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https://github.com/trustgraph-ai/trustgraph.git
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144 lines
3.7 KiB
Python
Executable file
144 lines
3.7 KiB
Python
Executable file
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"""
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Simple RAG service, performs query using document RAG an LLM.
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Input is query, output is response.
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"""
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import logging
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from ... schema import DocumentRagQuery, DocumentRagResponse, Error
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from . document_rag import DocumentRag
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from ... base import FlowProcessor, ConsumerSpec, ProducerSpec
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from ... base import PromptClientSpec, EmbeddingsClientSpec
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from ... base import DocumentEmbeddingsClientSpec
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "document-rag"
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class Processor(FlowProcessor):
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def __init__(self, **params):
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id = params.get("id", default_ident)
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doc_limit = params.get("doc_limit", 5)
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super(Processor, self).__init__(
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**params | {
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"id": id,
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"doc_limit": doc_limit,
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}
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)
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self.doc_limit = doc_limit
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self.register_specification(
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ConsumerSpec(
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name = "request",
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schema = DocumentRagQuery,
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handler = self.on_request,
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)
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)
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self.register_specification(
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EmbeddingsClientSpec(
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request_name = "embeddings-request",
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response_name = "embeddings-response",
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)
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)
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self.register_specification(
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DocumentEmbeddingsClientSpec(
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request_name = "document-embeddings-request",
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response_name = "document-embeddings-response",
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)
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)
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self.register_specification(
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PromptClientSpec(
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request_name = "prompt-request",
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response_name = "prompt-response",
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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 = "response",
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schema = DocumentRagResponse,
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)
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)
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async def on_request(self, msg, consumer, flow):
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try:
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self.rag = DocumentRag(
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embeddings_client = flow("embeddings-request"),
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doc_embeddings_client = flow("document-embeddings-request"),
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prompt_client = flow("prompt-request"),
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verbose=True,
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)
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v = msg.value()
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# Sender-produced ID
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id = msg.properties()["id"]
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logger.info(f"Handling input {id}...")
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if v.doc_limit:
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doc_limit = v.doc_limit
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else:
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doc_limit = self.doc_limit
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response = await self.rag.query(
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v.query,
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user=v.user,
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collection=v.collection,
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doc_limit=doc_limit
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)
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await flow("response").send(
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DocumentRagResponse(
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response = response,
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error = None
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),
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properties = {"id": id}
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)
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logger.info("Request processing complete")
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except Exception as e:
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logger.error(f"Document RAG service exception: {e}", exc_info=True)
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logger.debug("Sending error response...")
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await flow("response").send(
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DocumentRagResponse(
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response = None,
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error = Error(
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type = "document-rag-error",
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message = str(e),
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),
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),
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properties = {"id": id}
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)
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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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'-d', '--doc-limit',
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type=int,
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default=20,
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help=f'Default document fetch limit (default: 10)'
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)
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def run():
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Processor.launch(default_ident, __doc__)
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