Machine Learning Model APIs - Lucidworks documentation

Use these APIs to deploy machine learning (ML) models and generate predictions. Fusion’s Machine Learning (ML) Model service stores deployed models and runs prediction jobs.

ML Model API
Use this API to deploy ML models and generate predictions.
Serialized Model API
The Serialized Model API is for fetching, updating, or deleting deployed models.

For more information, view the API specification. Fusion also provides convenient jobs you can configure to deploy your models via the ML Model service, plus pipelines and pipeline stages for querying them. See these topics:

Develop and Deploy a Machine Learning Model provides general instructions for using ML models with Fusion, including pre-trained models and your own custom models.
Lucidworks provides pre-trained models for sentiment analysis and prediction and Smart Answers.
Then you can query your model using the Machine Learning query pipeline stage.
Train a Smart Answers Supervised Model explains how to configure the Smart Answers Supervised Training job to train a model on an existing body of question/answer data and deploy it for use with your Configure the Smart Answers Pipelines (5.3 and later).
If your existing question/answer data is sparse or your data is in another format, use the Train a Smart Answers cold start model to train and deploy a model you can use to get started.
Lucidworks also provides Set Up a Pre-Trained Cold Start Model for Smart Answers you can use.

Develop and Deploy a Machine Learning Model

This tutorial walks you through deploying your own model to Fusion with Seldon Core.

Prerequisites

A Fusion instance with an app and indexed data
An understanding of Python and the ability to write Python code
Docker installed locally, plus a private or public Docker repository
Seldon-core installed locally: pip install seldon-core
Code editor; you can use any editor, but Visual Studio Code is used in the example
Model: paraphrase-multilingual-MiniLM-L12-v2 from Hugging Face
Docker image: example_sbert_model

Tips

Always test your Python code locally before uploading to Docker and then Fusion. This simplifies troubleshooting significantly.
Once you’ve created your Docker you can also test locally by doing docker run with a specified port, like 9000, which you can then curl to confirm functionality in Fusion. See the testing example below.

LucidAcademyLucidworks offers free training to help you get started.The Course for Intro to Machine Learning in Fusion focuses on using machine learning to to infer the goals of customers and users in order to deliver a more sophisticated search experience:

Visit the LucidAcademy to see the full training catalog.

Local testing example

The examples in this section use the following models:

Docker command:

 docker run -p 127.0.0.1:9000:9000 <your-docker-image>

Curl to hit Docker:

 curl -X POST -H 'Content-Type: application/json' -d '{"data": { "ndarray": ["Sentence to test"], "names":["text"]} }' https://localhost:9000/api/v1.0/predictions

Curl model in Fusion:

 curl -u $FUSION_USER:$FUSION_PASSWORD -X POST -H 'Content-Type: application/json' -d '{"text": "i love fusion"}' https://<your-fusion>.lucidworks.com:6764/api/ai/ml-models/<your-model>/prediction

See all your deployed models:

 https://<your-fusion-host>/api/ai/ml-models

Download the model

This tutorial uses the paraphrase-multilingual-MiniLM-L12-v2 model from Hugging Face, but any pre-trained model from https://huggingface.co will work with this tutorial.If you want to use your own model instead, you can do so, but your model must have been trained and then saved though a function similar to the PyTorch’s torch.save(model, PATH) function. See Saving and Loading Models in the PyTorch documentation.

Format a Python class

The next step is to format a Python class which will be invoked by Fusion to get the results from your model. The skeleton below represents the format that you should follow. See also Packaging a Python model for Seldon Core using Docker in the Seldon Core documentation.

class MyModel(object):
    """
    Model template. You can load your model parameters in __init__ from a
    location accessible at runtime
    """

    def __init__(self):
        """
        Add any initialization parameters. These will be passed at runtime
        from the graph definition parameters defined in your seldondeployment
        kubernetes resource manifest.
        """
        print("Initializing")

    def predict(self,X,features_names,**kwargs):
        """
        Return a prediction.

        Parameters
        ----------
        X : array-like
        feature_names : array of feature names (optional)
        """
        print("Predict called - will run identity function")
        return X

    def  class_names(self):
        return ["X_name"]

A real instance of this class with the Paraphrase Multilingual MiniLM L12 v2 model is as follows:

import logging
import os

from transformers import AutoTokenizer, AutoModel
from torch.nn import functional as F
from typing import Iterable
import numpy as np
import torch

log = logging.getLogger()

class mini():
    def __init__(self):
        self.tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
        self.model= AutoModel.from_pretrained('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')

    #Mean Pooling
    def mean_pooling(self, model_output, attention_mask):
        token_embeddings = model_output[0] #First element of model_output contains all token embeddings
        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

    def predict(self, X:np.ndarray, names=None, **kwargs):
        #   In Fusion there are several variables passed in the numpy array with the Milvus Query stage,
        #   Encode to Milvus index stage, and Vectorize Seldon index and query stage:
        #   [pipeline, bool, and text]. Text is what variable will be encoded, so that is what will be set to 'text'
        #   When using the Machine Learning stage, the input map keys should match what what is in this file.

        model_input = dict(zip(names, X))
        text = model_input["text"]

        with torch.inference_mode(): # Allows torch to run more quickly
          # Tokenize sentences
          encoded_input = self.tokenizer(text, padding=True, truncation=True, return_tensors='pt')
          log.debug('encoded input',str(encoded_input))
          model_output = self.model(**encoded_input)
          log.debug('model output',str(model_output))

          # Perform pooling. In this case, max pooling.
          sentence_embeddings = self.mean_pooling(model_output, encoded_input['attention_mask'])
          # Normalize embeddings, because Fusion likes it that way.
          sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=-1)
          # Fixing the shape of the emebbedings to match (1, 384).
          final = [sentence_embeddings.squeeze().cpu().detach().numpy().tolist()]
        return final

    def class_names(self) -> Iterable[str]:
        return ["vector"]

In the above code, an additional function has been added in the class; this is completely fine to do. Logging has also been added for debugging purposes.Two functions are non-negotiable:

init: The init function is where models, tokenizers, vectorizers, and the like should be set to self for invoking. It is recommended that you include your model’s trained parameters directly into the Docker container rather than reaching out to external storage inside init.
predict: The predict function processes the field or query that Fusion passes to the model. The predict function must be able to handle any text processing needed for the model to accept input invoked in its model.evaluate(), model.predict(), or equivalent function to get the expected model result. If the output needs additional manipulation, that should be done before the result is returned. For embedding models the return value must have the shape of (1, DIM), where DIM (dimension) is a consistent integer, to enable Fusion to handle the vector encoding into Milvus or Solr.

Use the exact name of the class when naming this file.

For the example, above the Python file is named mini.py and the class name is mini().

Create a Dockerfile

The next step is to create a Dockerfile. The Dockerfile should follow this general outline; read the comments for additional details:

#It is important that python version is 3.x-slim for seldon-core
FROM python:3.9-slim
# Whatever directory(folder)the python file for your python class, Dockerfile, and
# requirements.txt is in should be copied then denoted as the work directory.
COPY . /app
WORKDIR /app

# The requirements file for the Docker container
COPY requirements.txt requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY . .

# GRPC - Allows Fusion to do a Remote Procedure Call
EXPOSE 5000

# Define environment variable for seldon-core
# !!!MODEL_NAME must be the EXACT same as the python file & python class name!!!
ENV MODEL_NAME mini
ENV SERVICE_TYPE MODEL
ENV PERSISTENCE 0

# Changing active directory folder (same one as above on lines 5 & 6) to default user, required for Fusion
RUN chown -R 8888 /app

# Command to wrap python class with seldon-core to allow it to be usable in Fusion
CMD ["sh", "-c", "seldon-core-microservice $MODEL_NAME --service-type $SERVICE_TYPE --persistence $PERSISTENCE"]

# You can use the following if You need shell features like environment variable expansion or
# You need to use shell constructs like pipes, redirects, etc.
# See https://docs.docker.com/reference/dockerfile/#cmd for more details.
# CMD exec seldon-core-microservice $MODEL_NAME --service-type $SERVICE_TYPE --persistence $PERSISTENCE

Create a requirements file

The requirements.txt file is a list of installs for the Dockerfile to run to ensure the Docker container has the right resources to run the model.
For the Paraphrase Mulitilingual MiniLM L12 v2 model, the requirements are as follows:

seldon-core
torch
transformers
numpy

In general, if an item was used in an import statement in your Python file, it should be included in the requirements file.An easy way to populate the requirements is by using in the following command in the terminal, inside the directory that contains your code:

pip freeze > requirements.txt

If you use pip freeze, you must manually add seldon-core to the requirements file because it is not invoked in the Python file but is required for containerization.

Build and push the Docker image

After creating the <your_model>.py, Dockerfile, and requirements.txt files, you need to run a few Docker commands. Run the commands below in order:

DOCKER_DEFAULT_PLATFORM=linux/amd64 docker build . -t [DOCKERHUB-USERNAME]/[REPOSITORY]:[VERSION-TAG]

docker push [DOCKERHUB USERNAME]/[REPOSITORY]:[VERSION-TAG]

Using the example model, the terminal commands would be as follows:

DOCKER_DEFAULT_PLATFORM=linux/amd64 docker build . -t jstrmec/example_sbert_model:0.14; docker push jstrmec/example_sbert_model:0.14

This repository is public and you can visit it here: example_sbert_model

Deploy the model in Fusion

Now you can go to Fusion to deploy your model.

In Fusion, navigate to Collections > Jobs.
Add a job by clicking the Add+ Button and selecting Create Seldon Core Model Deployment.

Fill in each of the text fields:

Create a Seldon Core model deployment job

Parameter	Description
Job ID	A string used by the Fusion API to reference the job after its creation.
Model name	A name for the deployed model. This is used to generate the deployment name in Seldon Core. It is also the name that you reference as a `model-id` when making predictions with the ML Service.
Model replicas	The number of load-balanced replicas of the model to deploy; specify multiple replicas for a higher-volume intake.
Docker Repository	The public or private repository where the Docker image is located. If you’re using Docker Hub, fill in the Docker Hub username here.
Image name	The name of the image with an optional tag. If no tag is given, `latest` is used.
Kubernetes secret	If you’re using a private repository, supply the name of the Kubernetes secret used for access.
Output columns	A list of column names that the model’s `predict` method returns.

Click Save, then Run and Start. When the job finishes successfully, you can proceed to the next section.

Now that the model is in Fusion, it can be utilized in either index or query pipelines, depending on the model’s purpose. In this case the model is a word vectorizer or semantic vector search implementation, so both pipelines must invoke the model.

Apply an API key to the deployment

These steps are only needed if your model utilizes any kind of secret, such as an API key. If not, skip this section and proceed to the next.

Create and modify a <seldon_model_name>_sdep.yaml file.
In the first line, kubectl get sdep gets the details for the currently running Seldon Deployment job and saves those details to a YAML file. kubectl apply -f open_sdep.yaml adds the key to the Seldon Deployment job the next time it launches.
```
kubectl get sdep <seldon_model_name> -o yaml > <seldon_model_name>_sdep.yaml
# Modify <seldon_model_name>_sdep.yaml to add
       - env:
         - name: API_KEY
           value: "your-api-key-here"
kubectl apply -f <seldon_model_name>_sdep.yaml
```
Delete sdep before redeploying the model. The currently running Seldon Deployment job does not have the key applied to it. Delete it before redeploying and the new job will have the key.
```
kubectl delete sdep <seldon_model_name>
```
Lastly, you can encode into Milvus.

Create a Milvus collection

In Fusion, navigate to Collections > Jobs.
Click the Add+ Button and select Create Collections in Milvus.
This job creates a collection in Milvus for storing the vectors sent to it. The job is needed because a collection does not automatically spawn at indexing or query time if it does not already exist.
Name the job and the collection.
Click Add on the right side of the job panel.
The key to creating the collection is the Dimension text field; this must exactly match the shape value your output prediction has.
In our example the shape is (1,384), so 384 will be in the collections Dimension field: The Metric field should typically be left at the default of Inner Product, but this also depends on use case and model type.
Click Save, then Run and Start.

Configure the Fusion pipelines

Your real-world pipeline configuration depends on your use case and model, but for our example we will configure the index pipeline and then the query pipeline.Configure the index pipeline

Create a new index pipeline or load an existing one for editing.
Click Add a Stage and then Encode to Milvus.
In the new stage, fill in these fields:
- The name of your model
- The output name you have for your model job
- The field you’d like to encode
- The collection name
Save the stage in the pipeline and index your data with it.

Configure the query pipeline

Create a new query pipeline or load an existing one for editing.
Click Add a Stage and then Milvus Query.
Fill in the configuration fields, then save the stage.
Add a Milvus Ensemble Query stage.
This stage is necessary to have the Milvus collection scores taken into account in ranking and to weight multiple collections. The Milvus Results Context Key from the Milvus Query Stage is used in this stage to preform math on the Milvus result scores. One (1) is a typical multiplier for the Milvus results but any number can be used.
Save the stage and then run a query by typing a search term.
To verify the Milvus results are correct, use the Compare+ button to see another pipeline without the model implementation and compare the number of results.

You have now successfully uploaded a Seldon Core model to Fusion and deployed it.

Train a Smart Answers Supervised Model

The Supervised solution for Smart Answers begins with training a model using your existing data and the Smart Answers Supervised Training job, as explained in this topic. The job includes an auto-tune feature that you can use instead of manually tuning the configuration.

Training job requirements

Storage150GB plus 2.5 times the total input data size.Processor and memoryThe memory requirements depend on whether you choose GPU or CPU processing:


GPU	CPU
one core 11GB RAM	32 cores 32GB RAM

If your training data contains more than 1 million entries, use GPU.

Prepare the input data

Format your input data as question/answer pairs, that is, a query and its corresponding response in each row. You can do this in any format that Managed Fusion supports. If there are multiple possible answers for a unique question, then repeat the questions and put the pair into different rows to make sure each row has one question and one answer, as in the example JSON below:
```
[{"question":"How to transfer personal auto lease to business auto lease?","answer":"I would approach the lender that you are getting the lease from..."}
 {"question":"How to transfer personal auto lease to business auto lease?","answer":"See what the contract says about transfers or subleases..."}]
```
Index the input data in Managed Fusion. If you wish to have the training data in Managed Fusion, index it into a separate collection for training data such as model_training_input. Otherwise you can use it directly from the cloud storage.

Configure the training job

In Managed Fusion, navigate to Collections > Jobs.
Select Add > Smart Answers Supervised Training:
In the Training Collection field, specify the input data collection that you created when you prepared the input data.
You can also configure this job to read from or write to cloud storage.
Enter the names of the Question Field and the Answer Field in the training collection.
Enter a Model Deployment Name. The new machine learning model will be saved in the blob store with this name. You will reference it later when you configure your pipelines.
Configure the Model base. There are several pre-trained word and BPE embeddings for different languages, as well as a few pre-trained BERT models. If you want to train custom embeddings, select word_custom or bpe_custom. This trains Word2vec on the provided data and specified fields. It might be useful in cases when your content includes unusual or domain-specific vocabulary. If you have content in addition to the query/response pairs that can be used to train the model, then specify it in the Texts Data Path. When you use the pre-trained embeddings, the log shows the percentage of processed vocabulary words. If this value is high, then try using custom embeddings. The job trains a few (configurable) RNN layers on top of word embeddings or fine-tunes a BERT model on the provided training data. The result model uses an attention mechanism to average word embeddings to obtain the final single dense vector for the content.
Dimension size of vectors for Transformer-based models is 768. For RNN-based models it is 2 times the number units of the last layer. To find the dimension size: download the model, expand the zip, open the log and search for Encoder output dim size: line. You might need this information when creating collections in Milvus.
Optional: Check Perform auto hyperparameter tuning to use auto-tune. Although training module tries to select the most optimal default parameters based on the training data statistics, auto-tune can extend it by automatically finding even better training configuration through hyper-parameter search. Although this is a resource-intensive operation, it can be useful to identify the best possible RNN-based configuration. Transformer-based models like BERT are not used during auto hyperparameter tuning as they usually perform better yet they are much more expensive on both training and inference time.
Click Save.
If using solr as the training data source ensure that the source collection contains the random_* dynamic field defined in its managed-schema.xml. This field is required for sampling the data. If it is not present, add the following entry to the managed-schema.xml alongside other dynamic fields <dynamicField name="random_*" type="random"/> and <fieldType class=“solr.RandomSortField” indexed=“true” name=“random”/> alongside other field types.
Click Run > Start.

After training is finished the model is deployed into the cluster and can be used in index and query pipelines.

Next steps

See A Smart Answers Supervised Job’s Status and Output
Configure The Smart Answers Pipelines
Evaluate a Smart Answers Query Pipeline

Configure the Smart Answers Pipelines (5.3 and later)

Before beginning this procedure, train a machine learning model using either the FAQ method or the cold start method.Regardless of how you set up your model, the deployment procedure is the same:

Create the Milvus collection.
Configure the smart-answers index pipeline.
Configure the smart-answers query pipeline.

See also Best Practices](/fusion/5.9/462/best-practices), Advanced Model Training Configuration for Smart Answers, and Smart Answers Detailed Pipeline Setup.

Create the Milvus collection

For complete details about job configuration options, see the Create Collections in Milvus job.

Navigate to Collections > Jobs > Add + and select Create Collections in Milvus.
Configure the job:
1. Enter an ID for this job.
2. Under Collections, click Add.
3. Enter a collection name.
4. In the Dimension field, enter the dimension size of vectors to store in this Milvus collection. The Dimension should match the size of the vectors returned by the encoding model. For example, the Smart Answers Pre-trained Coldstart models outputs vectors of 512 dimension size. Dimensionality of encoders trained by Smart Answers Supervised Training job depends on the provided parameters and printed in the training job logs.
Click Save. The Create Collections in Milvus job can be used to create multiple collections at once. In this image, the first collection is used in the indexing and query steps. The other two collections are used in the example.
Click Run > Start to run the job.

Configure the index pipeline

Open the Index Workbench.
Load or create your datasource using the default smart-answers index pipeline.
Configure the Encode into Milvus stage:
1. change the value of Model ID to match the model deployment name you chose when you configured the model training job.
2. Change Field to Encode to the document field name to be processed and encoded into dense vectors.
3. Ensure the Encoder Output Vector matches the output vector from the chosen model.
4. Ensure the Milvus Collection Name matches the collection name created via the Create Milvus Collection job.
  To test out your settings, turn on Fail on Error in the Encode into Milvus stage and Apply the changes. This will cause an error message to display if any settings need to be changed.
Save the datasource.
Index your data.

Configure the query pipeline

Open the Query Workbench.
Load the default smart-answers query pipeline.
Configure the Milvus Query stage:
1. Change the Model ID value to match the model deployment name you chose when you configured the model training job.
2. Ensure the Encoder Output Vector matches the output vector from the chosen model.
3. Ensure the Milvus Collection Name matches the collection name created via the Create Milvus Collection job.
4. Milvus Results Context Key can be changed as needed. It will be used in the Milvus Ensemble Query Stage to calculate the query score.
In the Milvus Ensemble Query stage, update the Ensemble math expression as needed based on your model and the name used in the prior stage for the storing the Milvus results. In versions 5.4 and later, you can also set the Threshold so that the Milvus Ensemble Query Stage will only return items with a score greater than or equal to the configured value.
Save the query pipeline.

Pipeline Setup Example

Index and retrieve the question and answer together

To show question and answer together in one document (that is, treat the question as the title and the answer as the description), you can index them together in the same document. You can still use the default smart-answers index and query pipelines with a few additional changes.Prior to configuring the Smart Answers pipelines, use the Create Milvus Collection job to create two collections, question_collection and answer_collection, to store the encoded “questions” and the encoded “answers”, respectively.

Index Pipeline

As shown in the pictures below, you will need two Encode into Milvus stages, named Encode Question and Encode Answer respectively.Encode Question (Encode Into Milvus) stage

Pipeline setup example - Encode Question stage

Encode Answer (Encode Into Milvus) stage

Pipeline setup example - Encode Answer stage

In the Encode Question stage, specify Field to Encode to be title_t and change the Milvus Collection Name to match the new Milvus collection, question_collection.In the Encode Answer stage, specify Field to Encode to be description_t and change the Milvus Collection Name to match the new Milvus collection, answer_collection.

Query Pipeline

Since we have two dense vectors generated during indexing, at query time we need to compute both query to question distance and query to answer distance. This can be set up as the pictures shown below with two Milvus Query Stages, one for each of the two Milvus collections. To store those two distances separately, the Milvus Results Context Key needs to be different in each of these two stages.In the Query Questions stage, we set the Milvus Results Context Key to milvus_questions and the Milvus collection name to question_collection.Query Questions (Milvus Query) stage:

Pipeline setup example - Query Questions stage

In the Query Answers stage, we set the Milvus Results Context Key to milvus_answers and the Milvus collection name to answer_collection.Query Answers (Milvus Query) stage:

Pipeline setup example - Query Answers stage

Now we can ensemble them together with the Milvus Ensemble Query Stage with the Ensemble math expression combining the results from the two query stages. If we want the question scores and answer scores weighted equally, we would use: 0.5 * milvus_questions + 0.5 * milvus_answers. This is recommended especially when you have limited FAQ dataset and want to utilize both question and answer information.Milvus Ensemble Query stage

Pipeline setup example - Milvus Ensemble Query stage

Evaluate the query pipeline

The Evaluate QnA Pipeline job evaluates the rankings of results from any Smart Answers pipeline and finds the best set of weights in the ensemble score.

Detailed pipeline setup

Typically, you can use the default pipelines included with Fusion AI. These pipelines now utilize Milvus to store encoded vectors and to calculate vector similarity. This topic provides information you can use to customize the Smart Answers pipelines.


”smart-answers” index pipeline		Encode into Milvus stage
”smart-answers” query pipeline		Query Fields Milvus Query Milvus Ensemble Query Milvus Response Update

Create the Milvus collection

Prior to indexing data, the Create Collections in Milvus job can be used to create the Milvus collection(s) used by the Smart Answers pipelines (see Milvus overview).

Job ID. A unique identifier for the job.
Collection Name. A name for the Milvus collection you are creating. This name is used in both the Smart Answer Index and the Smart Answer Query pipelines.
Dimension. The dimension size of the vectors to store in this Milvus collection. The Dimension should match the size of the vectors returned by the encryption model. For example, if the model was created with either the Smart Answers Coldstart Training job or the Smart Answers Supervised Training job with the Model Base word_en_300d_2M, then the dimension would be 300.
Index file size. Files with more documents than this will cause Milvus to build an index on this collection.
Metric. The type of metric used to calculate vector similarity scores. Inner Product is recommended. It produces values between 0 and 1, where a higher value means higher similarity.

Index pipeline setup

Stages in the default “smart-answers” index pipeline

Only one custom index stage needs to be configured in your index pipeline, the Encode into Milvus index stage.

The Encode into Milvus Index Stage

If you are using a dynamic schema, make sure this stage is added after the Solr Dynamic Field Name Mapping stage.

The Encode into Milvus index stage uses the specified model to encode the Field to Encode and store it in Milvus in the given Milvus collection. There are several required parameters:

Model ID. The ID of the model.
Encoder Output Vector. The name of the field that stores the compressed dense vectors output from the model. Default value: vector.
Field to Encode. The text field to encode into a dense vector, such as answer_t or body_t.
Milvus Collection Name. The name of the collection you created via the Create Milvus Collection job, which will store the dense vectors. When creating the collection you specify the type of Metric to use to calculate vector similarity. This stage can be used multiple times to encode additional fields, each into a different Milvus collection.

Query pipeline setup

The Query Fields stage

The first stage is Query Fields. For more information see the Query Fields stage.

The Milvus Query stage

The Milvus Query stage encodes the query into a vector using the specified model. It then performs a vector similarity search against the specified Milvus collection and returns a list of the best document matches.

Model ID. The ID of the model used when configuring the model training job.
Encoder Output Vector. The name of the output vector from the specified model, which will contain the query encoded as a vector. Defaults to vector.
Milvus Collection Name. The name of the collection that you used in the Encode into Milvus index stage to store the encoded vectors.
Milvus Results Context Key. The name of the variable used to store the vector distances. It can be changed as needed. It will be used in the Milvus Ensemble Query Stage to calculate the query score for the document.
Number of Results. The number of highest scoring results returned from Milvus. This stage would typically be used the same number of times that the Encode into Milvus index stage is used, each with a different Milvus collection and a different Milvus Results Context Key.

The Milvus Ensemble Query stage

The Milvus Ensemble Query takes the results of the Milvus Query stage(s) and calculates the ensemble score, which is used to return the best matches.

Ensemble math expression. The mathematical expression used to calculate the ensemble score. It should reference the value(s) variable name specified in the Milvus Results Context Key parameter in the Milvus Query stage.
Result field name. The name of the field used to store the ensemble score. It defaults to ensemble_score.
Threshold- A parameter that filters the stage results to remove items that fall below the configured score. Items with a score at, or above, the threshold will be returned.

The Threshold feature is only available in Fusion 5.4 and later.

The Milvus Response Update Query stage

The Milvus Response Update Query stage does not need to be configured and can be skipped if desired. It inserts the Milvus values, including the ensemble_score, into each of the returned documents, which is particularly useful when there is more than one Milvus Query Stage. This stage needs to come after the Solr Query stage.

Short answer extraction

By default, the question-answering query pipelines return complete documents that answer questions. Optionally, you can extract just a paragraph, a sentence, or a few words that answer the question.

Train a Smart Answers cold start model

Set Up a Pre-Trained Cold Start Model for Smart Answers

Introduction to Fusion

Getting Data In

Getting Data Out

Operations

Reference

Developer Docs

Neural Hybrid Search

Release Notes