invalid docstore strategy: {self.docstore_strategy}") elif self.docstore is not None and self.vector_store is None: if self.docstore_strategy == DocstoreStrategy.UPSERTS: print( "Docstore strategy set to upserts, but no vector store. " "Switching to duplicates_only strategy." ) self.docstore_strategy = DocstoreStrategy.DUPLICATES_ONLY elif self.docstore_strategy == DocstoreStrategy.UPSERTS_AND_DELETE: print( "Docstore strategy set to upserts and delete, but no vector store. " "Switching to duplicates_only strategy." ) self.docstore_strategy = DocstoreStrategy.DUPLICATES_ONLY nodes_to_run = await self._ahandle_duplicates( input_nodes, store_doc_text=store_doc_text ) else: nodes_to_run = input_nodes if num_workers and num_workers > 1: num_cpus = multiprocessing.cpu_count() if num_workers > num_cpus: warnings.warn( "Specified num_workers exceed number of CPUs in the system. " "Setting `num_workers` down to the maximum CPU count." ) num_workers = num_cpus loop = asyncio.get_event_loop() with ProcessPoolExecutor(max_workers=num_workers) as p: node_batches = self._node_batcher( num_batches=num_workers, nodes=nodes_to_run ) tasks = [ loop.run_in_executor( p, partial( arun_transformations_wrapper, transformations=self.transformations, in_place=in_place, cache=self.cache if not self.disable_cache else None, cache_collection=cache_collection, ), batch, ) for batch in node_batches ] result: List[List[BaseNode]] = await asyncio.gather(*tasks) nodes = reduce(lambda x, y: x + y, result, []) else: nodes = await arun_transformations( nodes_to_run, self.transformations, show_progress=show_progress, cache=self.cache if not self.disable_cache else None, cache_collection=cache_collection, in_place=in_place, **kwargs, ) if self.vector_store is not None: await self.vector_store.async_add( [n for n in nodes if n.embedding is not None] ) return nodes