mirror of
https://github.com/trustgraph-ai/trustgraph.git
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9b91d5eee3
* Starting to spawn base package * More package hacking * Bedrock and VertexAI * Parquet split * Updated templates * Utils
132 lines
3.6 KiB
Python
132 lines
3.6 KiB
Python
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from . clients.document_embeddings_client import DocumentEmbeddingsClient
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from . clients.triples_query_client import TriplesQueryClient
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from . clients.embeddings_client import EmbeddingsClient
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from . clients.prompt_client import PromptClient
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from . schema import DocumentEmbeddingsRequest, DocumentEmbeddingsResponse
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from . schema import TriplesQueryRequest, TriplesQueryResponse
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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 . schema import embeddings_request_queue
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from . schema import embeddings_response_queue
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from . schema import document_embeddings_request_queue
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from . schema import document_embeddings_response_queue
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LABEL="http://www.w3.org/2000/01/rdf-schema#label"
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DEFINITION="http://www.w3.org/2004/02/skos/core#definition"
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class DocumentRag:
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def __init__(
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self,
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pulsar_host="pulsar://pulsar:6650",
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pr_request_queue=None,
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pr_response_queue=None,
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emb_request_queue=None,
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emb_response_queue=None,
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de_request_queue=None,
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de_response_queue=None,
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verbose=False,
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module="test",
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):
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self.verbose=verbose
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if pr_request_queue is None:
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pr_request_queue = prompt_request_queue
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if pr_response_queue is None:
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pr_response_queue = prompt_response_queue
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if emb_request_queue is None:
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emb_request_queue = embeddings_request_queue
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if emb_response_queue is None:
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emb_response_queue = embeddings_response_queue
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if de_request_queue is None:
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de_request_queue = document_embeddings_request_queue
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if de_response_queue is None:
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de_response_queue = document_embeddings_response_queue
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if self.verbose:
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print("Initialising...", flush=True)
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# FIXME: Configurable
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self.entity_limit = 20
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self.de_client = DocumentEmbeddingsClient(
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pulsar_host=pulsar_host,
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subscriber=module + "-de",
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input_queue=de_request_queue,
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output_queue=de_response_queue,
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)
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self.embeddings = EmbeddingsClient(
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pulsar_host=pulsar_host,
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input_queue=emb_request_queue,
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output_queue=emb_response_queue,
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subscriber=module + "-emb",
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)
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self.lang = PromptClient(
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pulsar_host=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 + "-de-prompt",
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)
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if self.verbose:
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print("Initialised", flush=True)
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def get_vector(self, query):
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if self.verbose:
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print("Compute embeddings...", flush=True)
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qembeds = self.embeddings.request(query)
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if self.verbose:
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print("Done.", flush=True)
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return qembeds
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def get_docs(self, query):
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vectors = self.get_vector(query)
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if self.verbose:
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print("Get entities...", flush=True)
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docs = self.de_client.request(
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vectors, self.entity_limit
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)
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if self.verbose:
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print("Docs:", flush=True)
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for doc in docs:
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print(doc, flush=True)
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return docs
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def query(self, query):
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if self.verbose:
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print("Construct prompt...", flush=True)
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docs = self.get_docs(query)
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if self.verbose:
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print("Invoke LLM...", flush=True)
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print(docs)
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print(query)
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resp = self.lang.request_document_prompt(query, docs)
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if self.verbose:
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print("Done", flush=True)
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return resp
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