Whether the model should be enabled

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_CHAT_MODEL_ENABLED

Whether the model should be enabled

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_EMBEDDING_MODEL_ENABLED

Base URL where the Ollama serving is running

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_BASE_URL

Timeout for Ollama calls

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_TIMEOUT

Whether the Ollama client should log requests

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_LOG_REQUESTS

Whether the Ollama client should log responses

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_LOG_RESPONSES

Whether or not to enable the integration. Defaults to true, which means requests are made to the OpenAI provider. Set to false to disable all requests.

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_ENABLE_INTEGRATION

Model to use. According to Ollama docs, the default value is llama2

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_CHAT_MODEL_MODEL_ID

The temperature of the model. Increasing the temperature will make the model answer with more variability. A lower temperature will make the model answer more conservatively.

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_CHAT_MODEL_TEMPERATURE

Maximum number of tokens to predict when generating text

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_CHAT_MODEL_NUM_PREDICT

Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_CHAT_MODEL_STOP

Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_CHAT_MODEL_TOP_P

Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_CHAT_MODEL_TOP_K

With a static number the result is always the same. With a random number the result varies Example: Random random = new Random(); int x = random.nextInt(Integer.MAX_VALUE);

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_CHAT_MODEL_SEED

Model to use. According to Ollama docs, the default value is nomic-embed-text

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_EMBEDDING_MODEL_MODEL_ID

The temperature of the model. Increasing the temperature will make the model answer with more variability. A lower temperature will make the model answer more conservatively.

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_EMBEDDING_MODEL_TEMPERATURE

Maximum number of tokens to predict when generating text

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_EMBEDDING_MODEL_NUM_PREDICT

Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_EMBEDDING_MODEL_STOP

Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_EMBEDDING_MODEL_TOP_P

Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA_EMBEDDING_MODEL_TOP_K

Base URL where the Ollama serving is running

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__BASE_URL

Timeout for Ollama calls

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__TIMEOUT

Whether the Ollama client should log requests

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__LOG_REQUESTS

Whether the Ollama client should log responses

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__LOG_RESPONSES

Whether or not to enable the integration. Defaults to true, which means requests are made to the OpenAI provider. Set to false to disable all requests.

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__ENABLE_INTEGRATION

Model to use. According to Ollama docs, the default value is llama2

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__CHAT_MODEL_MODEL_ID

The temperature of the model. Increasing the temperature will make the model answer with more variability. A lower temperature will make the model answer more conservatively.

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__CHAT_MODEL_TEMPERATURE

Maximum number of tokens to predict when generating text

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__CHAT_MODEL_NUM_PREDICT

Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__CHAT_MODEL_STOP

Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__CHAT_MODEL_TOP_P

Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__CHAT_MODEL_TOP_K

With a static number the result is always the same. With a random number the result varies Example: Random random = new Random(); int x = random.nextInt(Integer.MAX_VALUE);

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__CHAT_MODEL_SEED

Model to use. According to Ollama docs, the default value is nomic-embed-text

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__EMBEDDING_MODEL_MODEL_ID

The temperature of the model. Increasing the temperature will make the model answer with more variability. A lower temperature will make the model answer more conservatively.

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__EMBEDDING_MODEL_TEMPERATURE

Maximum number of tokens to predict when generating text

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__EMBEDDING_MODEL_NUM_PREDICT

Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__EMBEDDING_MODEL_STOP

Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__EMBEDDING_MODEL_TOP_P

Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative

Environment variable: QUARKUS_LANGCHAIN4J_OLLAMA__MODEL_NAME__EMBEDDING_MODEL_TOP_K