alessandrobenedetti commented on code in PR #2809: URL: https://github.com/apache/solr/pull/2809#discussion_r1863793135
########## solr/solr-ref-guide/modules/query-guide/pages/embedding-text.adoc: ########## @@ -0,0 +1,269 @@ += Embedding Text +// 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. + +With the *Large Language Model* (or *LLM* for short) module you can interact with Large Language Models in Solr to encode text to vectors at indexing and query time. + + +== Text Embedding Concepts + +=== From Text to Vector + +The task of sentence similarity aims to encode text to vector in a way that sentences semantically similar are encoded to vectors close in a vector space (using a vector distance metric). + + +=== Large Language Models + +Large Language Models can be fine-tuned for such task. +The resulting model is able to encode text to a numerical vector. + +For additional information you can refer to this https://sease.io/2021/12/using-bert-to-improve-search-relevance.html[blog post]. + +==== Embedding Services + +Training, fine-tuning and operating such Large Language Models is expensive. + +Many companies focus on this aspect and let users access APIs to encode the text (at the price of a license fee). + +Apache Solr uses https://github.com/langchain4j/langchain4j[LangChain4j] to connect to such apis. + +[IMPORTANT] +==== +At the moment a subset of the embedding models supported by LangChain4j is supported by Solr. + +*Disclaimer*: Apache Solr is *in no way* affiliated to any of these corporations or services. + +If you want to add support for additional services or improve the support for the existing ones, feel free to contribute: + +* https://github.com/apache/solr/blob/main/CONTRIBUTING.md[Contributing to Solr] +==== + +== Module + +This is provided via the `llm` xref:configuration-guide:solr-modules.adoc[Solr Module] that needs to be enabled before use. + +At the moment the only supported way to interact with Large Language Models is via embedding text. + +In the future additional components to empower Solr with LLM will be added. + + +== LLM Configuration + +Large-Language-Model is a module and therefore its plugins must be configured in `solrconfig.xml`. + +=== Minimum Requirements + +* Declaration of the `text_to_vector` query parser. ++ +[source,xml] +---- +<queryParser name="text_to_vector" class="org.apache.solr.llm.search.TextToVectorQParserPlugin"/> +---- + +== Text Embedding Lifecycle + + +=== Models + +* A model encodes text to a vector. +* A model in Solr is a reference to an external API that runs the Large Language Model responsible for text embedding. + +*N.B.* the Solr embedding model specifies the parameters to access the APIs, the model doesn't run internally in Solr + + +A model is described by these parameters: + + +`class`:: ++ +[%autowidth,frame=none] +|=== +s|Required |Default: none +|=== ++ +The model implementation. +Accepted values: + +* `dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel`. +* `dev.langchain4j.model.mistralai.MistralAiEmbeddingModel`. +* `dev.langchain4j.model.openai.OpenAiEmbeddingModel`. +* `dev.langchain4j.model.cohere.CohereEmbeddingModel`. + + +`name`:: ++ +[%autowidth,frame=none] +|=== +s|Required |Default: none +|=== ++ +The identifier of your model, this is used by any component that intends to use the model (`text_to_vector` query parser). + +`params`:: ++ +[%autowidth,frame=none] +|=== +|Optional |Default: none +|=== ++ +Each model class has potentially different params. +Many are shared but for the full set of parameters of the model you are interested in please refer to the official documentation of the LangChain4j version included in Solr: https://docs.langchain4j.dev/category/embedding-models[Embedding Models in LangChain4j]. + + +=== Supported Models +Apache Solr uses https://github.com/langchain4j/langchain4j[LangChain4j] to support text embedding. +The models currently supported are: + +[tabs#supported-models] +====== +Hugging Face:: ++ +==== + +[source,json] +---- +{ + "class": "dev.langchain4j.model.huggingface.HuggingFaceEmbeddingModel", + "name": "<a-name-for-your-model>", + "params": { + "accessToken": "<your-huggingface-api-key>", + "modelId": "<a-huggingface-embedding-model>" + } +} +---- +==== + +MistralAI:: ++ +==== +[source,json] +---- +{ + "class": "dev.langchain4j.model.mistralai.MistralAiEmbeddingModel", + "name": "<a-name-for-your-model>", + "params": { + "baseUrl": "https://api.mistral.ai/v1";, + "apiKey": "<your-mistralAI-api-key>", + "modelName": "<a-mistralAI-embedding-model>", + "timeout": 60, + "logRequests": true, + "logResponses": true, + "maxRetries": 5 + } +} +---- +==== + +OpenAI:: ++ +==== +[source,json] +---- +{ + "class": "dev.langchain4j.model.openai.OpenAiEmbeddingModel", + "name": "<a-name-for-your-model>", + "params": { + "baseUrl": "https://api.openai.com/v1";, + "apiKey": "<your-openAI-api-key>", + "modelName": "<a-openAI-embedding-model>", + "timeout": 60, + "logRequests": true, + "logResponses": true, + "maxRetries": 5 + } +} +---- +==== + +Cohere:: ++ +==== +[source,json] +---- +{ + "class": "dev.langchain4j.model.cohere.CohereEmbeddingModel", + "name": "<a-name-for-your-model>", + "params": { + "baseUrl": "https://api.cohere.ai/v1/";, + "apiKey": "<your-cohere-api-key>", + "modelName": "<a-cohere-embedding-model>", + "inputType": "search_document", + "timeout": 60, + "logRequests": true, + "logResponses": true + } +} +---- +==== +====== + +=== Uploading a Model + +To upload the model in a `/path/myModel.json` file, please run: + +[source,bash] +---- +curl -XPUT 'http://localhost:8983/solr/techproducts/schema/embedding-model-store' --data-binary "@/path/myModel.json" -H 'Content-type:application/json' +---- + + +To view all models: + +[source,text] +http://localhost:8983/solr/techproducts/schema/embedding-model-store + +To delete the `currentModel` model: + +[source,bash] +---- +curl -XDELETE 'http://localhost:8983/solr/techproducts/schema/embedding-model-store/currentModel' +---- + + +To view the model you just uploaded please open the following URL in a browser: + +[source,text] +http://localhost:8983/solr/techproducts/schema/embedding-model-store + +.Example: /path/myModel.json +[source,json] +---- +{ + "class": "dev.langchain4j.model.openai.OpenAiEmbeddingModel", + "name": "openai-1", + "params": { + "baseUrl": "https://api.openai.com/v1";, + "apiKey": "apiKey-openAI", + "modelName": "text-embedding-3-small", + "timeout": 60, + "logRequests": true, + "logResponses": true, + "maxRetries": 5 + } +} + +---- + +=== Running an embedding Query +To run a query that embeds your query text, using a model you previously uploaded is simple: + +[source,text] +?q={!text_to_vector model=a-model f=vector topK=10}hello world query Review Comment: I considered that, but proceeded this way for simplicity, happy for others to evlove this later! -- This is an automated message from the Apache Git Service. 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