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llama-index-integrations/llms/llama-index-llms-keywordsai/.gitignore
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llama_index/_static | ||
.DS_Store | ||
# Byte-compiled / optimized / DLL files | ||
__pycache__/ | ||
*.py[cod] | ||
*$py.class | ||
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# C extensions | ||
*.so | ||
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# Distribution / packaging | ||
.Python | ||
bin/ | ||
build/ | ||
develop-eggs/ | ||
dist/ | ||
downloads/ | ||
eggs/ | ||
.eggs/ | ||
etc/ | ||
include/ | ||
lib/ | ||
lib64/ | ||
parts/ | ||
sdist/ | ||
share/ | ||
var/ | ||
wheels/ | ||
pip-wheel-metadata/ | ||
share/python-wheels/ | ||
*.egg-info/ | ||
.installed.cfg | ||
*.egg | ||
MANIFEST | ||
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# PyInstaller | ||
# Usually these files are written by a python script from a template | ||
# before PyInstaller builds the exe, so as to inject date/other infos into it. | ||
*.manifest | ||
*.spec | ||
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# Installer logs | ||
pip-log.txt | ||
pip-delete-this-directory.txt | ||
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# Unit test / coverage reports | ||
htmlcov/ | ||
.tox/ | ||
.nox/ | ||
.coverage | ||
.coverage.* | ||
.cache | ||
nosetests.xml | ||
coverage.xml | ||
*.cover | ||
*.py,cover | ||
.hypothesis/ | ||
.pytest_cache/ | ||
.ruff_cache | ||
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# Translations | ||
*.mo | ||
*.pot | ||
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# Django stuff: | ||
*.log | ||
local_settings.py | ||
db.sqlite3 | ||
db.sqlite3-journal | ||
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# Flask stuff: | ||
instance/ | ||
.webassets-cache | ||
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# Scrapy stuff: | ||
.scrapy | ||
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# Sphinx documentation | ||
docs/_build/ | ||
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# PyBuilder | ||
target/ | ||
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# Jupyter Notebook | ||
.ipynb_checkpoints | ||
notebooks/ | ||
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# IPython | ||
profile_default/ | ||
ipython_config.py | ||
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# pyenv | ||
.python-version | ||
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# pipenv | ||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. | ||
# However, in case of collaboration, if having platform-specific dependencies or dependencies | ||
# having no cross-platform support, pipenv may install dependencies that don't work, or not | ||
# install all needed dependencies. | ||
#Pipfile.lock | ||
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow | ||
__pypackages__/ | ||
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# Celery stuff | ||
celerybeat-schedule | ||
celerybeat.pid | ||
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# SageMath parsed files | ||
*.sage.py | ||
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# Environments | ||
.env | ||
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env/ | ||
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env.bak/ | ||
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pyvenv.cfg | ||
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# Spyder project settings | ||
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# mkdocs documentation | ||
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*.swp | ||
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# pipenv | ||
Pipfile | ||
Pipfile.lock | ||
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# pyright | ||
pyrightconfig.json |
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llama-index-integrations/llms/llama-index-llms-keywordsai/BUILD
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poetry_requirements( | ||
name="poetry", | ||
) |
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llama-index-integrations/llms/llama-index-llms-keywordsai/Makefile
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GIT_ROOT ?= $(shell git rev-parse --show-toplevel) | ||
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help: ## Show all Makefile targets. | ||
@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' | ||
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format: ## Run code autoformatters (black). | ||
pre-commit install | ||
git ls-files | xargs pre-commit run black --files | ||
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lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy | ||
pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files | ||
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test: ## Run tests via pytest. | ||
pytest tests | ||
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watch-docs: ## Build and watch documentation. | ||
sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ |
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llama-index-integrations/llms/llama-index-llms-keywordsai/README.md
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# LlamaIndex Llms Integration: KeywordsAI | ||
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## Installation | ||
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To install the required package, run: | ||
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```bash | ||
%pip install llama-index-llms-keywordsai | ||
``` | ||
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## Setup | ||
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1. Set your KeywordsAI API key as an environment variable. You can replace `"sk-..."` with your actual API key: | ||
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```python | ||
import os | ||
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os.environ["OPENAI_API_KEY"] = "sk-..." | ||
``` | ||
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## Basic Usage | ||
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### Generate Completions | ||
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To generate a completion for a prompt, use the `complete` method: | ||
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```python | ||
from llama_index.llms.keywordsai import KeywordsAI | ||
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resp = KeywordsAI().complete("Paul Graham is ") | ||
print(resp) | ||
``` | ||
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### Chat Responses | ||
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To send a chat message and receive a response, create a list of `ChatMessage` instances and use the `chat` method: | ||
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```python | ||
from llama_index.core.llms import ChatMessage | ||
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messages = [ | ||
ChatMessage( | ||
role="system", content="You are a pirate with a colorful personality." | ||
), | ||
ChatMessage(role="user", content="What is your name?"), | ||
] | ||
resp = KeywordsAI().chat(messages) | ||
print(resp) | ||
``` | ||
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## Streaming Responses | ||
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### Stream Complete | ||
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To stream responses for a prompt, use the `stream_complete` method: | ||
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```python | ||
from llama_index.llms.keywordsai import KeywordsAI | ||
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llm = KeywordsAI() | ||
resp = llm.stream_complete("Paul Graham is ") | ||
for r in resp: | ||
print(r.delta, end="") | ||
``` | ||
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### Stream Chat | ||
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To stream chat responses, use the `stream_chat` method: | ||
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```python | ||
from llama_index.llms.keywordsai import KeywordsAI | ||
from llama_index.core.llms import ChatMessage | ||
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llm = KeywordsAI() | ||
messages = [ | ||
ChatMessage( | ||
role="system", content="You are a pirate with a colorful personality." | ||
), | ||
ChatMessage(role="user", content="What is your name?"), | ||
] | ||
resp = llm.stream_chat(messages) | ||
for r in resp: | ||
print(r.delta, end="") | ||
``` | ||
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## Configure Model | ||
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You can specify a particular model when creating the `KeywordsAI` instance: | ||
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```python | ||
llm = KeywordsAI(model="gpt-3.5-turbo") | ||
resp = llm.complete("Paul Graham is ") | ||
print(resp) | ||
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messages = [ | ||
ChatMessage( | ||
role="system", content="You are a pirate with a colorful personality." | ||
), | ||
ChatMessage(role="user", content="What is your name?"), | ||
] | ||
resp = llm.chat(messages) | ||
print(resp) | ||
``` | ||
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## Asynchronous Usage | ||
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You can also use asynchronous methods for completion: | ||
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```python | ||
from llama_index.llms.keywordsai import KeywordsAI | ||
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llm = KeywordsAI(model="gpt-3.5-turbo") | ||
resp = await llm.acomplete("Paul Graham is ") | ||
print(resp) | ||
``` | ||
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## Set API Key at a Per-Instance Level | ||
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If desired, you can have separate LLM instances use different API keys: | ||
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```python | ||
from llama_index.llms.keywordsai import KeywordsAI | ||
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llm = KeywordsAI(model="gpt-3.5-turbo", api_key="BAD_KEY") | ||
resp = KeywordsAI().complete("Paul Graham is ") | ||
print(resp) | ||
``` | ||
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### LLM Implementation example | ||
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https://docs.llamaindex.ai/en/stable/examples/llm/keywordsai/ |
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llama-index-integrations/llms/llama-index-llms-keywordsai/llama_index/llms/keywordsai/BUILD
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python_sources() |
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...dex-integrations/llms/llama-index-llms-keywordsai/llama_index/llms/keywordsai/__init__.py
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from llama_index.llms.keywordsai.base import KeywordsAI | ||
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__all__ = ["KeywordsAI"] |
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