> ## Documentation Index > Fetch the complete documentation index at: https://doc.lucidworks.com/llms.txt > Use this file to discover all available pages before exploring further. # Content-Based Recommender > Job configuration specifications export const schema = { "type": "object", "title": "Content based Recommender", "description": "Use this job when you want to compute item similarities based on their content such as product descriptions. ", "required": ["id", "trainingCollection", "trainingFormat", "outputCollection", "outputFormat", "itemIdField", "contentField", "type"], "properties": { "id": { "type": "string", "title": "Job ID", "description": "The ID for this job. Used in the API to reference this job. Allowed characters: a-z, A-Z, dash (-) and underscore (_)", "maxLength": 63, "pattern": "[a-zA-Z][_\\-a-zA-Z0-9]*[a-zA-Z0-9]?" }, "sparkConfig": { "type": "array", "title": "Additional parameters", "description": "Provide additional key/value pairs to be injected into the training JSON map at runtime. Values will be inserted as-is, so use \" to surround string values", "hints": ["advanced"], "items": { "type": "object", "required": ["key"], "properties": { "key": { "type": "string", "title": "Parameter Name" }, "value": { "type": "string", "title": "Parameter Value" } } } }, "writeOptions": { "type": "array", "title": "Write Options", "description": "Options used when writing output to Solr or other sources", "hints": ["advanced"], "items": { "type": "object", "required": ["key"], "properties": { "key": { "type": "string", "title": "Parameter Name" }, "value": { "type": "string", "title": "Parameter Value" } } } }, "readOptions": { "type": "array", "title": "Read Options", "description": "Options used when reading input from Solr or other sources.", "hints": ["advanced"], "items": { "type": "object", "required": ["key"], "properties": { "key": { "type": "string", "title": "Parameter Name" }, "value": { "type": "string", "title": "Parameter Value" } } } }, "outputBatchSize": { "type": "string", "title": "Output Batch Size", "description": "Batch size of documents when pushing results to solr", "default": "15000", "hints": ["advanced"] }, "unidecodeText": { "type": "boolean", "title": "Unidecode Text", "description": "Select if you want the text to be unidecoded.", "default": true }, "lowercaseText": { "type": "boolean", "title": "Lowercase Text", "description": "Select if you want the text to be lowercased.", "default": true }, "vectorizationUseDl": { "type": "boolean", "title": "Use Deep Learning for vectorization", "description": "Select if you want to use deep learning as the method for vectorization. You can choose the other methods too in which case an ensemble will be used.", "default": true }, "vectorizationUseFasttext": { "type": "boolean", "title": "Use Word2Vec for vectorization", "description": "Select if you want to use word2vec as the method for vectorization. You can choose the other methods too in which case an ensemble will be used. Custom embeddings will be learned. Useful for jargon." }, "vectorizationUseTfidf": { "type": "boolean", "title": "Use Tf-Idf for vectorization", "description": "Select if you want to use Tf-idf as the method for vectorization. You can choose the other methods too in which case an ensemble will be used." }, "vectorizationDlEnsembleWeight": { "type": "number", "title": "Deep learning vectorization ensemble weight", "description": "Ensemble weight for deep learning based vectorization if more than one method of vectorization is selected.", "default": 1 }, "vectorizationFasttextVectorsSize": { "type": "integer", "title": "Size of word vectors", "description": "Word vector dimensions for Word2Vec vectorizer.", "default": 150, "minimum": 1, "exclusiveMinimum": false }, "vectorizationFasttextWindowSize": { "type": "integer", "title": "Word2Vec window size", "description": "The window size (context words from [-window, window]) for Word2Vec.", "default": 5, "hints": ["advanced"], "minimum": 1, "exclusiveMinimum": false }, "vectorizationFasttextEpochs": { "type": "integer", "title": "Word2Vec training epochs", "description": "Number of epochs to train custom Word2Vec embeddings.", "default": 15, "minimum": 1, "exclusiveMinimum": false }, "vectorizationFasttextSkipGram": { "type": "boolean", "title": "Use SkipGram model", "description": "Whether to use skip gram for training. If unchecked, CBOW will be used.", "default": true, "hints": ["hidden"] }, "vectorizationFasttextMinCount": { "type": "integer", "title": "Min count of words", "description": "Minimum times a token needs to occur in the text to be considered for the vocab.", "default": 1, "hints": ["hidden"], "minimum": 1, "exclusiveMinimum": false }, "vectorizationFasttextMaxVocabSize": { "type": "integer", "title": "Max vocab size", "description": "Maximum number of tokens to consider for the vocab. Less frequent tokens will be omitted.", "minimum": 1, "exclusiveMinimum": false }, "vectorizationFasttextUseSubwordNgram": { "type": "boolean", "title": "Use subword ngrams", "description": "Whether to use subword (character) ngrams.", "default": true, "hints": ["hidden"] }, "vectorizationFasttextMinNgram": { "type": "integer", "title": "Min Ngram size", "description": "Minimum size for ngrams generated.", "default": 3, "hints": ["hidden"], "minimum": 1, "exclusiveMinimum": false }, "vectorizationFasttextMaxNgram": { "type": "integer", "title": "Max Ngram size", "description": "Maximum size for ngrams generated.", "default": 6, "hints": ["hidden"], "minimum": 1, "exclusiveMinimum": false }, "vectorizationFasttextEnsembleWeight": { "type": "number", "title": "Word2Vec vectorization ensemble weight", "description": "Ensemble weight for Fasttext based vectorization if more than one method of vectorization is selected.", "default": 1 }, "vectorizationTfidfUseCharacters": { "type": "boolean", "title": "Use characters ngrams", "description": "Whether to use characters. By default words are used." }, "vectorizationTfidfFilterStopwords": { "type": "boolean", "title": "Filter stopwords", "description": "Whether to filter out stopwords before generating Tf-Idf weights.", "default": true }, "vectorizationTfidfMinDf": { "type": "number", "title": "Min Document Frequency", "description": "Minimum Df for token to be considered.", "hints": ["hidden"] }, "vectorizationTfidfMaxDf": { "type": "number", "title": "Max Document Frequency", "description": "Maximum Df for token to be considered.", "default": 1, "hints": ["hidden"] }, "vectorizationTfidfMinNgram": { "type": "integer", "title": "Min Ngram size", "description": "Minimum Ngram size to be used.", "default": 1, "minimum": 1, "exclusiveMinimum": false }, "vectorizationTfidfMaxNgram": { "type": "integer", "title": "Max Ngram size", "description": "Maximum Ngram size to be used.", "default": 3, "minimum": 1, "exclusiveMinimum": false }, "vectorizationTfIdfMaxVocabSize": { "type": "integer", "title": "Max vocab size", "description": "Maximum number of tokens to consider for the vocab. Less frequent tokens will be omitted.", "minimum": 1, "exclusiveMinimum": false }, "vectorizationTfidfEnsembleWeight": { "type": "number", "title": "Tf-Idf vectorization ensemble weight", "description": "Ensemble weight for Tf-Idf based vectorization if more than one method of vectorization is selected.", "default": 1 }, "topKAnn": { "type": "integer", "title": "No. of Item Recs to compute for ensemble", "description": "This is used to fetch additional recommendations so that the value specified for the Number of User Recommendations to Compute is most likely satisfied after filtering. This is normally set to 10 * (No. of item recommendations to compute)", "default": 100, "minimum": 1, "exclusiveMinimum": false }, "jobRunName": { "type": "string", "title": "Job Run Name", "description": "Identifier for this job run. Use it to filter recommendations from particular runs", "hints": ["advanced"] }, "trainingCollection": { "type": "string", "title": "Training data path", "description": "Solr collection or cloud storage path where training data is present.", "minLength": 1 }, "trainingFormat": { "type": "string", "title": "Training data format", "description": "The format of the training data - solr, parquet etc.", "default": "solr", "minLength": 1 }, "secretName": { "type": "string", "title": "Cloud storage secret name", "description": "Name of the secret used to access cloud storage as defined in the K8s namespace", "hints": ["advanced"], "minLength": 1 }, "outputCollection": { "type": "string", "title": "Output data path", "description": "Solr collection or cloud storage path where output data is to be written." }, "outputFormat": { "type": "string", "title": "Output data format", "description": "The format of the output data - solr, parquet etc.", "default": "solr", "minLength": 1 }, "partitionFields": { "type": "string", "title": "Partition fields", "description": "If writing to non-Solr sources, this field will accept a comma-delimited list of column names for partitioning the dataframe before writing to the external output ", "hints": ["advanced"] }, "numSimsPerItem": { "type": "integer", "title": "No. of Item Recs to Compute", "description": "Number of recommendations that will be saved per item.", "default": 10, "minimum": 1, "exclusiveMinimum": false }, "deleteOldRecs": { "type": "boolean", "title": "Delete Old Recommendations", "description": "Should previous recommendations be deleted. If this box is unchecked, then old recommendations will not be deleted but new recommendations will be appended with a different Job ID. Both sets of recommendations will be contained within the same collection. Will only work when output path is solr.", "default": true }, "excludeFromDeleteFilter": { "type": "string", "title": "Exclude from Delete Filter", "description": "If the 'Delete Old Recommendations' flag is enabled, then use this query filter to identify existing recommendation docs to exclude from delete. The filter should identify recommendation docs you want to keep.", "hints": ["advanced"] }, "metadataCategoryFields": { "type": "array", "title": "Metadata fields for item-item evaluation", "description": "These fields will be used for item-item evaluation and for determining if the recommendation pair belongs to the same category.", "hints": ["advanced"], "items": { "type": "string" } }, "trainingDataFilterQuery": { "type": "string", "title": "Training Data Filter Query", "description": "Solr or SQL query to filter training data. Use solr query when solr collection is specified in Training Path. Use SQL query when cloud storage location is specified. The table name for SQL is `spark_input`.", "hints": ["code/sql", "advanced"] }, "trainingSampleFraction": { "type": "number", "title": "Training Data Sampling Fraction", "description": "Choose a fraction of the data for training.", "default": 1, "hints": ["advanced"], "maximum": 1, "exclusiveMaximum": false }, "itemIdField": { "type": "string", "title": "Training Item Id Field", "description": "Field name containing stored item ids", "default": "item_id_s", "minLength": 1 }, "contentField": { "type": "array", "title": "Training Content Field", "description": "Field name containing item content such as product description", "items": { "type": "string" } }, "randomSeed": { "type": "integer", "title": "Random Seed", "description": "Pseudorandom determinism fixed by keeping this seed constant", "default": 12345, "hints": ["advanced"] }, "itemMetadataFields": { "type": "array", "title": "Item Metadata Fields", "description": "List of item metadata fields to include in the recommendation output documents.", "hints": ["advanced"], "items": { "type": "string" } }, "vectorizationDlBatchSize": { "type": "integer", "title": "Batch size to compute encodings", "description": "Compute encodings in batches in case hardware out of memory.", "hints": ["advanced"], "minimum": 1, "exclusiveMinimum": false }, "performANN": { "type": "boolean", "title": "Perform approximate nearest neighbor search", "description": "Whether to perform approximate nearest neighbor search (ANN). ANN will drastically reduce training time, but accuracy will drop a little. Disable only if dataset is very small.", "default": true }, "maxNeighbors": { "type": "integer", "title": "Max neighbors for indexing", "description": "If perform ANN, size of the potential neighbors for the indexing phase. Higher value leads to better recall and shorter retrieval times (at the expense of longer indexing time).Reasonable range: 5~100", "hints": ["advanced"], "maximum": 100, "exclusiveMaximum": false, "minimum": 5, "exclusiveMinimum": false }, "searchNN": { "type": "integer", "title": "Search Depth", "description": "If perform ANN, the depth of search used to find neighbors. Higher value improves recall at the expense of longer retrieval time.Reasonable range: 100~2000", "hints": ["advanced"], "maximum": 2000, "exclusiveMaximum": false, "minimum": 100, "exclusiveMinimum": false }, "indexNN": { "type": "integer", "title": "Indexing Depth", "description": "If perform ANN, the depth of constructed index. Higher value improves recall at the expense of longer indexing time.Reasonable range: 100~2000", "hints": ["advanced"], "maximum": 2000, "exclusiveMaximum": false, "minimum": 100, "exclusiveMinimum": false }, "type": { "type": "string", "title": "Spark Job Type", "enum": ["argo-item-recommender-content"], "default": "argo-item-recommender-content", "hints": ["readonly"] } }, "additionalProperties": true, "category": "Other", "categoryPriority": 1, "propertyGroups": [{ "label": "Input/Output Parameters", "properties": ["trainingCollection", "trainingFormat", "outputCollection", "outputFormat", "outputBatchSize", "secretName", "partitionFields"] }, { "label": "Training Data Settings", "properties": ["trainingDataFilterQuery", "trainingSampleFraction", "randomSeed", "itemIdField", "contentField"] }, { "label": "Model Tuning Parameters", "properties": ["numSimsPerItem", "topKAnn", "performANN", "maxNeighbors", "searchNN", "indexNN", "unidecodeText", "lowercaseText", "deleteOldRecs", "excludeFromDeleteFilter"] }, { "label": "Vectorization Parameters", "properties": ["vectorizationUseDl", "vectorizationUseFasttext", "vectorizationUseTfidf"] }, { "label": "Deep Learning Vectorization Parameters", "properties": ["vectorizationDlBatchSize", "vectorizationDlEnsembleWeight"] }, { "label": "Word2Vec Vectorization Parameters", "properties": ["vectorizationFasttextVectorsSize", "vectorizationFasttextWindowSize", "vectorizationFasttextEpochs", "vectorizationFasttextMinNgram", "vectorizationFasttextEnsembleWeight", "vectorizationFasttextMaxVocabSize"] }, { "label": "Tf-Idf Vectorization Parameters", "properties": ["vectorizationTfidfUseCharacters", "vectorizationTfidfFilterStopwords", "vectorizationTfidfMinNgram", "vectorizationTfidfMaxNgram", "vectorizationTfIdfMaxVocabSize", "vectorizationTfidfEnsembleWeight"] }, { "label": "Item Metadata Settings", "properties": ["itemMetadataFields", "metadataCategoryFields"] }] }; export const SchemaParamFields = ({schema}) => { const sanitize = str => { if (typeof str !== "string") return str; return str.replace(/^"(.*)"$/s, "$1").replace(/\\/g, "").replace(/"/g, "'"); }; const formatDescription = str => { const s = sanitize(str); return (/[.!?]\)*$/).test(s) ? s : `${s}.`; }; const {description, properties = {}, required: requiredProps = []} = schema; const visibleProps = useMemo(() => Object.entries(properties).filter(([, prop]) => !prop.hints?.includes("hidden")), [properties]); return
{description &&

{formatDescription(description)}

} {visibleProps.map(([name, prop]) => { const isRequired = requiredProps.includes(name); const hasDefault = prop.default !== undefined; const rawDefault = prop.default; const isComplexDefault = hasDefault && (typeof rawDefault === "object" || typeof rawDefault === "string" && (rawDefault.length > 20 || rawDefault.includes('"'))); const fieldProps = { key: name, body: prop.title || name, type: prop.type, ...prop.title && ({ post: [<>API property: {name}] }), ...isRequired && ({ required: true }), ...!isComplexDefault && hasDefault ? { default: sanitize(String(rawDefault)) } : {} }; const isObject = prop.type === "object" && prop.properties; const isArrayOfObjects = prop.type === "array" && prop.items?.type === "object" && prop.items.properties; return {prop.description &&

{formatDescription(prop.description)}

} {isComplexDefault &&

Default: 

                  
                    {JSON.stringify(rawDefault, null, 2)}
} {isArrayOfObjects &&

Object attributes: 

                
                  {'{\n'}
                  {Object.entries(prop.items.properties).map(([iname, iprop]) => <>
                      {`  ${iname}`}
                      {prop.items?.required?.includes(iname) &&  required}
                      {`: {\n    display name: ${sanitize(iprop.title || '')}\n    type: ${iprop.type}\n  }\n`}
                    )}
                  {'}'}
} {isObject && } ; })}
; }; export const LwTemplate = ({title = "Key questions to get you started", icon = "sparkles", cta = "Powered by Agent Studio", linkHref = "https://lucidworks.com/demo/?utm_source=docs&utm_medium=referral&utm_campaign=docs_cta_ai"}) => { const [isLoaded, setIsLoaded] = useState(false); useEffect(() => { const timer = setTimeout(() => { setIsLoaded(true); }, 500); return () => clearTimeout(timer); }, []); return
{isLoaded && ` }} />} Powered by Lucidworks Agent Studio
; }; [localhost link]: http://localhost:3000/docs/lucidworks-search/09-developer-documentation/config-specs/jobs/content-recommend [mintlify link]: https://doc.lucidworks.com/docs/lucidworks-search/09-developer-documentation/config-specs/jobs/content-recommend [old doc.lw link]: https://doc.lucidworks.com/managed-fusion/5.9/g02utf Use this job when you want to compute item similarities based on their content, such as product descriptions. | | | | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Default job name** | `COLLECTION_NAME_content_recs` | | **Input** | Searchable content from the primary collection. | | **Output** | [Items-for-item recommendations](/docs/lucidworks-search/07-improve-your-queries/recommendations/items-for-item) (the `COLLECTION_NAME_content_recs` collection by default) | First, item content is vectorized; different vectorization methods are available. Then, similar items are selected based on cosine similarity ("nearest neighbor") between their vectors. At a minimum, you must specify these: * An ID for this job * The name of the training collection, that is, the collection with your content * An output collection; create a separate collection for this * The name of the ID field for documents in the training collection, such as `item_id_s` * The names of one or more content fields in the training collection Content-based recommendations dataflow ## Tuning tips * Configure **Metadata fields for item-item evaluation** to use those fields during evaluation to determine whether pairs belong to the same category. * **Perform approximate nearest neighbor search** is enabled by default to significantly reduce the job’s running time, with a small decrease in accuracy. If your training dataset is very small, then you can disable this option. * If your content contains a lot of domain-specific jargon, enable **Use Word2Vec for vectorization**. * If your documents are too short or too long, enable **Use TF-IDF for vectorization**. ## Query pipeline setup Download the APPName\_item\_item\_rec\_pipelines\_content.json file and **Migrate Lucidworks Search Objects** to create the query pipeline that consumes this job’s output. See **Fetch Items-for-Item Recommendations (Content-Based Method)** for details. You can use the Fusion UI and the [Objects API](/api-reference/objects/get-objects-service-status) to migrate collections and related objects, including your searchable data, configuration data, pipelines, aggregations, and other objects on which your collections depend. You can also migrate entire apps. To upgrade from one Fusion version to a 5.x version, see Fusion 5 Upgrades to migrate objects. You might need to migrate objects in the following circumstances: * When migrating data from development environments into testing and production environments * To back up data, so you can restore it after an incident of data loss * When the migrator script was not able to migrate an object automatically When you export and import objects to migrate them, make sure you that any data gets to where it is going before deleting the sources. ## Migration approaches Several approaches are available for migrating Fusion objects. This table summarizes the approaches. | | Export an app | Import an app | Export an object | Import an object | Add an object to an app | | --------------------------------- | -------------------------- | ------------------------------------------------------------------ | -------------------------- | ------------------------- | ----------------------- | | **Fusion UI** | App configuration | Launcher
(entire app) App configuration
(combine apps) | - | - | Object Explorer | | **Objects API**
(endpoints) | GET from `export` endpoint | POST to `import` endpoint | GET from `export` endpoint | POST to `import` endpoint | - | For more information about using the Objects API to export and import objects, see [Objects API](/api-reference/objects/get-objects-service-status). The remainder of this topic describes approaches in the Fusion UI. Use the parts of the Fusion UI indicated in the table to export and import apps and specific objects. Exporting creates a zip file. To import, you select a data file and possibly a variable file. The approach with Object Explorer differs. With Object Explorer, you can add objects from other apps (or that are not linked to any apps) to the currently open app. ## Export an app with the Fusion UI **How to export an app with the Fusion UI** 1. Navigate to the launcher. 2. Hover over the app you want to export and click the Configure icon: App config button 3. In the app config window, click **Export app to zip**: See import for information to import the downloaded zip file into other instances of Fusion 5.x. ## Import an app with the Fusion UI **How to import an app with the Fusion UI** 1. Navigate to the launcher. 2. Click **Import app**. 3. Under **Data File**, click **Choose File** and select the zip file containing the app you want to import. 4. If your app has [usernames and passwords](/api-reference/objects/list-imported-variables) in a separate file, select it under **Variables File**. If the Variables File is needed, it *must* be a separate file that is not in a .zip file. It is a .json map of variables to values. The following is an example: ```json theme={"dark"} { "secret.dataSources.Inventory_BR_S3_DS.password":"SOMETHING", "secret.dataSources.LLM_A_BR_S3_DS.password":"FmJSaDE9Tj5REDACTED", "secret.dataSources.LLM_BR_Inventory_S3_DS.password":"FmJSaDE9Tj5GzIVvethAC4Huh", "secret.dataSources.LLM_BR_Load_S3_DS.password":"FmJSaDE9Tj5GzIVvethAC4" } ``` 5. You can sometimes edit parameter values to use the new values in the imported app. If this is the case, Fusion displays a dialog box that lets you edit the parameter values. Edit parameter values Make desired changes, and then click **Import**. ## Copy an app To copy an app from one deployment to a different one, [export the app](#export-an-app-with-the-fusion-ui) on the source deployment, and then [import the app](#import-an-app-with-the-fusion-ui) on the target deployment. ## Import objects into an app You can import objects into the currently open app. **How to import objects into an open app** 1. In the Fusion launcher, click the app into which you want to import objects. The Fusion workspace appears. 2. Click **System > Import Fusion Objects**. The Import Fusion Objects window opens. Import Objects Window 3. Select the data file from your local filesystem. If you are importing [usernames and passwords](/api-reference/objects/import-objects) in a separate file, select it under **Variables File**. 4. Click **Import**. If there are conflicts, Fusion prompts you to specify an import policy: Import Conflicts * Click **Overwrite** to overwrite the objects on the target system with the ones in the import file. * Click **Merge** to skip all conflicting objects and import only the non-conflicting objects. * Click **Start Over** to abort the import. Fusion confirms that the import was successful: Successful Import 5. Click **Close** to close the Import Fusion Objects window. ## Add an object to an app You can add objects present in other apps (or in no apps) to the open app. Some objects are linked to other apps. You can also add those directly to an app. * **Add an object to an app** – While in the Fusion workspace for the app to which you want to add an object, open Object Explorer and click **In Any App**. Search for or browse to the object you want to add. Hover over the object, click the App App icon, and then click **Add to this app**. Add to this app * **Add an object to an app directly** – In cases when an object is linked to an app, but is not linked directly to the app (it is linked via some dependency), you can add the object to an app directly. While in the Fusion workspace for the app to which you want to add an object directly, open Object Explorer and click **In Any App**. Search for or browse to the object you want to add. Hover over the object, click the App App icon, and then click **Add to this app directly**. Add to this app directly You can download and import a query pipeline that works out of the box to fetch the items-for-item recommendations generated by the default [Content-Based Recommender](/docs/lucidworks-search/09-developer-documentation/config-specs/jobs/content-recommend) job. There are two separate pipelines attached below which work in different ways to query and return recommendations. **Query for recommendations only** 1. Download the `APPName_item_item_rec_pipelines_content.json` file. 2. Rename the file to replace `APPName` with the name of your Lucidworks Search app, such as `ProductCatalog_item_item_rec_pipelines_bpr.json`. 3. Open the JSON file, replace all instances of `APPName` with the name of your Lucidworks Search app, such as `ProductCatalog`, and save it. 4. Import the JSON file into your Lucidworks Search instance using the Query Pipelines REST API: ``` curl -u USERNAME:PASSWORD https://EXAMPLE_COMPANY.b.lucidworks.cloud/api/query-pipelines -XPOST -H 'content-type:application/json' -d@ ``` 5. In the Lucidworks Search UI, navigate to **Query** > **Query Pipelines** to verify that the new pipeline is available. This pipeline should be used to query the collection where the recommendations are stored. It makes a query against the `itemId` field and returns the recommended `itemId` values. To get the actual items, you need to make a second query to the respective catalog collection with the returned `itemId` values. No additional configuration is needed to use this pipeline with the default BPR job configuration. **Boost recommended products from catalog** 1. Download the `APPName_content_boost.json` file 2. Rename the file to replace `APPName` with the name of your Lucidworks Search app, such as `ProductCatalog_item_item_rec_pipelines_bpr.json`. 3. Open the JSON file, replace all instances of `APPName` with the name of your Lucidworks Search app, such as `ProductCatalog`. 4. Fill in the `collection` name field in the first `Recommend Items for Item` stage and save the file. 5. Import the JSON file into your Lucidworks Search instance using the Query Pipelines REST API: ``` curl -u USERNAME:PASSWORD https://EXAMPLE_COMPANY.b.lucidworks.cloud/api/query-pipelines -XPOST -H 'content-type:application/json' -d@ ``` 6. In the Lucidworks Search UI, navigate to **Query** > **Query Pipelines** to verify that the new pipeline is available. This pipeline queries the recommendations collection and then makes a subsequent query to the actual catalog collection boosting the recommended items and returning the actual items from the catalog. This pipeline will therefore also return recommendations even if none were generated/available. This pipeline expects a request parameter called `id=itemId` to be appended to the request. An example query URL to this pipeline would look like `https://EXAMPLE_COMPANY.b.lucidworks.cloud/api/query-pipelines/APPName_items_for_user_bpr_boost/collections/catalog/select?q=**:**&id=SomeItemId` If the pipeline does not appear in the **Query Pipelines** panel, you may need to attach it to your app like this: Go to **System** > **Object Explorer**, click the **In No Apps** filter, hover over the pipeline, click the oe-app-menu icon, and select **Add to this app**. **See also** * [Items-For-Item Recommendations](/docs/lucidworks-search/07-improve-your-queries/recommendations/items-for-item) * [Content-Based Recommender job](/docs/lucidworks-search/09-developer-documentation/config-specs/jobs/content-recommend) * [Get Started with Recommendations and Boosting](/docs/lucidworks-search/07-improve-your-queries/recommendations/getting-started) ## Configuration properties