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import asyncio
import copy
import logging
import uuid
from typing import Any, AsyncGenerator, Optional, Union
from r2r.base import (
AsyncState,
EmbeddingProvider,
Extraction,
Fragment,
FragmentType,
KVLoggingSingleton,
PipeType,
R2RDocumentProcessingError,
TextSplitter,
Vector,
VectorEntry,
generate_id_from_label,
)
from r2r.base.pipes.base_pipe import AsyncPipe
logger = logging.getLogger(__name__)
class EmbeddingPipe(AsyncPipe):
"""
Embeds and stores documents using a specified embedding model and database.
"""
class Input(AsyncPipe.Input):
message: AsyncGenerator[
Union[Extraction, R2RDocumentProcessingError], None
]
def __init__(
self,
embedding_provider: EmbeddingProvider,
text_splitter: TextSplitter,
embedding_batch_size: int = 1,
id_prefix: str = "demo",
pipe_logger: Optional[KVLoggingSingleton] = None,
type: PipeType = PipeType.INGESTOR,
config: Optional[AsyncPipe.PipeConfig] = None,
*args,
**kwargs,
):
"""
Initializes the embedding pipe with necessary components and configurations.
"""
super().__init__(
pipe_logger=pipe_logger,
type=type,
config=config
or AsyncPipe.PipeConfig(name="default_embedding_pipe"),
)
self.embedding_provider = embedding_provider
self.text_splitter = text_splitter
self.embedding_batch_size = embedding_batch_size
self.id_prefix = id_prefix
self.pipe_run_info = None
async def fragment(
self, extraction: Extraction, run_id: uuid.UUID
) -> AsyncGenerator[Fragment, None]:
"""
Splits text into manageable chunks for embedding.
"""
if not isinstance(extraction, Extraction):
raise ValueError(
f"Expected an Extraction, but received {type(extraction)}."
)
if not isinstance(extraction.data, str):
raise ValueError(
f"Expected a string, but received {type(extraction.data)}."
)
text_chunks = [
ele.page_content
for ele in self.text_splitter.create_documents([extraction.data])
]
for iteration, chunk in enumerate(text_chunks):
fragment = Fragment(
id=generate_id_from_label(f"{extraction.id}-{iteration}"),
type=FragmentType.TEXT,
data=chunk,
metadata=copy.deepcopy(extraction.metadata),
extraction_id=extraction.id,
document_id=extraction.document_id,
)
yield fragment
iteration += 1
async def transform_fragments(
self, fragments: list[Fragment], metadatas: list[dict]
) -> AsyncGenerator[Fragment, None]:
"""
Transforms text chunks based on their metadata, e.g., adding prefixes.
"""
async for fragment, metadata in zip(fragments, metadatas):
if "chunk_prefix" in metadata:
prefix = metadata.pop("chunk_prefix")
fragment.data = f"{prefix}\n{fragment.data}"
yield fragment
async def embed(self, fragments: list[Fragment]) -> list[float]:
return await self.embedding_provider.async_get_embeddings(
[fragment.data for fragment in fragments],
EmbeddingProvider.PipeStage.BASE,
)
async def _process_batch(
self, fragment_batch: list[Fragment]
) -> list[VectorEntry]:
"""
Embeds a batch of fragments and yields vector entries.
"""
vectors = await self.embed(fragment_batch)
return [
VectorEntry(
id=fragment.id,
vector=Vector(data=raw_vector),
metadata={
"document_id": fragment.document_id,
"extraction_id": fragment.extraction_id,
"text": fragment.data,
**fragment.metadata,
},
)
for raw_vector, fragment in zip(vectors, fragment_batch)
]
async def _process_and_enqueue_batch(
self, fragment_batch: list[Fragment], vector_entry_queue: asyncio.Queue
):
try:
batch_result = await self._process_batch(fragment_batch)
for vector_entry in batch_result:
await vector_entry_queue.put(vector_entry)
except Exception as e:
logger.error(f"Error processing batch: {e}")
await vector_entry_queue.put(
R2RDocumentProcessingError(
error_message=str(e),
document_id=fragment_batch[0].document_id,
)
)
finally:
await vector_entry_queue.put(None) # Signal completion
async def _run_logic(
self,
input: Input,
state: AsyncState,
run_id: uuid.UUID,
*args: Any,
**kwargs: Any,
) -> AsyncGenerator[Union[R2RDocumentProcessingError, VectorEntry], None]:
"""
Executes the embedding pipe: chunking, transforming, embedding, and storing documents.
"""
vector_entry_queue = asyncio.Queue()
fragment_batch = []
active_tasks = 0
fragment_info = {}
async for extraction in input.message:
if isinstance(extraction, R2RDocumentProcessingError):
yield extraction
continue
async for fragment in self.fragment(extraction, run_id):
if extraction.document_id in fragment_info:
fragment_info[extraction.document_id] += 1
else:
fragment_info[extraction.document_id] = 0 # Start with 0
fragment.metadata["chunk_order"] = fragment_info[
extraction.document_id
]
version = fragment.metadata.get("version", "v0")
# Ensure fragment ID is set correctly
if not fragment.id:
fragment.id = generate_id_from_label(
f"{extraction.id}-{fragment_info[extraction.document_id]}-{version}"
)
fragment_batch.append(fragment)
if len(fragment_batch) >= self.embedding_batch_size:
asyncio.create_task(
self._process_and_enqueue_batch(
fragment_batch.copy(), vector_entry_queue
)
)
active_tasks += 1
fragment_batch.clear()
logger.debug(
f"Fragmented the input document ids into counts as shown: {fragment_info}"
)
if fragment_batch:
asyncio.create_task(
self._process_and_enqueue_batch(
fragment_batch.copy(), vector_entry_queue
)
)
active_tasks += 1
while active_tasks > 0:
vector_entry = await vector_entry_queue.get()
if vector_entry is None: # Check for termination signal
active_tasks -= 1
elif isinstance(vector_entry, Exception):
yield vector_entry # Propagate the exception
active_tasks -= 1
else:
yield vector_entry
|
