mirror of
https://github.com/trustgraph-ai/trustgraph.git
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156 lines
4.3 KiB
Python
Executable file
156 lines
4.3 KiB
Python
Executable file
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"""
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Simple decoder, accepts embeddings+text chunks input, applies entity analysis to
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get entity definitions which are output as graph edges.
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"""
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import urllib.parse
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import json
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from .... schema import ChunkEmbeddings, Triple, Triples, Metadata, Value
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from .... schema import chunk_embeddings_ingest_queue, triples_store_queue
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from .... schema import prompt_request_queue
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from .... schema import prompt_response_queue
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from .... log_level import LogLevel
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from .... clients.prompt_client import PromptClient
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from .... rdf import TRUSTGRAPH_ENTITIES, DEFINITION
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from .... base import ConsumerProducer
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DEFINITION_VALUE = Value(value=DEFINITION, is_uri=True)
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module = ".".join(__name__.split(".")[1:-1])
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default_input_queue = chunk_embeddings_ingest_queue
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default_output_queue = triples_store_queue
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default_subscriber = module
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class Processor(ConsumerProducer):
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def __init__(self, **params):
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input_queue = params.get("input_queue", default_input_queue)
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output_queue = params.get("output_queue", default_output_queue)
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subscriber = params.get("subscriber", default_subscriber)
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pr_request_queue = params.get(
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"prompt_request_queue", prompt_request_queue
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)
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pr_response_queue = params.get(
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"prompt_response_queue", prompt_response_queue
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)
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super(Processor, self).__init__(
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**params | {
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"input_queue": input_queue,
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"output_queue": output_queue,
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"subscriber": subscriber,
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"input_schema": ChunkEmbeddings,
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"output_schema": Triples,
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"prompt_request_queue": pr_request_queue,
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"prompt_response_queue": pr_response_queue,
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}
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)
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self.prompt = PromptClient(
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pulsar_host=self.pulsar_host,
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input_queue=pr_request_queue,
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output_queue=pr_response_queue,
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subscriber = module + "-prompt",
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)
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def to_uri(self, text):
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part = text.replace(" ", "-").lower().encode("utf-8")
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quoted = urllib.parse.quote(part)
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uri = TRUSTGRAPH_ENTITIES + quoted
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return uri
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def get_definitions(self, chunk):
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return self.prompt.request_definitions(chunk)
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def emit_edges(self, metadata, triples):
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t = Triples(
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metadata=metadata,
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triples=triples,
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)
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self.producer.send(t)
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def handle(self, msg):
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v = msg.value()
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print(f"Indexing {v.metadata.id}...", flush=True)
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chunk = v.chunk.decode("utf-8")
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try:
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defs = self.get_definitions(chunk)
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triples = []
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# FIXME: Putting metadata into triples store is duplicated in
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# relationships extractor too
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for t in v.metadata.metadata:
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triples.append(t)
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for defn in defs:
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s = defn.name
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o = defn.definition
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if s == "": continue
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if o == "": continue
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if s is None: continue
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if o is None: continue
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s_uri = self.to_uri(s)
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s_value = Value(value=str(s_uri), is_uri=True)
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o_value = Value(value=str(o), is_uri=False)
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triples.append(Triple(
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s=s_value, p=DEFINITION_VALUE, o=o_value
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))
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self.emit_edges(
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Metadata(
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id=v.metadata.id,
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metadata=[],
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user=v.metadata.user,
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collection=v.metadata.collection,
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),
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triples
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)
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except Exception as e:
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print("Exception: ", e, flush=True)
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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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ConsumerProducer.add_args(
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parser, default_input_queue, default_subscriber,
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default_output_queue,
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)
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parser.add_argument(
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'--prompt-request-queue',
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default=prompt_request_queue,
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help=f'Prompt request queue (default: {prompt_request_queue})',
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)
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parser.add_argument(
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'--prompt-completion-response-queue',
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default=prompt_response_queue,
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help=f'Prompt response queue (default: {prompt_response_queue})',
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)
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def run():
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Processor.start(module, __doc__)
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