Knowledge Discovery 25.4.0
25.4 Release Notes

# Answer Server

Answer Server is an ACI server. For details of changes that affect all ACI servers, see ACI Server Framework.

## 25.4.0

### New Features

There were no new features in this release.

### Resolved Issues

- When using a PassageExtractorLLM type answer system, requests sometimes unexpectedly gave no results, with a warning `Success: HTTP Error 414: Request-URI Too Long`.

- Answer Server would not start correctly on Windows platforms if the `RequirementsFile` configuration parameter was set.

- Answer Server would not start correctly if the `RequirementsFile` configuration parameter was set and the path to the installation contained a space character.

## 25.3.0

### New Features

- The `VectorField` parameter in the `MyVectorField` configuration section now supports a comma-separated list of different field names as input. Answer Server generates embeddings by using the `EmbeddingsConfig` in the `MyVectorField` configuration section and then search for matching vectors in any of the vector fields supplied in the `VectorField` list.

### Resolved Issues

- The Passage Extractor answer system was overly restrictive about what input it considered to be an answerable question. For example, it would not provide an answer to some questions if they were surrounded with parentheses.

## 25.2.0

### New Features

There were no new features in this release.

### Resolved Issues

- Answer Server did not log errors that occurred when fetching answers from answer systems. Previously, these errors were returned only in the ACI response, as errors if no answers were found at all, or warnings if answers were found. Now they are included in the log files.

## 25.1.0

### New Features

- The RAG answer system has been updated so that you can use the configured LLM to determine the contextual topics of a conversation that you pass in as session_data. Answer Server then uses these topics in the query that it sends to the Content component, to improve the relevance of candidate documents that it returns.

  To use this option, you must create a topic template and set the `TopicTemplatePath` configuration parameter. For more information, refer to the Answer Server Help.

- You can now configure how many processors (CPUs) that your generative LLMs can use by setting the `NumProcessors` parameter in the relevant generative LLM configuration section.

### Resolved Issues

There were no resolved issues in this release.

## 24.4.0

### New Features

- The RAG system has been updated so that you can add additional metadata to the prompts that Answer Server supplies to the LLM. The new `ContextJSONFields` parameter accepts a list of fields to include, and Knowledge Discovery generates a JSON object with these fields and the corresponding values. This option can improve the ability of the LLM to differentiate between document summaries and parse the information. For more information, refer to the Answer Server Help.

- The `Ask` action `CustomizationData` parameter now allows you to specify session data for all systems, by using the new global option for the `system_name` object in the customization data JSON object. The `session_data` option for this global system contains results from previous questions and answers from a single answer session. For example:

      CustomizationData=[{"system_name": "global", "session_data": "[{\"question\":\"How tall is the Eiffel Tower?\", \"answer\": \"300m\"}]"}]

  For more information, refer to the Answer Server Help.

- The RAG system `GenerativePython` and `GenerativeLua` options have been updated to allow you to use session data in your scripts to provide additional context. You access the session data by passing in a second argument to the `generate` function. The sample vllm Python script in `passageextractor/rag/vllm.py` has also been updated with an example of how to use chat history with vLLM. For more information about how to access and use the session data, refer to the Answer Server Help.

- LLM models are now downloaded when the server starts, rather than when they are first used. This change prevents long request times when there are a large amount of requests sent in a short time frame before the model has been downloaded.

### Resolved Issues

There were no resolved issues in this release.

## 24.3.0

### New Features

- A new answer system has been added for Retrieval Augmented Generation (RAG). Retrieval Augmented Generation (RAG) is a method to combine the generative capabilities of large language models (LLMs) with a system to ensure that the answers generated are from a verifiable source. RAG finds answers in a body of data, and then uses the natural language generation of the LLM to create a human-readable answer.

  You configure an Answer Server RAG system in a similar way to a passage extractor LLM system, with configurations for the LLM modules that you use to generate embeddings for queries to find candidate documents, as well as the LLM module that you use to generate answers. Answer Server retrieves the summaries from candidate documents in your IDOL index, and combines them with the question in a prompt template that you can to send to the LLM.

  You can use a Python or Lua script to connect to the LLM and generate the answers to Answer Server.

  For more information, refer to the Answer Server Help.

- The following improvements have been made to the LLM configuration that you can use to generate embeddings or perform generative or extractive question answering:

  - You can now perform embedding generation and generative question answering by using a Python script, in the same way as for Lua. To use this option, you set the `Type` parameter to Python in your embedding generation configuration, or GenerativePython in your LLM Module configuration. You must also set `Script` to the path to the Python script to use. You can also set `RequirementsFile` to the path to your `requirements.txt` file.

  - You can now use a specific revision of a model from Hugging Face, by setting the new `ModelRevision` parameter in your model configuration.

  - You can now use a private model from Hugging Face, by setting the new `AccessToken` parameter in your model configuration.

  - You can now use only an offline (cached) version of your model, rather than downloading the latest, by setting the new `OfflineMode` parameter in your model configuration.

  - You can now use an alternative algorithm to generate answers, by setting the new `GreedySearch` parameter to False. By default, Answer Server uses the greedy search algorithm, which uses the token with the highest probability as the next token. When you set `GreedySearch` to False, it uses a multinomial sampling algorithm to choose a random token based on a probability distribution, which can give better results for long sentences.

  - You can now set a limit on the amount of data to generate embeddings for, by setting the new `DataLimit` parameter in your model configuration.

  For more information, refer to the Answer Server Help

### Resolved Issues

- Using generative models with small input (less than 2 tokens for generative question answering) caused an error.

## 24.2.0

### New Features

- The method for using LLMs to generate embeddings and to perform generative and extractive question answering has been improved.

  Rather than create your own model files by using a script, Answer Server can now download and cache the models directly from Hugging Face. This change means that Answer Server supports a wider range of models, and it does not require the python script or external python libraries.

  To use LLMs, you must now use the `ModelName` parameter in your LLM configuration to specify the model to use from Hugging Face. You can also optionally set `CacheDirectory` to the location that Answer Server must use to store the cached model files.

  For information about the models that you can use for different configurations, refer to the Answer Server Help.

  IMPORTANT: As part of this change, the `ModelPath` and `TokenizerPath` parameters have been removed, and are no longer supported.

- You can now control the precision of the embeddings that Answer Server generates to run vector queries, by using the `EmbeddingPrecision` parameter in the configuration section for your embedding system. This parameter sets the number of decimal places to use in the embedding values.

- You can now configure embedding models and generative LLMs to use a CUDA-compatible GPU device, by setting the new `Device` configuration parameter in the model configuration.

### Resolved Issues

There were no resolved issues in this release.

## 24.1.0

### New Features

- You can now configure Passage Extractor systems to use an LLM to extract or generate answers. To configure these systems, you set the `Type` to `PassageExtractorLLM`. Like a standard passage extractor, you must configure the location of an IDOL index to use to find answers, and classifier files to describe the types of different questions.

  For LLM passage extractor, you must also configure the location of model and tokenizer files for the LLM to use to generate or extract answers.

  You can also use these models in a Lua script, for example so that you can access an LLM through a HTTP endpoint.

  For example:

      [passageextractorLLM]
      // Data store IDOL
      IdolHost=localhost
      IdolAciport=6002
      Type=PassageExtractorLLM
      // Classifier Files
      ClassifierFile=./passageextractor/classifiertraining/svm_en.dat
      LabelFile=./passageextractor/classifiertraining/labels_en.dat
      // Module to use
      ModuleID=LLMExtractiveQuestionAnswering-Small

      [LLMExtractiveQuestionAnswering-Small]
      Type=ExtractiveQuestionAnsweringLLM
      ModelPath=modelfiles/model.pt
      TokenizerPath=modelfiles/tokenizer.spiece.model

  For more information, refer to the Answer Server Help.

- When you use a passage extract LLM system, the `Ask` action returns a highlighted paragraph in the response metadata to show the passage that the answer was extracted from, to allow you to verify automatically generated answers.

- You can now configure a Passage Extractor or Passage Extractor LLM system to run vector queries against the IDOL Content component to identify candidate documents that might contain answers to an input question. You can use this option when you index vectors in your IDOL Content component and want to use vector search to retrieve answers.

  To use this option, you must set the `AnswerCandidateEmbeddingsSettings` parameter in your system configuration section to the name of a configuration section where you configure the Content vector field and an embeddings configuration for how to generate embeddings to send to Content. For example:

      [PassageExtractorSystem]
      idolhost=localhost
      idolaciport=6002
      type=passageextractor
      ...
      AnswerCandidateEmbeddingsSettings=VectorSettings

      [VectorSettings]
      EmbeddingsConfig=EmbeddingsSystem
      VectorField=VECTORA

      [EmbeddingsSystem]
      Type=Transformer
      ModelPath=path/to/model.pt
      TokenizerPath=path/to/tokenizer.spiece.model
      ModelMaxSequenceLength=128

  For more information, refer to the Answer Server Help.

### Resolved Issues

There were no resolved issues in this release.

## 23.4.0

There were no new features or resolved issues in this release.

## 23.3.0

There were no new features or resolved issues in this release.

## 23.2.0

There were no new features or resolved issues in this release.

Copyright 2025 Open Text
Version 25.4.0 | Last updated October 2025

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Product: Knowledge Discovery 25.4.0

Topic Title: Answer Server