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# This workflow will build a package using Maven and then publish it to GitHub packages when a release is created
# For more information see: https://github.com/actions/setup-java/blob/main/docs/advanced-usage.md#apache-maven-with-a-settings-path
name: Build on PR Create
on:
pull_request:
types: [ opened, reopened ]
branches: [ "main" ]
jobs:
build:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
steps:
- uses: actions/checkout@v3
- name: Set up JDK 11
uses: actions/setup-java@v3
with:
java-version: '11'
distribution: 'adopt-hotspot'
server-id: github # Value of the distributionManagement/repository/id field of the pom.xml
settings-path: ${{ github.workspace }} # location for the settings.xml file
- name: Build with Maven
run: mvn --file pom.xml -U clean package
- name: Run Tests
run: mvn --file pom.xml -U clean test -Punit-tests

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name: Run Tests
on:
pull_request:
# types: [opened, reopened, synchronize, edited]
branches: [ "main" ]
paths:
- 'src/**' # Run if changes occur in the 'src' folder
- 'pom.xml' # Run if changes occur in the 'pom.xml' file
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
run-tests:
runs-on: ubuntu-latest
permissions:
contents: read
steps:
- uses: actions/checkout@v3
- name: Set up JDK 11
uses: actions/setup-java@v3
with:
java-version: '11'
distribution: 'adopt-hotspot'
server-id: github # Value of the distributionManagement/repository/id field of the pom.xml
settings-path: ${{ github.workspace }} # location for the settings.xml file
- name: Build with Maven
run: mvn --file pom.xml -U clean package
- name: Run unit tests
run: mvn --file pom.xml -U clean test -Punit-tests
- name: Run integration tests
run: mvn --file pom.xml -U clean verify -Pintegration-tests
- name: Use Node.js
uses: actions/setup-node@v3
with:
node-version: '20.x'
- run: cd docs && npm ci
- run: cd docs && npm run build

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name: Release Artifacts to GitHub Maven Packages
on:
release:
types: [ created ]
jobs:
build:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
steps:
- uses: actions/checkout@v3
- name: Set up JDK 17
uses: actions/setup-java@v3
with:
java-version: '17'
distribution: 'temurin'
server-id: github
settings-path: ${{ github.workspace }}
- name: maven-settings-xml-action
uses: whelk-io/maven-settings-xml-action@v22
with:
servers: '[{ "id": "${repo.id}", "username": "${repo.user}", "password": "${repo.pass}" }]'
- name: Find and Replace
uses: jacobtomlinson/gha-find-replace@v3
with:
find: "ollama4j-revision"
replace: ${{ github.ref_name }}
regex: false
- name: Find and Replace
uses: jacobtomlinson/gha-find-replace@v3
with:
find: "mvn-repo-id"
replace: github
regex: false
- name: Import GPG key
uses: crazy-max/ghaction-import-gpg@v6
with:
gpg_private_key: ${{ secrets.GPG_PRIVATE_KEY }}
passphrase: ${{ secrets.GPG_PASSPHRASE }}
- name: List keys
run: gpg -K
- name: Build with Maven
run: mvn --file pom.xml -U clean package -Punit-tests
- name: Publish to GitHub Packages Apache Maven
run: mvn deploy -Punit-tests -s $GITHUB_WORKSPACE/settings.xml -Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }} -Drepo.id=github -Drepo.user=${{ secrets.GH_MVN_USER }} -Drepo.pass=${{ secrets.GH_MVN_PASS }} -DaltDeploymentRepository=github::default::https://maven.pkg.github.com/ollama4j/ollama4j
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

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name: Mark stale issues
on:
workflow_dispatch: # for manual run
schedule:
- cron: '0 0 * * *' # Runs every day at midnight
permissions:
contents: write # only for delete-branch option
issues: write
jobs:
stale:
runs-on: ubuntu-latest
steps:
- name: Mark stale issues
uses: actions/stale@v8
with:
repo-token: ${{ github.token }}
days-before-stale: 15
stale-issue-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs.'
days-before-close: 7
stale-issue-label: 'stale'
exempt-issue-labels: 'pinned,security'

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# This workflow will build a package using Maven and then publish it to GitHub packages when a release is created
# For more information see: https://github.com/actions/setup-java/blob/main/docs/advanced-usage.md#apache-maven-with-a-settings-path
name: Release Artifacts to Maven Central
on:
release:
types: [ created ]
name: Test and Publish Package
#on:
# pull_request:
# types: [ opened, reopened ]
# branches: [ "main" ]
# release:
# types: [ "created" ]
on:
push:
branches: [ "main" ]
workflow_dispatch:
jobs:
build:
runs-on: ubuntu-latest
permissions:
contents: write
packages: write
steps:
- uses: actions/checkout@v3
- name: Set up JDK 17
- name: Set up JDK 11
uses: actions/setup-java@v3
with:
java-version: '17'
distribution: 'temurin'
java-version: '11'
distribution: 'adopt-hotspot'
server-id: github # Value of the distributionManagement/repository/id field of the pom.xml
settings-path: ${{ github.workspace }} # location for the settings.xml file
- name: maven-settings-xml-action
uses: whelk-io/maven-settings-xml-action@v22
- name: Build with Maven
run: mvn --file pom.xml -U clean package -Punit-tests
- name: Set up Apache Maven Central (Overwrite settings.xml)
uses: actions/setup-java@v3
with: # running setup-java again overwrites the settings.xml
java-version: '11'
distribution: 'adopt-hotspot'
cache: 'maven'
server-id: ossrh
server-username: MAVEN_USERNAME
server-password: MAVEN_PASSWORD
gpg-private-key: ${{ secrets.GPG_PRIVATE_KEY }}
gpg-passphrase: MAVEN_GPG_PASSPHRASE
- name: Set up Maven cache
uses: actions/cache@v3
with:
servers: '[{ "id": "${repo.id}", "username": "${repo.user}", "password": "${repo.pass}" }]'
- name: Import GPG key
uses: crazy-max/ghaction-import-gpg@v6
with:
gpg_private_key: ${{ secrets.GPG_PRIVATE_KEY }}
passphrase: ${{ secrets.GPG_PASSPHRASE }}
- name: List keys
run: gpg -K
- name: Find and Replace
uses: jacobtomlinson/gha-find-replace@v3
with:
find: "ollama4j-revision"
replace: ${{ github.ref_name }}
regex: false
- name: Find and Replace
uses: jacobtomlinson/gha-find-replace@v3
with:
find: "mvn-repo-id"
replace: central
regex: false
- name: Publish to Maven Central
run: mvn deploy -Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }} -Drepo.id=central -Drepo.user=${{ secrets.MVN_USER }} -Drepo.pass=${{ secrets.MVN_PASS }}
- name: Upload Release Asset - JAR
uses: actions/upload-release-asset@v1
path: ~/.m2/repository
key: ${{ runner.os }}-maven-${{ hashFiles('**/pom.xml') }}
restore-keys: |
${{ runner.os }}-maven-
- name: Build
run: mvn -B -ntp clean install
- name: Publish to GitHub Packages Apache Maven
# if: >
# github.event_name != 'pull_request' &&
# github.ref_name == 'main' &&
# contains(github.event.head_commit.message, 'release')
run: |
git config --global user.email "koujalgi.amith@gmail.com"
git config --global user.name "amithkoujalgi"
mvn -B -ntp -DskipTests -Pci-cd -Darguments="-DskipTests -Pci-cd" release:clean release:prepare release:perform
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
upload_url: ${{ github.event.release.upload_url }}
asset_path: target/ollama4j-${{ github.ref_name }}.jar
asset_name: ollama4j-${{ github.ref_name }}.jar
asset_content_type: application/x-jar
- name: Upload Release Asset - Javadoc JAR
uses: actions/upload-release-asset@v1
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
upload_url: ${{ github.event.release.upload_url }}
asset_path: target/ollama4j-${{ github.ref_name }}-javadoc.jar
asset_name: ollama4j-${{ github.ref_name }}-javadoc.jar
asset_content_type: application/x-jar
- name: Upload Release Asset - Sources JAR
uses: actions/upload-release-asset@v1
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
upload_url: ${{ github.event.release.upload_url }}
asset_path: target/ollama4j-${{ github.ref_name }}-sources.jar
asset_name: ollama4j-${{ github.ref_name }}-sources.jar
asset_content_type: application/x-jar
MAVEN_USERNAME: ${{ secrets.OSSRH_USERNAME }}
MAVEN_PASSWORD: ${{ secrets.OSSRH_PASSWORD }}
MAVEN_GPG_PASSPHRASE: ${{ secrets.GPG_PASSPHRASE }}

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# Simple workflow for deploying static content to GitHub Pages
name: Publish Docs to GH Pages
name: Deploy Docs to GH Pages
on:
release:
types: [ created ]
# Runs on pushes targeting the default branch
push:
branches: [ "main" ]
# Allows you to run this workflow manually from the Actions tab
workflow_dispatch:
@ -46,29 +47,16 @@ jobs:
- run: cd docs && npm ci
- run: cd docs && npm run build
- name: Find and Replace
uses: jacobtomlinson/gha-find-replace@v3
with:
find: "ollama4j-revision"
replace: ${{ github.ref_name }}
regex: false
- name: Build with Maven
run: mvn --file pom.xml -U clean package && cp -r ./target/apidocs/. ./docs/build/apidocs
- name: Doxygen Action
uses: mattnotmitt/doxygen-action@v1.1.0
with:
doxyfile-path: "./Doxyfile"
working-directory: "."
- name: Setup Pages
uses: actions/configure-pages@v5
uses: actions/configure-pages@v3
- name: Upload artifact
uses: actions/upload-pages-artifact@v3
uses: actions/upload-pages-artifact@v2
with:
# Upload entire repository
path: './docs/build/.'
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v4
uses: actions/deploy-pages@v2

52
.github/workflows/publish-javadoc.yml vendored Normal file
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# Simple workflow for deploying static content to GitHub Pages
name: Deploy Javadoc content to Pages
on:
# Runs on pushes targeting the default branch
push:
branches: [ "none" ]
# Allows you to run this workflow manually from the Actions tab
workflow_dispatch:
# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
permissions:
contents: read
pages: write
id-token: write
packages: write
# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
concurrency:
group: "pages"
cancel-in-progress: false
jobs:
# Single deploy job since we're just deploying
deploy:
runs-on: ubuntu-latest
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
steps:
- uses: actions/checkout@v3
- name: Set up JDK 11
uses: actions/setup-java@v3
with:
java-version: '11'
distribution: 'adopt-hotspot'
server-id: github # Value of the distributionManagement/repository/id field of the pom.xml
settings-path: ${{ github.workspace }} # location for the settings.xml file
- name: Build with Maven
run: mvn --file pom.xml -U clean package
- name: Setup Pages
uses: actions/configure-pages@v3
- name: Upload artifact
uses: actions/upload-pages-artifact@v2
with:
# Upload entire repository
path: './target/apidocs/.'
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v2

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name: Run Unit and Integration Tests
on:
# push:
# branches:
# - main
workflow_dispatch:
inputs:
branch:
description: 'Branch name to run the tests on'
required: true
default: 'main'
jobs:
run-tests:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
with:
ref: ${{ github.event.inputs.branch }}
- name: Set up JDK 17
uses: actions/setup-java@v3
with:
java-version: '17'
distribution: 'temurin'
server-id: github
settings-path: ${{ github.workspace }}
- name: Run unit tests
run: mvn clean test -Punit-tests
- name: Run integration tests
run: mvn clean verify -Pintegration-tests

6
.gitignore vendored
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### Mac OS ###
.DS_Store
/.idea/
/src/main/java/io/github/amithkoujalgi/ollama4j/core/localtests/
pom.xml.*
release.properties
!.idea/icon.svg
src/main/java/io/github/ollama4j/localtests
release.properties

18
.idea/icon.svg generated
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repos:
# pre-commit hooks
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: "v5.0.0"
hooks:
- id: no-commit-to-branch
args: ['--branch', 'main']
- id: check-merge-conflict
- id: check-added-large-files
- id: check-yaml
- id: check-xml
- id: check-json
- id: pretty-format-json
args: ['--no-sort-keys', '--autofix', '--indent=4']
- id: end-of-file-fixer
exclude: \.json$
files: \.java$|\.xml$
- id: trailing-whitespace
- id: mixed-line-ending
# for commit message formatting
- repo: https://github.com/commitizen-tools/commitizen
rev: v4.4.1
hooks:
- id: commitizen
stages: [commit-msg]
# # for java code quality
# - repo: https://github.com/gherynos/pre-commit-java
# rev: v0.6.10
# hooks:
# - id: pmd
# exclude: /test/
# - id: cpd
# exclude: /test/
# - id: checkstyle
# exclude: /test/

View File

@ -1,128 +0,0 @@
# Contributor Covenant Code of Conduct
## Our Pledge
We as members, contributors, and leaders pledge to make participation in our
community a harassment-free experience for everyone, regardless of age, body
size, visible or invisible disability, ethnicity, sex characteristics, gender
identity and expression, level of experience, education, socio-economic status,
nationality, personal appearance, race, religion, or sexual identity
and orientation.
We pledge to act and interact in ways that contribute to an open, welcoming,
diverse, inclusive, and healthy community.
## Our Standards
Examples of behavior that contributes to a positive environment for our
community include:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
overall community
Examples of unacceptable behavior include:
* The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Enforcement Responsibilities
Community leaders are responsible for clarifying and enforcing our standards of
acceptable behavior and will take appropriate and fair corrective action in
response to any behavior that they deem inappropriate, threatening, offensive,
or harmful.
Community leaders have the right and responsibility to remove, edit, or reject
comments, commits, code, wiki edits, issues, and other contributions that are
not aligned to this Code of Conduct, and will communicate reasons for moderation
decisions when appropriate.
## Scope
This Code of Conduct applies within all community spaces, and also applies when
an individual is officially representing the community in public spaces.
Examples of representing our community include using an official e-mail address,
posting via an official social media account, or acting as an appointed
representative at an online or offline event.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported to the community leaders responsible for enforcement at
koujalgi.amith@gmail.com.
All complaints will be reviewed and investigated promptly and fairly.
All community leaders are obligated to respect the privacy and security of the
reporter of any incident.
## Enforcement Guidelines
Community leaders will follow these Community Impact Guidelines in determining
the consequences for any action they deem in violation of this Code of Conduct:
### 1. Correction
**Community Impact**: Use of inappropriate language or other behavior deemed
unprofessional or unwelcome in the community.
**Consequence**: A private, written warning from community leaders, providing
clarity around the nature of the violation and an explanation of why the
behavior was inappropriate. A public apology may be requested.
### 2. Warning
**Community Impact**: A violation through a single incident or series
of actions.
**Consequence**: A warning with consequences for continued behavior. No
interaction with the people involved, including unsolicited interaction with
those enforcing the Code of Conduct, for a specified period of time. This
includes avoiding interactions in community spaces as well as external channels
like social media. Violating these terms may lead to a temporary or
permanent ban.
### 3. Temporary Ban
**Community Impact**: A serious violation of community standards, including
sustained inappropriate behavior.
**Consequence**: A temporary ban from any sort of interaction or public
communication with the community for a specified period of time. No public or
private interaction with the people involved, including unsolicited interaction
with those enforcing the Code of Conduct, is allowed during this period.
Violating these terms may lead to a permanent ban.
### 4. Permanent Ban
**Community Impact**: Demonstrating a pattern of violation of community
standards, including sustained inappropriate behavior, harassment of an
individual, or aggression toward or disparagement of classes of individuals.
**Consequence**: A permanent ban from any sort of public interaction within
the community.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
version 2.0, available at
https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
Community Impact Guidelines were inspired by [Mozilla's code of conduct
enforcement ladder](https://github.com/mozilla/diversity).
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see the FAQ at
https://www.contributor-covenant.org/faq. Translations are available at
https://www.contributor-covenant.org/translations.

413
Doxyfile
View File

@ -1,413 +0,0 @@
# Doxyfile 1.10.0
#---------------------------------------------------------------------------
# Project related configuration options
#---------------------------------------------------------------------------
DOXYFILE_ENCODING = UTF-8
PROJECT_NAME = "Ollama4j"
PROJECT_NUMBER =
PROJECT_BRIEF = "A Java library (wrapper/binding) for Ollama server."
PROJECT_LOGO = ./logo-small.png
PROJECT_ICON = ./logo-small.png
OUTPUT_DIRECTORY = ./docs/build/doxygen
CREATE_SUBDIRS = NO
CREATE_SUBDIRS_LEVEL = 8
ALLOW_UNICODE_NAMES = NO
OUTPUT_LANGUAGE = English
BRIEF_MEMBER_DESC = YES
REPEAT_BRIEF = YES
ABBREVIATE_BRIEF = "The $name class" \
"The $name widget" \
"The $name file" \
is \
provides \
specifies \
contains \
represents \
a \
an \
the
ALWAYS_DETAILED_SEC = NO
INLINE_INHERITED_MEMB = NO
FULL_PATH_NAMES = YES
STRIP_FROM_PATH =
STRIP_FROM_INC_PATH =
SHORT_NAMES = NO
JAVADOC_AUTOBRIEF = NO
JAVADOC_BANNER = NO
QT_AUTOBRIEF = NO
MULTILINE_CPP_IS_BRIEF = NO
PYTHON_DOCSTRING = YES
INHERIT_DOCS = YES
SEPARATE_MEMBER_PAGES = NO
TAB_SIZE = 4
ALIASES =
OPTIMIZE_OUTPUT_FOR_C = NO
OPTIMIZE_OUTPUT_JAVA = YES
OPTIMIZE_FOR_FORTRAN = NO
OPTIMIZE_OUTPUT_VHDL = NO
OPTIMIZE_OUTPUT_SLICE = NO
EXTENSION_MAPPING =
MARKDOWN_SUPPORT = YES
TOC_INCLUDE_HEADINGS = 5
MARKDOWN_ID_STYLE = DOXYGEN
AUTOLINK_SUPPORT = YES
BUILTIN_STL_SUPPORT = NO
CPP_CLI_SUPPORT = NO
SIP_SUPPORT = NO
IDL_PROPERTY_SUPPORT = YES
DISTRIBUTE_GROUP_DOC = NO
GROUP_NESTED_COMPOUNDS = NO
SUBGROUPING = YES
INLINE_GROUPED_CLASSES = NO
INLINE_SIMPLE_STRUCTS = NO
TYPEDEF_HIDES_STRUCT = NO
LOOKUP_CACHE_SIZE = 0
NUM_PROC_THREADS = 1
TIMESTAMP = NO
#---------------------------------------------------------------------------
# Build related configuration options
#---------------------------------------------------------------------------
EXTRACT_ALL = YES
EXTRACT_PRIVATE = NO
EXTRACT_PRIV_VIRTUAL = NO
EXTRACT_PACKAGE = NO
EXTRACT_STATIC = NO
EXTRACT_LOCAL_CLASSES = YES
EXTRACT_LOCAL_METHODS = NO
EXTRACT_ANON_NSPACES = NO
RESOLVE_UNNAMED_PARAMS = YES
HIDE_UNDOC_MEMBERS = NO
HIDE_UNDOC_CLASSES = NO
HIDE_FRIEND_COMPOUNDS = NO
HIDE_IN_BODY_DOCS = NO
INTERNAL_DOCS = NO
CASE_SENSE_NAMES = SYSTEM
HIDE_SCOPE_NAMES = NO
HIDE_COMPOUND_REFERENCE= NO
SHOW_HEADERFILE = YES
SHOW_INCLUDE_FILES = YES
SHOW_GROUPED_MEMB_INC = NO
FORCE_LOCAL_INCLUDES = NO
INLINE_INFO = YES
SORT_MEMBER_DOCS = YES
SORT_BRIEF_DOCS = NO
SORT_MEMBERS_CTORS_1ST = NO
SORT_GROUP_NAMES = NO
SORT_BY_SCOPE_NAME = NO
STRICT_PROTO_MATCHING = NO
GENERATE_TODOLIST = YES
GENERATE_TESTLIST = YES
GENERATE_BUGLIST = YES
GENERATE_DEPRECATEDLIST= YES
ENABLED_SECTIONS =
MAX_INITIALIZER_LINES = 30
SHOW_USED_FILES = YES
SHOW_FILES = YES
SHOW_NAMESPACES = YES
FILE_VERSION_FILTER =
LAYOUT_FILE =
CITE_BIB_FILES =
#---------------------------------------------------------------------------
# Configuration options related to warning and progress messages
#---------------------------------------------------------------------------
QUIET = NO
WARNINGS = YES
WARN_IF_UNDOCUMENTED = YES
WARN_IF_DOC_ERROR = YES
WARN_IF_INCOMPLETE_DOC = YES
WARN_NO_PARAMDOC = NO
WARN_IF_UNDOC_ENUM_VAL = NO
WARN_AS_ERROR = NO
WARN_FORMAT = "$file:$line: $text"
WARN_LINE_FORMAT = "at line $line of file $file"
WARN_LOGFILE =
#---------------------------------------------------------------------------
# Configuration options related to the input files
#---------------------------------------------------------------------------
INPUT = ./src/main
INPUT_ENCODING = UTF-8
INPUT_FILE_ENCODING =
FILE_PATTERNS = *.c \
*.cc \
*.cxx \
*.cxxm \
*.cpp \
*.cppm \
*.ccm \
*.c++ \
*.c++m \
*.java \
*.ii \
*.ixx \
*.ipp \
*.i++ \
*.inl \
*.idl \
*.ddl \
*.odl \
*.h \
*.hh \
*.hxx \
*.hpp \
*.h++ \
*.ixx \
*.l \
*.cs \
*.d \
*.php \
*.php4 \
*.php5 \
*.phtml \
*.inc \
*.m \
*.markdown \
*.md \
*.mm \
*.dox \
*.py \
*.pyw \
*.f90 \
*.f95 \
*.f03 \
*.f08 \
*.f18 \
*.f \
*.for \
*.vhd \
*.vhdl \
*.ucf \
*.qsf \
*.ice
RECURSIVE = YES
EXCLUDE =
EXCLUDE_SYMLINKS = NO
EXCLUDE_PATTERNS =
EXCLUDE_SYMBOLS =
EXAMPLE_PATH =
EXAMPLE_PATTERNS = *
EXAMPLE_RECURSIVE = NO
IMAGE_PATH =
INPUT_FILTER =
FILTER_PATTERNS =
FILTER_SOURCE_FILES = NO
FILTER_SOURCE_PATTERNS =
USE_MDFILE_AS_MAINPAGE =
FORTRAN_COMMENT_AFTER = 72
#---------------------------------------------------------------------------
# Configuration options related to source browsing
#---------------------------------------------------------------------------
SOURCE_BROWSER = YES
INLINE_SOURCES = NO
STRIP_CODE_COMMENTS = YES
REFERENCED_BY_RELATION = NO
REFERENCES_RELATION = NO
REFERENCES_LINK_SOURCE = YES
SOURCE_TOOLTIPS = YES
USE_HTAGS = NO
VERBATIM_HEADERS = YES
CLANG_ASSISTED_PARSING = NO
CLANG_ADD_INC_PATHS = YES
CLANG_OPTIONS =
CLANG_DATABASE_PATH =
#---------------------------------------------------------------------------
# Configuration options related to the alphabetical class index
#---------------------------------------------------------------------------
ALPHABETICAL_INDEX = YES
IGNORE_PREFIX =
#---------------------------------------------------------------------------
# Configuration options related to the HTML output
#---------------------------------------------------------------------------
GENERATE_HTML = YES
HTML_OUTPUT = html
HTML_FILE_EXTENSION = .html
HTML_HEADER =
HTML_FOOTER =
HTML_STYLESHEET =
HTML_EXTRA_STYLESHEET =
HTML_EXTRA_FILES =
HTML_COLORSTYLE = LIGHT
HTML_COLORSTYLE_HUE = 220
HTML_COLORSTYLE_SAT = 100
HTML_COLORSTYLE_GAMMA = 80
HTML_DYNAMIC_MENUS = YES
HTML_DYNAMIC_SECTIONS = NO
HTML_CODE_FOLDING = YES
HTML_COPY_CLIPBOARD = YES
HTML_PROJECT_COOKIE =
HTML_INDEX_NUM_ENTRIES = 100
GENERATE_DOCSET = NO
DOCSET_FEEDNAME = "Doxygen generated docs"
DOCSET_FEEDURL =
DOCSET_BUNDLE_ID = org.doxygen.Project
DOCSET_PUBLISHER_ID = org.doxygen.Publisher
DOCSET_PUBLISHER_NAME = Publisher
GENERATE_HTMLHELP = NO
CHM_FILE =
HHC_LOCATION =
GENERATE_CHI = NO
CHM_INDEX_ENCODING =
BINARY_TOC = NO
TOC_EXPAND = NO
SITEMAP_URL =
GENERATE_QHP = NO
QCH_FILE =
QHP_NAMESPACE = org.doxygen.Project
QHP_VIRTUAL_FOLDER = doc
QHP_CUST_FILTER_NAME =
QHP_CUST_FILTER_ATTRS =
QHP_SECT_FILTER_ATTRS =
QHG_LOCATION =
GENERATE_ECLIPSEHELP = NO
ECLIPSE_DOC_ID = org.doxygen.Project
DISABLE_INDEX = NO
GENERATE_TREEVIEW = YES
FULL_SIDEBAR = NO
ENUM_VALUES_PER_LINE = 4
TREEVIEW_WIDTH = 250
EXT_LINKS_IN_WINDOW = NO
OBFUSCATE_EMAILS = YES
HTML_FORMULA_FORMAT = png
FORMULA_FONTSIZE = 10
FORMULA_MACROFILE =
USE_MATHJAX = NO
MATHJAX_VERSION = MathJax_2
MATHJAX_FORMAT = HTML-CSS
MATHJAX_RELPATH =
MATHJAX_EXTENSIONS =
MATHJAX_CODEFILE =
SEARCHENGINE = YES
SERVER_BASED_SEARCH = NO
EXTERNAL_SEARCH = NO
SEARCHENGINE_URL =
SEARCHDATA_FILE = searchdata.xml
EXTERNAL_SEARCH_ID =
EXTRA_SEARCH_MAPPINGS =
#---------------------------------------------------------------------------
# Configuration options related to the LaTeX output
#---------------------------------------------------------------------------
GENERATE_LATEX = YES
LATEX_OUTPUT = latex
LATEX_CMD_NAME =
MAKEINDEX_CMD_NAME = makeindex
LATEX_MAKEINDEX_CMD = makeindex
COMPACT_LATEX = NO
PAPER_TYPE = a4
EXTRA_PACKAGES =
LATEX_HEADER =
LATEX_FOOTER =
LATEX_EXTRA_STYLESHEET =
LATEX_EXTRA_FILES =
PDF_HYPERLINKS = YES
USE_PDFLATEX = YES
LATEX_BATCHMODE = NO
LATEX_HIDE_INDICES = NO
LATEX_BIB_STYLE = plain
LATEX_EMOJI_DIRECTORY =
#---------------------------------------------------------------------------
# Configuration options related to the RTF output
#---------------------------------------------------------------------------
GENERATE_RTF = NO
RTF_OUTPUT = rtf
COMPACT_RTF = NO
RTF_HYPERLINKS = NO
RTF_STYLESHEET_FILE =
RTF_EXTENSIONS_FILE =
#---------------------------------------------------------------------------
# Configuration options related to the man page output
#---------------------------------------------------------------------------
GENERATE_MAN = NO
MAN_OUTPUT = man
MAN_EXTENSION = .3
MAN_SUBDIR =
MAN_LINKS = NO
#---------------------------------------------------------------------------
# Configuration options related to the XML output
#---------------------------------------------------------------------------
GENERATE_XML = NO
XML_OUTPUT = xml
XML_PROGRAMLISTING = YES
XML_NS_MEMB_FILE_SCOPE = NO
#---------------------------------------------------------------------------
# Configuration options related to the DOCBOOK output
#---------------------------------------------------------------------------
GENERATE_DOCBOOK = NO
DOCBOOK_OUTPUT = docbook
#---------------------------------------------------------------------------
# Configuration options for the AutoGen Definitions output
#---------------------------------------------------------------------------
GENERATE_AUTOGEN_DEF = NO
#---------------------------------------------------------------------------
# Configuration options related to Sqlite3 output
#---------------------------------------------------------------------------
GENERATE_SQLITE3 = NO
SQLITE3_OUTPUT = sqlite3
SQLITE3_RECREATE_DB = YES
#---------------------------------------------------------------------------
# Configuration options related to the Perl module output
#---------------------------------------------------------------------------
GENERATE_PERLMOD = NO
PERLMOD_LATEX = NO
PERLMOD_PRETTY = YES
PERLMOD_MAKEVAR_PREFIX =
#---------------------------------------------------------------------------
# Configuration options related to the preprocessor
#---------------------------------------------------------------------------
ENABLE_PREPROCESSING = YES
MACRO_EXPANSION = NO
EXPAND_ONLY_PREDEF = NO
SEARCH_INCLUDES = YES
INCLUDE_PATH =
INCLUDE_FILE_PATTERNS =
PREDEFINED =
EXPAND_AS_DEFINED =
SKIP_FUNCTION_MACROS = YES
#---------------------------------------------------------------------------
# Configuration options related to external references
#---------------------------------------------------------------------------
TAGFILES =
GENERATE_TAGFILE =
ALLEXTERNALS = NO
EXTERNAL_GROUPS = YES
EXTERNAL_PAGES = YES
#---------------------------------------------------------------------------
# Configuration options related to diagram generator tools
#---------------------------------------------------------------------------
HIDE_UNDOC_RELATIONS = YES
HAVE_DOT = NO
DOT_NUM_THREADS = 0
DOT_COMMON_ATTR = "fontname=Helvetica,fontsize=10"
DOT_EDGE_ATTR = "labelfontname=Helvetica,labelfontsize=10"
DOT_NODE_ATTR = "shape=box,height=0.2,width=0.4"
DOT_FONTPATH =
CLASS_GRAPH = YES
COLLABORATION_GRAPH = YES
GROUP_GRAPHS = YES
UML_LOOK = NO
UML_LIMIT_NUM_FIELDS = 10
DOT_UML_DETAILS = NO
DOT_WRAP_THRESHOLD = 17
TEMPLATE_RELATIONS = NO
INCLUDE_GRAPH = YES
INCLUDED_BY_GRAPH = YES
CALL_GRAPH = NO
CALLER_GRAPH = NO
GRAPHICAL_HIERARCHY = YES
DIRECTORY_GRAPH = YES
DIR_GRAPH_MAX_DEPTH = 1
DOT_IMAGE_FORMAT = png
INTERACTIVE_SVG = NO
DOT_PATH =
DOTFILE_DIRS =
DIA_PATH =
DIAFILE_DIRS =
PLANTUML_JAR_PATH =
PLANTUML_CFG_FILE =
PLANTUML_INCLUDE_PATH =
DOT_GRAPH_MAX_NODES = 50
MAX_DOT_GRAPH_DEPTH = 0
DOT_MULTI_TARGETS = NO
GENERATE_LEGEND = YES
DOT_CLEANUP = YES
MSCGEN_TOOL =
MSCFILE_DIRS =

View File

@ -1,42 +1,13 @@
dev:
@echo "Setting up dev environment..."
@command -v pre-commit >/dev/null 2>&1 || { echo "Error: pre-commit is not installed. Please install it first."; exit 1; }
@command -v docker >/dev/null 2>&1 || { echo "Error: docker is not installed. Please install it first."; exit 1; }
pre-commit install
pre-commit autoupdate
pre-commit install --install-hooks
build:
mvn -B clean install -Dgpg.skip=true
full-build:
mvn -B clean install
unit-tests:
ut:
mvn clean test -Punit-tests
integration-tests:
export USE_EXTERNAL_OLLAMA_HOST=false && mvn clean verify -Pintegration-tests
integration-tests-remote:
export USE_EXTERNAL_OLLAMA_HOST=true && export OLLAMA_HOST=http://192.168.29.223:11434 && mvn clean verify -Pintegration-tests -Dgpg.skip=true
doxygen:
doxygen Doxyfile
it:
mvn clean verify -Pintegration-tests
list-releases:
curl 'https://central.sonatype.com/api/internal/browse/component/versions?sortField=normalizedVersion&sortDirection=desc&page=0&size=20&filter=namespace%3Aio.github.ollama4j%2Cname%3Aollama4j' \
curl 'https://central.sonatype.com/api/internal/browse/component/versions?sortField=normalizedVersion&sortDirection=asc&page=0&size=12&filter=namespace%3Aio.github.amithkoujalgi%2Cname%3Aollama4j' \
--compressed \
--silent | jq -r '.components[].version'
docs-build:
npm i --prefix docs && npm run build --prefix docs
docs-serve:
npm i --prefix docs && npm run start --prefix docs
start-cpu:
docker run -it -v ~/ollama:/root/.ollama -p 11434:11434 ollama/ollama
start-gpu:
docker run -it --gpus=all -v ~/ollama:/root/.ollama -p 11434:11434 ollama/ollama
--silent | jq '.components[].version'

336
README.md
View File

@ -1,60 +1,9 @@
### Ollama4j
<p align="center">
<img src='https://raw.githubusercontent.com/ollama4j/ollama4j/65a9d526150da8fcd98e2af6a164f055572bf722/ollama4j.jpeg' width='100' alt="ollama4j-icon">
</p>
<img src='https://raw.githubusercontent.com/amithkoujalgi/ollama4j/65a9d526150da8fcd98e2af6a164f055572bf722/ollama4j.jpeg' width='100' alt="ollama4j-icon">
<div align="center">
A Java library (wrapper/binding) for Ollama server.
Find more details on the [website](https://ollama4j.github.io/ollama4j/).
![GitHub stars](https://img.shields.io/github/stars/ollama4j/ollama4j)
![GitHub forks](https://img.shields.io/github/forks/ollama4j/ollama4j)
![GitHub watchers](https://img.shields.io/github/watchers/ollama4j/ollama4j)
![Contributors](https://img.shields.io/github/contributors/ollama4j/ollama4j?style=social)
![GitHub License](https://img.shields.io/github/license/ollama4j/ollama4j)
[//]: # (![GitHub repo size]&#40;https://img.shields.io/github/repo-size/ollama4j/ollama4j&#41;)
[//]: # (![GitHub top language]&#40;https://img.shields.io/github/languages/top/ollama4j/ollama4j&#41;)
[//]: # (![JitPack Downloads This Month Badge]&#40;https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fjitpack.io%2Fapi%2Fdownloads%2Fio.github.ollama4j%2Follama4j&query=%24.month&label=JitPack%20Downloads%20-%20This%20Month&#41;)
[//]: # (![JitPack Downloads This Week Badge]&#40;https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fjitpack.io%2Fapi%2Fdownloads%2Fio.github.ollama4j%2Follama4j&query=%24.week&label=JitPack%20Downloads%20-%20This%20Week&#41;)
[//]: # (![JitPack Downloads Per Month Badge]&#40;https://jitpack.io/v/ollama4j/ollama4j/month.svg&#41;)
[//]: # (![GitHub Downloads &#40;all assets, all releases&#41;]&#40;https://img.shields.io/github/downloads/ollama4j/ollama4j/total?label=GitHub%20Package%20Downloads&#41;)
![GitHub last commit](https://img.shields.io/github/last-commit/ollama4j/ollama4j?color=green)
[![codecov](https://codecov.io/gh/ollama4j/ollama4j/graph/badge.svg?token=U0TE7BGP8L)](https://codecov.io/gh/ollama4j/ollama4j)
[![Run Unit and Integration Tests](https://github.com/ollama4j/ollama4j/actions/workflows/run-tests.yml/badge.svg)](https://github.com/ollama4j/ollama4j/actions/workflows/run-tests.yml)
![Build Status](https://github.com/ollama4j/ollama4j/actions/workflows/maven-publish.yml/badge.svg)
</div>
[//]: # (![Hits]&#40;https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Follama4j%2Follama4j&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false&#41;)
[//]: # (![GitHub language count]&#40;https://img.shields.io/github/languages/count/ollama4j/ollama4j&#41;)
## Table of Contents
- [How does it work?](#how-does-it-work)
- [Requirements](#requirements)
- [Installation](#installation)
- [API Spec](https://ollama4j.github.io/ollama4j/category/apis---model-management)
- [Examples](#examples)
- [Javadoc](https://ollama4j.github.io/ollama4j/apidocs/)
- [Development](#development)
- [Contributions](#get-involved)
- [References](#references)
#### How does it work?
A Java library (wrapper/binding)
for [Ollama](https://github.com/jmorganca/ollama/blob/main/docs/api.md) APIs.
```mermaid
flowchart LR
@ -68,271 +17,122 @@ Find more details on the [website](https://ollama4j.github.io/ollama4j/).
end
```
![Build Status](https://github.com/amithkoujalgi/ollama4j/actions/workflows/maven-publish.yml/badge.svg)
![GitHub stars](https://img.shields.io/github/stars/amithkoujalgi/ollama4j?style=social)
![GitHub forks](https://img.shields.io/github/forks/amithkoujalgi/ollama4j?style=social)
![GitHub watchers](https://img.shields.io/github/watchers/amithkoujalgi/ollama4j?style=social)
![GitHub repo size](https://img.shields.io/github/repo-size/amithkoujalgi/ollama4j?style=plastic)
![GitHub language count](https://img.shields.io/github/languages/count/amithkoujalgi/ollama4j?style=plastic)
![GitHub top language](https://img.shields.io/github/languages/top/amithkoujalgi/ollama4j?style=plastic)
![GitHub last commit](https://img.shields.io/github/last-commit/amithkoujalgi/ollama4j?color=red&style=plastic)
![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Famithkoujalgi%2Follama4j&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)
## Table of Contents
- [Requirements](#requirements)
- [Installation](#installation)
- [API Spec](#api-spec)
- [Demo APIs](#try-out-the-apis-with-ollama-server)
- [Development](#development)
- [Contributions](#get-involved)
#### Requirements
![Java](https://img.shields.io/badge/Java-11_+-green.svg?style=for-the-badge&labelColor=gray&label=Java&color=orange)
![Java](https://img.shields.io/badge/Java-11_+-green.svg?style=for-the-badge&labelColor=gray)
[![][ollama-shield]][ollama] Or [![][ollama-docker-shield]][ollama-docker]
<a href="https://ollama.com/" target="_blank">
<img src="https://img.shields.io/badge/v0.3.0-green.svg?style=for-the-badge&labelColor=gray&label=Ollama&color=blue" alt=""/>
</a>
[ollama]: https://ollama.ai/
## Installation
[ollama-shield]: https://img.shields.io/badge/Ollama-Local_Installation-blue.svg?style=for-the-badge&labelColor=gray
> [!NOTE]
> We are now publishing the artifacts to both Maven Central and GitHub package repositories.
>
> Track the releases [here](https://github.com/ollama4j/ollama4j/releases) and update the dependency version
> according to your requirements.
[ollama-docker]: https://hub.docker.com/r/ollama/ollama
### For Maven
[ollama-docker-shield]: https://img.shields.io/badge/Ollama-Docker-blue.svg?style=for-the-badge&labelColor=gray
#### Using [Maven Central](https://central.sonatype.com/)
[![][ollama4j-mvn-releases-shield]][ollama4j-mvn-releases-link]
[ollama4j-mvn-releases-link]: https://central.sonatype.com/artifact/io.github.ollama4j/ollama4j/overview
[ollama4j-mvn-releases-shield]: https://img.shields.io/maven-central/v/io.github.ollama4j/ollama4j?display_name=release&style=for-the-badge&label=From%20Maven%20Central
#### Installation
In your Maven project, add this dependency:
```xml
<dependency>
<groupId>io.github.ollama4j</groupId>
<groupId>io.github.amithkoujalgi</groupId>
<artifactId>ollama4j</artifactId>
<version>1.0.100</version>
<version>1.0.20</version>
</dependency>
```
#### Using GitHub's Maven Package Repository
Latest release: ![Maven Central](https://img.shields.io/maven-central/v/io.github.amithkoujalgi/ollama4j)
[![][ollama4j-releases-shield]][ollama4j-releases-link]
[![][lib-shield]][lib]
[ollama4j-releases-link]: https://github.com/ollama4j/ollama4j/releases
[lib]: https://central.sonatype.com/artifact/io.github.amithkoujalgi/ollama4j
[ollama4j-releases-shield]: https://img.shields.io/github/v/release/ollama4j/ollama4j?display_name=release&style=for-the-badge&label=From%20GitHub%20Packages
1. Add `GitHub Maven Packages` repository to your project's `pom.xml` or your `settings.xml`:
```xml
<repositories>
<repository>
<id>github</id>
<name>GitHub Apache Maven Packages</name>
<url>https://maven.pkg.github.com/ollama4j/ollama4j</url>
<releases>
<enabled>true</enabled>
</releases>
<snapshots>
<enabled>true</enabled>
</snapshots>
</repository>
</repositories>
```
2. Add `GitHub` server to settings.xml. (Usually available at ~/.m2/settings.xml)
```xml
<settings xmlns="http://maven.apache.org/SETTINGS/1.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0
http://maven.apache.org/xsd/settings-1.0.0.xsd">
<servers>
<server>
<id>github</id>
<username>YOUR-USERNAME</username>
<password>YOUR-TOKEN</password>
</server>
</servers>
</settings>
```
3. In your Maven project, add this dependency:
```xml
<dependency>
<groupId>io.github.ollama4j</groupId>
<artifactId>ollama4j</artifactId>
<version>1.0.100</version>
</dependency>
```
### For Gradle
1. Add the dependency
```groovy
dependencies {
implementation 'io.github.ollama4j:ollama4j:1.0.100'
}
```
[//]: # (Latest release:)
[//]: # ()
[//]: # (![Maven Central]&#40;https://img.shields.io/maven-central/v/io.github.ollama4j/ollama4j&#41;)
[//]: # ()
[//]: # ([![][lib-shield]][lib])
[lib]: https://central.sonatype.com/artifact/io.github.ollama4j/ollama4j
[lib-shield]: https://img.shields.io/badge/ollama4j-get_latest_version-blue.svg?style=just-the-message&labelColor=gray
[lib-shield]: https://img.shields.io/badge/ollama4j-get_latest_version-blue.svg?style=for-the-badge&labelColor=gray
#### API Spec
> [!TIP]
> Find the full API specifications on the [website](https://ollama4j.github.io/ollama4j/).
Find the full `Javadoc` (API specifications) [here](https://amithkoujalgi.github.io/ollama4j/).
### Development
#### Development
Make sure you have `pre-commit` installed.
With `brew`:
```shell
brew install pre-commit
```
With `pip`:
```shell
pip install pre-commit
```
#### Setup dev environment
> **Note**
> If you're on Windows, install [Chocolatey Package Manager for Windows](https://chocolatey.org/install) and then install `make` by running `choco install make`. Just a little tip - run the command with administrator privileges if installation faiils.
```shell
make dev
```
#### Build
Build:
```shell
make build
```
#### Run unit tests
Run unit tests:
```shell
make unit-tests
make ut
```
#### Run integration tests
Make sure you have Docker running as this uses [testcontainers](https://testcontainers.com/) to run the integration
tests on Ollama Docker container.
Run integration tests:
```shell
make integration-tests
make it
```
### Releases
#### Releases
Newer artifacts are published via GitHub Actions CI workflow when a new release is created from `main` branch.
Releases (newer artifact versions) are done automatically on pushing the code to the `main` branch through GitHub
Actions CI workflow.
## Examples
#### Traction
The `ollama4j-examples` repository contains examples for using the Ollama4j library. You can explore
it [here](https://github.com/ollama4j/ollama4j-examples).
[![Star History Chart](https://api.star-history.com/svg?repos=amithkoujalgi/ollama4j&type=Date)](https://star-history.com/#amithkoujalgi/ollama4j&Date)
## ⭐ Give us a Star!
### Areas of improvement
If you like or are using this project to build your own, please give us a star. It's a free way to show your support.
## Who's using Ollama4j?
| # | Project Name | Description | Link |
|----|-------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | Datafaker | A library to generate fake data | [GitHub](https://github.com/datafaker-net/datafaker-experimental/tree/main/ollama-api) |
| 2 | Vaadin Web UI | UI-Tester for interactions with Ollama via ollama4j | [GitHub](https://github.com/TEAMPB/ollama4j-vaadin-ui) |
| 3 | ollama-translator | A Minecraft 1.20.6 Spigot plugin that translates all messages into a specific target language via Ollama | [GitHub](https://github.com/liebki/ollama-translator) |
| 4 | AI Player | A Minecraft mod that adds an intelligent "second player" to the game | [Website](https://modrinth.com/mod/ai-player), [GitHub](https://github.com/shasankp000/AI-Player), <br/> [Reddit Thread](https://www.reddit.com/r/fabricmc/comments/1e65x5s/comment/ldr2vcf/) |
| 5 | Ollama4j Web UI | A web UI for Ollama written in Java using Spring Boot, Vaadin, and Ollama4j | [GitHub](https://github.com/ollama4j/ollama4j-web-ui) |
| 6 | JnsCLI | A command-line tool for Jenkins that manages jobs, builds, and configurations, with AI-powered error analysis | [GitHub](https://github.com/mirum8/jnscli) |
| 7 | Katie Backend | An open-source AI-based question-answering platform for accessing private domain knowledge | [GitHub](https://github.com/wyona/katie-backend) |
| 8 | TeleLlama3 Bot | A question-answering Telegram bot | [Repo](https://git.hiast.edu.sy/mohamadbashar.disoki/telellama3-bot) |
| 9 | moqui-wechat | A moqui-wechat component | [GitHub](https://github.com/heguangyong/moqui-wechat) |
| 10 | B4X | A set of simple and powerful RAD tool for Desktop and Server development | [Website](https://www.b4x.com/android/forum/threads/ollama4j-library-pnd_ollama4j-your-local-offline-llm-like-chatgpt.165003/) |
| 11 | Research Article | Article: `Large language model based mutations in genetic improvement` - published on National Library of Medicine (National Center for Biotechnology Information) | [Website](https://pmc.ncbi.nlm.nih.gov/articles/PMC11750896/) |
## Traction
[![Star History Chart](https://api.star-history.com/svg?repos=ollama4j/ollama4j&type=Date)](https://star-history.com/#ollama4j/ollama4j&Date)
## Get Involved
<div align="center">
<a href="">![Open Issues](https://img.shields.io/github/issues-raw/ollama4j/ollama4j)</a>
<a href="">![Closed Issues](https://img.shields.io/github/issues-closed-raw/ollama4j/ollama4j)</a>
<a href="">![Open PRs](https://img.shields.io/github/issues-pr-raw/ollama4j/ollama4j)</a>
<a href="">![Closed PRs](https://img.shields.io/github/issues-pr-closed-raw/ollama4j/ollama4j)</a>
<a href="">![Discussions](https://img.shields.io/github/discussions/ollama4j/ollama4j)</a>
</div>
[//]: # (![GitHub Issues or Pull Requests]&#40;https://img.shields.io/github/issues-raw/ollama4j/ollama4j&#41;)
[//]: # (![GitHub Issues or Pull Requests]&#40;https://img.shields.io/github/issues-closed-raw/ollama4j/ollama4j&#41;)
[//]: # (![GitHub Issues or Pull Requests]&#40;https://img.shields.io/github/issues-pr-raw/ollama4j/ollama4j&#41;)
[//]: # (![GitHub Issues or Pull Requests]&#40;https://img.shields.io/github/issues-pr-closed-raw/ollama4j/ollama4j&#41;)
[//]: # (![GitHub Discussions]&#40;https://img.shields.io/github/discussions/ollama4j/ollama4j&#41;)
- [x] Use Java-naming conventions for attributes in the request/response models instead of the
snake-case conventions. (
possibly with Jackson-mapper's `@JsonProperty`)
- [x] Fix deprecated HTTP client code
- [x] Setup logging
- [x] Use lombok
- [x] Update request body creation with Java objects
- [ ] Async APIs for images
- [ ] Add additional params for `ask` APIs such as:
- `options`: additional model parameters for the Modelfile such as `temperature`
- `system`: system prompt to (overrides what is defined in the Modelfile)
- `template`: the full prompt or prompt template (overrides what is defined in the Modelfile)
- `context`: the context parameter returned from a previous request, which can be used to keep a
short
conversational memory
- `stream`: Add support for streaming responses from the model
- [ ] Add test cases
- [ ] Handle exceptions better (maybe throw more appropriate exceptions)
### Get Involved
Contributions are most welcome! Whether it's reporting a bug, proposing an enhancement, or helping
with code - any sort
of contribution is much appreciated.
## 🏷️ License and Citation
The code is available under [MIT License](./LICENSE).
If you find this project helpful in your research, please cite this work at
```
@misc{ollama4j2024,
author = {Amith Koujalgi},
title = {Ollama4j: A Java Library (Wrapper/Binding) for Ollama Server},
year = {2024},
month = {January},
url = {https://github.com/ollama4j/ollama4j}
}
```
### References
- [Ollama REST APIs](https://github.com/jmorganca/ollama/blob/main/docs/api.md)
### Credits
The nomenclature and the icon have been adopted from the incredible [Ollama](https://ollama.ai/)
project.
**Thanks to the amazing contributors**
<p align="center">
<a href="https://github.com/ollama4j/ollama4j/graphs/contributors">
<img src="https://contrib.rocks/image?repo=ollama4j/ollama4j" alt=""/>
</a>
</p>
### Appreciate the work?
<p align="center">
<a href="https://www.buymeacoffee.com/amithkoujalgi" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
</p>

View File

@ -0,0 +1,9 @@
---
slug: welcome
title: Welcome
authors: [ amith ]
tags: [ Java, AI, LLM, GenAI, GenerativeAI, Ollama, Ollama4J, OpenSource, Developers
]
---
Welcome Java Developers!

View File

@ -1,6 +1,6 @@
---
slug: release-post
title: First Release 🚀
title: Release
authors: [ amith ]
tags: [ Java, AI, LLM, GenAI, GenerativeAI, Ollama, Ollama4j, OpenSource, Developers
]
@ -11,7 +11,7 @@ Hey there, my fellow Java Developers! 🚀
I am glad to announce the release of Ollama4j, a library that unites Ollama (an LLM manager and runner) and your Java
applications! 🌐🚀
👉 GitHub Repository: Ollama4j on GitHub (https://github.com/ollama4j/ollama4j)
👉 GitHub Repository: Ollama4j on GitHub (https://github.com/amithkoujalgi/ollama4j)
🌟 Key Features:
@ -58,9 +58,9 @@ elevate your projects.
I look forward to seeing the incredible applications/projects you'll build with Ollama4j! 🌟
Find the full API spec here: https://ollama4j.github.io/ollama4j/
Find the full API spec here: https://amithkoujalgi.github.io/ollama4j/
Find the Javadoc here: https://ollama4j.github.io/ollama4j/apidocs/
Find the Javadoc here: https://amithkoujalgi.github.io/ollama4j/apidocs/
Ollama4j Docs is powered by [Docusaurus](https://docusaurus.io).

View File

@ -1,709 +0,0 @@
---
slug: talk-to-your-data-on-couchbase-via-ollama4j
title: "Talk to Your Data Using Natural Language: A Guide to Interacting with Couchbase via Ollama4j"
authors: [ amith ]
tags: [ Java, AI, LLM, GenAI, GenerativeAI, Generative AI Tools, Ollama, Ollama4J, OpenSource, Developers,
]
---
Sometime back, I created a small wrapper called Ollama4j to interact with the Ollama server over the REST API in Java as
a side project and made the [repository](https://github.com/ollama4j/ollama4j) public on GitHub. Over time, the project
gained traction, with many fellow Java
developers contributing, and it now boasts over _300 stars_! 😍
Weve consistently introduced new features, and when we added the tool-calling capability, the library became incredibly
powerful, opening up so many possibilities. With this addition, we could automate numerous tasks using natural language!
I wanted to share how to make the most of this functionality.
In this article, well explore how to use Ollama4j, a Java SDK for interacting with Ollama-hosted models, to leverage
tool-calling models like Mistral for querying a Couchbase database. The goal is to create a system where you can query
your database using natural, conversational language — just like interacting with a virtual assistant. Well walk you
through the code, explain the key components, and show you how to set up your environment to ensure everything runs
smoothly.
### Overview of the Technologies Involved
Before diving into the implementation, lets understand the core technologies were using:
- **Ollama4j**: A Java SDK that interacts with hosted AI models through a convenient API. Ollama allows you to interact
with
pre-trained models (like Mistral) and access additional tools that can be applied to real-world tasks.
- **Mistral**: A powerful, language-based model that can be used for a variety of tasks, including answering questions,
text
generation, and data retrieval from external sources. While Ive used Mistral in this instance, you can easily replace
it with [any other model](https://ollama.com/search?c=tools) that supports tool-calling capabilities.
- **Couchbase**: A NoSQL database that provides a flexible and scalable data model. In this example, well query a
Couchbase
database to retrieve airline information.
The magic happens when we combine these technologies to allow the model to query the database in a more intuitive and
human-like way, acting as an interface between the users natural language and Couchbases structured data.
> Oh, by the way, you can either set up
> a [Couchbase server](https://www.couchbase.com/downloads/?family=couchbase-server) on your own or, if you prefer a
> more
> effortless approach like
> I
> do, give [Couchbase Capella](https://www.couchbase.com/products/capella/) a spin. Its a fully managed
> Database-as-a-Service (DBaaS) with a free tier 🎉 thats so
> easy
> to set up, youll be querying your data in no time. Its perfect for developers who want to dive in without any
> hassle —
> its like having your own cloud database, minus the headache!
In the following section, we will walk you through the simple steps to create your free Couchbase Capella database
cluster. If youd prefer to set up your own Couchbase server elsewhere, feel free to skip this section and go directly
to the [Code Environment Setup](#setting-up-the-environment-for-code) section.
Sign up for a free database cluster on Couchbase Capella
Head over to https://cloud.couchbase.com/sign-in and sign up for an account.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*vsJC0ugfoh9vpYNapt4-5A.png'} />
Once youre in, you will be able to create a new database cluster. Click on the _**Operational**_ tab and click on the
**_Create Cluster_** button.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*ZNicgmYNkclgaBIxwRN7Ug.png'} />
Select the default project named **_My First Project_** and click on the **_Continue_** button.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*vfc2cF7IgkjLtNXvls8giQ.png'} />
Youll now see the available cluster options. Go ahead and select the **_Free_** option! 😍
Next, choose your preferred cloud provider (you can select any provider or stick with the default AWS provider).
Pick a region (or leave it set to the default).
Finally, click on the Create Cluster button to proceed.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*rdWpeSrUaBKC6Y5q8Kd6EA.png'} />
Give it a couple of minutes, and let the magic happen as your cluster gets deployed.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*no3uHx8cIzVBn7qccYEZ3A.png'} />
Once your cluster is deployed, youll see the status of your cluster as **_Healthy_**.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*Jyu9uiSDSE0o-EQRb53CJA.png'} />
Click on the listed cluster to open its details. Here, you can view the version of the deployed Couchbase server, the
enabled services, as well as the cloud provider and region.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*Sv-7wQuAoD0l0bjbI5I7Aw.png'} />
Click on **_Explore Data_** button. Notice that a default bucket called **_travel-sample_** with some sample data has
been created
for you.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*z85GsgMBvdR2mrvKUrIjJg.png'} />
Browse through the collection to explore the pre-created buckets, scopes and collections available to you.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*Qr84bs1dvn6m9ZjkNxXvUg.png'} />
Open up a sample document from the **_travel-sample_** (bucket) > **_inventory_** (scope) > **_airline_** (collection)
to see the contents
of the document.
The document shown in the image below is about an airline named **_Astraeus_**, whose call sign (a unique name or code
used to
identify an airline or aircraft in communication) is **_FLYSTAR_**.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*AmvixYfdNNKC6nXNNXbe4Q.png'} />
Navigate to the **_Connect_** tab, and you will see a **_Public Connection String_** that allows you to access the
Capella cluster
endpoint from your client application, which looks like the following URL:
```
couchbases://cb.uniqueclusteridentifer.cloud.couchbase.com
```
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*jwnVdj5ZOQMHoggj9JZeJQ.png'} />
To access this cluster endpoint, you need to allow the IP addresses that are permitted to connect. Click on the
**_Settings_**
tab, which will take you to the **_Cluster Settings_** view. Then, click on **_Allowed IP Addresses_** in the left pane
under
**_Networking_**, where you can add allowed IP addresses. Then, click on the **_Add Allowed IP_** button.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*tS83AJaNzlBa4Q3aadxohw.png'} />
You can either click on the **_Add Current IP Address_** button to limit access to your cluster to your IP address
alone, or
if youd like to allow access from anywhere, click on the **_Allow Access from Anywhere_** button.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*XBgqQoXQQJyYg51Ztugw6w.png'} />
Confirm that you want to allow the IP addresses.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*WjfYQQaiT2WqwNnWvUCyww.png'} />
The IP addresses have now been added to the allow list, and the networking is set up.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*5BHIp2rqUf7E_GNX8TENoA.png'} />
Now that youve allowed IP addresses, its time to create credentials for accessing the cluster using a username and
password. Click on the **_Cluster Access_** tab in the left pane, then click on the **_Create Cluster Access_** button.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*Q5l_EE3gGtxiANdkKilVTQ.png'} />
Enter a username of your choice in the **_Cluster Access Name_** text field, and then enter a password of your choice in
the
**_Password_** text field.
Next, select the bucket, scope, and the read/write permissions you want these credentials to have access to. In this
example, Ive granted access to all buckets and scopes with both read and write permissions.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*j2DRB1oDWE78SKpcsIb2SA.png'} />
Alright, your cluster access is now set up.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*8TY-5DPDfQlwz0-2IYR8Sg.png'} />
One last step: you just need to select the **_Cluster Access Credentials_** that you want to allow to connect to your
Capella
cluster. Head over to the **_Connect_** tab, then click on the **_SDKs_** tab in the left pane. Under Choose the
**_Cluster Access Credentials you want to use to connect to your Capella cluster_**, select the cluster credentials you
just created.
<img src={'https://miro.medium.com/v2/resize:fit:1400/format:webp/1*sIlH51v2HllTzBDV8K-9Aw.png'} />
Awesome! Your cluster access is all set up, and youre ready to connect to your Capella cluster using a Couchbase
client. Thats it — youre all set and good to go!
### Setting Up the Environment For Code
Before you begin, ensure you have the following components setup.
**Java**: Make sure you have Java 11+ installed on your system. Set it up
from [here](https://www.oracle.com/in/java/technologies/downloads/). Verify it by running the following
command in your terminal.
```shell
java --version
```
**Maven**: Make sure you have the Maven build system set up. Set it up from [here](https://maven.apache.org/download.cgi).
Verify it by running the following command
in your terminal.
```
mvn --version
```
**Ollama Server**: Make sure you have installed the latest version of [Ollama server](https://ollama.com/) and it is up
and running. Verify it by
running the following command in your terminal.
```shell
ollama --version
```
**Model**: Youll need [tool-calling model](https://ollama.com/search?c=tools) (such as Mistral) downloaded and ready to
serve from your Ollama server.
To download/pull the model into your Ollama server, run the following command in your terminal.
```shell
ollama pull mistral
```
You can list the models available on your model server by running the following command in your terminal.
```shell
ollama list
```
Once you have these, you can start setting up the application.
Setup `pom.xml` for your Maven project.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>io.github.ollama4j.couchbase</groupId>
<artifactId>ollama4j-couchbase</artifactId>
<version>0.0.1</version>
<name>Ollama4j Couchbase</name>
<description>Talk to your data in Couchbase over Ollama4j</description>
<packaging>jar</packaging>
<properties>
<maven.compiler.release>11</maven.compiler.release>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<lombok.version>1.18.30</lombok.version>
</properties>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-source-plugin</artifactId>
<version>3.3.1</version>
<executions>
<execution>
<id>attach-sources</id>
<goals>
<goal>jar-no-fork</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-javadoc-plugin</artifactId>
<version>3.11.2</version>
<configuration>
<!-- to disable the "missing" warnings. Remove the doclint to enable warnings-->
<doclint>all,-missing</doclint>
</configuration>
<executions>
<execution>
<id>attach-javadocs</id>
<goals>
<goal>jar</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
<pluginManagement>
<plugins>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.14.0</version>
</plugin>
</plugins>
</pluginManagement>
</build>
<dependencies>
<dependency>
<groupId>io.github.ollama4j</groupId>
<artifactId>ollama4j</artifactId>
<version>ollama4j-revision</version>
</dependency>
<!-- SLF4J API -->
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>2.0.0</version>
</dependency>
<!-- Logback Classic (SLF4J binding) -->
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>
<version>1.4.12</version>
</dependency>
<dependency>
<groupId>com.couchbase.client</groupId>
<artifactId>java-client</artifactId>
<version>3.7.8</version>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>1.18.30</version>
<scope>provided</scope>
</dependency>
</dependencies>
</project>
```
### Code Walkthrough
Heres the main part of the implementation in the Java code.
```java
package io.github.ollama4j.examples;
import com.couchbase.client.java.Bucket;
import com.couchbase.client.java.Cluster;
import com.couchbase.client.java.ClusterOptions;
import com.couchbase.client.java.Scope;
import com.couchbase.client.java.json.JsonObject;
import com.couchbase.client.java.query.QueryResult;
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.exceptions.OllamaBaseException;
import io.github.ollama4j.exceptions.ToolInvocationException;
import io.github.ollama4j.tools.OllamaToolsResult;
import io.github.ollama4j.tools.ToolFunction;
import io.github.ollama4j.tools.Tools;
import io.github.ollama4j.utils.OptionsBuilder;
import io.github.ollama4j.utils.Utilities;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import java.io.IOException;
import java.time.Duration;
import java.util.Arrays;
import java.util.Map;
public class CouchbaseToolCallingExample {
public static void main(String[] args) throws IOException, ToolInvocationException, OllamaBaseException, InterruptedException {
String connectionString = Utilities.getFromEnvVar("CB_CLUSTER_URL");
String username = Utilities.getFromEnvVar("CB_CLUSTER_USERNAME");
String password = Utilities.getFromEnvVar("CB_CLUSTER_PASSWORD");
String bucketName = "travel-sample";
Cluster cluster = Cluster.connect(
connectionString,
ClusterOptions.clusterOptions(username, password).environment(env -> {
env.applyProfile("wan-development");
})
);
String host = Utilities.getFromConfig("host");
String modelName = Utilities.getFromConfig("tools_model_mistral");
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.setVerbose(false);
ollamaAPI.setRequestTimeoutSeconds(60);
Tools.ToolSpecification callSignFinderToolSpec = getCallSignFinderToolSpec(cluster, bucketName);
Tools.ToolSpecification callSignUpdaterToolSpec = getCallSignUpdaterToolSpec(cluster, bucketName);
ollamaAPI.registerTool(callSignFinderToolSpec);
ollamaAPI.registerTool(callSignUpdaterToolSpec);
String prompt1 = "What is the call-sign of Astraeus?";
for (OllamaToolsResult.ToolResult r : ollamaAPI.generateWithTools(modelName, new Tools.PromptBuilder()
.withToolSpecification(callSignFinderToolSpec)
.withPrompt(prompt1)
.build(), new OptionsBuilder().build()).getToolResults()) {
AirlineDetail airlineDetail = (AirlineDetail) r.getResult();
System.out.println(String.format("[Result of tool '%s']: Call-sign of %s is '%s'! ✈️", r.getFunctionName(), airlineDetail.getName(), airlineDetail.getCallsign()));
}
String prompt2 = "I want to code name Astraeus as STARBOUND";
for (OllamaToolsResult.ToolResult r : ollamaAPI.generateWithTools(modelName, new Tools.PromptBuilder()
.withToolSpecification(callSignUpdaterToolSpec)
.withPrompt(prompt2)
.build(), new OptionsBuilder().build()).getToolResults()) {
Boolean updated = (Boolean) r.getResult();
System.out.println(String.format("[Result of tool '%s']: Call-sign is %s! ✈️", r.getFunctionName(), updated ? "updated" : "not updated"));
}
String prompt3 = "What is the call-sign of Astraeus?";
for (OllamaToolsResult.ToolResult r : ollamaAPI.generateWithTools(modelName, new Tools.PromptBuilder()
.withToolSpecification(callSignFinderToolSpec)
.withPrompt(prompt3)
.build(), new OptionsBuilder().build()).getToolResults()) {
AirlineDetail airlineDetail = (AirlineDetail) r.getResult();
System.out.println(String.format("[Result of tool '%s']: Call-sign of %s is '%s'! ✈️", r.getFunctionName(), airlineDetail.getName(), airlineDetail.getCallsign()));
}
}
public static Tools.ToolSpecification getCallSignFinderToolSpec(Cluster cluster, String bucketName) {
return Tools.ToolSpecification.builder()
.functionName("airline-lookup")
.functionDescription("You are a tool who finds only the airline name and do not worry about any other parameters. You simply find the airline name and ignore the rest of the parameters. Do not validate airline names as I want to use fake/fictitious airline names as well.")
.toolFunction(new AirlineCallsignQueryToolFunction(bucketName, cluster))
.toolPrompt(
Tools.PromptFuncDefinition.builder()
.type("prompt")
.function(
Tools.PromptFuncDefinition.PromptFuncSpec.builder()
.name("get-airline-name")
.description("Get the airline name")
.parameters(
Tools.PromptFuncDefinition.Parameters.builder()
.type("object")
.properties(
Map.of(
"airlineName", Tools.PromptFuncDefinition.Property.builder()
.type("string")
.description("The name of the airline. e.g. Emirates")
.required(true)
.build()
)
)
.required(java.util.List.of("airline-name"))
.build()
)
.build()
)
.build()
)
.build();
}
public static Tools.ToolSpecification getCallSignUpdaterToolSpec(Cluster cluster, String bucketName) {
return Tools.ToolSpecification.builder()
.functionName("airline-update")
.functionDescription("You are a tool who finds the airline name and its callsign and do not worry about any validations. You simply find the airline name and its callsign. Do not validate airline names as I want to use fake/fictitious airline names as well.")
.toolFunction(new AirlineCallsignUpdateToolFunction(bucketName, cluster))
.toolPrompt(
Tools.PromptFuncDefinition.builder()
.type("prompt")
.function(
Tools.PromptFuncDefinition.PromptFuncSpec.builder()
.name("get-airline-name-and-callsign")
.description("Get the airline name and callsign")
.parameters(
Tools.PromptFuncDefinition.Parameters.builder()
.type("object")
.properties(
Map.of(
"airlineName", Tools.PromptFuncDefinition.Property.builder()
.type("string")
.description("The name of the airline. e.g. Emirates")
.required(true)
.build(),
"airlineCallsign", Tools.PromptFuncDefinition.Property.builder()
.type("string")
.description("The callsign of the airline. e.g. Maverick")
.enumValues(Arrays.asList("petrol", "diesel"))
.required(true)
.build()
)
)
.required(java.util.List.of("airlineName", "airlineCallsign"))
.build()
)
.build()
)
.build()
)
.build();
}
}
class AirlineCallsignQueryToolFunction implements ToolFunction {
private final String bucketName;
private final Cluster cluster;
public AirlineCallsignQueryToolFunction(String bucketName, Cluster cluster) {
this.bucketName = bucketName;
this.cluster = cluster;
}
@Override
public AirlineDetail apply(Map<String, Object> arguments) {
String airlineName = arguments.get("airlineName").toString();
Bucket bucket = cluster.bucket(bucketName);
bucket.waitUntilReady(Duration.ofSeconds(10));
Scope inventoryScope = bucket.scope("inventory");
QueryResult result = inventoryScope.query(String.format("SELECT * FROM airline WHERE name = '%s';", airlineName));
JsonObject row = (JsonObject) result.rowsAsObject().get(0).get("airline");
return new AirlineDetail(row.getString("callsign"), row.getString("name"), row.getString("country"));
}
}
class AirlineCallsignUpdateToolFunction implements ToolFunction {
private final String bucketName;
private final Cluster cluster;
public AirlineCallsignUpdateToolFunction(String bucketName, Cluster cluster) {
this.bucketName = bucketName;
this.cluster = cluster;
}
@Override
public Boolean apply(Map<String, Object> arguments) {
String airlineName = arguments.get("airlineName").toString();
String airlineNewCallsign = arguments.get("airlineCallsign").toString();
Bucket bucket = cluster.bucket(bucketName);
bucket.waitUntilReady(Duration.ofSeconds(10));
Scope inventoryScope = bucket.scope("inventory");
String query = String.format("SELECT * FROM airline WHERE name = '%s';", airlineName);
QueryResult result;
try {
result = inventoryScope.query(query);
} catch (Exception e) {
throw new RuntimeException("Error executing query", e);
}
if (result.rowsAsObject().isEmpty()) {
throw new RuntimeException("Airline not found with name: " + airlineName);
}
JsonObject row = (JsonObject) result.rowsAsObject().get(0).get("airline");
if (row == null) {
throw new RuntimeException("Airline data is missing or corrupted.");
}
String currentCallsign = row.getString("callsign");
if (!airlineNewCallsign.equals(currentCallsign)) {
JsonObject updateQuery = JsonObject.create()
.put("callsign", airlineNewCallsign);
inventoryScope.query(String.format(
"UPDATE airline SET callsign = '%s' WHERE name = '%s';",
airlineNewCallsign, airlineName
));
return true;
}
return false;
}
}
@SuppressWarnings("ALL")
@Data
@AllArgsConstructor
@NoArgsConstructor
class AirlineDetail {
private String callsign;
private String name;
private String country;
}
```
### Key Concepts
#### 1. Ollama API Client Setup
```javascript
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.setRequestTimeoutSeconds(60);
```
Here, we initialize the Ollama API client and configure it with the host of the Ollama server, where the model is hosted
and can handle API requests. Additionally, we set the request timeout to 60 seconds to ensure that even if the model
takes longer to respond, the request will still be processed.
#### 2. Tool Specification
The ToolSpecification class defines how the model will interact with the Couchbase database. We define a function that
queries the database for airline details based on the airline name.
```javascript
Tools.ToolSpecification callSignFinderToolSpec = getCallSignFinderToolSpec(cluster, bucketName);
ollamaAPI.registerTool(callSignFinderToolSpec);
```
This step registers custom tools with Ollama that allows the tool-calling model to invoke database queries.
#### 3. Query Execution
The tool will execute a Couchbase N1QL query to retrieve the airline details:
```javascript
QueryResult result = inventoryScope.query(String.format("SELECT * FROM airline WHERE name = '%s';", airlineName));
```
The result is processed and returned as an AirlineDetail object.
#### 4. Set up your prompt (question)
```javascript
String prompt = "What is the call-sign of Astraeus?";
```
#### 5. Generating Results with Tools
```javascript
for (OllamaToolsResult.ToolResult r : ollamaAPI.generateWithTools(modelName, new Tools.PromptBuilder()
.withToolSpecification(callSignFinderToolSpec)
.withPrompt(prompt)
.build(), new OptionsBuilder().build()).getToolResults()) {
AirlineDetail airlineDetail = (AirlineDetail) r.getResult();
System.out.printf("[Result of tool '%s']: Call-sign of %s is '%s'! ✈️", r.getFunctionName(), airlineDetail.getName(), airlineDetail.getCallsign());
}
```
This invokes the tool-calling model (Mistral in this case) with the provided prompt and uses the registered tool to
query the database. The result is returned and printed to the console.
So, we ask the following question to the model.
> **What is the call-sign of Astraeus?**
And, heres what the model responds:
> **Call-sign of Astraeus is FLYSTAR! ✈️**
Isnt that amazing? Now, lets enhance it further by adding a function that allows us to update an airlines call sign
using natural language.
Lets define another `ToolSpecificationclass` that defines how the model will interact with the Couchbase database to
update the database. We define a function that queries the database for airline details based on the airline name and
then update the airlines callsign.
```javascript
Tools.ToolSpecification callSignUpdaterToolSpec = getCallSignUpdaterToolSpec(cluster, bucketName);
ollamaAPI.registerTool(callSignUpdaterToolSpec);
```
The tool will execute a Couchbase N1QL query to update the airlines callsign.
```javascript
inventoryScope.query(String.format(
"UPDATE airline SET callsign = '%s' WHERE name = '%s';",
airlineNewCallsign, airlineName
));
```
Setup the prompt to instruct the model to update the airlines callsign.
```javascript
String prompt = "I want to code name Astraeus as STARBOUND";
```
And then we invoke the model with the new prompt.
```javascript
String prompt = "I want to code name Astraeus as STARBOUND";
for (OllamaToolsResult.ToolResult r : ollamaAPI.generateWithTools(modelName, new Tools.PromptBuilder()
.withToolSpecification(callSignUpdaterToolSpec)
.withPrompt(prompt)
.build(), new OptionsBuilder().build()).getToolResults()) {
Boolean updated = (Boolean) r.getResult();
System.out.println(String.format("[Result of tool '%s']: Call-sign is %s! ✈️", r.getFunctionName(), updated ? "updated" : "not updated"));
}
```
This invokes the tool-calling model (Mistral in this case) with the new prompt and uses the registered tool to update
the database.
So, we ask the following question to the model.
> **I want to code name Astraeus as STARBOUND.**
And, heres what the model responds:
> **Call-sign is updated! ✈️**
How amazing is that? The possibilities for interacting with your data using natural language are endless. You could
integrate features like checking flight availability, booking tickets, retrieving ticket details, and so much more!
Feel free to extend this example further by adding more sophisticated capabilities! 🚀
### Conclusion
With the code above, you can use Ollamas hosted models (like Mistral) to query a Couchbase database using natural
language prompts. This makes it possible to interact with databases in a more intuitive and human-like way.
By leveraging Ollama4j, you can connect AI models to real-world applications and build powerful tools that can automate
complex tasks or simply make querying your data more conversational.
You can find the full code and more such examples from
the [ollama4j-examples](https://github.com/ollama4j/ollama4j-examples) GitHub repository.
Credit to Couchbase, Ollama, and all the model teams for providing us with such amazing software!

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@ -0,0 +1,8 @@
{
"label": "APIs - Ask",
"position": 10,
"link": {
"type": "generated-index",
"description": "Specs of APIs and their usage."
}
}

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@ -0,0 +1,42 @@
---
sidebar_position: 2
---
# Ask - Async
This API lets you ask questions to the LLMs in a asynchronous way.
These APIs correlate to
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) APIs.
```java
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
String prompt = "Who are you?";
OllamaAsyncResultCallback callback = ollamaAPI.askAsync(OllamaModelType.LLAMA2, prompt);
while (!callback.isComplete() || !callback.getStream().isEmpty()) {
// poll for data from the response stream
String result = callback.getStream().poll();
if (response != null) {
System.out.print(result.getResponse());
}
Thread.sleep(100);
}
}
}
```
You will get a response similar to:
> I am LLaMA, an AI assistant developed by Meta AI that can understand and respond to human input in a conversational
> manner. I am trained on a massive dataset of text from the internet and can generate human-like responses to a wide
> range of topics and questions. I can be used to create chatbots, virtual assistants, and other applications that
> require
> natural language understanding and generation capabilities.

View File

@ -2,32 +2,43 @@
sidebar_position: 3
---
import CodeEmbed from '@site/src/components/CodeEmbed';
# Generate with Image Files
# Ask - With Image Files
This API lets you ask questions along with the image files to the LLMs.
This API corresponds to
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) API.
These APIs correlate to
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) APIs.
:::note
:::caution
Executing this on Ollama server running in CPU-mode will take longer to generate response. Hence, GPU-mode is
recommended.
:::
## Synchronous mode
## Ask (Sync)
If you have this image downloaded and you pass the path to the downloaded image to the following code:
![Img](https://t3.ftcdn.net/jpg/02/96/63/80/360_F_296638053_0gUVA4WVBKceGsIr7LNqRWSnkusi07dq.jpg)
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateWithImageFile.java" />
```java
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.setRequestTimeoutSeconds(10);
OllamaResult result = ollamaAPI.askWithImageFiles(OllamaModelType.LLAVA,
"What's in this image?",
List.of(
new File("/path/to/image")));
System.out.println(result.getResponse());
}
}
```
You will get a response similar to:
::::tip[LLM Response]
> This image features a white boat with brown cushions, where a dog is sitting on the back of the boat. The dog seems to
> be enjoying its time outdoors, perhaps on a lake.
::::
> be enjoying its time outdoors, perhaps on a lake.

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@ -0,0 +1,44 @@
---
sidebar_position: 4
---
# Ask - With Image URLs
This API lets you ask questions along with the image files to the LLMs.
These APIs correlate to
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) APIs.
:::caution
Executing this on Ollama server running in CPU-mode will take longer to generate response. Hence, GPU-mode is
recommended.
:::
## Ask (Sync)
Passing the link of this image the following code:
![Img](https://t3.ftcdn.net/jpg/02/96/63/80/360_F_296638053_0gUVA4WVBKceGsIr7LNqRWSnkusi07dq.jpg)
```java
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.setRequestTimeoutSeconds(10);
OllamaResult result = ollamaAPI.askWithImageURLs(OllamaModelType.LLAVA,
"What's in this image?",
List.of(
"https://t3.ftcdn.net/jpg/02/96/63/80/360_F_296638053_0gUVA4WVBKceGsIr7LNqRWSnkusi07dq.jpg"));
System.out.println(result.getResponse());
}
}
```
You will get a response similar to:
> This image features a white boat with brown cushions, where a dog is sitting on the back of the boat. The dog seems to
> be enjoying its time outdoors, perhaps on a lake.

106
docs/docs/apis-ask/ask.md Normal file
View File

@ -0,0 +1,106 @@
---
sidebar_position: 1
---
# Ask - Sync
This API lets you ask questions to the LLMs in a synchronous way.
These APIs correlate to
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) APIs.
## Try asking a question about the model.
```java
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
OllamaResult result = ollamaAPI.ask(OllamaModelType.LLAMA2, "Who are you?");
System.out.println(result.getResponse());
}
}
```
You will get a response similar to:
> I am LLaMA, an AI assistant developed by Meta AI that can understand and respond to human input in a conversational
> manner. I am trained on a massive dataset of text from the internet and can generate human-like responses to a wide
> range of topics and questions. I can be used to create chatbots, virtual assistants, and other applications that
> require
> natural language understanding and generation capabilities.
## Try asking a question from general topics.
```java
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
String prompt = "List all cricket world cup teams of 2019.";
OllamaResult result = ollamaAPI.ask(OllamaModelType.LLAMA2, prompt);
System.out.println(result.getResponse());
}
}
```
You'd then get a response from the model:
> The 2019 ICC Cricket World Cup was held in England and Wales from May 30 to July 14, 2019. The
> following teams
> participated in the tournament:
>
> 1. Afghanistan
> 2. Australia
> 3. Bangladesh
> 4. England
> 5. India
> 6. New Zealand
> 7. Pakistan
> 8. South Africa
> 9. Sri Lanka
> 10. West Indies
>
> These teams competed in a round-robin format, with the top four teams advancing to the
> semi-finals. The tournament was
> won by the England cricket team, who defeated New Zealand in the final.
## Try asking for a Database query for your data schema.
```java
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
String prompt = SamplePrompts.getSampleDatabasePromptWithQuestion(
"List all customer names who have bought one or more products");
OllamaResult result = ollamaAPI.ask(OllamaModelType.SQLCODER, prompt);
System.out.println(result.getResponse());
}
}
```
_Note: Here I've used
a [sample prompt](https://github.com/amithkoujalgi/ollama4j/blob/main/src/main/resources/sample-db-prompt-template.txt)
containing a database schema from within this library for demonstration purposes._
You'd then get a response from the model:
```sql
SELECT customers.name
FROM sales
JOIN customers ON sales.customer_id = customers.customer_id
GROUP BY customers.name;
```

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@ -0,0 +1,46 @@
---
sidebar_position: 5
---
# Generate Embeddings
Generate embeddings from a model.
Parameters:
- `model`: name of model to generate embeddings from
- `prompt`: text to generate embeddings for
```java
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
List<Double> embeddings = ollamaAPI.generateEmbeddings(OllamaModelType.LLAMA2,
"Here is an article about llamas...");
embeddings.forEach(System.out::println);
}
}
```
You will get a response similar to:
```json
[
0.5670403838157654,
0.009260174818336964,
0.23178744316101074,
-0.2916173040866852,
-0.8924556970596313,
0.8785552978515625,
-0.34576427936553955,
0.5742510557174683,
-0.04222835972905159,
-0.137906014919281
]
```

View File

@ -1,8 +0,0 @@
{
"label": "APIs - Extras",
"position": 4,
"link": {
"type": "generated-index",
"description": "Details of APIs to handle bunch of extra stuff."
}
}

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@ -1,26 +0,0 @@
---
sidebar_position: 2
---
# Set Basic Authentication
This API lets you set the basic authentication for the Ollama client. This would help in scenarios where
Ollama server would be setup behind a gateway/reverse proxy with basic auth.
After configuring basic authentication, all subsequent requests will include the Basic Auth header.
```java
import io.github.ollama4j.OllamaAPI;
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.setBasicAuth("username", "password");
}
}
```

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@ -1,26 +0,0 @@
---
sidebar_position: 2
---
# Set Bearer Authentication
This API lets you set the bearer authentication for the Ollama client. This would help in scenarios where
Ollama server would be setup behind a gateway/reverse proxy with bearer auth.
After configuring bearer authentication, all subsequent requests will include the Bearer Auth header.
```java
import io.github.ollama4j.OllamaAPI;
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.setBearerAuth("YOUR-TOKEN");
}
}
```

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@ -1,79 +0,0 @@
---
sidebar_position: 1
---
# Options Builder
This lets you build options for the `ask()` API.
Following are the parameters supported by Ollama:
| Parameter | Description | Value Type | Example Usage |
|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|----------------------|
| mirostat | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | int | mirostat 0 |
| mirostat_eta | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1) | float | mirostat_eta 0.1 |
| mirostat_tau | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0) | float | mirostat_tau 5.0 |
| num_ctx | Sets the size of the context window used to generate the next token. (Default: 2048) | int | num_ctx 4096 |
| num_gqa | The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b | int | num_gqa 1 |
| num_gpu | The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. | int | num_gpu 50 |
| num_thread | Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). | int | num_thread 8 |
| repeat_last_n | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) | int | repeat_last_n 64 |
| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1) | float | repeat_penalty 1.1 |
| temperature | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8) | float | temperature 0.7 |
| seed | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0) | int | seed 42 |
| stop | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile. | string | stop "AI assistant:" |
| tfs_z | Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1) | float | tfs_z 1 |
| num_predict | Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context) | int | num_predict 42 |
| top_k | 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. (Default: 40) | int | top_k 40 |
| top_p | 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. (Default: 0.9) | float | top_p 0.9 |
Link to [source](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
Also, see how to set those Ollama parameters using
the `OptionsBuilder`
from [javadoc](https://ollama4j.github.io/ollama4j/apidocs/io/github/ollama4j/ollama4j/core/utils/OptionsBuilder.html).
## Build an empty `Options` object
```java
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.utils.Options;
import io.github.ollama4j.utils.OptionsBuilder;
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
Options options = new OptionsBuilder().build();
}
}
```
## Build the `Options` object with values
```java
import io.github.ollama4j.utils.Options;
import io.github.ollama4j.utils.OptionsBuilder;
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
Options options =
new OptionsBuilder()
.setMirostat(10)
.setMirostatEta(0.5f)
.setNumGpu(2)
.setTemperature(1.5f)
.build();
}
}
```

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# Ping
This API lets you check the reachability of Ollama server.
```java
import io.github.ollama4j.OllamaAPI;
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.ping();
}
}
```

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# PS
This API provides a list of running models and details about each model currently loaded into memory.
This API corresponds to the [PS](https://github.com/ollama/ollama/blob/main/docs/api.md#list-running-models) API.
```java
package io.github.ollama4j.localtests;
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.exceptions.OllamaBaseException;
import io.github.ollama4j.models.ps.ModelsProcessResponse;
import java.io.IOException;
public class Main {
public static void main(String[] args) {
OllamaAPI ollamaAPI = new OllamaAPI("http://localhost:11434");
ModelsProcessResponse response = ollamaAPI.ps();
System.out.println(response);
}
}
```

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# Set Request Timeout
This API lets you set the request timeout for the Ollama client.
```java
import io.github.ollama4j.OllamaAPI;
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.setRequestTimeoutSeconds(10);
}
}
```

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# Set Verbosity
This API lets you set the verbosity of the Ollama client.
## Try asking a question about the model.
```java
import io.github.ollama4j.OllamaAPI;
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.setVerbose(true);
}
}
```

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{
"label": "APIs - Generate",
"position": 3,
"link": {
"type": "generated-index",
"description": "Details of APIs to interact with LLMs."
}
}

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---
import CodeEmbed from '@site/src/components/CodeEmbed';
# Chat with Tools
### Using Tools in Chat
If you want to have a natural back-and-forth chat experience with tools, you can directly integrate tools into
the `chat()` method, instead of using the `generateWithTools()` method. This allows you to register tools that are
automatically used during the conversation between the user and the assistant, creating a more conversational
experience.
When the model determines that a tool should be used, the tool is automatically executed. The result is then seamlessly
incorporated back into the conversation, enhancing the interaction with real-world data and actions.
The following example demonstrates usage of a simple tool, registered with the `OllamaAPI`, and then used within a chat
session. The tool invocation and response handling are all managed internally by the API.
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatWithTools.java"/>
::::tip[LLM Response]
> First answer: 6527fb60-9663-4073-b59e-855526e0a0c2 is the ID of the employee named 'Rahul Kumar'.
>
> Second answer: Kumar is the last name of the employee named 'Rahul Kumar'.
::::
This tool calling can also be done using the streaming API.
### Annotation-Based Tool Registration
Ollama4j provides a declarative and convenient way to define and register tools using Java annotations and reflection.
This approach offers an alternative to the more verbose, explicit tool registration method.
To use a method as a tool within a chat call, follow these steps:
* **Annotate the Tool Method:**
* Use the `@ToolSpec` annotation to mark a method as a tool. This annotation describes the tool's purpose.
* Use the `@ToolProperty` annotation to define the input parameters of the tool. The following data types are
currently supported:
* `java.lang.String`
* `java.lang.Integer`
* `java.lang.Boolean`
* `java.math.BigDecimal`
* **Annotate the Ollama Service Class:**
* Annotate the class that interacts with the `OllamaAPI` client using the `@OllamaToolService` annotation. Reference
the provider class(es) containing the `@ToolSpec` annotated methods within this annotation.
* **Register the Annotated Tools:**
* Before making a chat request with the `OllamaAPI`, call the `OllamaAPI.registerAnnotatedTools()` method. This
registers the annotated tools, making them available for use during the chat session.
Let's try an example. Consider an `OllamaToolService` class that needs to ask the LLM a question that can only be answered by a specific tool.
This tool is implemented within a `GlobalConstantGenerator` class. Following is the code that exposes an annotated method as a tool:
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/annotated/GlobalConstantGenerator.java"/>
The annotated method can then be used as a tool in the chat session:
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/annotated/AnnotatedToolCallingExample.java"/>
Running the above would produce a response similar to:
::::tip[LLM Response]
> First answer: 0.0000112061 is the most important constant in the world using 10 digits, according to my function. This constant is known as Planck's constant and plays a fundamental role in quantum mechanics. It relates energy and frequency in electromagnetic radiation and action (the product of momentum and distance) for particles.
>
> Second answer: 3-digit constant: 8.001
::::

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sidebar_position: 7
---
import CodeEmbed from '@site/src/components/CodeEmbed';
import TypewriterTextarea from '@site/src/components/TypewriterTextarea';
# Chat
This API lets you create a conversation with LLMs. Using this API enables you to ask questions to the model including
information using the history of already asked questions and the respective answers.
### Create a new conversation and use chat history to augment follow up questions
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatExample.java" />
You will get a response similar to:
::::tip[LLM Response]
> First answer: The capital of France is Paris.
>
> Second answer: The second-largest city in France is Marseille.
>
> Chat History:
```json
[{
"role" : "user",
"content" : "What is the capital of France?",
"images" : null,
"tool_calls" : [ ]
}, {
"role" : "assistant",
"content" : "The capital of France is Paris.",
"images" : null,
"tool_calls" : null
}, {
"role" : "user",
"content" : "And what is the second largest city?",
"images" : null,
"tool_calls" : [ ]
}, {
"role" : "assistant",
"content" : "The second-largest city in France is Marseille.",
"images" : null,
"tool_calls" : null
}]
```
::::
### Create a conversation where the answer is streamed
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatStreamingWithTokenConcatenationExample.java" />
<!-- ::::tip[LLM Response]
>
> The
>
> The capital
>
> The capital of
>
> The capital of France
>
> The capital of France is
>
> The capital of France is Paris
>
> The capital of France is Paris.
>
:::: -->
<TypewriterTextarea
textContent='The capital of France is Paris.'
typingSpeed={30}
pauseBetweenSentences={1200}
height='55px'
width='100%'
/>
### Using a simple Console Output Stream Handler
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ConsoleOutputStreamHandlerExample.java" />
### With a Stream Handler to receive the tokens as they are generated
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatStreamingExample.java" />
### Create a new conversation with custom system prompt
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatWithCustomSystemPrompt.java" />
You will get a response as:
::::tip[LLM Response]
> Shhh!
::::
## Create a conversation about an image (requires a vision model)
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ChatWithImage.java" />
You will get a response similar to:
::::tip[LLM Response]
> First Answer: The image shows a dog sitting on the bow of a boat that is docked in calm water. The boat has two
> levels, with the lower level containing seating and what appears to be an engine cover. The dog seems relaxed and
> comfortable on the boat, looking out over the water. The background suggests it might be late afternoon or early
> evening, given the warm lighting and the low position of the sun in the sky.
>
> Second Answer: Based on the image, it's difficult to definitively determine the breed of the dog. However, the dog
> appears to be medium-sized with a short coat and a brown coloration, which might suggest that it is a Golden Retriever
> or a similar breed. Without more details like ear shape and tail length, it's not possible to identify the exact breed
> confidently.
::::

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# Custom Roles
Allows to manage custom roles (apart from the base roles) for chat interactions with the models.
_Particularly helpful when you would need to use different roles that the newer models support other than the base
roles._
_Base roles are `SYSTEM`, `USER`, `ASSISTANT`, `TOOL`._
### Usage
#### Add new role
```java
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.models.chat.OllamaChatMessageRole;
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
OllamaChatMessageRole customRole = ollamaAPI.addCustomRole("custom-role");
}
}
```
#### List roles
```java
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.models.chat.OllamaChatMessageRole;
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
List<OllamaChatMessageRole> roles = ollamaAPI.listRoles();
}
}
```
#### Get role
```java
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.models.chat.OllamaChatMessageRole;
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
List<OllamaChatMessageRole> roles = ollamaAPI.getRole("custom-role");
}
}
```

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---
import CodeEmbed from '@site/src/components/CodeEmbed';
# Generate (Async)
This API lets you ask questions to the LLMs in a asynchronous way.
This is particularly helpful when you want to issue a generate request to the LLM and collect the response in the
background (such as threads) without blocking your code until the response arrives from the model.
This API corresponds to
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) API.
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateAsync.java" />
::::tip[LLM Response]
Here are the participating teams in the 2019 ICC Cricket World Cup:
1. Australia
2. Bangladesh
3. India
4. New Zealand
5. Pakistan
6. England
7. South Africa
8. West Indies (as a team)
9. Afghanistan
::::

File diff suppressed because one or more lines are too long

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import CodeEmbed from '@site/src/components/CodeEmbed';
# Generate with Image URLs
This API lets you ask questions along with the image files to the LLMs.
This API corresponds to
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) API.
:::note
Executing this on Ollama server running in CPU-mode will take longer to generate response. Hence, GPU-mode is
recommended.
:::
## Ask (Sync)
Passing the link of this image the following code:
![Img](https://t3.ftcdn.net/jpg/02/96/63/80/360_F_296638053_0gUVA4WVBKceGsIr7LNqRWSnkusi07dq.jpg)
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateWithImageURL.java" />
You will get a response similar to:
::::tip[LLM Response]
> This image features a white boat with brown cushions, where a dog is sitting on the back of the boat. The dog seems to
> be enjoying its time outdoors, perhaps on a lake.
::::

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import CodeEmbed from '@site/src/components/CodeEmbed';
# Generate with Tools
This API lets you perform [tool/function calling](https://docs.mistral.ai/capabilities/function_calling/) using LLMs in a
synchronous way.
This API corresponds to
the [generate](https://github.com/ollama/ollama/blob/main/docs/api.md#request-raw-mode) API with `raw` mode.
:::note
This is an only an experimental implementation and has a very basic design.
Currently, built and tested for [Mistral's latest model](https://ollama.com/library/mistral) only. We could redesign
this
in the future if tooling is supported for more models with a generic interaction standard from Ollama.
:::
## Tools/Function Calling
Assume you want to call a method/function in your code based on the response generated from the model.
For instance, let's say that based on a user's question, you'd want to identify a transaction and get the details of the
transaction from your database and respond to the user with the transaction details.
You could do that with ease with the `function calling` capabilities of the models by registering your `tools`.
### Create Tools/Functions
We can create static functions as our tools.
This function takes the arguments `location` and `fuelType` and performs an operation with these arguments and returns
fuel price value.
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/tools/FuelPriceTool.java"/ >
This function takes the argument `city` and performs an operation with the argument and returns the weather for a
location.
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/tools/WeatherTool.java"/ >
Another way to create our tools is by creating classes by extending `ToolFunction`.
This function takes the argument `employee-name` and performs an operation with the argument and returns employee
details.
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/tools/DBQueryFunction.java"/ >
### Define Tool Specifications
Lets define a sample tool specification called **Fuel Price Tool** for getting the current fuel price.
- Specify the function `name`, `description`, and `required` properties (`location` and `fuelType`).
- Associate the `getCurrentFuelPrice` function you defined earlier.
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/toolspecs/FuelPriceToolSpec.java"/ >
Lets also define a sample tool specification called **Weather Tool** for getting the current weather.
- Specify the function `name`, `description`, and `required` property (`city`).
- Associate the `getCurrentWeather` function you defined earlier.
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/toolspecs/WeatherToolSpec.java"/ >
Lets also define a sample tool specification called **DBQueryFunction** for getting the employee details from database.
- Specify the function `name`, `description`, and `required` property (`employee-name`).
- Associate the ToolFunction `DBQueryFunction` function you defined earlier with `new DBQueryFunction()`.
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/toolcalling/toolspecs/DatabaseQueryToolSpec.java"/ >
Now put it all together by registering the tools and prompting with tools.
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/MultiToolRegistryExample.java"/ >
Run this full example and you will get a response similar to:
::::tip[LLM Response]
[Result of executing tool 'current-fuel-price']: Current price of petrol in Bengaluru is Rs.103/L
[Result of executing tool 'current-weather']: Currently Bengaluru's weather is nice.
[Result of executing tool 'get-employee-details']: Employee Details `{ID: 6bad82e6-b1a1-458f-a139-e3b646e092b1, Name:
Rahul Kumar, Address: King St, Hyderabad, India, Phone: 9876543210}`
::::

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import CodeEmbed from '@site/src/components/CodeEmbed';
import TypewriterTextarea from '@site/src/components/TypewriterTextarea';
# Generate (Sync)
This API lets you ask questions to the LLMs in a synchronous way.
This API corresponds to
the [completion](https://github.com/jmorganca/ollama/blob/main/docs/api.md#generate-a-completion) API.
Use the `OptionBuilder` to build the `Options` object
with [extra parameters](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values).
Refer
to [this](/apis-extras/options-builder).
### Try asking a question about the model
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/Generate.java" />
You will get a response similar to:
::::tip[LLM Response]
> I am a large language model created by Alibaba Cloud. My purpose is to assist users in generating text, answering
> questions, and completing tasks. I aim to be user-friendly and easy to understand for everyone who interacts with me.
::::
### Try asking a question, receiving the answer streamed
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GenerateStreamingWithTokenConcatenation.java" />
You will get a response similar to:
<!-- ::::tip[LLM Response]
> The
>
> The capital
>
> The capital of
>
> The capital of France
>
> The capital of France is
>
> The capital of France is Paris
>
> The capital of France is Paris.
:::: -->
<TypewriterTextarea
textContent='The capital of France is Paris.'
typingSpeed={30}
pauseBetweenSentences={1200}
height='55px'
width='100%'
/>
## Generate structured output
### With response as a `Map`
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/StructuredOutput.java" />
You will get a response similar to:
::::tip[LLM Response]
```json
{
"available": true,
"age": 22
}
```
::::
### With response mapped to specified class type
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/StructuredOutputMappedToObject.java" />
::::tip[LLM Response]
Person(age=28, available=false)
::::

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# Prompt Builder
This is designed for prompt engineering. It allows you to easily build the prompt text for zero-shot, one-shot, few-shot
inferences.
```java
import io.github.ollama4j.OllamaAPI;
import io.github.ollama4j.models.response.OllamaResult;
import io.github.ollama4j.types.OllamaModelType;
import io.github.ollama4j.utils.OptionsBuilder;
import io.github.ollama4j.utils.PromptBuilder;
public class Main {
public static void main(String[] args) throws Exception {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.setRequestTimeoutSeconds(10);
String model = OllamaModelType.PHI;
PromptBuilder promptBuilder =
new PromptBuilder()
.addLine("You are an expert coder and understand different programming languages.")
.addLine("Given a question, answer ONLY with code.")
.addLine("Produce clean, formatted and indented code in markdown format.")
.addLine(
"DO NOT include ANY extra text apart from code. Follow this instruction very strictly!")
.addLine("If there's any additional information you want to add, use comments within code.")
.addLine("Answer only in the programming language that has been asked for.")
.addSeparator()
.addLine("Example: Sum 2 numbers in Python")
.addLine("Answer:")
.addLine("```python")
.addLine("def sum(num1: int, num2: int) -> int:")
.addLine(" return num1 + num2")
.addLine("```")
.addSeparator()
.add("How do I read a file in Go and print its contents to stdout?");
boolean raw = false;
OllamaResult response = ollamaAPI.generate(model, promptBuilder.build(), raw, new OptionsBuilder().build());
System.out.println(response.getResponse());
}
}
```
You will get a response similar to:
```go
package main
import (
"fmt"
"io/ioutil"
)
func readFile(fileName string) {
file, err := ioutil.ReadFile(fileName)
if err != nil {
fmt.Fprintln(os.Stderr, "Error reading file:", err.Error())
return
}
f, _ := ioutil.ReadFile("file.txt")
if f != nil {
fmt.Println(f.String())
}
}
```

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{
"label": "APIs - Model Management",
"position": 2,
"position": 4,
"link": {
"type": "generated-index",
"description": "Details of APIs to manage LLMs."
"description": "Specs of APIs and their usage."
}
}

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---
sidebar_position: 5
sidebar_position: 4
---
import CodeEmbed from '@site/src/components/CodeEmbed';
# Create Model
This API lets you create a custom model on the Ollama server.
### Create a custom model from an existing model in the Ollama server
```java title="CreateModel.java"
public class CreateModel {
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/CreateModel.java" />
public static void main(String[] args) {
You would see these logs while the custom model is being created:
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.createModel("mycustommodel", "/path/to/modelfile/on/ollama-server");
}
}
```
{"status":"using existing layer sha256:fad2a06e4cc705c2fa8bec5477ddb00dc0c859ac184c34dcc5586663774161ca"}
{"status":"using existing layer sha256:41c2cf8c272f6fb0080a97cd9d9bd7d4604072b80a0b10e7d65ca26ef5000c0c"}
{"status":"using existing layer sha256:1da0581fd4ce92dcf5a66b1da737cf215d8dcf25aa1b98b44443aaf7173155f5"}
{"status":"creating new layer sha256:941b69ca7dc2a85c053c38d9e8029c9df6224e545060954fa97587f87c044a64"}
{"status":"using existing layer sha256:f02dd72bb2423204352eabc5637b44d79d17f109fdb510a7c51455892aa2d216"}
{"status":"writing manifest"}
{"status":"success"}
```
Once created, you can see it when you use [list models](./list-models) API.
[Read more](https://github.com/ollama/ollama/blob/main/docs/api.md#create-a-model) about custom model creation and the parameters available for model creation.
[//]: # ()
[//]: # (### Example of a `Modelfile`)
[//]: # ()
[//]: # (```)
[//]: # (FROM llama2)
[//]: # (# sets the temperature to 1 [higher is more creative, lower is more coherent])
[//]: # (PARAMETER temperature 1)
[//]: # (# sets the context window size to 4096, this controls how many tokens the LLM can use as context to generate the next token)
[//]: # (PARAMETER num_ctx 4096)
[//]: # ()
[//]: # (# sets a custom system message to specify the behavior of the chat assistant)
[//]: # (SYSTEM You are Mario from super mario bros, acting as an assistant.)
[//]: # (```)
[//]: # ()
[//]: # (### Format of the `Modelfile`)
[//]: # ()
[//]: # (```modelfile)
[//]: # (# comment)
[//]: # (INSTRUCTION arguments)
[//]: # (```)
[//]: # ()
[//]: # (| Instruction | Description |)
[//]: # (|-------------------------------------|----------------------------------------------------------------|)
[//]: # (| [`FROM`]&#40;#from-required&#41; &#40;required&#41; | Defines the base model to use. |)
[//]: # (| [`PARAMETER`]&#40;#parameter&#41; | Sets the parameters for how Ollama will run the model. |)
[//]: # (| [`TEMPLATE`]&#40;#template&#41; | The full prompt template to be sent to the model. |)
[//]: # (| [`SYSTEM`]&#40;#system&#41; | Specifies the system message that will be set in the template. |)
[//]: # (| [`ADAPTER`]&#40;#adapter&#41; | Defines the &#40;Q&#41;LoRA adapters to apply to the model. |)
[//]: # (| [`LICENSE`]&#40;#license&#41; | Specifies the legal license. |)
[//]: # ()
[//]: # (#### PARAMETER)
[//]: # ()
[//]: # (The `PARAMETER` instruction defines a parameter that can be set when the model is run.)
[//]: # ()
[//]: # (| Parameter | Description | Value Type | Example Usage |)
[//]: # (|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|----------------------|)
[//]: # (| mirostat | Enable Mirostat sampling for controlling perplexity. &#40;default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0&#41; | int | mirostat 0 |)
[//]: # (| mirostat_eta | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. &#40;Default: 0.1&#41; | float | mirostat_eta 0.1 |)
[//]: # (| mirostat_tau | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. &#40;Default: 5.0&#41; | float | mirostat_tau 5.0 |)
[//]: # (| num_ctx | Sets the size of the context window used to generate the next token. &#40;Default: 2048&#41; | int | num_ctx 4096 |)
[//]: # (| num_gqa | The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b | int | num_gqa 1 |)
[//]: # (| num_gpu | The number of layers to send to the GPU&#40;s&#41;. On macOS it defaults to 1 to enable metal support, 0 to disable. | int | num_gpu 50 |)
[//]: # (| num_thread | Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has &#40;as opposed to the logical number of cores&#41;. | int | num_thread 8 |)
[//]: # (| repeat_last_n | Sets how far back for the model to look back to prevent repetition. &#40;Default: 64, 0 = disabled, -1 = num_ctx&#41; | int | repeat_last_n 64 |)
[//]: # (| repeat_penalty | Sets how strongly to penalize repetitions. A higher value &#40;e.g., 1.5&#41; will penalize repetitions more strongly, while a lower value &#40;e.g., 0.9&#41; will be more lenient. &#40;Default: 1.1&#41; | float | repeat_penalty 1.1 |)
[//]: # (| temperature | The temperature of the model. Increasing the temperature will make the model answer more creatively. &#40;Default: 0.8&#41; | float | temperature 0.7 |)
[//]: # (| seed | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. &#40;Default: 0&#41; | int | seed 42 |)
[//]: # (| stop | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile. | string | stop "AI assistant:" |)
[//]: # (| tfs_z | Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value &#40;e.g., 2.0&#41; will reduce the impact more, while a value of 1.0 disables this setting. &#40;default: 1&#41; | float | tfs_z 1 |)
[//]: # (| num_predict | Maximum number of tokens to predict when generating text. &#40;Default: 128, -1 = infinite generation, -2 = fill context&#41; | int | num_predict 42 |)
[//]: # (| top_k | Reduces the probability of generating nonsense. A higher value &#40;e.g. 100&#41; will give more diverse answers, while a lower value &#40;e.g. 10&#41; will be more conservative. &#40;Default: 40&#41; | int | top_k 40 |)
[//]: # (| top_p | Works together with top-k. A higher value &#40;e.g., 0.95&#41; will lead to more diverse text, while a lower value &#40;e.g., 0.5&#41; will generate more focused and conservative text. &#40;Default: 0.9&#41; | float | top_p 0.9 |)
[//]: # ()
[//]: # (#### TEMPLATE)
[//]: # ()
[//]: # (`TEMPLATE` of the full prompt template to be passed into the model. It may include &#40;optionally&#41; a system message and a)
[//]: # (user's prompt. This is used to create a full custom prompt, and syntax may be model specific. You can usually find the)
[//]: # (template for a given model in the readme for that model.)
[//]: # ()
[//]: # (#### Template Variables)
[//]: # ()
[//]: # (| Variable | Description |)
[//]: # (|-----------------|---------------------------------------------------------------------------------------------------------------|)
[//]: # (| `{{ .System }}` | The system message used to specify custom behavior, this must also be set in the Modelfile as an instruction. |)
[//]: # (| `{{ .Prompt }}` | The incoming prompt, this is not specified in the model file and will be set based on input. |)
[//]: # (| `{{ .First }}` | A boolean value used to render specific template information for the first generation of a session. |)
[//]: # ()
[//]: # (```modelfile)
[//]: # (TEMPLATE """)
[//]: # ({{- if .First }})
[//]: # (### System:)
[//]: # ({{ .System }})
[//]: # ({{- end }})
[//]: # ()
[//]: # (### User:)
[//]: # ({{ .Prompt }})
[//]: # ()
[//]: # (### Response:)
[//]: # (""")
[//]: # ()
[//]: # (SYSTEM """<system message>""")
[//]: # (```)
[//]: # ()
[//]: # (### SYSTEM)
[//]: # ()
[//]: # (The `SYSTEM` instruction specifies the system message to be used in the template, if applicable.)
[//]: # ()
[//]: # (```modelfile)
[//]: # (SYSTEM """<system message>""")
[//]: # (```)
[//]: # ()
[//]: # (### ADAPTER)
[//]: # ()
[//]: # (The `ADAPTER` instruction specifies the LoRA adapter to apply to the base model. The value of this instruction should be)
[//]: # (an absolute path or a path relative to the Modelfile and the file must be in a GGML file format. The adapter should be)
[//]: # (tuned from the base model otherwise the behaviour is undefined.)
[//]: # ()
[//]: # (```modelfile)
[//]: # (ADAPTER ./ollama-lora.bin)
[//]: # (```)
[//]: # ()
[//]: # (### LICENSE)
[//]: # ()
[//]: # (The `LICENSE` instruction allows you to specify the legal license under which the model used with this Modelfile is)
[//]: # (shared or distributed.)
[//]: # ()
[//]: # (```modelfile)
[//]: # (LICENSE """)
[//]: # (<license text>)
[//]: # (""")
[//]: # (```)
[//]: # ()
[//]: # (## Notes)
[//]: # ()
[//]: # (- the **`Modelfile` is not case sensitive**. In the examples, uppercase instructions are used to make it easier to)
[//]: # ( distinguish it from arguments.)
[//]: # (- Instructions can be in any order. In the examples, the `FROM` instruction is first to keep it easily readable.)
[//]: # ()
[//]: # (Read more about Modelfile: https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md)
Once created, you can see it when you use [list models](./list-models) API.

View File

@ -1,13 +1,26 @@
---
sidebar_position: 6
sidebar_position: 5
---
import CodeEmbed from '@site/src/components/CodeEmbed';
# Delete Model
This API lets you create a delete a model from the Ollama server.
<CodeEmbed src="https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/DeleteModel.java" />
```java title="DeleteModel.java"
public class Main {
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
ollamaAPI.setVerbose(false);
ollamaAPI.deleteModel("mycustommodel", true);
}
}
```
Once deleted, you can verify it using [list models](./list-models) API.

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@ -1,70 +0,0 @@
---
sidebar_position: 1
---
import CodeEmbed from '@site/src/components/CodeEmbed';
# Models from Ollama Library
These API retrieves a list of models directly from the Ollama library.
### List Models from Ollama Library
This API fetches available models from the Ollama library page, including details such as the model's name, pull count,
popular tags, tag count, and the last update time.
<CodeEmbed
src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ListLibraryModels.java'>
</CodeEmbed>
The following is the sample output:
```
[
LibraryModel(name=llama3.2-vision, description=Llama 3.2 Vision is a collection of instruction-tuned image reasoning generative models in 11B and 90B sizes., pullCount=21.1K, totalTags=9, popularTags=[vision, 11b, 90b], lastUpdated=yesterday),
LibraryModel(name=llama3.2, description=Meta's Llama 3.2 goes small with 1B and 3B models., pullCount=2.4M, totalTags=63, popularTags=[tools, 1b, 3b], lastUpdated=6 weeks ago)
]
```
### Get Tags of a Library Model
This API Fetches the tags associated with a specific model from Ollama library.
<CodeEmbed
src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/GetLibraryModelTags.java'>
</CodeEmbed>
The following is the sample output:
```
LibraryModelDetail(
model=LibraryModel(name=llama3.2-vision, description=Llama 3.2 Vision is a collection of instruction-tuned image reasoning generative models in 11B and 90B sizes., pullCount=21.1K, totalTags=9, popularTags=[vision, 11b, 90b], lastUpdated=yesterday),
tags=[
LibraryModelTag(name=llama3.2-vision, tag=latest, size=7.9GB, lastUpdated=yesterday),
LibraryModelTag(name=llama3.2-vision, tag=11b, size=7.9GB, lastUpdated=yesterday),
LibraryModelTag(name=llama3.2-vision, tag=90b, size=55GB, lastUpdated=yesterday)
]
)
```
### Find a model from Ollama library
This API finds a specific model using model `name` and `tag` from Ollama library.
<CodeEmbed
src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/FindLibraryModel.java'>
</CodeEmbed>
The following is the sample output:
```
LibraryModelTag(name=qwen2.5, tag=7b, size=4.7GB, lastUpdated=7 weeks ago)
```
### Pull model using `LibraryModelTag`
You can use `LibraryModelTag` to pull models into Ollama server.
<CodeEmbed
src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/PullLibraryModelTags.java'>
</CodeEmbed>

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@ -1,24 +1,30 @@
---
sidebar_position: 2
sidebar_position: 1
---
import CodeEmbed from '@site/src/components/CodeEmbed';
# List Models
# List Local Models
This API lets you list available models on the Ollama server.
This API lets you list downloaded/available models on the Ollama server.
```java title="ListModels.java"
public class ListModels {
<CodeEmbed
src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/ListLocalModels.java'>
</CodeEmbed>
public static void main(String[] args) {
String host = "http://localhost:11434/";
OllamaAPI ollamaAPI = new OllamaAPI(host);
List<Model> models = ollamaAPI.listModels();
models.forEach(model -> System.out.println(model.getName()));
}
}
```
If you have any models already downloaded on Ollama server, you would have them listed as follows:
```bash
llama2:latest
llama3.2:1b
qwen2:0.5b
qwen:0.5b
sqlcoder:latest
```

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@ -1,23 +1,23 @@
---
sidebar_position: 3
sidebar_position: 2
---
import CodeEmbed from '@site/src/components/CodeEmbed';
# Pull Model
This API lets you pull a model on the Ollama server.
<CodeEmbed
src='https://raw.githubusercontent.com/ollama4j/ollama4j-examples/refs/heads/main/src/main/java/io/github/ollama4j/examples/PullModel.java'>
</CodeEmbed>
```java title="PullModel.java"
public class Main {
Once downloaded, you can see them when you use [list models](./list-models) API.
public static void main(String[] args) {
String host = "http://localhost:11434/";
:::info
OllamaAPI ollamaAPI = new OllamaAPI(host);
You can even pull models using Ollama model library APIs. This looks up the models directly on the Ollama model library page. Refer
to [this](./list-library-models#pull-model-using-librarymodeltag).
:::
ollamaAPI.pullModel(OllamaModelType.LLAMA2);
}
}
```
Once downloaded, you can see them when you use [list models](./list-models) API.

View File

@ -1,52 +1,21 @@
---
sidebar_position: 1
title: Introduction
---
import LatestRelease from '@site/src/components/LatestRelease';
import AddToYourProject from '@site/src/components/AddToYourProject';
# Intro
# Introduction
Let's get started with **Ollama4j**.
### 🦙 What is Ollama?
## Getting Started
[Ollama](https://ollama.ai/) is an advanced AI tool that allows users to easily set up and run large language models
locally (in CPU and GPU
modes). With Ollama, users can leverage powerful language models such as Llama 2 and even customize and create their own
models.
### 👨‍💻 Why Ollama4j?
Ollama4j was built for the simple purpose of integrating Ollama with Java applications.
```mermaid
flowchart LR
o4j[Ollama4j]
o[Ollama Server]
o4j -->|Communicates with| o;
m[Models]
p[Your Java Project]
subgraph Your Java Environment
direction TB
p -->|Uses| o4j
end
subgraph Ollama Setup
direction TB
o -->|Manages| m
end
```
### Getting Started
#### What you'll need
### What you'll need
- **[Ollama](https://ollama.ai/download)**
- **[Oracle JDK](https://www.oracle.com/java/technologies/javase/jdk11-archive-downloads.html)** or
**[Open JDK](https://jdk.java.net/archive/)** 11.0 or above.
- **[Maven](https://maven.apache.org/download.cgi)**
#### Start Ollama server
### Start Ollama server
The easiest way of getting started with Ollama server is with [Docker](https://docs.docker.com/get-started/overview/).
But if you choose to run the
@ -72,17 +41,22 @@ terminal of your code editor.
The command runs the Ollama server locally at **http://localhost:11434/**.
#### Setup your project
### Setup your project
Get started by **creating a new Maven project** on your favorite IDE.
Add the dependency to your project's `pom.xml`.
<AddToYourProject/>
```xml
<div style={{ marginTop: '2rem', marginBottom: '2rem', fontSize: '1em', textAlign: 'left', display: 'flex', justifyContent: 'left'}}>
<LatestRelease showReleaseDate={true} style={{textAlign: 'left', fontWeight: 'normal'}}/>
</div>
<dependency>
<groupId>io.github.amithkoujalgi</groupId>
<artifactId>ollama4j</artifactId>
<version>1.0.27</version>
</dependency>
```
Find the latest version of the library from [Maven Central Repository](https://central.sonatype.com/artifact/io.github.ollama4j/ollama4j).
Find the latest version of the library [here](https://central.sonatype.com/artifact/io.github.amithkoujalgi/ollama4j).
You might want to include an implementation of [SL4J](https://www.slf4j.org/) logger in your `pom.xml` file. For
example,
@ -90,6 +64,7 @@ example,
Use `slf4j-jdk14` implementation:
```xml
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-jdk14</artifactId>
@ -100,6 +75,7 @@ Use `slf4j-jdk14` implementation:
or use `logback-classic` implementation:
```xml
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>
@ -112,26 +88,6 @@ or use other suitable implementations.
Create a new Java class in your project and add this code.
```java
import io.github.ollama4j.OllamaAPI;
public class OllamaAPITest {
public static void main(String[] args) {
OllamaAPI ollamaAPI = new OllamaAPI();
boolean isOllamaServerReachable = ollamaAPI.ping();
System.out.println("Is Ollama server running: " + isOllamaServerReachable);
}
}
```
This uses the default Ollama host as `http://localhost:11434`.
Specify a different Ollama host that you want to connect to.
```java
import io.github.ollama4j.OllamaAPI;
public class OllamaAPITest {
public static void main(String[] args) {
@ -143,7 +99,7 @@ public class OllamaAPITest {
boolean isOllamaServerReachable = ollamaAPI.ping();
System.out.println("Is Ollama server running: " + isOllamaServerReachable);
System.out.println("Is Ollama server alive: " + isOllamaServerReachable);
}
}
```

View File

@ -20,7 +20,7 @@ const config = {
// GitHub pages deployment config.
// If you aren't using GitHub pages, you don't need these.
organizationName: 'ollama4j', // Usually your GitHub org/user name.
organizationName: 'amithkoujalgi', // Usually your GitHub org/user name.
projectName: 'ollama4j', // Usually your repo name.
onBrokenLinks: 'throw',
@ -40,28 +40,22 @@ const config = {
/** @type {import('@docusaurus/preset-classic').Options} */
({
docs: {
path: 'docs',
routeBasePath: '', // change this to any URL route you'd want. For example: `home` - if you want /home/intro.
sidebarPath: './sidebars.js',
// Please change this to your repo.
// Remove this to remove the "edit this page" links.
editUrl:
'https://github.com/ollama4j/ollama4j/blob/main/docs',
'https://github.com/amithkoujalgi/ollama4j/blob/main/docs',
},
blog: {
showReadingTime: true,
// Please change this to your repo.
// Remove this to remove the "edit this page" links.
editUrl:
'https://github.com/ollama4j/ollama4j/blob/main/docs',
'https://github.com/amithkoujalgi/ollama4j/blob/main/docs',
},
theme: {
customCss: './src/css/custom.css',
},
gtag: {
trackingID: 'G-G7FLH6FNDC',
anonymizeIP: false,
},
}),
],
],
@ -82,14 +76,12 @@ const config = {
type: 'docSidebar',
sidebarId: 'tutorialSidebar',
position: 'left',
label: 'Docs',
label: 'Usage',
},
{to: 'https://github.com/ollama4j/ollama4j-examples', label: 'Examples', position: 'left'},
{to: 'https://ollama4j.github.io/ollama4j/apidocs/', label: 'Javadoc', position: 'left'},
{to: 'https://ollama4j.github.io/ollama4j/doxygen/html/', label: 'Doxygen', position: 'left'},
{to: 'https://amithkoujalgi.github.io/ollama4j/apidocs/', label: 'Javadoc', position: 'left'},
{to: '/blog', label: 'Blog', position: 'left'},
{
href: 'https://github.com/ollama4j/ollama4j',
href: 'https://github.com/amithkoujalgi/ollama4j',
label: 'GitHub',
position: 'right',
},
@ -99,32 +91,11 @@ const config = {
style: 'dark',
links: [
{
title: 'Quick Links',
title: 'Docs',
items: [
{
label: 'Ollama4j Examples',
to: 'https://github.com/ollama4j/ollama4j-examples',
},
{
label: 'Blog',
to: '/blog',
},
{
label: 'GitHub',
href: 'https://github.com/ollama4j/ollama4j',
},
],
},
{
title: 'Stuff built with Ollama4j',
items: [
{
label: 'Ollama4j Web UI',
to: 'https://github.com/ollama4j/ollama4j-web-ui',
},
{
label: 'Ollama4j Desktop UI with Swing',
to: 'https://github.com/ollama4j/ollama4j-ui',
label: 'Tutorial',
to: '/docs/intro',
},
],
},
@ -140,43 +111,28 @@ const config = {
href: 'https://twitter.com/ollama4j',
},
],
}
},
{
title: 'More',
items: [
{
label: 'Blog',
to: '/blog',
},
{
label: 'GitHub',
href: 'https://github.com/amithkoujalgi/ollama4j',
},
],
},
],
copyright: `Ollama4j Documentation ${new Date().getFullYear()}. Built with Docusaurus.`,
},
prism: {
theme: prismThemes.github,
darkTheme: prismThemes.dracula,
additionalLanguages: ['java'],
},
algolia: {
// The application ID provided by Algolia
appId: '7HJ3MZ6GBX',
// Public API key: it is safe to commit it
apiKey: '3037a6d8706a6347b1844ca6ecd582b0',
indexName: 'ollama4jio',
// Optional: see doc section below
contextualSearch: true,
// Optional: Specify domains where the navigation should occur through window.location instead on history.push. Useful when our Algolia config crawls multiple documentation sites and we want to navigate with window.location.href to them.
externalUrlRegex: 'external\\.com|domain\\.com',
// Optional: Replace parts of the item URLs from Algolia. Useful when using the same search index for multiple deployments using a different baseUrl. You can use regexp or string in the `from` param. For example: localhost:3000 vs myCompany.com/docs
replaceSearchResultPathname: {
from: '/docs/', // or as RegExp: /\/docs\//
to: '/',
},
// Optional: Algolia search parameters
searchParameters: {},
// Optional: path for search page that enabled by default (`false` to disable it)
searchPagePath: 'search',
// Optional: whether the insights feature is enabled or not on Docsearch (`false` by default)
insights: false,
//... other Algolia params
},
}),
markdown: {
mermaid: true,
},
themes: ['@docusaurus/theme-mermaid']
};
export default config;

30892
docs/package-lock.json generated

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View File

@ -1,51 +1,44 @@
{
"name": "ollama-4-j",
"version": "0.0.0",
"private": true,
"scripts": {
"docusaurus": "docusaurus",
"start": "docusaurus start",
"build": "docusaurus build",
"swizzle": "docusaurus swizzle",
"deploy": "docusaurus deploy",
"clear": "docusaurus clear",
"serve": "docusaurus serve",
"write-translations": "docusaurus write-translations",
"write-heading-ids": "docusaurus write-heading-ids"
},
"dependencies": {
"@docsearch/js": "^3.9.0",
"@docusaurus/core": "^3.4.0",
"@docusaurus/plugin-google-gtag": "^3.4.0",
"@docusaurus/preset-classic": "^3.4.0",
"@docusaurus/theme-mermaid": "^3.4.0",
"@iconify/react": "^5.2.1",
"@mdx-js/react": "^3.0.0",
"clsx": "^2.0.0",
"font-awesome": "^4.7.0",
"prism-react-renderer": "^2.3.0",
"react": "^18.0.0",
"react-dom": "^18.0.0",
"react-icons": "^5.5.0",
"react-image-gallery": "^1.4.0"
},
"devDependencies": {
"@docusaurus/module-type-aliases": "^3.4.0",
"@docusaurus/types": "^3.4.0"
},
"browserslist": {
"production": [
">0.5%",
"not dead",
"not op_mini all"
],
"development": [
"last 3 chrome version",
"last 3 firefox version",
"last 5 safari version"
]
},
"engines": {
"node": ">=18.0"
}
"name": "ollama-4-j",
"version": "0.0.0",
"private": true,
"scripts": {
"docusaurus": "docusaurus",
"start": "docusaurus start",
"build": "docusaurus build",
"swizzle": "docusaurus swizzle",
"deploy": "docusaurus deploy",
"clear": "docusaurus clear",
"serve": "docusaurus serve",
"write-translations": "docusaurus write-translations",
"write-heading-ids": "docusaurus write-heading-ids"
},
"dependencies": {
"@docusaurus/core": "3.0.1",
"@docusaurus/preset-classic": "3.0.1",
"@mdx-js/react": "^3.0.0",
"clsx": "^2.0.0",
"prism-react-renderer": "^2.3.0",
"react": "^18.0.0",
"react-dom": "^18.0.0"
},
"devDependencies": {
"@docusaurus/module-type-aliases": "3.0.1",
"@docusaurus/types": "3.0.1"
},
"browserslist": {
"production": [
">0.5%",
"not dead",
"not op_mini all"
],
"development": [
"last 3 chrome version",
"last 3 firefox version",
"last 5 safari version"
]
},
"engines": {
"node": ">=18.0"
}
}

View File

@ -1,65 +0,0 @@
import React, { useState, useEffect } from 'react';
import CodeBlock from '@theme/CodeBlock';
const AddToYourProject = () => {
const [releaseInfo, setReleaseInfo] = useState(null);
const [loading, setLoading] = useState(true);
const [error, setError] = useState(null);
useEffect(() => {
const fetchLatestRelease = async () => {
setLoading(true);
setError(null);
try {
const response = await fetch('https://api.github.com/repos/ollama4j/ollama4j/releases/latest');
if (!response.ok) {
throw new Error(`HTTP error! status: ${response.status}`);
}
const data = await response.json();
setReleaseInfo(data);
} catch (err) {
console.error('Failed to fetch release info:', err);
setError(err);
} finally {
setLoading(false);
}
};
fetchLatestRelease();
}, []);
return (
<div style={{ width: '100%' }}>
{loading ? (
<div>Loading latest release info...</div>
) : error ? (
<div>Error: {error.message}</div>
) : releaseInfo ? (
<>
<h4>Using Maven <code>pom.xml</code></h4>
<CodeBlock className="language-xml">
{`<dependency>
<groupId>io.github.ollama4j</groupId>
<artifactId>ollama4j</artifactId>
<version>${releaseInfo.name}</version>
</dependency>`}
</CodeBlock>
<h4>Using Groovy-based <code>build.gradle</code></h4>
<CodeBlock className="language-groovy">
{`dependencies {
implementation 'io.github.ollama4j:ollama4j:${releaseInfo.name}'
}`}
</CodeBlock>
<h4>For Kotlin-based <code>build.gradle.kts</code></h4>
<CodeBlock className="language-kotlin">
{`dependencies {
implementation("io.github.ollama4j:ollama4j:${releaseInfo.name}")
}`}
</CodeBlock>
</>
) : null}
</div>
);
};
export default AddToYourProject;

View File

@ -1,41 +0,0 @@
import React from "react";
class BuyMeACoffee extends React.Component {
constructor(props) {
super(props)
let script = document.createElement("script");
script.src = 'https://cdnjs.buymeacoffee.com/1.0.0/widget.prod.min.js';
script.dataset.name = 'BMC-Widget';
script.dataset.cfasync = 'false';
script.dataset.id = 'amithkoujalgi';
script.dataset.description = 'Support me on Buy me a coffee!';
script.dataset.message = 'If you like my work and want to say thanks, or encourage me to do more, you can buy me a coffee! 😊';
script.dataset.color = '#2e8555';
script.dataset.position = 'Right';
script.dataset.x_margin = '18';
script.dataset.y_margin = '18';
script.async = true
script.onload = function () {
let evt = document.createEvent('Event');
evt.initEvent('DOMContentLoaded', false, false);
window.dispatchEvent(evt);
}
this.script = script
}
componentDidMount() {
document.head.appendChild(this.script)
}
// componentWillUnmount() {
// document.head.removeChild(this.script);
// document.body.removeChild(document.getElementById("bmc-wbtn"))
// }
render() {
return null
}
}
export default BuyMeACoffee;

View File

@ -1,170 +0,0 @@
// import React, { useState, useEffect } from 'react';
// import CodeBlock from '@theme/CodeBlock';
// import Icon from '@site/src/components/Icon';
// const CodeEmbed = ({ src }) => {
// const [code, setCode] = useState('');
// const [loading, setLoading] = useState(true);
// const [error, setError] = useState(null);
// useEffect(() => {
// let isMounted = true;
// const fetchCodeFromUrl = async (url) => {
// if (!isMounted) return;
// setLoading(true);
// setError(null);
// try {
// const response = await fetch(url);
// if (!response.ok) {
// throw new Error(`HTTP error! status: ${response.status}`);
// }
// const data = await response.text();
// if (isMounted) {
// setCode(data);
// }
// } catch (err) {
// console.error('Failed to fetch code:', err);
// if (isMounted) {
// setError(err);
// setCode(`// Failed to load code from ${url}\n// ${err.message}`);
// }
// } finally {
// if (isMounted) {
// setLoading(false);
// }
// }
// };
// if (src) {
// fetchCodeFromUrl(src);
// }
// return () => {
// isMounted = false;
// };
// }, [src]);
// const githubUrl = src ? src.replace('https://raw.githubusercontent.com', 'https://github.com').replace('/refs/heads/', '/blob/') : null;
// const fileName = src ? src.substring(src.lastIndexOf('/') + 1) : null;
// return (
// loading ? (
// <div>Loading code...</div>
// ) : error ? (
// <div>Error: {error.message}</div>
// ) : (
// <div style={{ backgroundColor: 'transparent', padding: '0px', borderRadius: '5px' }}>
// <div style={{ textAlign: 'right' }}>
// {githubUrl && (
// <a href={githubUrl} target="_blank" rel="noopener noreferrer" style={{ paddingRight: '15px', color: 'gray', fontSize: '0.8em', fontStyle: 'italic', display: 'inline-flex', alignItems: 'center' }}>
// View on GitHub
// <Icon icon="mdi:github" height="48" />
// </a>
// )}
// </div>
// <CodeBlock title={fileName} className="language-java">{code}</CodeBlock>
// </div>
// )
// );
// };
// export default CodeEmbed;
import React, { useState, useEffect } from 'react';
import CodeBlock from '@theme/CodeBlock';
import Icon from '@site/src/components/Icon';
const CodeEmbed = ({ src }) => {
const [code, setCode] = useState('');
const [loading, setLoading] = useState(true);
const [error, setError] = useState(null);
useEffect(() => {
let isMounted = true;
const fetchCodeFromUrl = async (url) => {
if (!isMounted) return;
setLoading(true);
setError(null);
try {
const response = await fetch(url);
if (!response.ok) {
throw new Error(`HTTP error! status: ${response.status}`);
}
const data = await response.text();
if (isMounted) {
setCode(data);
}
} catch (err) {
console.error('Failed to fetch code:', err);
if (isMounted) {
setError(err);
setCode(`// Failed to load code from ${url}\n// ${err.message}`);
}
} finally {
if (isMounted) {
setLoading(false);
}
}
};
if (src) {
fetchCodeFromUrl(src);
}
return () => {
isMounted = false;
};
}, [src]);
const githubUrl = src ? src.replace('https://raw.githubusercontent.com', 'https://github.com').replace('/refs/heads/', '/blob/') : null;
const fileName = src ? src.substring(src.lastIndexOf('/') + 1) : null;
const title = (
<div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
<a
href={githubUrl}
target="_blank"
rel="noopener noreferrer"
style={{
color: 'gray',
textDecoration: 'none',
}}
onMouseOver={e => {
e.target.style.textDecoration = 'underline';
}}
onMouseOut={e => {
e.target.style.textDecoration = 'none';
}}
>
<span>{fileName}</span>
</a>
{githubUrl && (
<a href={githubUrl} target="_blank" rel="noopener noreferrer" style={{ color: 'gray', fontSize: '0.9em', fontStyle: 'italic', display: 'inline-flex', alignItems: 'center' }}>
View on GitHub
<Icon icon="mdi:github" height="1em" />
</a>
)}
</div>
);
return (
loading ? (
<div>Loading code...</div>
) : error ? (
<div>Error: {error.message}</div>
) : (
<div style={{ backgroundColor: 'transparent', padding: '0px', borderRadius: '5px' }}>
<CodeBlock title={title} className="language-java">{code}</CodeBlock>
</div>
)
);
};
export default CodeEmbed;

View File

@ -1,107 +1,39 @@
import clsx from 'clsx';
import Heading from '@theme/Heading';
import styles from './styles.module.css';
import Ollama4jUIImageCarousel from '@site/src/components/Ollama4jUIImageCarousel';
const FeatureList = [
{
title: 'Easy LLM Integration',
Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default,
description: (
<>
Easy integration with Ollama, enabling the execution of large language models locally.
</>
),
},
{
title: 'Developer-Friendly',
Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default,
description: (
<>
Provides clean and simple <code>APIs</code> for interacting with Ollama, including model management, chat functionalities, and authentication capability when Ollama is deployed behind proxy server.
Clean and simple <code>APIs</code>, focused on seamless interaction with Ollama.
</>
),
},
{
title: 'Chat with Images',
Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default,
description: (
<>
Supports interactions with vision/image models, allowing you to build applications that can understand and respond to visual content. Upload images directly into your chat sessions and receive intelligent, context-aware replies.
</>
),
},
{
title: 'Tools Support',
title: 'Powered by Java',
Svg: require('@site/static/img/undraw_docusaurus_react.svg').default,
description: (
<>
Supports tool/function calling with tool calling models such as mistral, llama3.x, qwen, etc. Empower your models to interact with external services and data sources, enabling more complex and dynamic AI workflows.
Empowers Java developers to harness the full capabilities of Ollama.
</>
),
},
];
const UsageList = [
{
title: 'Datafaker',
Svg: require('@site/static/img/datafaker.svg').default,
link: 'https://www.datafaker.net/',
description: (
<>
A powerful fake data generation library designed for JVM programs, offering over 200 data providers to easily create realistic and diverse datasets within minutes.
</>
),
imageSize: '20%',
},
{
title: 'Katie',
Svg: require('@site/static/img/katie_logo_v3.svg').default,
link: 'https://katie.qa/home',
description: (
<>
An Open Source AI-based question-answering platform that helps companies and organizations make their private domain knowledge accessible and useful to their employees and customers.
</>
),
imageSize: '30%',
},
{
title: 'AI Player',
Svg: require('@site/static/img/ai-player.svg').default,
link: 'https://modrinth.com/mod/ai-player',
description: (
<>
A minecraft mod which aims to add a "second player" into the game which will actually be intelligent.
</>
),
imageSize: '15%',
},
{
title: 'Ollama Translator Plugin',
Svg: require('@site/static/img/minecraft-spigot.svg').default,
link: 'https://github.com/liebki/ollama-translator',
description: (
<>
A minecraft 1.21 spigot plugin allows to easily break language barriers by using ollama on the server to translate all messages into a specfic target language.
</>
),
imageSize: '20%',
},
{
title: 'JnsCLI',
Svg: require('@site/static/img/jnscli.svg').default,
link: 'https://github.com/mirum8/jnscli',
description: (
<>
JnsCLI is a command-line tool for Jenkins, allowing you to manage jobs, builds, and configurations directly from the terminal. It also features AI-powered error analysis for quick troubleshooting.
</>
),
imageSize: '20%',
},
{
title: 'Featured in a Research Article on AI-Assisted Code Optimization',
Svg: require('@site/static/img/pmc-logo.svg').default,
link: 'https://pmc.ncbi.nlm.nih.gov/articles/PMC11750896/',
description: (
<>
Ollama4j was used in a research article Large Language Model Based Mutations in Genetic Improvement (PubMed Central).
</>
),
imageSize: '50%',
},
];
function Feature({ Svg, title, description }) {
function Feature({Svg, title, description}) {
return (
<div className={clsx('col col--4')}>
<div className="text--center">
@ -115,58 +47,16 @@ function Feature({ Svg, title, description }) {
);
}
function Usage({ Svg, title, description, link, imageSize }) {
return (
<div className={clsx('col col--4')}>
<div className="text--center">
<Svg className={styles.featureSvg} role="img" style={{ width: imageSize }} />
</div>
<div className="text--center padding-horiz--md">
<Heading as="h3" style={{ color: 'red' }}>
{link ? (
<a href={link} target="_blank" rel="noopener noreferrer" style={{ color: '#11bc11' }}>
{title}
</a>
) : (
<span style={{ color: 'red' }}>{title}</span>
)}
</Heading>
<p>{description}</p>
</div>
</div>
);
}
export default function HomepageFeatures() {
return (
<>
<section className={styles.features}>
<div className="container">
<div className="row">
{FeatureList.map((props, idx) => (
<Feature key={idx} {...props} />
))}
</div>
<section className={styles.features}>
<div className="container">
<div className="row">
{FeatureList.map((props, idx) => (
<Feature key={idx} {...props} />
))}
</div>
</section>
<hr />
<section className={styles.features}>
<div className="container">
<div style={{ fontSize: '20px', fontWeight: 'bold', display: 'flex', flexDirection: 'column', alignItems: 'center' }}>
Check out who's using Ollama4j
</div>
<div className="row">
{UsageList.map((props, idx) => (
<Usage key={idx} {...props} />
))}
</div>
</div>
</section>
<section className={styles.features}>
<div className="container">
<Ollama4jUIImageCarousel></Ollama4jUIImageCarousel>
</div>
</section>
</>
</div>
</section>
);
}

View File

@ -1,9 +0,0 @@
// @site/src/components/Icon.js
import React from 'react';
import { Icon as IconifyIcon } from '@iconify/react';
const IIcon = ({ icon, color, width = '24', height = '24' }) => (
<IconifyIcon icon={icon} color={color} width={width} height={height} />
);
export default IIcon;

View File

@ -1,52 +0,0 @@
import React, { useState, useEffect } from 'react';
const LatestRelease = ({ showReleaseDate }) => {
const [releaseInfo, setReleaseInfo] = useState(null);
const [loading, setLoading] = useState(true);
const [error, setError] = useState(null);
useEffect(() => {
const fetchLatestRelease = async () => {
setLoading(true);
setError(null);
try {
const response = await fetch('https://api.github.com/repos/ollama4j/ollama4j/releases/latest');
if (!response.ok) {
throw new Error(`HTTP error! status: ${response.status}`);
}
const data = await response.json();
setReleaseInfo(data);
} catch (err) {
console.error('Failed to fetch release info:', err);
setError(err);
} finally {
setLoading(false);
}
};
fetchLatestRelease();
}, []);
return (
<div style={{ display: 'flex', justifyContent: 'center' }}>
{loading ? (
<div>Loading latest release info...</div>
) : error ? (
<div>Error: {error.message}</div>
) : releaseInfo ? (
<div>
{/* <h4 style={{ display: 'flex', justifyContent: 'center'}}>Latest Release</h4> */}
<div>
<span style={{ fontWeight: 'bold'}}>Latest Version</span>: <a href={releaseInfo.html_url} target='_blank' rel="noopener noreferrer"><span style={{color: 'white', fontWeight: 'bold', backgroundColor:'#11bc11', borderRadius: '15px', padding: '5px'}}>{releaseInfo.name}</span></a>
{showReleaseDate && ` released on ${new Date(releaseInfo.published_at).toLocaleDateString(undefined, { year: 'numeric', month: 'long', day: 'numeric' })}`}
</div>
{/* <pre style={{ whiteSpace: 'pre-wrap' }}>
{JSON.stringify(releaseInfo, null, 2)}
</pre> */}
</div>
) : null}
</div>
);
};
export default LatestRelease;

View File

@ -1,54 +0,0 @@
import React from 'react';
import "react-image-gallery/styles/css/image-gallery.css";
import ImageGallery from "react-image-gallery";
const images = [
{
original: require('@site/static/img/ollama4j-ui/1.png').default,
thumbnail: require('@site/static/img/ollama4j-ui/1.png').default,
},
{
original: require('@site/static/img/ollama4j-ui/2.png').default,
thumbnail: require('@site/static/img/ollama4j-ui/2.png').default,
},
{
original: require('@site/static/img/ollama4j-ui/3.png').default,
thumbnail: require('@site/static/img/ollama4j-ui/3.png').default,
},
{
original: require('@site/static/img/ollama4j-ui/4.png').default,
thumbnail: require('@site/static/img/ollama4j-ui/4.png').default,
},
];
class Ollama4jUIImageCarousel extends React.Component {
renderItem = (item) => {
return (
<div className="image-gallery-image" style={{ textAlign: 'center' }}>
<img
src={item.original}
alt=""
style={{ maxHeight: '500px', width: 'auto', maxWidth: '100%' }}
/>
</div>
);
};
render() {
return (
<div style={{ margin: '0 auto', maxWidth: '800px' }}>
<div style={{ display: 'flex', flexDirection: 'column', justifyContent: 'center', alignItems: 'center', textAlign: 'center' }}>
<h2>Explore the stuff we have built with Ollama4j</h2>
<h4>
<a href="https://github.com/ollama4j/ollama4j-ui" target='_blank' rel="noopener noreferrer">
Ollama4j UI - Desktop UI built in Java with Swing
</a>
</h4>
</div>
<ImageGallery items={images} renderItem={this.renderItem} />
</div>
);
}
}
export default Ollama4jUIImageCarousel;

View File

@ -1,66 +0,0 @@
import React, { useEffect, useState, useRef } from 'react';
const TypewriterTextarea = ({ textContent, typingSpeed = 50, pauseBetweenSentences = 1000, height = '200px', width = '100%' }) => {
const [text, setText] = useState('');
const [sentenceIndex, setSentenceIndex] = useState(0);
const [charIndex, setCharIndex] = useState(0);
const sentences = textContent ? textContent.split('\n') : [];
const isTyping = useRef(false);
useEffect(() => {
if (!textContent) return;
if (!isTyping.current) {
isTyping.current = true;
}
if (sentenceIndex >= sentences.length) {
// Reset to start from the beginning
setSentenceIndex(0);
setCharIndex(0);
setText('');
return;
}
const currentSentence = sentences[sentenceIndex];
if (charIndex < currentSentence.length) {
const timeout = setTimeout(() => {
setText((prevText) => prevText + currentSentence[charIndex]);
setCharIndex((prevCharIndex) => prevCharIndex + 1);
}, typingSpeed);
return () => clearTimeout(timeout);
} else {
// Wait a bit, then go to the next sentence
const timeout = setTimeout(() => {
setSentenceIndex((prev) => prev + 1);
setCharIndex(0);
}, pauseBetweenSentences);
return () => clearTimeout(timeout);
}
}, [charIndex, sentenceIndex, sentences, typingSpeed, pauseBetweenSentences, textContent]);
return (
<textarea
value={text}
readOnly
rows={10}
cols={5}
style={{
width: typeof width === 'number' ? `${width}px` : width,
height: height,
padding: '1rem',
fontFamily: 'monospace',
fontSize: '1rem',
backgroundColor: '#f4f4f4',
border: '1px solid #ccc',
resize: 'none',
whiteSpace: 'pre-wrap',
}}
/>
);
};
export default TypewriterTextarea;

View File

@ -4,47 +4,27 @@
* work well for content-centric websites.
*/
@import 'font-awesome/css/font-awesome.min.css';
/* You can override the default Infima variables here. */
:root {
--ifm-color-primary: #2e8555;
--ifm-color-primary-dark: #29784c;
--ifm-color-primary-darker: #277148;
--ifm-color-primary-darkest: #205d3b;
--ifm-color-primary-light: #33925d;
--ifm-color-primary-lighter: #359962;
--ifm-color-primary-lightest: #3cad6e;
--ifm-code-font-size: 95%;
--docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1);
--ifm-color-primary: #2e8555;
--ifm-color-primary-dark: #29784c;
--ifm-color-primary-darker: #277148;
--ifm-color-primary-darkest: #205d3b;
--ifm-color-primary-light: #33925d;
--ifm-color-primary-lighter: #359962;
--ifm-color-primary-lightest: #3cad6e;
--ifm-code-font-size: 95%;
--docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1);
}
/* For readability concerns, you should choose a lighter palette in dark mode. */
[data-theme='dark'] {
--ifm-color-primary: #25c2a0;
--ifm-color-primary-dark: #21af90;
--ifm-color-primary-darker: #1fa588;
--ifm-color-primary-darkest: #1a8870;
--ifm-color-primary-light: #29d5b0;
--ifm-color-primary-lighter: #32d8b4;
--ifm-color-primary-lightest: #4fddbf;
--docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3);
--ifm-color-primary: #25c2a0;
--ifm-color-primary-dark: #21af90;
--ifm-color-primary-darker: #1fa588;
--ifm-color-primary-darkest: #1a8870;
--ifm-color-primary-light: #29d5b0;
--ifm-color-primary-lighter: #32d8b4;
--ifm-color-primary-lightest: #4fddbf;
--docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3);
}
article > header > h1 {
font-size: 2rem !important;
}
div > h1,
header > h1,
h2 > a {
font-size: 2rem !important;
}
#bmc-wbtn{
bottom: 15px;
}
#bmc-wbtn + div{
bottom:15px;
}

View File

@ -3,63 +3,38 @@ import Link from '@docusaurus/Link';
import useDocusaurusContext from '@docusaurus/useDocusaurusContext';
import Layout from '@theme/Layout';
import HomepageFeatures from '@site/src/components/HomepageFeatures';
import BuyMeACoffee from '@site/src/components/BuyMeACoffee';
import Heading from '@theme/Heading';
import styles from './index.module.css';
import BrowserOnly from '@docusaurus/BrowserOnly';
import LatestRelease from '@site/src/components/LatestRelease';
import TypewriterTextarea from '@site/src/components/TypewriterTextarea';
function HomepageHeader() {
const {siteConfig} = useDocusaurusContext();
return (
<header className={clsx('hero hero--primary', styles.heroBanner)}>
<div className="container">
<Heading as="h1" className="hero__title">
{siteConfig.title}
</Heading>
<img
src="img/logo.svg"
alt="Ollama4j Logo"
className={styles.logo}
style={{ maxWidth: '20vh' }}
/>
<p className="hero__subtitle">{siteConfig.tagline}</p>
<div style={{ marginTop: '2rem' }}>
<TypewriterTextarea
textContent='Hello there! Im a handy little Java library that helps you talk to an Ollama server — nice and easy.'
typingSpeed={30}
pauseBetweenSentences={1200}
height='130px'
width='100%'
/>
const {siteConfig} = useDocusaurusContext();
return (<header className={clsx('hero hero--primary', styles.heroBanner)}>
<div className="container">
<Heading as="h1" className="hero__title">
{siteConfig.title}
</Heading>
<img src="img/logo.svg" alt="Ollama4j Logo" className={styles.logo} style={{maxWidth: '20vh'}}/>
<p className="hero__subtitle">{siteConfig.tagline}</p>
<div className={styles.buttons}>
<Link
className="button button--secondary button--lg"
to="/docs/intro">
Getting Started
</Link>
</div>
</div>
<div className={styles.buttons} >
<Link className="button button--secondary button--lg" to="/intro" style={{ marginTop:'2rem' }}>
Get Started
</Link>
</div>
<div style={{ marginTop: '3rem' }}>
<LatestRelease showReleaseDate={false} />
</div>
</div>
</header>
);
</header>);
}
export default function Home() {
const {siteConfig} = useDocusaurusContext();
return (
<Layout
title={`${siteConfig.title}`}
description="Description will go into a meta tag in <head />">
<HomepageHeader />
<main>
<HomepageFeatures />
<BrowserOnly>
{() => <BuyMeACoffee />}
</BrowserOnly>
</main>
</Layout>
);
}
const {siteConfig} = useDocusaurusContext();
return (<Layout
title={`Hello from ${siteConfig.title}`}
description="Description will go into a meta tag in <head />">
<HomepageHeader/>
<main>
<HomepageFeatures/>
</main>
</Layout>);
}

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250
pom.xml
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@ -1,20 +1,18 @@
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>io.github.ollama4j</groupId>
<groupId>io.github.amithkoujalgi</groupId>
<artifactId>ollama4j</artifactId>
<version>ollama4j-revision</version>
<version>1.0.28</version>
<name>Ollama4j</name>
<description>Java library for interacting with Ollama API.</description>
<url>https://github.com/ollama4j/ollama4j</url>
<packaging>jar</packaging>
<url>https://github.com/amithkoujalgi/ollama4j</url>
<properties>
<maven.compiler.release>11</maven.compiler.release>
<project.build.outputTimestamp>${git.commit.time}</project.build.outputTimestamp><!-- populated via git-commit-id-plugin -->
<maven.compiler.source>11</maven.compiler.source>
<maven.compiler.target>11</maven.compiler.target>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<maven-surefire-plugin.version>3.0.0-M5</maven-surefire-plugin.version>
<maven-failsafe-plugin.version>3.0.0-M5</maven-failsafe-plugin.version>
@ -33,15 +31,15 @@
<licenses>
<license>
<name>MIT License</name>
<url>https://raw.githubusercontent.com/ollama4j/ollama4j/main/LICENSE</url>
<url>https://raw.githubusercontent.com/amithkoujalgi/ollama4j/main/LICENSE</url>
</license>
</licenses>
<scm>
<connection>scm:git:git@github.com:ollama4j/ollama4j.git</connection>
<developerConnection>scm:git:https://github.com/ollama4j/ollama4j.git</developerConnection>
<url>https://github.com/ollama4j/ollama4j</url>
<tag>ollama4j-revision</tag>
<connection>scm:git:git@github.com:amithkoujalgi/ollama4j.git</connection>
<developerConnection>scm:git:https://github.com/amithkoujalgi/ollama4j.git</developerConnection>
<url>https://github.com/amithkoujalgi/ollama4j</url>
<tag>v1.0.28</tag>
</scm>
<build>
@ -49,7 +47,7 @@
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-source-plugin</artifactId>
<version>3.3.1</version>
<version>3.3.0</version>
<executions>
<execution>
<id>attach-sources</id>
@ -62,25 +60,37 @@
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-javadoc-plugin</artifactId>
<version>3.11.2</version>
<configuration>
<!-- to disable the "missing" warnings. Remove the doclint to enable warnings-->
<doclint>all,-missing</doclint>
</configuration>
<version>3.5.0</version>
<executions>
<execution>
<id>attach-javadocs</id>
<phase>package</phase>
<goals>
<goal>jar</goal>
</goals>
<configuration>
<outputDirectory>${project.build.directory}</outputDirectory>
</configuration>
</execution>
</executions>
</plugin>
<!-- <plugin>-->
<!-- <groupId>org.apache.maven.plugins</groupId>-->
<!-- <artifactId>maven-gpg-plugin</artifactId>-->
<!-- <version>1.5</version>-->
<!-- <executions>-->
<!-- <execution>-->
<!-- <id>sign-artifacts</id>-->
<!-- <phase>verify</phase>-->
<!-- <goals>-->
<!-- <goal>sign</goal>-->
<!-- </goals>-->
<!-- <configuration>-->
<!-- &lt;!&ndash; This is necessary for gpg to not try to use the pinentry programs &ndash;&gt;-->
<!-- <gpgArguments>-->
<!-- <arg>&#45;&#45;pinentry-mode</arg>-->
<!-- <arg>loopback</arg>-->
<!-- </gpgArguments>-->
<!-- </configuration>-->
<!-- </execution>-->
<!-- </executions>-->
<!-- </plugin>-->
<!-- Surefire Plugin for Unit Tests -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
@ -89,7 +99,7 @@
<configuration>
<skipTests>${skipUnitTests}</skipTests>
<includes>
<include>**/unittests/**/*.java</include>
<include>**/unittests/*.java</include>
</includes>
</configuration>
</plugin>
@ -117,52 +127,16 @@
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-gpg-plugin</artifactId>
<version>1.5</version>
<executions>
<execution>
<id>sign-artifacts</id>
<phase>verify</phase>
<goals>
<goal>sign</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>io.github.git-commit-id</groupId>
<artifactId>git-commit-id-maven-plugin</artifactId>
<version>9.0.1</version>
<executions>
<execution>
<goals>
<goal>revision</goal>
</goals>
</execution>
</executions>
<artifactId>maven-release-plugin</artifactId>
<version>3.0.1</version>
<configuration>
<dateFormat>yyyy-MM-dd'T'HH:mm:ss'Z'</dateFormat>
<dateFormatTimeZone>Etc/UTC</dateFormatTimeZone>
<!-- <goals>install</goals>-->
<tagNameFormat>v@{project.version}</tagNameFormat>
</configuration>
</plugin>
</plugins>
<pluginManagement>
<plugins>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.14.0</version>
</plugin>
<plugin>
<artifactId>maven-jar-plugin</artifactId>
<version>3.4.2</version>
</plugin>
</plugins>
</pluginManagement>
</build>
<dependencies>
@ -172,25 +146,15 @@
<version>${lombok.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.jsoup</groupId>
<artifactId>jsoup</artifactId>
<version>1.18.1</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>2.17.1</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.datatype</groupId>
<artifactId>jackson-datatype-jsr310</artifactId>
<version>2.17.1</version>
<version>2.15.3</version>
</dependency>
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>
<version>1.5.6</version>
<version>1.3.11</version>
<scope>test</scope>
</dependency>
<dependency>
@ -210,105 +174,30 @@
<version>4.1.0</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.json</groupId>
<artifactId>json</artifactId>
<version>20240205</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.testcontainers</groupId>
<artifactId>ollama</artifactId>
<version>1.20.2</version>
<scope>test</scope>
</dependency>
</dependencies>
<distributionManagement>
<snapshotRepository>
<id>ossrh</id>
<url>https://s01.oss.sonatype.org/content/repositories/snapshots</url>
</snapshotRepository>
<repository>
<id>mvn-repo-id</id>
<id>ossrh</id>
<url>https://s01.oss.sonatype.org/service/local/staging/deploy/maven2</url>
</repository>
</distributionManagement>
<profiles>
<profile>
<id>ossrh</id>
<activation>
<activeByDefault>true</activeByDefault>
</activation>
<properties>
<gpg.executable>gpg2</gpg.executable>
<test.env>unit</test.env>
<skipUnitTests>false</skipUnitTests>
<skipIntegrationTests>true</skipIntegrationTests>
</properties>
<build>
<plugins>
<plugin>
<groupId>org.sonatype.central</groupId>
<artifactId>central-publishing-maven-plugin</artifactId>
<version>0.5.0</version>
<extensions>true</extensions>
<configuration>
<publishingServerId>mvn-repo-id</publishingServerId>
<autoPublish>true</autoPublish>
</configuration>
</plugin>
</plugins>
</build>
</profile>
<profile>
<id>unit-tests</id>
<properties>
<test.env>unit</test.env>
<skipUnitTests>false</skipUnitTests>
<skipIntegrationTests>true</skipIntegrationTests>
<skipGpgPluginDuringTests>true</skipGpgPluginDuringTests>
</properties>
<activation>
<activeByDefault>false</activeByDefault>
<activeByDefault>true</activeByDefault>
</activation>
<build>
<plugins>
<plugin>
<groupId>org.jacoco</groupId>
<artifactId>jacoco-maven-plugin</artifactId>
<version>0.8.11</version>
<executions>
<execution>
<goals>
<goal>prepare-agent</goal>
</goals>
</execution>
<execution>
<id>report</id>
<phase>test</phase>
<goals>
<goal>report</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-gpg-plugin</artifactId>
<version>1.5</version>
<executions>
<execution>
<id>sign-artifacts</id>
<phase>verify</phase>
<goals>
<goal>sign</goal>
</goals>
<configuration>
<skip>${skipGpgPluginDuringTests}</skip>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</profile>
<profile>
<id>integration-tests</id>
@ -316,29 +205,7 @@
<test.env>integration</test.env>
<skipUnitTests>true</skipUnitTests>
<skipIntegrationTests>false</skipIntegrationTests>
<skipGpgPluginDuringTests>true</skipGpgPluginDuringTests>
</properties>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-gpg-plugin</artifactId>
<version>1.5</version>
<executions>
<execution>
<id>sign-artifacts</id>
<phase>verify</phase>
<goals>
<goal>sign</goal>
</goals>
<configuration>
<skip>${skipGpgPluginDuringTests}</skip>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</profile>
<profile>
<id>ci-cd</id>
@ -382,28 +249,9 @@
<autoReleaseAfterClose>true</autoReleaseAfterClose>
</configuration>
</plugin>
<plugin>
<groupId>org.jacoco</groupId>
<artifactId>jacoco-maven-plugin</artifactId>
<version>0.8.7</version>
<executions>
<execution>
<goals>
<goal>prepare-agent</goal>
</goals>
</execution>
<execution>
<id>report</id>
<phase>test</phase>
<goals>
<goal>report</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</profile>
</profiles>
</project>
</project>

View File

@ -0,0 +1,466 @@
package io.github.amithkoujalgi.ollama4j.core;
import io.github.amithkoujalgi.ollama4j.core.exceptions.OllamaBaseException;
import io.github.amithkoujalgi.ollama4j.core.models.*;
import io.github.amithkoujalgi.ollama4j.core.models.request.CustomModelFileContentsRequest;
import io.github.amithkoujalgi.ollama4j.core.models.request.CustomModelFilePathRequest;
import io.github.amithkoujalgi.ollama4j.core.models.request.ModelEmbeddingsRequest;
import io.github.amithkoujalgi.ollama4j.core.models.request.ModelRequest;
import io.github.amithkoujalgi.ollama4j.core.utils.Utils;
import java.io.BufferedReader;
import java.io.ByteArrayOutputStream;
import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.net.URI;
import java.net.URISyntaxException;
import java.net.URL;
import java.net.http.HttpClient;
import java.net.http.HttpConnectTimeoutException;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.time.Duration;
import java.util.ArrayList;
import java.util.Base64;
import java.util.List;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/** The base Ollama API class. */
@SuppressWarnings("DuplicatedCode")
public class OllamaAPI {
private static final Logger logger = LoggerFactory.getLogger(OllamaAPI.class);
private final String host;
private long requestTimeoutSeconds = 3;
private boolean verbose = true;
/**
* Instantiates the Ollama API.
*
* @param host the host address of Ollama server
*/
public OllamaAPI(String host) {
if (host.endsWith("/")) {
this.host = host.substring(0, host.length() - 1);
} else {
this.host = host;
}
}
public void setRequestTimeoutSeconds(long requestTimeoutSeconds) {
this.requestTimeoutSeconds = requestTimeoutSeconds;
}
/**
* Set/unset logging of responses
*
* @param verbose true/false
*/
public void setVerbose(boolean verbose) {
this.verbose = verbose;
}
/**
* API to check the reachability of Ollama server.
*
* @return true if the server is reachable, false otherwise.
*/
public boolean ping() {
String url = this.host + "/api/tags";
HttpClient httpClient = HttpClient.newHttpClient();
HttpRequest httpRequest = null;
try {
httpRequest =
HttpRequest.newBuilder()
.uri(new URI(url))
.header("Accept", "application/json")
.header("Content-type", "application/json")
.timeout(Duration.ofSeconds(requestTimeoutSeconds))
.GET()
.build();
} catch (URISyntaxException e) {
throw new RuntimeException(e);
}
HttpResponse<String> response = null;
try {
response = httpClient.send(httpRequest, HttpResponse.BodyHandlers.ofString());
} catch (HttpConnectTimeoutException e) {
return false;
} catch (IOException | InterruptedException e) {
throw new RuntimeException(e);
}
int statusCode = response.statusCode();
return statusCode == 200;
}
/**
* List available models from Ollama server.
*
* @return the list
*/
public List<Model> listModels()
throws OllamaBaseException, IOException, InterruptedException, URISyntaxException {
String url = this.host + "/api/tags";
HttpClient httpClient = HttpClient.newHttpClient();
HttpRequest httpRequest =
HttpRequest.newBuilder()
.uri(new URI(url))
.header("Accept", "application/json")
.header("Content-type", "application/json")
.timeout(Duration.ofSeconds(requestTimeoutSeconds))
.GET()
.build();
HttpResponse<String> response =
httpClient.send(httpRequest, HttpResponse.BodyHandlers.ofString());
int statusCode = response.statusCode();
String responseString = response.body();
if (statusCode == 200) {
return Utils.getObjectMapper()
.readValue(responseString, ListModelsResponse.class)
.getModels();
} else {
throw new OllamaBaseException(statusCode + " - " + responseString);
}
}
/**
* Pull a model on the Ollama server from the list of <a
* href="https://ollama.ai/library">available models</a>.
*
* @param modelName the name of the model
*/
public void pullModel(String modelName)
throws OllamaBaseException, IOException, URISyntaxException, InterruptedException {
String url = this.host + "/api/pull";
String jsonData = new ModelRequest(modelName).toString();
HttpRequest request =
HttpRequest.newBuilder()
.uri(new URI(url))
.POST(HttpRequest.BodyPublishers.ofString(jsonData))
.header("Accept", "application/json")
.header("Content-type", "application/json")
.timeout(Duration.ofSeconds(requestTimeoutSeconds))
.build();
HttpClient client = HttpClient.newHttpClient();
HttpResponse<InputStream> response =
client.send(request, HttpResponse.BodyHandlers.ofInputStream());
int statusCode = response.statusCode();
InputStream responseBodyStream = response.body();
String responseString = "";
try (BufferedReader reader =
new BufferedReader(new InputStreamReader(responseBodyStream, StandardCharsets.UTF_8))) {
String line;
while ((line = reader.readLine()) != null) {
ModelPullResponse modelPullResponse =
Utils.getObjectMapper().readValue(line, ModelPullResponse.class);
if (verbose) {
logger.info(modelPullResponse.getStatus());
}
}
}
if (statusCode != 200) {
throw new OllamaBaseException(statusCode + " - " + responseString);
}
}
/**
* Gets model details from the Ollama server.
*
* @param modelName the model
* @return the model details
*/
public ModelDetail getModelDetails(String modelName)
throws IOException, OllamaBaseException, InterruptedException {
String url = this.host + "/api/show";
String jsonData = new ModelRequest(modelName).toString();
HttpRequest request =
HttpRequest.newBuilder()
.uri(URI.create(url))
.header("Accept", "application/json")
.header("Content-type", "application/json")
.timeout(Duration.ofSeconds(requestTimeoutSeconds))
.POST(HttpRequest.BodyPublishers.ofString(jsonData))
.build();
HttpClient client = HttpClient.newHttpClient();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
int statusCode = response.statusCode();
String responseBody = response.body();
if (statusCode == 200) {
return Utils.getObjectMapper().readValue(responseBody, ModelDetail.class);
} else {
throw new OllamaBaseException(statusCode + " - " + responseBody);
}
}
/**
* Create a custom model from a model file. Read more about custom model file creation <a
* href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md">here</a>.
*
* @param modelName the name of the custom model to be created.
* @param modelFilePath the path to model file that exists on the Ollama server.
*/
public void createModelWithFilePath(String modelName, String modelFilePath)
throws IOException, InterruptedException, OllamaBaseException {
String url = this.host + "/api/create";
String jsonData = new CustomModelFilePathRequest(modelName, modelFilePath).toString();
HttpRequest request =
HttpRequest.newBuilder()
.uri(URI.create(url))
.header("Accept", "application/json")
.header("Content-Type", "application/json")
.timeout(Duration.ofSeconds(requestTimeoutSeconds))
.POST(HttpRequest.BodyPublishers.ofString(jsonData, StandardCharsets.UTF_8))
.build();
HttpClient client = HttpClient.newHttpClient();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
int statusCode = response.statusCode();
String responseString = response.body();
if (statusCode != 200) {
throw new OllamaBaseException(statusCode + " - " + responseString);
}
// FIXME: Ollama API returns HTTP status code 200 for model creation failure cases. Correct this
// if the issue is fixed in the Ollama API server.
if (responseString.contains("error")) {
throw new OllamaBaseException(responseString);
}
if (verbose) {
logger.info(responseString);
}
}
/**
* Create a custom model from a model file. Read more about custom model file creation <a
* href="https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md">here</a>.
*
* @param modelName the name of the custom model to be created.
* @param modelFileContents the path to model file that exists on the Ollama server.
*/
public void createModelWithModelFileContents(String modelName, String modelFileContents)
throws IOException, InterruptedException, OllamaBaseException {
String url = this.host + "/api/create";
String jsonData = new CustomModelFileContentsRequest(modelName, modelFileContents).toString();
HttpRequest request =
HttpRequest.newBuilder()
.uri(URI.create(url))
.header("Accept", "application/json")
.header("Content-Type", "application/json")
.timeout(Duration.ofSeconds(requestTimeoutSeconds))
.POST(HttpRequest.BodyPublishers.ofString(jsonData, StandardCharsets.UTF_8))
.build();
HttpClient client = HttpClient.newHttpClient();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
int statusCode = response.statusCode();
String responseString = response.body();
if (statusCode != 200) {
throw new OllamaBaseException(statusCode + " - " + responseString);
}
if (responseString.contains("error")) {
throw new OllamaBaseException(responseString);
}
if (verbose) {
logger.info(responseString);
}
}
/**
* Delete a model from Ollama server.
*
* @param modelName the name of the model to be deleted.
* @param ignoreIfNotPresent - ignore errors if the specified model is not present on Ollama
* server.
*/
public void deleteModel(String modelName, boolean ignoreIfNotPresent)
throws IOException, InterruptedException, OllamaBaseException {
String url = this.host + "/api/delete";
String jsonData = new ModelRequest(modelName).toString();
HttpRequest request =
HttpRequest.newBuilder()
.uri(URI.create(url))
.method("DELETE", HttpRequest.BodyPublishers.ofString(jsonData, StandardCharsets.UTF_8))
.header("Accept", "application/json")
.header("Content-type", "application/json")
.timeout(Duration.ofSeconds(requestTimeoutSeconds))
.build();
HttpClient client = HttpClient.newHttpClient();
HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
int statusCode = response.statusCode();
String responseBody = response.body();
if (statusCode == 404 && responseBody.contains("model") && responseBody.contains("not found")) {
return;
}
if (statusCode != 200) {
throw new OllamaBaseException(statusCode + " - " + responseBody);
}
}
/**
* Generate embeddings for a given text from a model
*
* @param model name of model to generate embeddings from
* @param prompt text to generate embeddings for
* @return embeddings
*/
public List<Double> generateEmbeddings(String model, String prompt)
throws IOException, InterruptedException, OllamaBaseException {
String url = this.host + "/api/embeddings";
String jsonData = new ModelEmbeddingsRequest(model, prompt).toString();
HttpClient httpClient = HttpClient.newHttpClient();
HttpRequest request =
HttpRequest.newBuilder()
.uri(URI.create(url))
.header("Accept", "application/json")
.header("Content-type", "application/json")
.timeout(Duration.ofSeconds(requestTimeoutSeconds))
.POST(HttpRequest.BodyPublishers.ofString(jsonData))
.build();
HttpResponse<String> response = httpClient.send(request, HttpResponse.BodyHandlers.ofString());
int statusCode = response.statusCode();
String responseBody = response.body();
if (statusCode == 200) {
EmbeddingResponse embeddingResponse =
Utils.getObjectMapper().readValue(responseBody, EmbeddingResponse.class);
return embeddingResponse.getEmbedding();
} else {
throw new OllamaBaseException(statusCode + " - " + responseBody);
}
}
/**
* Ask a question to a model running on Ollama server. This is a sync/blocking call.
*
* @param model the ollama model to ask the question to
* @param promptText the prompt/question text
* @return OllamaResult - that includes response text and time taken for response
*/
public OllamaResult ask(String model, String promptText)
throws OllamaBaseException, IOException, InterruptedException {
OllamaRequestModel ollamaRequestModel = new OllamaRequestModel(model, promptText);
return askSync(ollamaRequestModel);
}
/**
* Ask a question to a model running on Ollama server and get a callback handle that can be used
* to check for status and get the response from the model later. This would be an
* async/non-blocking call.
*
* @param model the ollama model to ask the question to
* @param promptText the prompt/question text
* @return the ollama async result callback handle
*/
public OllamaAsyncResultCallback askAsync(String model, String promptText) {
OllamaRequestModel ollamaRequestModel = new OllamaRequestModel(model, promptText);
HttpClient httpClient = HttpClient.newHttpClient();
URI uri = URI.create(this.host + "/api/generate");
OllamaAsyncResultCallback ollamaAsyncResultCallback =
new OllamaAsyncResultCallback(httpClient, uri, ollamaRequestModel, requestTimeoutSeconds);
ollamaAsyncResultCallback.start();
return ollamaAsyncResultCallback;
}
/**
* With one or more image files, ask a question to a model running on Ollama server. This is a
* sync/blocking call.
*
* @param model the ollama model to ask the question to
* @param promptText the prompt/question text
* @param imageFiles the list of image files to use for the question
* @return OllamaResult - that includes response text and time taken for response
*/
public OllamaResult askWithImageFiles(String model, String promptText, List<File> imageFiles)
throws OllamaBaseException, IOException, InterruptedException {
List<String> images = new ArrayList<>();
for (File imageFile : imageFiles) {
images.add(encodeFileToBase64(imageFile));
}
OllamaRequestModel ollamaRequestModel = new OllamaRequestModel(model, promptText, images);
return askSync(ollamaRequestModel);
}
/**
* With one or more image URLs, ask a question to a model running on Ollama server. This is a
* sync/blocking call.
*
* @param model the ollama model to ask the question to
* @param promptText the prompt/question text
* @param imageURLs the list of image URLs to use for the question
* @return OllamaResult - that includes response text and time taken for response
*/
public OllamaResult askWithImageURLs(String model, String promptText, List<String> imageURLs)
throws OllamaBaseException, IOException, InterruptedException, URISyntaxException {
List<String> images = new ArrayList<>();
for (String imageURL : imageURLs) {
images.add(encodeByteArrayToBase64(loadImageBytesFromUrl(imageURL)));
}
OllamaRequestModel ollamaRequestModel = new OllamaRequestModel(model, promptText, images);
return askSync(ollamaRequestModel);
}
private static String encodeFileToBase64(File file) throws IOException {
return Base64.getEncoder().encodeToString(Files.readAllBytes(file.toPath()));
}
private static String encodeByteArrayToBase64(byte[] bytes) {
return Base64.getEncoder().encodeToString(bytes);
}
private static byte[] loadImageBytesFromUrl(String imageUrl)
throws IOException, URISyntaxException {
URL url = new URI(imageUrl).toURL();
try (InputStream in = url.openStream();
ByteArrayOutputStream out = new ByteArrayOutputStream()) {
byte[] buffer = new byte[1024];
int bytesRead;
while ((bytesRead = in.read(buffer)) != -1) {
out.write(buffer, 0, bytesRead);
}
return out.toByteArray();
}
}
private OllamaResult askSync(OllamaRequestModel ollamaRequestModel)
throws OllamaBaseException, IOException, InterruptedException {
long startTime = System.currentTimeMillis();
HttpClient httpClient = HttpClient.newHttpClient();
URI uri = URI.create(this.host + "/api/generate");
HttpRequest request =
HttpRequest.newBuilder(uri)
.POST(
HttpRequest.BodyPublishers.ofString(
Utils.getObjectMapper().writeValueAsString(ollamaRequestModel)))
.header("Content-Type", "application/json")
.timeout(Duration.ofSeconds(requestTimeoutSeconds))
.build();
HttpResponse<InputStream> response =
httpClient.send(request, HttpResponse.BodyHandlers.ofInputStream());
int statusCode = response.statusCode();
InputStream responseBodyStream = response.body();
StringBuilder responseBuffer = new StringBuilder();
try (BufferedReader reader =
new BufferedReader(new InputStreamReader(responseBodyStream, StandardCharsets.UTF_8))) {
String line;
while ((line = reader.readLine()) != null) {
if (statusCode == 404) {
OllamaErrorResponseModel ollamaResponseModel =
Utils.getObjectMapper().readValue(line, OllamaErrorResponseModel.class);
responseBuffer.append(ollamaResponseModel.getError());
} else {
OllamaResponseModel ollamaResponseModel =
Utils.getObjectMapper().readValue(line, OllamaResponseModel.class);
if (!ollamaResponseModel.isDone()) {
responseBuffer.append(ollamaResponseModel.getResponse());
}
}
}
}
if (statusCode != 200) {
throw new OllamaBaseException(responseBuffer.toString());
} else {
long endTime = System.currentTimeMillis();
return new OllamaResult(responseBuffer.toString().trim(), endTime - startTime, statusCode);
}
}
}

View File

@ -1,4 +1,4 @@
package io.github.ollama4j.exceptions;
package io.github.amithkoujalgi.ollama4j.core.exceptions;
public class OllamaBaseException extends Exception {

View File

@ -1,4 +1,4 @@
package io.github.ollama4j.models.embeddings;
package io.github.amithkoujalgi.ollama4j.core.models;
import com.fasterxml.jackson.annotation.JsonProperty;
@ -7,8 +7,7 @@ import lombok.Data;
@SuppressWarnings("unused")
@Data
@Deprecated(since="1.0.90")
public class OllamaEmbeddingResponseModel {
public class EmbeddingResponse {
@JsonProperty("embedding")
private List<Double> embedding;
}

View File

@ -1,7 +1,6 @@
package io.github.ollama4j.models.response;
package io.github.amithkoujalgi.ollama4j.core.models;
import java.util.List;
import lombok.Data;
@Data

View File

@ -0,0 +1,36 @@
package io.github.amithkoujalgi.ollama4j.core.models;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
@Data
public class Model {
private String name;
@JsonProperty("modified_at")
private String modifiedAt;
private String digest;
private long size;
@JsonProperty("details")
private ModelMeta modelMeta;
/**
* Returns the model name without its version
*
* @return model name
*/
public String getModelName() {
return name.split(":")[0];
}
/**
* Returns the model version without its name
*
* @return model version
*/
public String getModelVersion() {
return name.split(":")[1];
}
}

View File

@ -0,0 +1,20 @@
package io.github.amithkoujalgi.ollama4j.core.models;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.Map;
import lombok.Data;
@Data
@JsonIgnoreProperties(ignoreUnknown = true)
public class ModelDetail {
private String license;
@JsonProperty("modelfile")
private String modelFile;
private String parameters;
private String template;
private String system;
private Map<String, String> details;
}

View File

@ -1,9 +1,7 @@
package io.github.ollama4j.models.response;
package io.github.amithkoujalgi.ollama4j.core.models;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.core.JsonProcessingException;
import io.github.ollama4j.utils.Utils;
import lombok.Data;
@Data
@ -23,13 +21,4 @@ public class ModelMeta {
@JsonProperty("quantization_level")
private String quantizationLevel;
@Override
public String toString() {
try {
return Utils.getObjectMapper().writerWithDefaultPrettyPrinter().writeValueAsString(this);
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
}
}

View File

@ -1,4 +1,4 @@
package io.github.ollama4j.models.response;
package io.github.amithkoujalgi.ollama4j.core.models;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import lombok.Data;

View File

@ -0,0 +1,155 @@
package io.github.amithkoujalgi.ollama4j.core.models;
import io.github.amithkoujalgi.ollama4j.core.exceptions.OllamaBaseException;
import io.github.amithkoujalgi.ollama4j.core.utils.Utils;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStream;
import java.io.InputStreamReader;
import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.nio.charset.StandardCharsets;
import java.time.Duration;
import java.util.LinkedList;
import java.util.Queue;
@SuppressWarnings("unused")
public class OllamaAsyncResultCallback extends Thread {
private final HttpClient client;
private final URI uri;
private final OllamaRequestModel ollamaRequestModel;
private final Queue<String> queue = new LinkedList<>();
private String result;
private boolean isDone;
private boolean succeeded;
private long requestTimeoutSeconds;
private int httpStatusCode;
private long responseTime = 0;
public OllamaAsyncResultCallback(
HttpClient client,
URI uri,
OllamaRequestModel ollamaRequestModel,
long requestTimeoutSeconds) {
this.client = client;
this.ollamaRequestModel = ollamaRequestModel;
this.uri = uri;
this.isDone = false;
this.result = "";
this.queue.add("");
this.requestTimeoutSeconds = requestTimeoutSeconds;
}
@Override
public void run() {
try {
long startTime = System.currentTimeMillis();
HttpRequest request =
HttpRequest.newBuilder(uri)
.POST(
HttpRequest.BodyPublishers.ofString(
Utils.getObjectMapper().writeValueAsString(ollamaRequestModel)))
.header("Content-Type", "application/json")
.timeout(Duration.ofSeconds(requestTimeoutSeconds))
.build();
HttpResponse<InputStream> response =
client.send(request, HttpResponse.BodyHandlers.ofInputStream());
int statusCode = response.statusCode();
this.httpStatusCode = statusCode;
InputStream responseBodyStream = response.body();
try (BufferedReader reader =
new BufferedReader(new InputStreamReader(responseBodyStream, StandardCharsets.UTF_8))) {
String line;
StringBuilder responseBuffer = new StringBuilder();
while ((line = reader.readLine()) != null) {
if (statusCode == 404) {
OllamaErrorResponseModel ollamaResponseModel =
Utils.getObjectMapper().readValue(line, OllamaErrorResponseModel.class);
queue.add(ollamaResponseModel.getError());
responseBuffer.append(ollamaResponseModel.getError());
} else {
OllamaResponseModel ollamaResponseModel =
Utils.getObjectMapper().readValue(line, OllamaResponseModel.class);
queue.add(ollamaResponseModel.getResponse());
if (!ollamaResponseModel.isDone()) {
responseBuffer.append(ollamaResponseModel.getResponse());
}
}
}
this.isDone = true;
this.succeeded = true;
this.result = responseBuffer.toString();
long endTime = System.currentTimeMillis();
responseTime = endTime - startTime;
}
if (statusCode != 200) {
throw new OllamaBaseException(this.result);
}
} catch (IOException | InterruptedException | OllamaBaseException e) {
this.isDone = true;
this.succeeded = false;
this.result = "[FAILED] " + e.getMessage();
}
}
/**
* Returns the status of the thread. This does not indicate that the request was successful or a
* failure, rather it is just a status flag to indicate if the thread is active or ended.
*
* @return boolean - status
*/
public boolean isComplete() {
return isDone;
}
/**
* Returns the HTTP response status code for the request that was made to Ollama server.
*
* @return int - the status code for the request
*/
public int getHttpStatusCode() {
return httpStatusCode;
}
/**
* Returns the status of the request. Indicates if the request was successful or a failure. If the
* request was a failure, the `getResponse()` method will return the error message.
*
* @return boolean - status
*/
public boolean isSucceeded() {
return succeeded;
}
/**
* Returns the final response when the execution completes. Does not return intermediate results.
*
* @return String - response text
*/
public String getResponse() {
return result;
}
public Queue<String> getStream() {
return queue;
}
/**
* Returns the response time in milliseconds.
*
* @return long - response time in milliseconds.
*/
public long getResponseTime() {
return responseTime;
}
public void setRequestTimeoutSeconds(long requestTimeoutSeconds) {
this.requestTimeoutSeconds = requestTimeoutSeconds;
}
}

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@ -0,0 +1,13 @@
package io.github.amithkoujalgi.ollama4j.core.models;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
import lombok.Data;
@Data
@JsonIgnoreProperties(ignoreUnknown = true)
public class OllamaErrorResponseModel {
private String error;
}

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@ -0,0 +1,35 @@
package io.github.amithkoujalgi.ollama4j.core.models;
import static io.github.amithkoujalgi.ollama4j.core.utils.Utils.getObjectMapper;
import com.fasterxml.jackson.core.JsonProcessingException;
import java.util.List;
import lombok.Data;
@Data
public class OllamaRequestModel {
private String model;
private String prompt;
private List<String> images;
public OllamaRequestModel(String model, String prompt) {
this.model = model;
this.prompt = prompt;
}
public OllamaRequestModel(String model, String prompt, List<String> images) {
this.model = model;
this.prompt = prompt;
this.images = images;
}
public String toString() {
try {
return getObjectMapper().writerWithDefaultPrettyPrinter().writeValueAsString(this);
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
}
}

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@ -1,4 +1,4 @@
package io.github.ollama4j.models.generate;
package io.github.amithkoujalgi.ollama4j.core.models;
import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
import com.fasterxml.jackson.annotation.JsonProperty;
@ -8,7 +8,7 @@ import lombok.Data;
@Data
@JsonIgnoreProperties(ignoreUnknown = true)
public class OllamaGenerateResponseModel {
public class OllamaResponseModel {
private String model;
private @JsonProperty("created_at") String createdAt;
private String response;

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@ -0,0 +1,52 @@
package io.github.amithkoujalgi.ollama4j.core.models;
import static io.github.amithkoujalgi.ollama4j.core.utils.Utils.getObjectMapper;
import com.fasterxml.jackson.core.JsonProcessingException;
import lombok.Data;
import lombok.Getter;
/** The type Ollama result. */
@Getter
@SuppressWarnings("unused")
@Data
public class OllamaResult {
/**
* -- GETTER --
* Get the response text
*
* @return String - response text
*/
private final String response;
/**
* -- GETTER --
* Get the response status code.
*
* @return int - response status code
*/
private int httpStatusCode;
/**
* -- GETTER --
* Get the response time in milliseconds.
*
* @return long - response time in milliseconds
*/
private long responseTime = 0;
public OllamaResult(String response, long responseTime, int httpStatusCode) {
this.response = response;
this.responseTime = responseTime;
this.httpStatusCode = httpStatusCode;
}
@Override
public String toString() {
try {
return getObjectMapper().writerWithDefaultPrettyPrinter().writeValueAsString(this);
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
}
}

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@ -1,6 +1,6 @@
package io.github.ollama4j.models.request;
package io.github.amithkoujalgi.ollama4j.core.models.request;
import static io.github.ollama4j.utils.Utils.getObjectMapper;
import static io.github.amithkoujalgi.ollama4j.core.utils.Utils.getObjectMapper;
import com.fasterxml.jackson.core.JsonProcessingException;
import lombok.AllArgsConstructor;

View File

@ -1,6 +1,6 @@
package io.github.ollama4j.models.request;
package io.github.amithkoujalgi.ollama4j.core.models.request;
import static io.github.ollama4j.utils.Utils.getObjectMapper;
import static io.github.amithkoujalgi.ollama4j.core.utils.Utils.getObjectMapper;
import com.fasterxml.jackson.core.JsonProcessingException;
import lombok.AllArgsConstructor;

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@ -0,0 +1,23 @@
package io.github.amithkoujalgi.ollama4j.core.models.request;
import static io.github.amithkoujalgi.ollama4j.core.utils.Utils.getObjectMapper;
import com.fasterxml.jackson.core.JsonProcessingException;
import lombok.AllArgsConstructor;
import lombok.Data;
@Data
@AllArgsConstructor
public class ModelEmbeddingsRequest {
private String model;
private String prompt;
@Override
public String toString() {
try {
return getObjectMapper().writerWithDefaultPrettyPrinter().writeValueAsString(this);
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
}
}

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@ -1,6 +1,6 @@
package io.github.ollama4j.models.request;
package io.github.amithkoujalgi.ollama4j.core.models.request;
import static io.github.ollama4j.utils.Utils.getObjectMapper;
import static io.github.amithkoujalgi.ollama4j.core.utils.Utils.getObjectMapper;
import com.fasterxml.jackson.core.JsonProcessingException;
import lombok.AllArgsConstructor;

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@ -0,0 +1,63 @@
package io.github.amithkoujalgi.ollama4j.core.types;
/**
* A class to provide constants for all the supported models by Ollama.
*
* <p>Refer to the full list of models and the details here: <a
* href="https://ollama.ai/library">https://ollama.ai/library</a>
*/
@SuppressWarnings("ALL")
public class OllamaModelType {
public static final String LLAMA2 = "llama2";
public static final String MISTRAL = "mistral";
public static final String LLAVA = "llava";
public static final String MIXTRAL = "mixtral";
public static final String STARLING_LM = "starling-lm";
public static final String NEURAL_CHAT = "neural-chat";
public static final String CODELLAMA = "codellama";
public static final String LLAMA2_UNCENSORED = "llama2-uncensored";
public static final String DOLPHIN_MIXTRAL = "dolphin-mixtral";
public static final String ORCA_MINI = "orca-mini";
public static final String VICUNA = "vicuna";
public static final String WIZARD_VICUNA_UNCENSORED = "wizard-vicuna-uncensored";
public static final String PHIND_CODELLAMA = "phind-codellama";
public static final String ZEPHYR = "zephyr";
public static final String WIZARDCODER = "wizardcoder";
public static final String MISTRAL_OPENORCA = "mistral-openorca";
public static final String NOUS_HERMES = "nous-hermes";
public static final String DEEPSEEK_CODER = "deepseek-coder";
public static final String WIZARD_MATH = "wizard-math";
public static final String LLAMA2_CHINESE = "llama2-chinese";
public static final String FALCON = "falcon";
public static final String ORCA2 = "orca2";
public static final String STABLE_BELUGA = "stable-beluga";
public static final String CODEUP = "codeup";
public static final String EVERYTHINGLM = "everythinglm";
public static final String MEDLLAMA2 = "medllama2";
public static final String WIZARDLM_UNCENSORED = "wizardlm-uncensored";
public static final String STARCODER = "starcoder";
public static final String DOLPHIN22_MISTRAL = "dolphin2.2-mistral";
public static final String OPENCHAT = "openchat";
public static final String WIZARD_VICUNA = "wizard-vicuna";
public static final String OPENHERMES25_MISTRAL = "openhermes2.5-mistral";
public static final String OPEN_ORCA_PLATYPUS2 = "open-orca-platypus2";
public static final String YI = "yi";
public static final String YARN_MISTRAL = "yarn-mistral";
public static final String SAMANTHA_MISTRAL = "samantha-mistral";
public static final String SQLCODER = "sqlcoder";
public static final String YARN_LLAMA2 = "yarn-llama2";
public static final String MEDITRON = "meditron";
public static final String STABLELM_ZEPHYR = "stablelm-zephyr";
public static final String OPENHERMES2_MISTRAL = "openhermes2-mistral";
public static final String DEEPSEEK_LLM = "deepseek-llm";
public static final String MISTRALLITE = "mistrallite";
public static final String DOLPHIN21_MISTRAL = "dolphin2.1-mistral";
public static final String WIZARDLM = "wizardlm";
public static final String CODEBOOGA = "codebooga";
public static final String MAGICODER = "magicoder";
public static final String GOLIATH = "goliath";
public static final String NEXUSRAVEN = "nexusraven";
public static final String ALFRED = "alfred";
public static final String XWINLM = "xwinlm";
public static final String BAKLLAVA = "bakllava";
}

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@ -1,6 +1,6 @@
package io.github.ollama4j.utils;
package io.github.amithkoujalgi.ollama4j.core.utils;
import io.github.ollama4j.OllamaAPI;
import io.github.amithkoujalgi.ollama4j.core.OllamaAPI;
import java.io.InputStream;
import java.util.Scanner;

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@ -0,0 +1,9 @@
package io.github.amithkoujalgi.ollama4j.core.utils;
import com.fasterxml.jackson.databind.ObjectMapper;
public class Utils {
public static ObjectMapper getObjectMapper() {
return new ObjectMapper();
}
}

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@ -1,8 +0,0 @@
package io.github.ollama4j.exceptions;
public class RoleNotFoundException extends Exception {
public RoleNotFoundException(String s) {
super(s);
}
}

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@ -1,12 +0,0 @@
package io.github.ollama4j.exceptions;
public class ToolInvocationException extends Exception {
public ToolInvocationException(String s) {
super(s);
}
public ToolInvocationException(String s, Exception e) {
super(s, e);
}
}

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@ -1,8 +0,0 @@
package io.github.ollama4j.exceptions;
public class ToolNotFoundException extends Exception {
public ToolNotFoundException(String s) {
super(s);
}
}

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@ -1,14 +0,0 @@
package io.github.ollama4j.impl;
import io.github.ollama4j.models.generate.OllamaStreamHandler;
public class ConsoleOutputStreamHandler implements OllamaStreamHandler {
private final StringBuffer response = new StringBuffer();
@Override
public void accept(String message) {
String substr = message.substring(response.length());
response.append(substr);
System.out.print(substr);
}
}

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