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https://github.com/trustgraph-ai/trustgraph.git
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a9197d11ee
- Keeps processing in different flows separate so that data can go to different stores / collections etc. - Potentially supports different processing flows - Tidies the processing API with common base-classes for e.g. LLMs, and automatic configuration of 'clients' to use the right queue names in a flow
106 lines
2.6 KiB
Python
Executable file
106 lines
2.6 KiB
Python
Executable file
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"""
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Simple decoder, accepts text documents on input, outputs chunks from the
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as text as separate output objects.
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"""
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from langchain_text_splitters import TokenTextSplitter
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from prometheus_client import Histogram
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from ... schema import TextDocument, Chunk
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from ... base import FlowProcessor
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default_ident = "chunker"
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class Processor(FlowProcessor):
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def __init__(self, **params):
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id = params.get("id")
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chunk_size = params.get("chunk_size", 250)
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chunk_overlap = params.get("chunk_overlap", 15)
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super(Processor, self).__init__(
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**params | { "id": id }
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)
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if not hasattr(__class__, "chunk_metric"):
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__class__.chunk_metric = Histogram(
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'chunk_size', 'Chunk size',
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["id", "flow"],
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buckets=[100, 160, 250, 400, 650, 1000, 1600,
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2500, 4000, 6400, 10000, 16000]
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)
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self.text_splitter = TokenTextSplitter(
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encoding_name="cl100k_base",
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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)
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self.register_specification(
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ConsumerSpec(
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name = "input",
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schema = TextDocument,
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handler = self.on_message,
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)
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)
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self.register_specification(
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ProducerSpec(
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name = "output",
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schema = Chunk,
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)
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)
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print("Chunker initialised", flush=True)
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async def on_message(self, msg, consumer, flow):
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v = msg.value()
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print(f"Chunking {v.metadata.id}...", flush=True)
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texts = self.text_splitter.create_documents(
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[v.text.decode("utf-8")]
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)
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for ix, chunk in enumerate(texts):
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print("Chunk", len(chunk.page_content), flush=True)
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r = Chunk(
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metadata=v.metadata,
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chunk=chunk.page_content.encode("utf-8"),
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)
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__class__.chunk_metric.labels(
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id=consumer.id, flow=consumer.flow
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).observe(len(chunk.page_content))
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await flow("output").send(r)
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print("Done.", flush=True)
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@staticmethod
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def add_args(parser):
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FlowProcessor.add_args(parser)
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parser.add_argument(
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'-z', '--chunk-size',
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type=int,
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default=250,
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help=f'Chunk size (default: 250)'
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)
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parser.add_argument(
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'-v', '--chunk-overlap',
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type=int,
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default=15,
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help=f'Chunk overlap (default: 15)'
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)
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def run():
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Processor.launch(default_ident, __doc__)
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