jrmccluskey commented on code in PR #31657: URL: https://github.com/apache/beam/pull/31657#discussion_r1698860457
########## examples/notebooks/beam-ml/rag_usecase/redis_enrichment.py: ########## @@ -0,0 +1,124 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""This module implements enrichment classes to implement semantic search on Redis Vector DB. + + +Redis :Enrichment Handler +----------------- +:class:`RedisEnrichmentHandler` is a ``EnrichmentSourceHandler`` that performs enrichment/search +by fetching the similar text to the user query/prompt from the knowledge base (redis vector DB) and returns +the similar text along with its embeddings as Beam.Row Object. + +Example usage:: + redis_handler = RedisEnrichmentHandler(redis_host='127.0.0.1', redis_port=6379) + + pipeline | Enrichment(redis_handler) + +No backward compatibility guarantees. Everything in this module is experimental. +""" + + +import logging + + +import numpy as np +import redis +from redis.commands.search.query import Query + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'RedisEnrichmentHandler', +] + + +_LOGGER = logging.getLogger(__name__) + + +class RedisEnrichmentHandler(EnrichmentSourceHandler[beam.Row, beam.Row]): + """A handler for :class:`apache_beam.transforms.enrichment.Enrichment` + transform to interact with redis vector DB. + + Args: + redis_host (str): Redis Host to connect to redis DB + redis_port (int): Redis Port to connect to redis DB + index_name (str): Index Name created for searching in Redis DB + vector_field (str): vector field to compute similarity score in vector DB + return_fields (list): returns list of similar text and its embeddings + hybrid_fields (str): fields to be selected + k (int): Value of K in KNN algorithm for searching in redis + """ + + def __init__( + self, + redis_host: str, + redis_port: int, + index_name: str = "embeddings-index", + vector_field: str = "text_vector", + return_fields: list = ["id", "title", "url", "text"], + hybrid_fields: str = "*", + k: int = 2, + ): + + self.redis_host = redis_host + self.redis_port = redis_port + self.index_name = index_name + self.vector_field = vector_field + self.return_fields = return_fields + self.hybrid_fields = hybrid_fields + self.k = k + self.client = None + + def __enter__(self): + """connect to the redis DB using redis client.""" + self.client = redis.Redis(host=self.redis_host, port=self.redis_port) + + + def __call__(self, request: beam.Row, *args, **kwargs): + """ + Reads a row from the redis Vector DB and returns + a `Tuple` of request and response. + + Args: + request: the input `beam.Row` to enrich. + """ + + + # read embedding vector for user query + + embedded_query = request['text'] + + + # Prepare the Query + base_query = f'{self.hybrid_fields}=>[KNN {self.k} @{self.vector_field} $vector AS vector_score]' + query = ( + Query(base_query) + .return_fields(*self.return_fields) + # .sort_by("vector_score") + .paging(0, self.k) + .dialect(2) + ) + + params_dict = {"vector": np.array(embedded_query).astype(dtype=np.float32).tobytes()} + + # perform vector search + results = self.client.ft(self.index_name).search(query, params_dict) + + return beam.Row(text=embedded_query), beam.Row(docs = results.docs) Review Comment: I still have this question. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: github-unsubscr...@beam.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
