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ai21: init package (#17592)

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Co-authored-by: Asaf Gardin <asafg@ai21.com>
Co-authored-by: etang <etang@ai21.com>
Co-authored-by: asafgardin <147075902+asafgardin@users.noreply.github.com>
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Erick Friis 2024-02-15 12:25:05 -08:00 committed by GitHub
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32 changed files with 2685 additions and 75 deletions
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1
.github/workflows/_integration_test.yml vendored
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@ -52,6 +52,7 @@ jobs:
- name: Run integration tests - name: Run integration tests
shell: bash shell: bash
env: env:
AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }} GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }} MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}

1
.github/workflows/_release.yml vendored
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@ -166,6 +166,7 @@ jobs:
- name: Run integration tests - name: Run integration tests
if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }} if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
env: env:
AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }} GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }} MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}

131
docs/docs/integrations/chat/ai21.ipynb Normal file
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@ -0,0 +1,131 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "e49f1e0d",
"metadata": {},
"source": [
"# ChatAI21\n",
"\n",
"This notebook covers how to get started with AI21 chat models.\n",
"\n",
"## Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c3bef91",
"metadata": {
"ExecuteTime": {
"end_time": "2024-02-15T06:50:44.929635Z",
"start_time": "2024-02-15T06:50:41.209704Z"
}
},
"outputs": [],
"source": [
"!pip install -qU langchain-ai21"
]
},
{
"cell_type": "markdown",
"id": "2b4f3e15",
"metadata": {},
"source": [
"## Environment Setup\n",
"\n",
"We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62e0dbc3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"AI21_API_KEY\"] = getpass()"
]
},
{
"cell_type": "markdown",
"id": "4828829d3da430ce",
"metadata": {
"collapsed": false
},
"source": [
"## Usage"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "39353473fce5dd2e",
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='Bonjour, comment vas-tu?')"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_ai21 import ChatAI21\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"chat = ChatAI21(model=\"j2-ultra\")\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant that translates English to French.\"),\n",
" (\"human\", \"Translate this sentence from English to French. {english_text}.\"),\n",
" ]\n",
")\n",
"\n",
"chain = prompt | chat\n",
"chain.invoke({\"english_text\": \"Hello, how are you?\"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c159a79f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

122
docs/docs/integrations/llms/ai21.ipynb
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@ -5,133 +5,100 @@
"id": "9597802c", "id": "9597802c",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# AI21\n", "# AI21LLM\n",
"\n", "\n",
"[AI21 Studio](https://docs.ai21.com/) provides API access to `Jurassic-2` large language models.\n", "This example goes over how to use LangChain to interact with `AI21` models.\n",
"\n", "\n",
"This example goes over how to use LangChain to interact with [AI21 models](https://docs.ai21.com/docs/jurassic-2-models)." "## Installation"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": null,
"id": "02be122d-04e8-4ec6-84d1-f1d8961d6828", "id": "59c710c4",
"metadata": { "metadata": {},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
"\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"# install the package:\n",
"%pip install --upgrade --quiet ai21"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "4229227e-6ca2-41ad-a3c3-5f29e3559091",
"metadata": {
"tags": []
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# get AI21_API_KEY. Use https://studio.ai21.com/account/account\n", "!pip install -qU langchain-ai21"
"\n",
"from getpass import getpass\n",
"\n",
"AI21_API_KEY = getpass()"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "markdown",
"execution_count": 7, "id": "560a2f9254963fd7",
"id": "6fb585dd",
"metadata": { "metadata": {
"tags": [] "collapsed": false
}, },
"outputs": [],
"source": [ "source": [
"from langchain_community.llms import AI21\n", "## Environment Setup\n",
"from langchain_core.prompts import PromptTemplate" "\n",
"We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 4,
"id": "035dea0f", "id": "035dea0f",
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"template = \"\"\"Question: {question}\n", "import os\n",
"from getpass import getpass\n",
"\n", "\n",
"Answer: Let's think step by step.\"\"\"\n", "os.environ[\"AI21_API_KEY\"] = getpass()"
"\n",
"prompt = PromptTemplate.from_template(template)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "markdown",
"execution_count": 9, "id": "1891df96eb076e1a",
"id": "3f3458d9",
"metadata": { "metadata": {
"tags": [] "collapsed": false
}, },
"outputs": [],
"source": [ "source": [
"llm = AI21(ai21_api_key=AI21_API_KEY)" "## Usage"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 6,
"id": "a641dbd9", "id": "98f70927a87e4745",
"metadata": { "metadata": {
"tags": [] "collapsed": false
},
"outputs": [],
"source": [
"llm_chain = prompt | llm"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9f0b1960",
"metadata": {
"tags": []
}, },
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"'\\nThe Super Bowl in the year Justin Beiber was born was in the year 1991.\\nThe Super Bowl in 1991 was won by the Washington Redskins.\\nFinal answer: Washington Redskins'" "'\\nLangChain is a decentralized blockchain network that leverages AI and machine learning to provide language translation services.'"
] ]
}, },
"execution_count": 13, "execution_count": 6,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n", "from langchain_ai21 import AI21LLM\n",
"from langchain_core.prompts import PromptTemplate\n",
"\n", "\n",
"llm_chain.invoke({\"question\": question})" "template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate.from_template(template)\n",
"\n",
"model = AI21LLM(model=\"j2-ultra\")\n",
"\n",
"chain = prompt | model\n",
"\n",
"chain.invoke({\"question\": \"What is LangChain?\"})"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"id": "22bce013", "id": "a52f765c",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [] "source": []
@ -139,7 +106,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3 (ipykernel)", "display_name": "Python 3.11.1 64-bit",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -153,7 +120,12 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.10.13" "version": "3.11.4"
},
"vscode": {
"interpreter": {
"hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
}
} }
}, },
"nbformat": 4, "nbformat": 4,

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docs/docs/integrations/text_embedding/ai21.ipynb Normal file
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@ -0,0 +1,128 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "cc3c6ef6bbd57ce9",
"metadata": {
"collapsed": false
},
"source": [
"# AI21Embeddings\n",
"\n",
"This notebook covers how to get started with AI21 embedding models.\n",
"\n",
"## Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c3bef91",
"metadata": {},
"outputs": [],
"source": [
"!pip install -qU langchain-ai21"
]
},
{
"cell_type": "markdown",
"id": "2b4f3e15",
"metadata": {},
"source": [
"## Environment Setup\n",
"\n",
"We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62e0dbc3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"AI21_API_KEY\"] = getpass()"
]
},
{
"cell_type": "markdown",
"id": "74ef9d8b40a1319e",
"metadata": {
"collapsed": false
},
"source": [
"## Usage"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "12fcfb4b",
"metadata": {},
"outputs": [],
"source": [
"from langchain_ai21 import AI21Embeddings\n",
"\n",
"embeddings = AI21Embeddings()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f2e6104",
"metadata": {},
"outputs": [],
"source": [
"embeddings.embed_query(\"My query to look up\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3465d7e63bfb3d1",
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"embeddings.embed_documents(\n",
" [\"This is a content of the document\", \"This is another document\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d60af6d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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libs/partners/ai21/.gitignore vendored Normal file
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@ -0,0 +1 @@
__pycache__

21
libs/partners/ai21/LICENSE Normal file
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@ -0,0 +1,21 @@
MIT License
Copyright (c) 2023 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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libs/partners/ai21/Makefile Normal file
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@ -0,0 +1,56 @@
.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
# Default target executed when no arguments are given to make.
all: help
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE = tests/integration_tests/
test tests integration_test integration_tests:
poetry run pytest $(TEST_FILE)
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/ai21 --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_ai21
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
poetry run ruff .
poetry run ruff format $(PYTHON_FILES) --diff
poetry run ruff --select I $(PYTHON_FILES)
mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
poetry run ruff format $(PYTHON_FILES)
poetry run ruff --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
check_imports: $(shell find langchain_ai21 -name '*.py')
poetry run python ./scripts/check_imports.py $^
######################
# HELP
######################
help:
@echo '----'
@echo 'check_imports - check imports'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'

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libs/partners/ai21/README.md Normal file
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@ -0,0 +1,75 @@
# langchain-ai21
This package contains the LangChain integrations for [AI21](https://docs.ai21.com/) through their [AI21](https://pypi.org/project/ai21/) SDK.
## Installation and Setup
- Install the AI21 partner package
```bash
pip install langchain-ai21
```
- Get an AI21 api key and set it as an environment variable (`AI21_API_KEY`)
## Chat Models
This package contains the `ChatAI21` class, which is the recommended way to interface with AI21 Chat models.
To use, install the requirements, and configure your environment.
```bash
export AI21_API_KEY=your-api-key
```
Then initialize
```python
from langchain_core.messages import HumanMessage
from langchain_ai21.chat_models import ChatAI21
chat = ChatAI21(model="j2-ultra")
messages = [HumanMessage(content="Hello from AI21")]
chat.invoke(messages)
```
## LLMs
You can use AI21's generative AI models as Langchain LLMs:
```python
from langchain.prompts import PromptTemplate
from langchain_ai21 import AI21LLM
llm = AI21LLM(model="j2-ultra")
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
chain = prompt | llm
question = "Which scientist discovered relativity?"
print(chain.invoke({"question": question}))
```
## Embeddings
You can use AI21's embeddings models as:
### Query
```python
from langchain_ai21 import AI21Embeddings
embeddings = AI21Embeddings()
embeddings.embed_query("Hello! This is some query")
```
### Document
```python
from langchain_ai21 import AI21Embeddings
embeddings = AI21Embeddings()
embeddings.embed_documents(["Hello! This is document 1", "And this is document 2!"])
```

9
libs/partners/ai21/langchain_ai21/__init__.py Normal file
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@ -0,0 +1,9 @@
from langchain_ai21.chat_models import ChatAI21
from langchain_ai21.embeddings import AI21Embeddings
from langchain_ai21.llms import AI21LLM
__all__ = [
"AI21LLM",
"ChatAI21",
"AI21Embeddings",
]

48
libs/partners/ai21/langchain_ai21/ai21_base.py Normal file
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@ -0,0 +1,48 @@
import os
from typing import Dict, Optional
from ai21 import AI21Client
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str
_DEFAULT_TIMEOUT_SEC = 300
class AI21Base(BaseModel):
class Config:
arbitrary_types_allowed = True
client: AI21Client = Field(default=None)
api_key: Optional[SecretStr] = None
api_host: Optional[str] = None
timeout_sec: Optional[float] = None
num_retries: Optional[int] = None
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
api_key = convert_to_secret_str(
values.get("api_key") or os.getenv("AI21_API_KEY") or ""
)
values["api_key"] = api_key
api_host = (
values.get("api_host")
or os.getenv("AI21_API_URL")
or "https://api.ai21.com"
)
values["api_host"] = api_host
timeout_sec = values.get("timeout_sec") or float(
os.getenv("AI21_TIMEOUT_SEC", _DEFAULT_TIMEOUT_SEC)
)
values["timeout_sec"] = timeout_sec
if values.get("client") is None:
values["client"] = AI21Client(
api_key=api_key.get_secret_value(),
api_host=api_host,
timeout_sec=None if timeout_sec is None else float(timeout_sec),
via="langchain",
)
return values

171
libs/partners/ai21/langchain_ai21/chat_models.py Normal file
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@ -0,0 +1,171 @@
import asyncio
from functools import partial
from typing import Any, List, Optional, Tuple, cast
from ai21.models import ChatMessage, Penalty, RoleType
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
BaseMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_ai21.ai21_base import AI21Base
def _get_system_message_from_message(message: BaseMessage) -> str:
if not isinstance(message.content, str):
raise ValueError(
f"System Message must be of type str. Got {type(message.content)}"
)
return message.content
def _convert_messages_to_ai21_messages(
messages: List[BaseMessage],
) -> Tuple[Optional[str], List[ChatMessage]]:
system_message = None
converted_messages: List[ChatMessage] = []
for i, message in enumerate(messages):
if message.type == "system":
if i != 0:
raise ValueError("System message must be at beginning of message list.")
else:
system_message = _get_system_message_from_message(message)
else:
converted_message = _convert_message_to_ai21_message(message)
converted_messages.append(converted_message)
return system_message, converted_messages
def _convert_message_to_ai21_message(
message: BaseMessage,
) -> ChatMessage:
content = cast(str, message.content)
role = None
if isinstance(message, HumanMessage):
role = RoleType.USER
elif isinstance(message, AIMessage):
role = RoleType.ASSISTANT
if not role:
raise ValueError(
f"Could not resolve role type from message {message}. "
f"Only support {HumanMessage.__name__} and {AIMessage.__name__}."
)
return ChatMessage(role=role, text=content)
def _pop_system_messages(messages: List[BaseMessage]) -> List[SystemMessage]:
system_message_indexes = [
i for i, message in enumerate(messages) if isinstance(message, SystemMessage)
]
return [cast(SystemMessage, messages.pop(i)) for i in system_message_indexes]
class ChatAI21(BaseChatModel, AI21Base):
"""ChatAI21 chat model.
Example:
.. code-block:: python
from langchain_ai21 import ChatAI21
model = ChatAI21()
"""
model: str
"""Model type you wish to interact with.
You can view the options at https://github.com/AI21Labs/ai21-python?tab=readme-ov-file#model-types"""
num_results: int = 1
"""The number of responses to generate for a given prompt."""
max_tokens: int = 16
"""The maximum number of tokens to generate for each response."""
min_tokens: int = 0
"""The minimum number of tokens to generate for each response."""
temperature: float = 0.7
"""A value controlling the "creativity" of the model's responses."""
top_p: float = 1
"""A value controlling the diversity of the model's responses."""
top_k_return: int = 0
"""The number of top-scoring tokens to consider for each generation step."""
frequency_penalty: Optional[Penalty] = None
"""A penalty applied to tokens that are frequently generated."""
presence_penalty: Optional[Penalty] = None
""" A penalty applied to tokens that are already present in the prompt."""
count_penalty: Optional[Penalty] = None
"""A penalty applied to tokens based on their frequency
in the generated responses."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "chat-ai21"
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
system, ai21_messages = _convert_messages_to_ai21_messages(messages)
response = self.client.chat.create(
model=self.model,
messages=ai21_messages,
system=system or "",
num_results=self.num_results,
temperature=self.temperature,
max_tokens=self.max_tokens,
min_tokens=self.min_tokens,
top_p=self.top_p,
top_k_return=self.top_k_return,
stop_sequences=stop,
frequency_penalty=self.frequency_penalty,
presence_penalty=self.presence_penalty,
count_penalty=self.count_penalty,
**kwargs,
)
outputs = response.outputs
message = AIMessage(content=outputs[0].text)
return ChatResult(generations=[ChatGeneration(message=message)])
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
return await asyncio.get_running_loop().run_in_executor(
None, partial(self._generate, **kwargs), messages, stop, run_manager
)

41
libs/partners/ai21/langchain_ai21/embeddings.py Normal file
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@ -0,0 +1,41 @@
from typing import Any, List
from ai21.models import EmbedType
from langchain_core.embeddings import Embeddings
from langchain_ai21.ai21_base import AI21Base
class AI21Embeddings(Embeddings, AI21Base):
"""AI21 Embeddings embedding model.
To use, you should have the 'AI21_API_KEY' environment variable set
or pass as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain_ai21 import AI21Embeddings
embeddings = AI21Embeddings()
query_result = embeddings.embed_query("Hello embeddings world!")
"""
def embed_documents(self, texts: List[str], **kwargs: Any) -> List[List[float]]:
"""Embed search docs."""
response = self.client.embed.create(
texts=texts,
type=EmbedType.SEGMENT,
**kwargs,
)
return [result.embedding for result in response.results]
def embed_query(self, text: str, **kwargs: Any) -> List[float]:
"""Embed query text."""
response = self.client.embed.create(
texts=[text],
type=EmbedType.QUERY,
**kwargs,
)
return [result.embedding for result in response.results][0]

142
libs/partners/ai21/langchain_ai21/llms.py Normal file
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@ -0,0 +1,142 @@
import asyncio
from functools import partial
from typing import (
Any,
List,
Optional,
)
from ai21.models import CompletionsResponse, Penalty
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import BaseLLM
from langchain_core.outputs import Generation, LLMResult
from langchain_ai21.ai21_base import AI21Base
class AI21LLM(BaseLLM, AI21Base):
"""AI21LLM large language models.
Example:
.. code-block:: python
from langchain_ai21 import AI21LLM
model = AI21LLM()
"""
model: str
"""Model type you wish to interact with.
You can view the options at https://github.com/AI21Labs/ai21-python?tab=readme-ov-file#model-types"""
num_results: int = 1
"""The number of responses to generate for a given prompt."""
max_tokens: int = 16
"""The maximum number of tokens to generate for each response."""
min_tokens: int = 0
"""The minimum number of tokens to generate for each response."""
temperature: float = 0.7
"""A value controlling the "creativity" of the model's responses."""
top_p: float = 1
"""A value controlling the diversity of the model's responses."""
top_k_returns: int = 0
"""The number of top-scoring tokens to consider for each generation step."""
frequency_penalty: Optional[Penalty] = None
"""A penalty applied to tokens that are frequently generated."""
presence_penalty: Optional[Penalty] = None
""" A penalty applied to tokens that are already present in the prompt."""
count_penalty: Optional[Penalty] = None
"""A penalty applied to tokens based on their frequency
in the generated responses."""
custom_model: Optional[str] = None
epoch: Optional[int] = None
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
@property
def _llm_type(self) -> str:
"""Return type of LLM."""
return "ai21-llm"
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
generations: List[List[Generation]] = []
token_count = 0
for prompt in prompts:
response = self._invoke_completion(
prompt=prompt, model=self.model, stop_sequences=stop, **kwargs
)
generation = self._response_to_generation(response)
generations.append(generation)
token_count += self.client.count_tokens(prompt)
llm_output = {"token_count": token_count, "model_name": self.model}
return LLMResult(generations=generations, llm_output=llm_output)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
# Change implementation if integration natively supports async generation.
return await asyncio.get_running_loop().run_in_executor(
None, partial(self._generate, **kwargs), prompts, stop, run_manager
)
def _invoke_completion(
self,
prompt: str,
model: str,
stop_sequences: Optional[List[str]] = None,
**kwargs: Any,
) -> CompletionsResponse:
return self.client.completion.create(
prompt=prompt,
model=model,
max_tokens=self.max_tokens,
num_results=self.num_results,
min_tokens=self.min_tokens,
temperature=self.temperature,
top_p=self.top_p,
top_k_return=self.top_k_returns,
custom_model=self.custom_model,
stop_sequences=stop_sequences,
frequency_penalty=self.frequency_penalty,
presence_penalty=self.presence_penalty,
count_penalty=self.count_penalty,
epoch=self.epoch,
)
def _response_to_generation(
self, response: CompletionsResponse
) -> List[Generation]:
return [
Generation(
text=completion.data.text,
generation_info=completion.to_dict(),
)
for completion in response.completions
]

0
libs/partners/ai21/langchain_ai21/py.typed Normal file
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975
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description = "Runtime inspection utilities for typing module."
optional = false
python-versions = "*"
files = [
{file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
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]
[package.dependencies]
mypy-extensions = ">=0.3.0"
typing-extensions = ">=3.7.4"
[[package]]
name = "urllib3"
version = "2.1.0"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.8"
files = [
{file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
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]
[package.extras]
brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "watchdog"
version = "3.0.0"
description = "Filesystem events monitoring"
optional = false
python-versions = ">=3.7"
files = [
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[package.extras]
watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "95343e4ae1dd67887432aa1acd09e5f49cbc33c2ddcfdbad9552a90a92adda79"

90
libs/partners/ai21/pyproject.toml Normal file
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[tool.poetry]
name = "langchain-ai21"
version = "0.0.1"
description = "An integration package connecting AI21 and LangChain"
authors = []
readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain-core = "^0.1.22"
ai21 = "^2.0.0"
[tool.poetry.group.test]
optional = true
[tool.poetry.group.test.dependencies]
pytest = "^7.3.0"
freezegun = "^1.2.2"
pytest-mock = "^3.10.0"
syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
langchain-core = {path = "../../core", develop = true}
[tool.poetry.group.codespell]
optional = true
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
[tool.poetry.group.test_integration]
optional = true
[tool.poetry.group.test_integration.dependencies]
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.lint.dependencies]
ruff = "^0.1.5"
[tool.poetry.group.typing.dependencies]
mypy = "^0.991"
langchain-core = {path = "../../core", develop = true}
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.dev.dependencies]
langchain-core = {path = "../../core", develop = true}
[tool.ruff]
select = [
"E", # pycodestyle
"F", # pyflakes
"I", # isort
]
[tool.mypy]
disallow_untyped_defs = "True"
[tool.coverage.run]
omit = [
"tests/*",
]
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.pytest.ini_options]
# --strict-markers will raise errors on unknown marks.
# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks
#
# https://docs.pytest.org/en/7.1.x/reference/reference.html
# --strict-config any warnings encountered while parsing the `pytest`
# section of the configuration file raise errors.
#
# https://github.com/tophat/syrupy
# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite.
addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5"
# Registering custom markers.
# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers
markers = [
"requires: mark tests as requiring a specific library",
"asyncio: mark tests as requiring asyncio",
"compile: mark placeholder test used to compile integration tests without running them",
"scheduled: mark tests to run in scheduled testing",
]
asyncio_mode = "auto"

17
libs/partners/ai21/scripts/check_imports.py Normal file
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import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_faillure = True
print(file)
traceback.print_exc()
print()
sys.exit(1 if has_failure else 0)

27
libs/partners/ai21/scripts/check_pydantic.sh Executable file
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#!/bin/bash
#
# This script searches for lines starting with "import pydantic" or "from pydantic"
# in tracked files within a Git repository.
#
# Usage: ./scripts/check_pydantic.sh /path/to/repository
# Check if a path argument is provided
if [ $# -ne 1 ]; then
echo "Usage: $0 /path/to/repository"
exit 1
fi
repository_path="$1"
# Search for lines matching the pattern within the specified repository
result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
# Check if any matching lines were found
if [ -n "$result" ]; then
echo "ERROR: The following lines need to be updated:"
echo "$result"
echo "Please replace the code with an import from langchain_core.pydantic_v1."
echo "For example, replace 'from pydantic import BaseModel'"
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
exit 1
fi

17
libs/partners/ai21/scripts/lint_imports.sh Executable file
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#!/bin/bash
set -eu
# Initialize a variable to keep track of errors
errors=0
# make sure not importing from langchain or langchain_experimental
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
# Decide on an exit status based on the errors
if [ "$errors" -gt 0 ]; then
exit 1
else
exit 0
fi

0
libs/partners/ai21/tests/__init__.py Normal file
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0
libs/partners/ai21/tests/integration_tests/__init__.py Normal file
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43
libs/partners/ai21/tests/integration_tests/test_chat_models.py Normal file
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"""Test ChatAI21 chat model."""
from langchain_core.messages import HumanMessage
from langchain_core.outputs import ChatGeneration
from langchain_ai21.chat_models import ChatAI21
def test_invoke() -> None:
"""Test invoke tokens from AI21."""
llm = ChatAI21(model="j2-ultra")
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
assert isinstance(result.content, str)
def test_generation() -> None:
"""Test invoke tokens from AI21."""
llm = ChatAI21(model="j2-ultra")
message = HumanMessage(content="Hello")
result = llm.generate([[message], [message]], config=dict(tags=["foo"]))
for generations in result.generations:
assert len(generations) == 1
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
async def test_ageneration() -> None:
"""Test invoke tokens from AI21."""
llm = ChatAI21(model="j2-ultra")
message = HumanMessage(content="Hello")
result = await llm.agenerate([[message], [message]], config=dict(tags=["foo"]))
for generations in result.generations:
assert len(generations) == 1
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content

7
libs/partners/ai21/tests/integration_tests/test_compile.py Normal file
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import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass

19
libs/partners/ai21/tests/integration_tests/test_embeddings.py Normal file
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"""Test AI21 embeddings."""
from langchain_ai21.embeddings import AI21Embeddings
def test_langchain_ai21_embedding_documents() -> None:
"""Test AI21 embeddings."""
documents = ["foo bar"]
embedding = AI21Embeddings()
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) > 0
def test_langchain_ai21_embedding_query() -> None:
"""Test AI21 embeddings."""
document = "foo bar"
embedding = AI21Embeddings()
output = embedding.embed_query(document)
assert len(output) > 0

103
libs/partners/ai21/tests/integration_tests/test_llms.py Normal file
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"""Test AI21LLM llm."""
from langchain_ai21.llms import AI21LLM
def _generate_llm() -> AI21LLM:
"""
Testing AI21LLm using non default parameters with the following parameters
"""
return AI21LLM(
model="j2-ultra",
max_tokens=2, # Use less tokens for a faster response
temperature=0, # for a consistent response
epoch=1,
)
def test_stream() -> None:
"""Test streaming tokens from AI21."""
llm = AI21LLM(
model="j2-ultra",
)
for token in llm.stream("I'm Pickle Rick"):
assert isinstance(token, str)
async def test_abatch() -> None:
"""Test streaming tokens from AI21LLM."""
llm = AI21LLM(
model="j2-ultra",
)
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token, str)
async def test_abatch_tags() -> None:
"""Test batch tokens from AI21LLM."""
llm = AI21LLM(
model="j2-ultra",
)
result = await llm.abatch(
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
)
for token in result:
assert isinstance(token, str)
def test_batch() -> None:
"""Test batch tokens from AI21LLM."""
llm = AI21LLM(
model="j2-ultra",
)
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token, str)
async def test_ainvoke() -> None:
"""Test invoke tokens from AI21LLM."""
llm = AI21LLM(
model="j2-ultra",
)
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
assert isinstance(result, str)
def test_invoke() -> None:
"""Test invoke tokens from AI21LLM."""
llm = AI21LLM(
model="j2-ultra",
)
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
assert isinstance(result, str)
def test__generate() -> None:
llm = _generate_llm()
llm_result = llm.generate(
prompts=["Hey there, my name is Pickle Rick. What is your name?"],
stop=["##"],
)
assert len(llm_result.generations) > 0
assert llm_result.llm_output["token_count"] != 0 # type: ignore
async def test__agenerate() -> None:
llm = _generate_llm()
llm_result = await llm.agenerate(
prompts=["Hey there, my name is Pickle Rick. What is your name?"],
stop=["##"],
)
assert len(llm_result.generations) > 0
assert llm_result.llm_output["token_count"] != 0 # type: ignore

0
libs/partners/ai21/tests/unit_tests/__init__.py Normal file
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91
libs/partners/ai21/tests/unit_tests/conftest.py Normal file
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import os
from contextlib import contextmanager
from typing import Generator
from unittest.mock import Mock
import pytest
from ai21 import AI21Client
from ai21.models import (
ChatOutput,
ChatResponse,
Completion,
CompletionData,
CompletionFinishReason,
CompletionsResponse,
FinishReason,
Penalty,
RoleType,
)
from pytest_mock import MockerFixture
DUMMY_API_KEY = "test_api_key"
BASIC_EXAMPLE_LLM_PARAMETERS = {
"num_results": 3,
"max_tokens": 20,
"min_tokens": 10,
"temperature": 0.5,
"top_p": 0.5,
"top_k_return": 0,
"frequency_penalty": Penalty(scale=0.2, apply_to_numbers=True),
"presence_penalty": Penalty(scale=0.2, apply_to_stopwords=True),
"count_penalty": Penalty(
scale=0.2, apply_to_punctuation=True, apply_to_emojis=True
),
}
@pytest.fixture
def mocked_completion_response(mocker: MockerFixture) -> Mock:
mocked_response = mocker.MagicMock(spec=CompletionsResponse)
mocked_response.prompt = "this is a test prompt"
mocked_response.completions = [
Completion(
data=CompletionData(text="test", tokens=[]),
finish_reason=CompletionFinishReason(reason=None, length=None),
)
]
return mocked_response
@pytest.fixture
def mock_client_with_completion(
mocker: MockerFixture, mocked_completion_response: Mock
) -> Mock:
mock_client = mocker.MagicMock(spec=AI21Client)
mock_client.completion = mocker.MagicMock()
mock_client.completion.create.side_effect = [
mocked_completion_response,
mocked_completion_response,
]
mock_client.count_tokens.side_effect = [10, 20]
return mock_client
@pytest.fixture
def mock_client_with_chat(mocker: MockerFixture) -> Mock:
mock_client = mocker.MagicMock(spec=AI21Client)
mock_client.chat = mocker.MagicMock()
output = ChatOutput(
text="Hello Pickle Rick!",
role=RoleType.ASSISTANT,
finish_reason=FinishReason(reason="testing"),
)
mock_client.chat.create.return_value = ChatResponse(outputs=[output])
return mock_client
@contextmanager
def temporarily_unset_api_key() -> Generator:
"""
Unset and set environment key for testing purpose for when an API KEY is not set
"""
api_key = os.environ.pop("API_KEY", None)
yield
if api_key is not None:
os.environ["API_KEY"] = api_key

239
libs/partners/ai21/tests/unit_tests/test_chat_models.py Normal file
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"""Test chat model integration."""
from typing import List, Optional
from unittest.mock import Mock, call
import pytest
from ai21 import MissingApiKeyError
from ai21.models import ChatMessage, Penalty, RoleType
from langchain_core.messages import (
AIMessage,
BaseMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.messages import (
ChatMessage as LangChainChatMessage,
)
from langchain_ai21.chat_models import (
ChatAI21,
_convert_message_to_ai21_message,
_convert_messages_to_ai21_messages,
)
from tests.unit_tests.conftest import (
BASIC_EXAMPLE_LLM_PARAMETERS,
DUMMY_API_KEY,
temporarily_unset_api_key,
)
def test_initialization__when_no_api_key__should_raise_exception() -> None:
"""Test integration initialization."""
with temporarily_unset_api_key():
with pytest.raises(MissingApiKeyError):
ChatAI21(model="j2-ultra")
def test_initialization__when_default_parameters_in_init() -> None:
"""Test chat model initialization."""
ChatAI21(api_key=DUMMY_API_KEY, model="j2-ultra")
def test_initialization__when_custom_parameters_in_init() -> None:
model = "j2-mid"
num_results = 1
max_tokens = 10
min_tokens = 20
temperature = 0.1
top_p = 0.1
top_k_returns = 0
frequency_penalty = Penalty(scale=0.2, apply_to_numbers=True)
presence_penalty = Penalty(scale=0.2, apply_to_stopwords=True)
count_penalty = Penalty(scale=0.2, apply_to_punctuation=True, apply_to_emojis=True)
llm = ChatAI21(
api_key=DUMMY_API_KEY,
model=model,
num_results=num_results,
max_tokens=max_tokens,
min_tokens=min_tokens,
temperature=temperature,
top_p=top_p,
top_k_returns=top_k_returns,
frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty,
count_penalty=count_penalty,
)
assert llm.model == model
assert llm.num_results == num_results
assert llm.max_tokens == max_tokens
assert llm.min_tokens == min_tokens
assert llm.temperature == temperature
assert llm.top_p == top_p
assert llm.top_k_return == top_k_returns
assert llm.frequency_penalty == frequency_penalty
assert llm.presence_penalty == presence_penalty
assert count_penalty == count_penalty
@pytest.mark.parametrize(
ids=[
"when_human_message",
"when_ai_message",
],
argnames=["message", "expected_ai21_message"],
argvalues=[
(
HumanMessage(content="Human Message Content"),
ChatMessage(role=RoleType.USER, text="Human Message Content"),
),
(
AIMessage(content="AI Message Content"),
ChatMessage(role=RoleType.ASSISTANT, text="AI Message Content"),
),
],
)
def test_convert_message_to_ai21_message(
message: BaseMessage, expected_ai21_message: ChatMessage
) -> None:
ai21_message = _convert_message_to_ai21_message(message)
assert ai21_message == expected_ai21_message
@pytest.mark.parametrize(
ids=[
"when_system_message",
"when_langchain_chat_message",
],
argnames=["message"],
argvalues=[
(SystemMessage(content="System Message Content"),),
(LangChainChatMessage(content="Chat Message Content", role="human"),),
],
)
def test_convert_message_to_ai21_message__when_invalid_role__should_raise_exception(
message: BaseMessage,
) -> None:
with pytest.raises(ValueError) as e:
_convert_message_to_ai21_message(message)
assert e.value.args[0] == (
f"Could not resolve role type from message {message}. "
f"Only support {HumanMessage.__name__} and {AIMessage.__name__}."
)
@pytest.mark.parametrize(
ids=[
"when_all_messages_are_human_messages__should_return_system_none",
"when_first_message_is_system__should_return_system",
],
argnames=["messages", "expected_system", "expected_messages"],
argvalues=[
(
[
HumanMessage(content="Human Message Content 1"),
HumanMessage(content="Human Message Content 2"),
],
None,
[
ChatMessage(role=RoleType.USER, text="Human Message Content 1"),
ChatMessage(role=RoleType.USER, text="Human Message Content 2"),
],
),
(
[
SystemMessage(content="System Message Content 1"),
HumanMessage(content="Human Message Content 1"),
],
"System Message Content 1",
[
ChatMessage(role=RoleType.USER, text="Human Message Content 1"),
],
),
],
)
def test_convert_messages(
messages: List[BaseMessage],
expected_system: Optional[str],
expected_messages: List[ChatMessage],
) -> None:
system, ai21_messages = _convert_messages_to_ai21_messages(messages)
assert ai21_messages == expected_messages
assert system == expected_system
def test_convert_messages_when_system_is_not_first__should_raise_value_error() -> None:
messages = [
HumanMessage(content="Human Message Content 1"),
SystemMessage(content="System Message Content 1"),
]
with pytest.raises(ValueError):
_convert_messages_to_ai21_messages(messages)
def test_invoke(mock_client_with_chat: Mock) -> None:
chat_input = "I'm Pickle Rick"
llm = ChatAI21(
model="j2-ultra",
api_key=DUMMY_API_KEY,
client=mock_client_with_chat,
**BASIC_EXAMPLE_LLM_PARAMETERS,
)
llm.invoke(input=chat_input, config=dict(tags=["foo"]))
mock_client_with_chat.chat.create.assert_called_once_with(
model="j2-ultra",
messages=[ChatMessage(role=RoleType.USER, text=chat_input)],
system="",
stop_sequences=None,
**BASIC_EXAMPLE_LLM_PARAMETERS,
)
def test_generate(mock_client_with_chat: Mock) -> None:
messages0 = [
HumanMessage(content="I'm Pickle Rick"),
AIMessage(content="Hello Pickle Rick! I am your AI Assistant"),
HumanMessage(content="Nice to meet you."),
]
messages1 = [
SystemMessage(content="system message"),
HumanMessage(content="What is 1 + 1"),
]
llm = ChatAI21(
model="j2-ultra",
client=mock_client_with_chat,
**BASIC_EXAMPLE_LLM_PARAMETERS,
)
llm.generate(messages=[messages0, messages1])
mock_client_with_chat.chat.create.assert_has_calls(
[
call(
model="j2-ultra",
messages=[
ChatMessage(
role=RoleType.USER,
text=str(messages0[0].content),
),
ChatMessage(
role=RoleType.ASSISTANT, text=str(messages0[1].content)
),
ChatMessage(role=RoleType.USER, text=str(messages0[2].content)),
],
system="",
stop_sequences=None,
**BASIC_EXAMPLE_LLM_PARAMETERS,
),
call(
model="j2-ultra",
messages=[
ChatMessage(role=RoleType.USER, text=str(messages1[1].content)),
],
system="system message",
stop_sequences=None,
**BASIC_EXAMPLE_LLM_PARAMETERS,
),
]
)

67
libs/partners/ai21/tests/unit_tests/test_embeddings.py Normal file
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@ -0,0 +1,67 @@
"""Test embedding model integration."""
from unittest.mock import Mock
import pytest
from ai21 import AI21Client, MissingApiKeyError
from ai21.models import EmbedResponse, EmbedResult, EmbedType
from pytest_mock import MockerFixture
from langchain_ai21.embeddings import AI21Embeddings
from tests.unit_tests.conftest import DUMMY_API_KEY, temporarily_unset_api_key
_EXAMPLE_EMBEDDING_0 = [1.0, 2.0, 3.0]
_EXAMPLE_EMBEDDING_1 = [4.0, 5.0, 6.0]
_EXAMPLE_EMBEDDING_2 = [7.0, 8.0, 9.0]
_EXAMPLE_EMBEDDING_RESPONSE = EmbedResponse(
results=[
EmbedResult(_EXAMPLE_EMBEDDING_0),
EmbedResult(_EXAMPLE_EMBEDDING_1),
EmbedResult(_EXAMPLE_EMBEDDING_2),
],
id="test_id",
)
def test_initialization__when_no_api_key__should_raise_exception() -> None:
"""Test integration initialization."""
with temporarily_unset_api_key():
with pytest.raises(MissingApiKeyError):
AI21Embeddings()
@pytest.fixture
def mock_client_with_embeddings(mocker: MockerFixture) -> Mock:
mock_client = mocker.MagicMock(spec=AI21Client)
mock_client.embed = mocker.MagicMock()
mock_client.embed.create.return_value = _EXAMPLE_EMBEDDING_RESPONSE
return mock_client
def test_embed_query(mock_client_with_embeddings: Mock) -> None:
llm = AI21Embeddings(client=mock_client_with_embeddings, api_key=DUMMY_API_KEY)
text = "Hello embeddings world!"
response = llm.embed_query(text=text)
assert response == _EXAMPLE_EMBEDDING_0
mock_client_with_embeddings.embed.create.assert_called_once_with(
texts=[text],
type=EmbedType.QUERY,
)
def test_embed_documents(mock_client_with_embeddings: Mock) -> None:
llm = AI21Embeddings(client=mock_client_with_embeddings, api_key=DUMMY_API_KEY)
texts = ["Hello embeddings world!", "Some other text", "Some more text"]
response = llm.embed_documents(texts=texts)
assert response == [
_EXAMPLE_EMBEDDING_0,
_EXAMPLE_EMBEDDING_1,
_EXAMPLE_EMBEDDING_2,
]
mock_client_with_embeddings.embed.create.assert_called_once_with(
texts=texts,
type=EmbedType.SEGMENT,
)

11
libs/partners/ai21/tests/unit_tests/test_imports.py Normal file
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from langchain_ai21 import __all__
EXPECTED_ALL = [
"AI21LLM",
"ChatAI21",
"AI21Embeddings",
]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)

107
libs/partners/ai21/tests/unit_tests/test_llms.py Normal file
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"""Test AI21 Chat API wrapper."""
from unittest.mock import Mock, call
import pytest
from ai21 import MissingApiKeyError
from ai21.models import (
Penalty,
)
from langchain_ai21 import AI21LLM
from tests.unit_tests.conftest import (
BASIC_EXAMPLE_LLM_PARAMETERS,
DUMMY_API_KEY,
temporarily_unset_api_key,
)
def test_initialization__when_no_api_key__should_raise_exception() -> None:
"""Test integration initialization."""
with temporarily_unset_api_key():
with pytest.raises(MissingApiKeyError):
AI21LLM(
model="j2-ultra",
)
def test_initialization__when_default_parameters() -> None:
"""Test integration initialization."""
AI21LLM(
api_key=DUMMY_API_KEY,
model="j2-ultra",
)
def test_initialization__when_custom_parameters_to_init() -> None:
"""Test integration initialization."""
AI21LLM(
api_key=DUMMY_API_KEY,
model="j2-mid",
num_results=2,
max_tokens=20,
min_tokens=10,
temperature=0.5,
top_p=0.5,
top_k_returns=0,
stop_sequences=["\n"],
frequency_penalty=Penalty(scale=0.2, apply_to_numbers=True),
presence_penalty=Penalty(scale=0.2, apply_to_stopwords=True),
count_penalty=Penalty(
scale=0.2, apply_to_punctuation=True, apply_to_emojis=True
),
custom_model="test_model",
epoch=1,
)
def test_generate(mock_client_with_completion: Mock) -> None:
# Setup test
prompt0 = "Hi, my name is what?"
prompt1 = "My name is who?"
stop = ["\n"]
custom_model = "test_model"
epoch = 1
ai21 = AI21LLM(
model="j2-ultra",
api_key=DUMMY_API_KEY,
client=mock_client_with_completion,
custom_model=custom_model,
epoch=epoch,
**BASIC_EXAMPLE_LLM_PARAMETERS,
)
# Make call to testing function
ai21.generate(
[prompt0, prompt1],
stop=stop,
)
# Assertions
mock_client_with_completion.count_tokens.assert_has_calls(
[
call(prompt0),
call(prompt1),
],
)
mock_client_with_completion.completion.create.assert_has_calls(
[
call(
prompt=prompt0,
model="j2-ultra",
custom_model=custom_model,
stop_sequences=stop,
epoch=epoch,
**BASIC_EXAMPLE_LLM_PARAMETERS,
),
call(
prompt=prompt1,
model="j2-ultra",
custom_model=custom_model,
stop_sequences=stop,
epoch=epoch,
**BASIC_EXAMPLE_LLM_PARAMETERS,
),
]
)