lordgamez commented on code in PR #8441: URL: https://github.com/apache/nifi/pull/8441#discussion_r1519730551
########## nifi-python-extensions/nifi-text-embeddings-module/src/main/python/vectorstores/PutOpenSearchVector.py: ########## @@ -0,0 +1,245 @@ +# 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. + +from langchain.vectorstores import OpenSearchVectorSearch +from nifiapi.flowfiletransform import FlowFileTransform, FlowFileTransformResult +from nifiapi.properties import PropertyDescriptor, StandardValidators, ExpressionLanguageScope, PropertyDependency +from OpenSearchVectorUtils import (L2, L1, LINF, COSINESIMIL, OPENAI_API_KEY, OPENAI_API_MODEL, HUGGING_FACE_API_KEY, + HUGGING_FACE_MODEL,HTTP_HOST, USERNAME, PASSWORD, INDEX_NAME, VECTOR_FIELD, Review Comment: There is a missing whitespace after HUGGING_FACE_MODEL ########## nifi-python-extensions/nifi-text-embeddings-module/src/main/python/vectorstores/PutOpenSearchVector.py: ########## @@ -0,0 +1,245 @@ +# 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. + +from langchain.vectorstores import OpenSearchVectorSearch +from nifiapi.flowfiletransform import FlowFileTransform, FlowFileTransformResult +from nifiapi.properties import PropertyDescriptor, StandardValidators, ExpressionLanguageScope, PropertyDependency +from OpenSearchVectorUtils import (L2, L1, LINF, COSINESIMIL, OPENAI_API_KEY, OPENAI_API_MODEL, HUGGING_FACE_API_KEY, + HUGGING_FACE_MODEL,HTTP_HOST, USERNAME, PASSWORD, INDEX_NAME, VECTOR_FIELD, + TEXT_FIELD, create_authentication_params, parse_documents) +from EmbeddingUtils import EMBEDDING_MODEL, create_embedding_service +from nifiapi.documentation import use_case, multi_processor_use_case, ProcessorConfiguration Review Comment: multi_processor_use_case and ProcessorConfiguration are unused imports that can be removed ########## nifi-python-extensions/nifi-text-embeddings-module/src/main/python/vectorstores/PutOpenSearchVector.py: ########## @@ -0,0 +1,245 @@ +# 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. + +from langchain.vectorstores import OpenSearchVectorSearch +from nifiapi.flowfiletransform import FlowFileTransform, FlowFileTransformResult +from nifiapi.properties import PropertyDescriptor, StandardValidators, ExpressionLanguageScope, PropertyDependency +from OpenSearchVectorUtils import (L2, L1, LINF, COSINESIMIL, OPENAI_API_KEY, OPENAI_API_MODEL, HUGGING_FACE_API_KEY, + HUGGING_FACE_MODEL,HTTP_HOST, USERNAME, PASSWORD, INDEX_NAME, VECTOR_FIELD, + TEXT_FIELD, create_authentication_params, parse_documents) +from EmbeddingUtils import EMBEDDING_MODEL, create_embedding_service +from nifiapi.documentation import use_case, multi_processor_use_case, ProcessorConfiguration + + +@use_case(description="Create vectors/embeddings that represent text content and send the vectors to OpenSearch", + notes="This use case assumes that the data has already been formatted in JSONL format with the text to store in OpenSearch provided in the 'text' field.", + keywords=["opensearch", "embedding", "vector", "text", "vectorstore", "insert"], + configuration=""" + Configure the 'HTTP Host' to an appropriate URL where OpenSearch is accessible. + Configure 'Embedding Model' to indicate whether OpenAI embeddings should be used or a HuggingFace embedding model should be used: 'Hugging Face Model' or 'OpenAI Model' + Configure the 'OpenAI API Key' or 'HuggingFace API Key', depending on the chosen Embedding Model. + Set 'Index Name' to the name of your OpenSearch Index. + Set 'Vector Field Name' to the name of the field in the document which will store the vector data. + Set 'Text Field Name' to the name of the field in the document which will store the text data. + + If the documents to send to OpenSearch contain a unique identifier, set the 'Document ID Field Name' property to the name of the field that contains the document ID. + This property can be left blank, in which case a unique ID will be generated based on the FlowFile's filename. + + If the provided index does not exists in OpenSearch then the processor is capable to create it. The 'New Index Strategy' property defines + that the index needs to be created from the default template or it should be configured with custom values. + """) +@use_case(description="Update vectors/embeddings in OpenSearch", + notes="This use case assumes that the data has already been formatted in JSONL format with the text to store in OpenSearch provided in the 'text' field.", + keywords=["opensearch", "embedding", "vector", "text", "vectorstore", "update", "upsert"], + configuration=""" + Configure the 'HTTP Host' to an appropriate URL where OpenSearch is accessible. + Configure 'Embedding Model' to indicate whether OpenAI embeddings should be used or a HuggingFace embedding model should be used: 'Hugging Face Model' or 'OpenAI Model' + Configure the 'OpenAI API Key' or 'HuggingFace API Key', depending on the chosen Embedding Model. + Set 'Index Name' to the name of your OpenSearch Index. + Set 'Vector Field Name' to the name of the field in the document which will store the vector data. + Set 'Text Field Name' to the name of the field in the document which will store the text data. + Set the 'Document ID Field Name' property to the name of the field that contains the identifier of the document in OpenSearch to update. + """) +class PutOpenSearchVector(FlowFileTransform): + class Java: + implements = ['org.apache.nifi.python.processor.FlowFileTransform'] + + class ProcessorDetails: + version = '2.0.0-SNAPSHOT' + description = """Publishes JSON data to OpenSearch. The Incoming data must be in single JSON per Line format, each with two keys: 'text' and 'metadata'. + The text must be a string, while metadata must be a map with strings for values. Any additional fields will be ignored.""" + tags = ["opensearch", "vector", "vectordb", "vectorstore", "embeddings", "ai", "artificial intelligence", "ml", + "machine learning", "text", "LLM"] + + # Engine types + NMSLIB = ("nmslib (Non-Metric Space Library)", "nmslib") + FAISS = ("faiss (Facebook AI Similarity Search)", "faiss") + LUCENE = ("lucene", "lucene") + + ENGINE_VALUES = dict([NMSLIB, FAISS, LUCENE]) + + # Space types + INNERPRODUCT = ("Inner product", "innerproduct") + + NMSLIB_SPACE_TYPE_VALUES = dict([L2, L1, LINF, COSINESIMIL, INNERPRODUCT]) + FAISS_SPACE_TYPE_VALUES = dict([L2, INNERPRODUCT]) + LUCENE_SPACE_TYPE_VALUES = dict([L2, COSINESIMIL]) + + # New Index Mapping Strategy + DEFAULT_INDEX_MAPPING = "Default index mapping" + CUSTOM_INDEX_MAPPING = "Custom index mapping" + + DOC_ID_FIELD_NAME = PropertyDescriptor( + name="Document ID Field Name", + description="""Specifies the name of the field in the 'metadata' element of each document where the document's ID can be found. + If not specified, an ID will be generated based on the FlowFile's filename and a one-up number.""", + required=False, + validators=[StandardValidators.NON_EMPTY_VALIDATOR], + expression_language_scope=ExpressionLanguageScope.FLOWFILE_ATTRIBUTES + ) + NEW_INDEX_STRATEGY = PropertyDescriptor( + name="New Index Strategy", + description="Specifies the Mapping strategy to use for new index creation. The default template values are the following: " + "{engine: nmslib, space_type: l2, ef_search: 512, ef_construction: 512, m: 16}", + allowable_values=[DEFAULT_INDEX_MAPPING, CUSTOM_INDEX_MAPPING], + default_value=DEFAULT_INDEX_MAPPING, + required=False, + ) + ENGINE = PropertyDescriptor( + name="Engine", + description="The approximate k-NN library to use for indexing and search.", + allowable_values=ENGINE_VALUES.keys(), + default_value=NMSLIB[0], + required=False, + dependencies=[PropertyDependency(NEW_INDEX_STRATEGY, CUSTOM_INDEX_MAPPING)] + ) + NMSLIB_SPACE_TYPE = PropertyDescriptor( + name="NMSLIB Space Type", + description="The vector space used to calculate the distance between vectors.", + allowable_values=NMSLIB_SPACE_TYPE_VALUES.keys(), + default_value=L2[0], + required=False, + dependencies=[PropertyDependency(NEW_INDEX_STRATEGY, CUSTOM_INDEX_MAPPING), + PropertyDependency(ENGINE, NMSLIB[0])] + ) + FAISS_SPACE_TYPE = PropertyDescriptor( + name="FAISS Space Type", + description="The vector space used to calculate the distance between vectors.", + allowable_values=FAISS_SPACE_TYPE_VALUES.keys(), + default_value=L2[0], + required=False, + dependencies=[PropertyDependency(NEW_INDEX_STRATEGY, CUSTOM_INDEX_MAPPING), + PropertyDependency(ENGINE, FAISS[0])] + ) + LUCENE_SPACE_TYPE = PropertyDescriptor( + name="Lucene Space Type", + description="The vector space used to calculate the distance between vectors.", + allowable_values=LUCENE_SPACE_TYPE_VALUES.keys(), + default_value=L2[0], + required=False, + dependencies=[PropertyDependency(NEW_INDEX_STRATEGY, CUSTOM_INDEX_MAPPING), + PropertyDependency(ENGINE, LUCENE[0])] + ) + EF_SEARCH = PropertyDescriptor( + name="EF Search", + description="The size of the dynamic list used during k-NN searches. Higher values lead to more accurate but slower searches.", + default_value="512", + required=False, + validators=[StandardValidators.NON_NEGATIVE_INTEGER_VALIDATOR], + dependencies=[PropertyDependency(NEW_INDEX_STRATEGY, CUSTOM_INDEX_MAPPING)] + ) + EF_CONSTRUCTION = PropertyDescriptor( + name="EF Construction", + description="The size of the dynamic list used during k-NN graph creation. Higher values lead to a more accurate graph but slower indexing speed.", + default_value="512", + required=False, + validators=[StandardValidators.NON_NEGATIVE_INTEGER_VALIDATOR], + dependencies=[PropertyDependency(NEW_INDEX_STRATEGY, CUSTOM_INDEX_MAPPING)] + ) + M = PropertyDescriptor( + name="M", + description="The number of bidirectional links that the plugin creates for each new element. Increasing and " + "decreasing this value can have a large impact on memory consumption. Keep this value between 2 and 100.", + default_value="16", + required=False, + validators=[StandardValidators.create_long_validator(2, 100, True)], Review Comment: Could you use `StandardValidators._standard_validators.createLongValidator(2, 100, True)` the same way it is done in the `PromptChatGPT` processor instead of introducing a new function in the nifiapi? This way this processor would also be compatible with MiNiFi C++, otherwise the [nifiapi implementation in MiNiFi C++](https://github.com/apache/nifi-minifi-cpp/tree/main/extensions/python/pythonprocessors/nifiapi) would also need to be updated. @exceptionfactory I think this is one other topic that could be discussed when extracting python processors to a separate repository. IMO we can either say that MiNiFi C++ is always updated on every NiFi release to support the latest version of the python nifiapi, or the nifiapi implementation of MiNiFi C++ could also be moved to the python repository and have both implementations updated if the nifiapi changes and have a few smoke tests to see if a new processor works with both nifiapi implementations. -- 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: issues-unsubscr...@nifi.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
