IBM watsonx.ai

You can develop generative AI solutions with foundation models in IBM watsonx.ai. You can use prompts to generate, classify, summarize, or extract content from your input text. Choose from IBM models or open source models from Hugging Face. You can tune foundation models to customize your prompt output or optimize inferencing performance.

Supported only for IBM watsonx as a service on IBM Cloud.

Using watsonx.ai

To employ watsonx.ai LLMs, integrate the following dependency into your project:

<dependency>
    <groupId>io.quarkiverse.langchain4j</groupId>
    <artifactId>quarkus-langchain4j-watsonx</artifactId>
    <version>0.24.0.CR1</version>
</dependency>

If no other extension is installed, AI Services will automatically utilize the configured watsonx dependency.

Configuration

To use the watsonx.ai dependency, you must configure some required values in the application.properties file.

Base URL

The base-url property depends on the region of the provided service instance, use one of the following values:

quarkus.langchain4j.watsonx.base-url=https://us-south.ml.cloud.ibm.com

Project ID

To prompt foundation models in watsonx.ai, you need to pass the identifier of a project.

To get the ID of a project, complete the following steps:

  1. Go to https://dataplatform.cloud.ibm.com/projects/?context=wx.

  2. Open the project, and then click the Manage tab.

  3. Copy the project ID from the Details section of the General page.

quarkus.langchain4j.watsonx.project-id=23d...
If you like, you can use the Space ID property with quarkus.langchain4j.watsonx.space-id

API Key

To use foundation models in IBM Watsonx.ai, you need an IBM Cloud API key.

Follow these steps to generate one:

  1. Go to https://cloud.ibm.com/iam/apikeys.

  2. Click Create + button.

  3. Provide a name and description for your new API key, then click Create.

  4. Copy or securely save the API key.

quarkus.langchain4j.watsonx.api-key=hG-...

Modes of Interaction

The watsonx.ai module provides two modes for interacting with large language models: chat and generation. These modes allow you to tailor interactions based on the complexity of your use case and the level of control you require over the prompt structure.

You can select the interaction mode using the property quarkus.langchain4j.watsonx.mode.

  • chat: This mode abstracts the complexity of tagging by automatically formatting prompts (default value).

  • generation: In this mode, you must explicitly structure the prompts using the required model-specific tags. This provides full control over the format of the prompt but requires in-depth knowledge of the model being used. For best results, always refer to the documentation provided for each model to maximize the effectiveness of your prompts.

Each mode uses its own property namespace for customization:

  • For chat mode the properties are under quarkus.langchain4j.watsonx.chat-model.

  • For generation mode properties are under quarkus.langchain4j.watsonx.generation-model.

Chat Mode Example

The following example demonstrates how to configure and use chat mode:

quarkus.langchain4j.watsonx.base-url=${BASE_URL}
quarkus.langchain4j.watsonx.api-key=${API_KEY}
quarkus.langchain4j.watsonx.project-id=${PROJECT_ID}
quarkus.langchain4j.watsonx.chat-model.model-id=mistralai/mistral-large
quarkus.langchain4j.watsonx.mode=chat // You can omit this property, as 'chat' is the default mode.
@RegisterAiService
public interface AiService {
    @SystemMessage("You are a helpful assistant")
    public String chat(@MemoryId String id, @UserMessage message);
}

Generation Mode Example

The following example demonstrates how to configure and use generation mode:

quarkus.langchain4j.watsonx.base-url=${BASE_URL}
quarkus.langchain4j.watsonx.api-key=${API_KEY}
quarkus.langchain4j.watsonx.project-id=${PROJECT_ID}
quarkus.langchain4j.watsonx.generation-model.model-id=mistralai/mistral-large
quarkus.langchain4j.watsonx.mode=generation
@RegisterAiService(chatMemoryProviderSupplier = RegisterAiService.NoChatMemoryProviderSupplier.class)
public interface AiService {
    @SystemMessage("<s>[INST] You are a helpful assistant [/INST]</s>")
    @UserMessage("[INST] What is the capital of {capital}? [/INST]")
    public String askCapital(String capital);
}
The @SystemMessage and @UserMessage annotations are joined by default with no separator (an empty string ""). If you want to change this behavior, use the property quarkus.langchain4j.watsonx.generation-model.prompt-joiner=<value>.

All configuration properties

Configuration property fixed at build time - All other configuration properties are overridable at runtime

Configuration property

Type

Default

quarkus.langchain4j.watsonx.chat-model.enabled

Whether the model should be enabled.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_ENABLED

boolean

true

quarkus.langchain4j.watsonx.embedding-model.enabled

Whether the embedding model should be enabled.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_EMBEDDING_MODEL_ENABLED

boolean

true

quarkus.langchain4j.watsonx.scoring-model.enabled

Whether the scoring model should be enabled.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_SCORING_MODEL_ENABLED

boolean

true

quarkus.langchain4j.watsonx.mode

Specifies the mode of interaction with the LLM model.

This property allows you to choose between two modes of operation:

  • chat: prompts are automatically enriched with the specific tags defined by the model

  • generation: prompts require manual specification of tags

Allowable values: [chat, generation]

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_MODE

string

chat

quarkus.langchain4j.watsonx.base-url

Base URL of the watsonx.ai API.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_BASE_URL

string

quarkus.langchain4j.watsonx.api-key

IBM Cloud API key.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_API_KEY

string

quarkus.langchain4j.watsonx.timeout

Timeout for watsonx.ai calls.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_TIMEOUT

Duration 

10s

quarkus.langchain4j.watsonx.version

The version date for the API of the form YYYY-MM-DD.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_VERSION

string

2024-03-14

quarkus.langchain4j.watsonx.space-id

The space that contains the resource.

Either space_id or project_id has to be given.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_SPACE_ID

string

quarkus.langchain4j.watsonx.project-id

The project that contains the resource.

Either space_id or project_id has to be given.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_PROJECT_ID

string

quarkus.langchain4j.watsonx.log-requests

Whether the watsonx.ai client should log requests.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_LOG_REQUESTS

boolean

false

quarkus.langchain4j.watsonx.log-responses

Whether the watsonx.ai client should log responses.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_LOG_RESPONSES

boolean

false

quarkus.langchain4j.watsonx.enable-integration

Whether to enable the integration. Defaults to true, which means requests are made to the watsonx.ai provider. Set to false to disable all requests.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_ENABLE_INTEGRATION

boolean

true

quarkus.langchain4j.watsonx.iam.base-url

Base URL of the IAM Authentication API.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_IAM_BASE_URL

URL

https://iam.cloud.ibm.com

quarkus.langchain4j.watsonx.iam.timeout

Timeout for IAM authentication calls.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_IAM_TIMEOUT

Duration 

10s

quarkus.langchain4j.watsonx.iam.grant-type

Grant type for the IAM Authentication API.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_IAM_GRANT_TYPE

string

urn:ibm:params:oauth:grant-type:apikey

quarkus.langchain4j.watsonx.chat-model.model-id

The model to use for the chat completion.

All available models are listed in the IBM Watsonx.ai documentation at the link: following link.

To use a model, locate the API model_id column in the table and copy the corresponding model ID.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_MODEL_ID

string

mistralai/mistral-large

quarkus.langchain4j.watsonx.chat-model.frequency-penalty

Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.

Possible values: -2 < value < 2

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_FREQUENCY_PENALTY

double

0

quarkus.langchain4j.watsonx.chat-model.logprobs

Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_LOGPROBS

boolean

false

quarkus.langchain4j.watsonx.chat-model.top-logprobs

An integer specifying the number of most likely tokens to return at each token position, each with an associated log probability. The option logprobs must be set to true if this parameter is used.

Possible values: 0 ≤ value ≤ 20

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_TOP_LOGPROBS

int

quarkus.langchain4j.watsonx.chat-model.max-tokens

The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model’s context length.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_MAX_TOKENS

int

1024

quarkus.langchain4j.watsonx.chat-model.n

How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_N

int

1

quarkus.langchain4j.watsonx.chat-model.presence-penalty

Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics.

Possible values: -2 < value < 2

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_PRESENCE_PENALTY

double

0

quarkus.langchain4j.watsonx.chat-model.seed

Random number generator seed to use in sampling mode for experimental repeatability.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_SEED

int

quarkus.langchain4j.watsonx.chat-model.stop

Stop sequences are one or more strings which will cause the text generation to stop if/when they are produced as part of the output. Stop sequences encountered prior to the minimum number of tokens being generated will be ignored.

Possible values: 0 ≤ number of items ≤ 4

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_STOP

list of string

quarkus.langchain4j.watsonx.chat-model.temperature

What sampling temperature to use. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

Possible values: 0 < value < 2

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_TEMPERATURE

double

${quarkus.langchain4j.temperature:1.0}

quarkus.langchain4j.watsonx.chat-model.top-p

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

Possible values: 0 < value < 1

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_TOP_P

double

1

quarkus.langchain4j.watsonx.chat-model.response-format

Specifies the desired format for the model’s output.

Allowable values: [json_object]

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_RESPONSE_FORMAT

string

quarkus.langchain4j.watsonx.chat-model.log-requests

Whether chat model requests should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_LOG_REQUESTS

boolean

false

quarkus.langchain4j.watsonx.chat-model.log-responses

Whether chat model responses should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_CHAT_MODEL_LOG_RESPONSES

boolean

false

quarkus.langchain4j.watsonx.generation-model.model-id

The id of the model to be used.

All available models are listed in the IBM Watsonx.ai documentation at the link: following link.

To use a model, locate the API model_id column in the table and copy the corresponding model ID.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_MODEL_ID

string

mistralai/mistral-large

quarkus.langchain4j.watsonx.generation-model.decoding-method

Represents the strategy used for picking the tokens during generation of the output text. During text generation when parameter value is set to greedy, each successive token corresponds to the highest probability token given the text that has already been generated. This strategy can lead to repetitive results especially for longer output sequences. The alternative sample strategy generates text by picking subsequent tokens based on the probability distribution of possible next tokens defined by (i.e., conditioned on) the already-generated text and the top_k and top_p parameters.

Allowable values: [sample,greedy]

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_DECODING_METHOD

string

greedy

quarkus.langchain4j.watsonx.generation-model.length-penalty.decay-factor

Represents the factor of exponential decay. Larger values correspond to more aggressive decay.

Possible values: > 1

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_LENGTH_PENALTY_DECAY_FACTOR

double

quarkus.langchain4j.watsonx.generation-model.length-penalty.start-index

A number of generated tokens after which this should take effect.

Possible values: ≥ 0

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_LENGTH_PENALTY_START_INDEX

int

quarkus.langchain4j.watsonx.generation-model.max-new-tokens

The maximum number of new tokens to be generated. The maximum supported value for this field depends on the model being used. How the "token" is defined depends on the tokenizer and vocabulary size, which in turn depends on the model. Often the tokens are a mix of full words and sub-words. Depending on the users plan, and on the model being used, there may be an enforced maximum number of new tokens.

Possible values: ≥ 0

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_MAX_NEW_TOKENS

int

200

quarkus.langchain4j.watsonx.generation-model.min-new-tokens

If stop sequences are given, they are ignored until minimum tokens are generated.

Possible values: ≥ 0

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_MIN_NEW_TOKENS

int

0

quarkus.langchain4j.watsonx.generation-model.random-seed

Random number generator seed to use in sampling mode for experimental repeatability.

Possible values: ≥ 1

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_RANDOM_SEED

int

quarkus.langchain4j.watsonx.generation-model.stop-sequences

Stop sequences are one or more strings which will cause the text generation to stop if/when they are produced as part of the output. Stop sequences encountered prior to the minimum number of tokens being generated will be ignored.

Possible values: 0 ≤ number of items ≤ 6

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_STOP_SEQUENCES

list of string

quarkus.langchain4j.watsonx.generation-model.temperature

A value used to modify the next-token probabilities in sampling mode. Values less than 1.0 sharpen the probability distribution, resulting in "less random" output. Values greater than 1.0 flatten the probability distribution, resulting in "more random" output. A value of 1.0 has no effect.

Possible values: 0 ≤ value ≤ 2

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_TEMPERATURE

double

${quarkus.langchain4j.temperature:1.0}

quarkus.langchain4j.watsonx.generation-model.top-k

The number of highest probability vocabulary tokens to keep for top-k-filtering. Only applies for sampling mode. When decoding_strategy is set to sample, only the top_k most likely tokens are considered as candidates for the next generated token.

Possible values: 1 ≤ value ≤ 100

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_TOP_K

int

quarkus.langchain4j.watsonx.generation-model.top-p

Similar to top_k except the candidates to generate the next token are the most likely tokens with probabilities that add up to at least top_p. Also known as nucleus sampling. A value of 1.0 is equivalent to disabled.

Possible values: 0 < value ≤ 1

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_TOP_P

double

quarkus.langchain4j.watsonx.generation-model.repetition-penalty

Represents the penalty for penalizing tokens that have already been generated or belong to the context. The value 1.0 means that there is no penalty.

Possible values: 1 ≤ value ≤ 2

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_REPETITION_PENALTY

double

quarkus.langchain4j.watsonx.generation-model.truncate-input-tokens

Represents the maximum number of input tokens accepted. This can be used to avoid requests failing due to input being longer than configured limits. If the text is truncated, then it truncates the start of the input (on the left), so the end of the input will remain the same. If this value exceeds the maximum sequence length (refer to the documentation to find this value for the model) then the call will fail if the total number of tokens exceeds the maximum sequence length. Zero means don’t truncate.

Possible values: ≥ 0

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_TRUNCATE_INPUT_TOKENS

int

quarkus.langchain4j.watsonx.generation-model.include-stop-sequence

Pass false to omit matched stop sequences from the end of the output text. The default is true, meaning that the output will end with the stop sequence text when matched.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_INCLUDE_STOP_SEQUENCE

boolean

quarkus.langchain4j.watsonx.generation-model.log-requests

Whether chat model requests should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_LOG_REQUESTS

boolean

false

quarkus.langchain4j.watsonx.generation-model.log-responses

Whether chat model responses should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_LOG_RESPONSES

boolean

false

quarkus.langchain4j.watsonx.generation-model.prompt-joiner

Delimiter used to concatenate the ChatMessage elements into a single string. By setting this property, you can define your preferred way of concatenating messages to ensure that the prompt is structured in the correct way.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_GENERATION_MODEL_PROMPT_JOINER

string

` `

quarkus.langchain4j.watsonx.embedding-model.model-id

The id of the model to be used.

All available models are listed in the IBM Watsonx.ai documentation at the link: following link.

To use a model, locate the API model_id column in the table and copy the corresponding model ID.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_EMBEDDING_MODEL_MODEL_ID

string

ibm/slate-125m-english-rtrvr

quarkus.langchain4j.watsonx.embedding-model.truncate-input-tokens

Represents the maximum number of input tokens accepted. This can be used to avoid requests failing due to input being longer than configured limits. If the text is truncated, then it truncates the end of the input (on the right), so the start of the input will remain the same. If this value exceeds the maximum sequence length (refer to the documentation to find this value for the model) then the call will fail if the total number of tokens exceeds the maximum sequence length.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_EMBEDDING_MODEL_TRUNCATE_INPUT_TOKENS

int

quarkus.langchain4j.watsonx.embedding-model.log-requests

Whether embedding model requests should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_EMBEDDING_MODEL_LOG_REQUESTS

boolean

false

quarkus.langchain4j.watsonx.embedding-model.log-responses

Whether embedding model responses should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_EMBEDDING_MODEL_LOG_RESPONSES

boolean

false

quarkus.langchain4j.watsonx.scoring-model.model-id

The id of the model to be used.

All available models are listed in the IBM Watsonx.ai documentation at the link: following link.

To use a model, locate the API model_id column in the table and copy the corresponding model ID.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_SCORING_MODEL_MODEL_ID

string

cross-encoder/ms-marco-minilm-l-12-v2

quarkus.langchain4j.watsonx.scoring-model.truncate-input-tokens

Represents the maximum number of input tokens accepted. This can be used to avoid requests failing due to input being longer than configured limits. If the text is truncated, then it truncates the end of the input (on the right), so the start of the input will remain the same. If this value exceeds the maximum sequence length (refer to the documentation to find this value for the model) then the call will fail if the total number of tokens exceeds the maximum sequence length.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_SCORING_MODEL_TRUNCATE_INPUT_TOKENS

int

quarkus.langchain4j.watsonx.scoring-model.log-requests

Whether embedding model requests should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_SCORING_MODEL_LOG_REQUESTS

boolean

false

quarkus.langchain4j.watsonx.scoring-model.log-responses

Whether embedding model responses should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX_SCORING_MODEL_LOG_RESPONSES

boolean

false

quarkus.langchain4j.watsonx."model-name".base-url

Base URL of the watsonx.ai API.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__BASE_URL

string

quarkus.langchain4j.watsonx."model-name".api-key

IBM Cloud API key.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__API_KEY

string

quarkus.langchain4j.watsonx."model-name".timeout

Timeout for watsonx.ai calls.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__TIMEOUT

Duration 

10s

quarkus.langchain4j.watsonx."model-name".version

The version date for the API of the form YYYY-MM-DD.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__VERSION

string

2024-03-14

quarkus.langchain4j.watsonx."model-name".space-id

The space that contains the resource.

Either space_id or project_id has to be given.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__SPACE_ID

string

quarkus.langchain4j.watsonx."model-name".project-id

The project that contains the resource.

Either space_id or project_id has to be given.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__PROJECT_ID

string

quarkus.langchain4j.watsonx."model-name".log-requests

Whether the watsonx.ai client should log requests.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__LOG_REQUESTS

boolean

false

quarkus.langchain4j.watsonx."model-name".log-responses

Whether the watsonx.ai client should log responses.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__LOG_RESPONSES

boolean

false

quarkus.langchain4j.watsonx."model-name".enable-integration

Whether to enable the integration. Defaults to true, which means requests are made to the watsonx.ai provider. Set to false to disable all requests.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__ENABLE_INTEGRATION

boolean

true

quarkus.langchain4j.watsonx."model-name".iam.base-url

Base URL of the IAM Authentication API.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__IAM_BASE_URL

URL

https://iam.cloud.ibm.com

quarkus.langchain4j.watsonx."model-name".iam.timeout

Timeout for IAM authentication calls.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__IAM_TIMEOUT

Duration 

10s

quarkus.langchain4j.watsonx."model-name".iam.grant-type

Grant type for the IAM Authentication API.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__IAM_GRANT_TYPE

string

urn:ibm:params:oauth:grant-type:apikey

quarkus.langchain4j.watsonx."model-name".chat-model.model-id

The model to use for the chat completion.

All available models are listed in the IBM Watsonx.ai documentation at the link: following link.

To use a model, locate the API model_id column in the table and copy the corresponding model ID.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_MODEL_ID

string

mistralai/mistral-large

quarkus.langchain4j.watsonx."model-name".chat-model.frequency-penalty

Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.

Possible values: -2 < value < 2

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_FREQUENCY_PENALTY

double

0

quarkus.langchain4j.watsonx."model-name".chat-model.logprobs

Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_LOGPROBS

boolean

false

quarkus.langchain4j.watsonx."model-name".chat-model.top-logprobs

An integer specifying the number of most likely tokens to return at each token position, each with an associated log probability. The option logprobs must be set to true if this parameter is used.

Possible values: 0 ≤ value ≤ 20

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_TOP_LOGPROBS

int

quarkus.langchain4j.watsonx."model-name".chat-model.max-tokens

The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model’s context length.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_MAX_TOKENS

int

1024

quarkus.langchain4j.watsonx."model-name".chat-model.n

How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_N

int

1

quarkus.langchain4j.watsonx."model-name".chat-model.presence-penalty

Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics.

Possible values: -2 < value < 2

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_PRESENCE_PENALTY

double

0

quarkus.langchain4j.watsonx."model-name".chat-model.seed

Random number generator seed to use in sampling mode for experimental repeatability.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_SEED

int

quarkus.langchain4j.watsonx."model-name".chat-model.stop

Stop sequences are one or more strings which will cause the text generation to stop if/when they are produced as part of the output. Stop sequences encountered prior to the minimum number of tokens being generated will be ignored.

Possible values: 0 ≤ number of items ≤ 4

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_STOP

list of string

quarkus.langchain4j.watsonx."model-name".chat-model.temperature

What sampling temperature to use. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

Possible values: 0 < value < 2

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_TEMPERATURE

double

${quarkus.langchain4j.temperature:1.0}

quarkus.langchain4j.watsonx."model-name".chat-model.top-p

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

Possible values: 0 < value < 1

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_TOP_P

double

1

quarkus.langchain4j.watsonx."model-name".chat-model.response-format

Specifies the desired format for the model’s output.

Allowable values: [json_object]

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_RESPONSE_FORMAT

string

quarkus.langchain4j.watsonx."model-name".chat-model.log-requests

Whether chat model requests should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_LOG_REQUESTS

boolean

false

quarkus.langchain4j.watsonx."model-name".chat-model.log-responses

Whether chat model responses should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__CHAT_MODEL_LOG_RESPONSES

boolean

false

quarkus.langchain4j.watsonx."model-name".generation-model.model-id

The id of the model to be used.

All available models are listed in the IBM Watsonx.ai documentation at the link: following link.

To use a model, locate the API model_id column in the table and copy the corresponding model ID.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_MODEL_ID

string

mistralai/mistral-large

quarkus.langchain4j.watsonx."model-name".generation-model.decoding-method

Represents the strategy used for picking the tokens during generation of the output text. During text generation when parameter value is set to greedy, each successive token corresponds to the highest probability token given the text that has already been generated. This strategy can lead to repetitive results especially for longer output sequences. The alternative sample strategy generates text by picking subsequent tokens based on the probability distribution of possible next tokens defined by (i.e., conditioned on) the already-generated text and the top_k and top_p parameters.

Allowable values: [sample,greedy]

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_DECODING_METHOD

string

greedy

quarkus.langchain4j.watsonx."model-name".generation-model.length-penalty.decay-factor

Represents the factor of exponential decay. Larger values correspond to more aggressive decay.

Possible values: > 1

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_LENGTH_PENALTY_DECAY_FACTOR

double

quarkus.langchain4j.watsonx."model-name".generation-model.length-penalty.start-index

A number of generated tokens after which this should take effect.

Possible values: ≥ 0

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_LENGTH_PENALTY_START_INDEX

int

quarkus.langchain4j.watsonx."model-name".generation-model.max-new-tokens

The maximum number of new tokens to be generated. The maximum supported value for this field depends on the model being used. How the "token" is defined depends on the tokenizer and vocabulary size, which in turn depends on the model. Often the tokens are a mix of full words and sub-words. Depending on the users plan, and on the model being used, there may be an enforced maximum number of new tokens.

Possible values: ≥ 0

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_MAX_NEW_TOKENS

int

200

quarkus.langchain4j.watsonx."model-name".generation-model.min-new-tokens

If stop sequences are given, they are ignored until minimum tokens are generated.

Possible values: ≥ 0

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_MIN_NEW_TOKENS

int

0

quarkus.langchain4j.watsonx."model-name".generation-model.random-seed

Random number generator seed to use in sampling mode for experimental repeatability.

Possible values: ≥ 1

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_RANDOM_SEED

int

quarkus.langchain4j.watsonx."model-name".generation-model.stop-sequences

Stop sequences are one or more strings which will cause the text generation to stop if/when they are produced as part of the output. Stop sequences encountered prior to the minimum number of tokens being generated will be ignored.

Possible values: 0 ≤ number of items ≤ 6

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_STOP_SEQUENCES

list of string

quarkus.langchain4j.watsonx."model-name".generation-model.temperature

A value used to modify the next-token probabilities in sampling mode. Values less than 1.0 sharpen the probability distribution, resulting in "less random" output. Values greater than 1.0 flatten the probability distribution, resulting in "more random" output. A value of 1.0 has no effect.

Possible values: 0 ≤ value ≤ 2

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_TEMPERATURE

double

${quarkus.langchain4j.temperature:1.0}

quarkus.langchain4j.watsonx."model-name".generation-model.top-k

The number of highest probability vocabulary tokens to keep for top-k-filtering. Only applies for sampling mode. When decoding_strategy is set to sample, only the top_k most likely tokens are considered as candidates for the next generated token.

Possible values: 1 ≤ value ≤ 100

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_TOP_K

int

quarkus.langchain4j.watsonx."model-name".generation-model.top-p

Similar to top_k except the candidates to generate the next token are the most likely tokens with probabilities that add up to at least top_p. Also known as nucleus sampling. A value of 1.0 is equivalent to disabled.

Possible values: 0 < value ≤ 1

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_TOP_P

double

quarkus.langchain4j.watsonx."model-name".generation-model.repetition-penalty

Represents the penalty for penalizing tokens that have already been generated or belong to the context. The value 1.0 means that there is no penalty.

Possible values: 1 ≤ value ≤ 2

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_REPETITION_PENALTY

double

quarkus.langchain4j.watsonx."model-name".generation-model.truncate-input-tokens

Represents the maximum number of input tokens accepted. This can be used to avoid requests failing due to input being longer than configured limits. If the text is truncated, then it truncates the start of the input (on the left), so the end of the input will remain the same. If this value exceeds the maximum sequence length (refer to the documentation to find this value for the model) then the call will fail if the total number of tokens exceeds the maximum sequence length. Zero means don’t truncate.

Possible values: ≥ 0

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_TRUNCATE_INPUT_TOKENS

int

quarkus.langchain4j.watsonx."model-name".generation-model.include-stop-sequence

Pass false to omit matched stop sequences from the end of the output text. The default is true, meaning that the output will end with the stop sequence text when matched.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_INCLUDE_STOP_SEQUENCE

boolean

quarkus.langchain4j.watsonx."model-name".generation-model.log-requests

Whether chat model requests should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_LOG_REQUESTS

boolean

false

quarkus.langchain4j.watsonx."model-name".generation-model.log-responses

Whether chat model responses should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_LOG_RESPONSES

boolean

false

quarkus.langchain4j.watsonx."model-name".generation-model.prompt-joiner

Delimiter used to concatenate the ChatMessage elements into a single string. By setting this property, you can define your preferred way of concatenating messages to ensure that the prompt is structured in the correct way.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__GENERATION_MODEL_PROMPT_JOINER

string

` `

quarkus.langchain4j.watsonx."model-name".embedding-model.model-id

The id of the model to be used.

All available models are listed in the IBM Watsonx.ai documentation at the link: following link.

To use a model, locate the API model_id column in the table and copy the corresponding model ID.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__EMBEDDING_MODEL_MODEL_ID

string

ibm/slate-125m-english-rtrvr

quarkus.langchain4j.watsonx."model-name".embedding-model.truncate-input-tokens

Represents the maximum number of input tokens accepted. This can be used to avoid requests failing due to input being longer than configured limits. If the text is truncated, then it truncates the end of the input (on the right), so the start of the input will remain the same. If this value exceeds the maximum sequence length (refer to the documentation to find this value for the model) then the call will fail if the total number of tokens exceeds the maximum sequence length.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__EMBEDDING_MODEL_TRUNCATE_INPUT_TOKENS

int

quarkus.langchain4j.watsonx."model-name".embedding-model.log-requests

Whether embedding model requests should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__EMBEDDING_MODEL_LOG_REQUESTS

boolean

false

quarkus.langchain4j.watsonx."model-name".embedding-model.log-responses

Whether embedding model responses should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__EMBEDDING_MODEL_LOG_RESPONSES

boolean

false

quarkus.langchain4j.watsonx."model-name".scoring-model.model-id

The id of the model to be used.

All available models are listed in the IBM Watsonx.ai documentation at the link: following link.

To use a model, locate the API model_id column in the table and copy the corresponding model ID.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__SCORING_MODEL_MODEL_ID

string

cross-encoder/ms-marco-minilm-l-12-v2

quarkus.langchain4j.watsonx."model-name".scoring-model.truncate-input-tokens

Represents the maximum number of input tokens accepted. This can be used to avoid requests failing due to input being longer than configured limits. If the text is truncated, then it truncates the end of the input (on the right), so the start of the input will remain the same. If this value exceeds the maximum sequence length (refer to the documentation to find this value for the model) then the call will fail if the total number of tokens exceeds the maximum sequence length.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__SCORING_MODEL_TRUNCATE_INPUT_TOKENS

int

quarkus.langchain4j.watsonx."model-name".scoring-model.log-requests

Whether embedding model requests should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__SCORING_MODEL_LOG_REQUESTS

boolean

false

quarkus.langchain4j.watsonx."model-name".scoring-model.log-responses

Whether embedding model responses should be logged.

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__SCORING_MODEL_LOG_RESPONSES

boolean

false

Named model config

Type

Default

quarkus.langchain4j.watsonx."model-name".mode

Specifies the mode of interaction with the LLM model.

This property allows you to choose between two modes of operation:

  • chat: prompts are automatically enriched with the specific tags defined by the model

  • generation: prompts require manual specification of tags

Allowable values: [chat, generation]

Environment variable: QUARKUS_LANGCHAIN4J_WATSONX__MODEL_NAME__MODE

string

chat
About the Duration format

To write duration values, use the standard java.time.Duration format. See the Duration#parse() Java API documentation for more information.

You can also use a simplified format, starting with a number:

  • If the value is only a number, it represents time in seconds.

  • If the value is a number followed by ms, it represents time in milliseconds.

In other cases, the simplified format is translated to the java.time.Duration format for parsing:

  • If the value is a number followed by h, m, or s, it is prefixed with PT.

  • If the value is a number followed by d, it is prefixed with P.