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ai21[patch]: AI21 Labs Contextual Answers support (#18270)

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Description: Added support for AI21 Labs model - Contextual Answers
Dependencies: ai21, ai21-tokenizer
Twitter handle: https://github.com/AI21Labs

---------

Co-authored-by: Asaf Gardin <asafg@ai21.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
pull/18614/head
Asaf Joseph Gardin 4 months ago committed by GitHub
parent e169ee8863
commit 27441555d0
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
10 changed files with 662 additions and 230 deletions
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97
docs/docs/integrations/llms/ai21.ipynb
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@ -24,9 +24,14 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"id": "59c710c4",
"metadata": {},
"metadata": {
"ExecuteTime": {
"end_time": "2024-03-05T20:58:42.397591Z",
"start_time": "2024-03-05T20:58:40.944729Z"
}
},
"outputs": [],
"source": [
"!pip install -qU langchain-ai21"
@ -46,10 +51,14 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"id": "035dea0f",
"metadata": {
"tags": []
"tags": [],
"ExecuteTime": {
"end_time": "2024-03-05T20:58:44.465443Z",
"start_time": "2024-03-05T20:58:42.399724Z"
}
},
"outputs": [],
"source": [
@ -74,14 +83,16 @@
"execution_count": 6,
"id": "98f70927a87e4745",
"metadata": {
"collapsed": false
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-03-05T20:58:45.859265Z",
"start_time": "2024-03-05T20:58:44.466637Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'\\nLangChain is a decentralized blockchain network that leverages AI and machine learning to provide language translation services.'"
]
"text/plain": "'\\nLangChain is a (database)\\nLangChain is a database for storing and processing documents'"
},
"execution_count": 6,
"metadata": {},
@ -105,13 +116,75 @@
"chain.invoke({\"question\": \"What is LangChain?\"})"
]
},
{
"cell_type": "markdown",
"source": [
"# AI21 Contextual Answer\n",
"\n",
"You can use AI21's contextual answers model to receives text or document, serving as a context,\n",
"and a question and returns an answer based entirely on this context.\n",
"\n",
"This means that if the answer to your question is not in the document,\n",
"the model will indicate it (instead of providing a false answer)"
],
"metadata": {
"collapsed": false
},
"id": "9965c10269159ed1"
},
{
"cell_type": "code",
"outputs": [],
"source": [
"from langchain_ai21 import AI21ContextualAnswers\n",
"\n",
"tsm = AI21ContextualAnswers()\n",
"\n",
"response = tsm.invoke(input={\"context\": \"Your context\", \"question\": \"Your question\"})"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-03-05T20:59:00.943426Z",
"start_time": "2024-03-05T20:59:00.263497Z"
}
},
"id": "411adf42eab80829",
"execution_count": 9
},
{
"cell_type": "markdown",
"source": [
"You can also use it with chains and output parsers and vector DBs"
],
"metadata": {
"collapsed": false
},
"id": "af59ffdbf4964875"
},
{
"cell_type": "code",
"execution_count": null,
"id": "a52f765c",
"metadata": {},
"outputs": [],
"source": []
"source": [
"from langchain_ai21 import AI21ContextualAnswers\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"\n",
"tsm = AI21ContextualAnswers()\n",
"chain = tsm | StrOutputParser()\n",
"\n",
"response = chain.invoke(\n",
" {\"context\": \"Your context\", \"question\": \"Your question\"},\n",
")"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-03-05T20:59:07.719225Z",
"start_time": "2024-03-05T20:59:07.102950Z"
}
},
"id": "bc63830f921b4ac9",
"execution_count": 10
}
],
"metadata": {

30
libs/partners/ai21/README.md
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@ -73,3 +73,33 @@ from langchain_ai21 import AI21Embeddings
embeddings = AI21Embeddings()
embeddings.embed_documents(["Hello! This is document 1", "And this is document 2!"])
```
## Task Specific Models
### Contextual Answers
You can use AI21's contextual answers model to receives text or document, serving as a context,
and a question and returns an answer based entirely on this context.
This means that if the answer to your question is not in the document,
the model will indicate it (instead of providing a false answer)
```python
from langchain_ai21 import AI21ContextualAnswers
tsm = AI21ContextualAnswers()
response = tsm.invoke(input={"context": "Your context", "question": "Your question"})
```
You can also use it with chains and output parsers and vector DBs:
```python
from langchain_ai21 import AI21ContextualAnswers
from langchain_core.output_parsers import StrOutputParser
tsm = AI21ContextualAnswers()
chain = tsm | StrOutputParser()
response = chain.invoke(
{"context": "Your context", "question": "Your question"},
)
```

2
libs/partners/ai21/langchain_ai21/__init__.py
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@ -1,4 +1,5 @@
from langchain_ai21.chat_models import ChatAI21
from langchain_ai21.contextual_answers import AI21ContextualAnswers
from langchain_ai21.embeddings import AI21Embeddings
from langchain_ai21.llms import AI21LLM
@ -6,4 +7,5 @@ __all__ = [
"AI21LLM",
"ChatAI21",
"AI21Embeddings",
"AI21ContextualAnswers",
]

108
libs/partners/ai21/langchain_ai21/contextual_answers.py
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@ -0,0 +1,108 @@
from typing import (
Any,
List,
Optional,
Tuple,
Type,
TypedDict,
Union,
)
from langchain_core.documents import Document
from langchain_core.runnables import RunnableConfig, RunnableSerializable, ensure_config
from langchain_ai21.ai21_base import AI21Base
ANSWER_NOT_IN_CONTEXT_RESPONSE = "Answer not in context"
ContextType = Union[str, List[Union[Document, str]]]
class ContextualAnswerInput(TypedDict):
context: ContextType
question: str
class AI21ContextualAnswers(RunnableSerializable[ContextualAnswerInput, str], AI21Base):
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@property
def InputType(self) -> Type[ContextualAnswerInput]:
"""Get the input type for this runnable."""
return ContextualAnswerInput
@property
def OutputType(self) -> Type[str]:
"""Get the input type for this runnable."""
return str
def invoke(
self,
input: ContextualAnswerInput,
config: Optional[RunnableConfig] = None,
response_if_no_answer_found: str = ANSWER_NOT_IN_CONTEXT_RESPONSE,
**kwargs: Any,
) -> str:
config = ensure_config(config)
return self._call_with_config(
func=lambda inner_input: self._call_contextual_answers(
inner_input, response_if_no_answer_found
),
input=input,
config=config,
run_type="llm",
)
def _call_contextual_answers(
self,
input: ContextualAnswerInput,
response_if_no_answer_found: str,
) -> str:
context, question = self._convert_input(input)
response = self.client.answer.create(context=context, question=question)
if response.answer is None:
return response_if_no_answer_found
return response.answer
def _convert_input(self, input: ContextualAnswerInput) -> Tuple[str, str]:
context, question = self._extract_context_and_question(input)
context = self._parse_context(context)
return context, question
def _extract_context_and_question(
self,
input: ContextualAnswerInput,
) -> Tuple[ContextType, str]:
context = input.get("context")
question = input.get("question")
if not context or not question:
raise ValueError(
f"Input must contain a 'context' and 'question' fields. Got {input}"
)
if not isinstance(context, list) and not isinstance(context, str):
raise ValueError(
f"Expected input to be a list of strings or Documents."
f" Received {type(input)}"
)
return context, question
def _parse_context(self, context: ContextType) -> str:
if isinstance(context, str):
return context
docs = [
item.page_content if isinstance(item, Document) else item
for item in context
]
return "\n".join(docs)

441
libs/partners/ai21/poetry.lock generated
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@ -2,13 +2,13 @@
[[package]]
name = "ai21"
version = "2.0.0"
version = "2.0.5"
description = ""
optional = false
python-versions = ">=3.8,<4.0"
files = [
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]
[package.dependencies]
@ -49,13 +49,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
[[package]]
name = "anyio"
version = "4.2.0"
version = "4.3.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.8"
files = [
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]
[package.dependencies]
@ -71,13 +71,13 @@ trio = ["trio (>=0.23)"]
[[package]]
name = "certifi"
version = "2023.11.17"
version = "2024.2.2"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
{file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
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]
[[package]]
@ -209,13 +209,13 @@ files = [
[[package]]
name = "dataclasses-json"
version = "0.6.3"
version = "0.6.4"
description = "Easily serialize dataclasses to and from JSON."
optional = false
python-versions = ">=3.7,<4.0"
files = [
{file = "dataclasses_json-0.6.3-py3-none-any.whl", hash = "sha256:4aeb343357997396f6bca1acae64e486c3a723d8f5c76301888abeccf0c45176"},
{file = "dataclasses_json-0.6.3.tar.gz", hash = "sha256:35cb40aae824736fdf959801356641836365219cfe14caeb115c39136f775d2a"},
{file = "dataclasses_json-0.6.4-py3-none-any.whl", hash = "sha256:f90578b8a3177f7552f4e1a6e535e84293cd5da421fcce0642d49c0d7bdf8df2"},
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]
[package.dependencies]
@ -299,7 +299,7 @@ files = [
[[package]]
name = "langchain-core"
version = "0.1.22"
version = "0.1.27"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.8.1,<4.0"
@ -309,7 +309,7 @@ develop = true
[package.dependencies]
anyio = ">=3,<5"
jsonpatch = "^1.33"
langsmith = "^0.0.87"
langsmith = "^0.1.0"
packaging = "^23.2"
pydantic = ">=1,<3"
PyYAML = ">=5.3"
@ -325,37 +325,37 @@ url = "../../core"
[[package]]
name = "langsmith"
version = "0.0.87"
version = "0.1.10"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
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]
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orjson = ">=3.9.14,<4.0.0"
pydantic = ">=1,<3"
requests = ">=2,<3"
[[package]]
name = "marshmallow"
version = "3.20.2"
version = "3.21.0"
description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
optional = false
python-versions = ">=3.8"
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]
[package.dependencies]
packaging = ">=17.0"
[package.extras]
dev = ["pre-commit (>=2.4,<4.0)", "pytest", "pytz", "simplejson", "tox"]
docs = ["alabaster (==0.7.15)", "autodocsumm (==0.2.12)", "sphinx (==7.2.6)", "sphinx-issues (==3.0.1)", "sphinx-version-warning (==1.1.2)"]
lint = ["pre-commit (>=2.4,<4.0)"]
dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.2.6)", "sphinx-issues (==4.0.0)", "sphinx-version-warning (==1.1.2)"]
tests = ["pytest", "pytz", "simplejson"]
[[package]]
@ -419,6 +419,65 @@ files = [
{file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
]
[[package]]
name = "orjson"
version = "3.9.15"
description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
optional = false
python-versions = ">=3.8"
files = [
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]
[package.dependencies]
pytest = ">=7.0.0,<8.0.0"
pytest = ">=7.0.0,<9.0.0"
[[package]]
name = "tenacity"
@ -890,13 +924,13 @@ files = [
[[package]]
name = "typing-extensions"
version = "4.9.0"
version = "4.10.0"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
files = [
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]
[[package]]
@ -916,54 +950,57 @@ typing-extensions = ">=3.7.4"
[[package]]
name = "urllib3"
version = "2.1.0"
version = "2.2.1"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.8"
files = [
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h2 = ["h2 (>=4,<5)"]
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
[[package]]
name = "watchdog"
version = "3.0.0"
version = "4.0.0"
description = "Filesystem events monitoring"
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
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]
[package.extras]
@ -972,4 +1009,4 @@ watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "95343e4ae1dd67887432aa1acd09e5f49cbc33c2ddcfdbad9552a90a92adda79"
content-hash = "f8512fa6e745dc32b49132bd7327ec204822c861c113b57db21f1f8cf2fac881"

22
libs/partners/ai21/pyproject.toml
Unescape Escape View File

@ -1,6 +1,6 @@
[tool.poetry]
name = "langchain-ai21"
version = "0.0.1"
version = "0.1.0"
description = "An integration package connecting AI21 and LangChain"
authors = []
readme = "README.md"
@ -8,7 +8,7 @@ readme = "README.md"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain-core = "^0.1.22"
ai21 = "^2.0.0"
ai21 = "2.0.5"
[tool.poetry.group.test]
optional = true
@ -16,11 +16,11 @@ optional = true
[tool.poetry.group.test.dependencies]
pytest = "^7.3.0"
freezegun = "^1.2.2"
pytest-mock = "^3.10.0"
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}
langchain-core = { path = "../../core", develop = true }
[tool.poetry.group.codespell]
optional = true
@ -41,28 +41,26 @@ ruff = "^0.1.5"
[tool.poetry.group.typing.dependencies]
mypy = "^0.991"
langchain-core = {path = "../../core", develop = true}
langchain-core = { path = "../../core", develop = true }
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.dev.dependencies]
langchain-core = {path = "../../core", develop = true}
langchain-core = { path = "../../core", develop = true }
[tool.ruff]
select = [
"E", # pycodestyle
"F", # pyflakes
"I", # isort
"E", # pycodestyle
"F", # pyflakes
"I", # isort
]
[tool.mypy]
disallow_untyped_defs = "True"
[tool.coverage.run]
omit = [
"tests/*",
]
omit = ["tests/*"]
[build-system]
requires = ["poetry-core>=1.0.0"]

61
libs/partners/ai21/tests/integration_tests/test_contextual_answers.py
Unescape Escape View File

@ -0,0 +1,61 @@
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import Runnable
from langchain_ai21.contextual_answers import (
ANSWER_NOT_IN_CONTEXT_RESPONSE,
AI21ContextualAnswers,
)
context = """
Albert Einstein German: 14 March 1879 – 18 April 1955)
was a German-born theoretical physicist who is widely held
to be one of the greatest and most influential scientists
"""
_GOOD_QUESTION = "When did Albert Einstein born?"
_BAD_QUESTION = "What color is Yoda's light saber?"
_EXPECTED_PARTIAL_RESPONSE = "March 14, 1879"
def test_invoke__when_good_question() -> None:
llm = AI21ContextualAnswers()
response = llm.invoke(
{"context": context, "question": _GOOD_QUESTION},
config={"metadata": {"name": "I AM A TEST"}},
)
assert response != ANSWER_NOT_IN_CONTEXT_RESPONSE
def test_invoke__when_bad_question__should_return_answer_not_in_context() -> None:
llm = AI21ContextualAnswers()
response = llm.invoke(input={"context": context, "question": _BAD_QUESTION})
assert response == ANSWER_NOT_IN_CONTEXT_RESPONSE
def test_invoke__when_response_if_no_answer_passed__should_use_it() -> None:
response_if_no_answer_found = "This should be the response"
llm = AI21ContextualAnswers()
response = llm.invoke(
input={"context": context, "question": _BAD_QUESTION},
response_if_no_answer_found=response_if_no_answer_found,
)
assert response == response_if_no_answer_found
def test_invoke_when_used_in_a_simple_chain_with_no_vectorstore() -> None:
tsm = AI21ContextualAnswers()
chain: Runnable = tsm | StrOutputParser()
response = chain.invoke(
{"context": context, "question": _GOOD_QUESTION},
)
assert response != ANSWER_NOT_IN_CONTEXT_RESPONSE

22
libs/partners/ai21/tests/unit_tests/conftest.py
Unescape Escape View File

@ -1,11 +1,11 @@
import os
from contextlib import contextmanager
from typing import Generator
from unittest.mock import Mock
import pytest
from ai21 import AI21Client
from ai21 import AI21Client, AI21EnvConfig
from ai21.models import (
AnswerResponse,
ChatOutput,
ChatResponse,
Completion,
@ -84,8 +84,22 @@ 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)
api_key = AI21EnvConfig.api_key
AI21EnvConfig.api_key = None
yield
if api_key is not None:
os.environ["API_KEY"] = api_key
AI21EnvConfig.api_key = api_key
@pytest.fixture
def mock_client_with_contextual_answers(mocker: MockerFixture) -> Mock:
mock_client = mocker.MagicMock(spec=AI21Client)
mock_client.answer = mocker.MagicMock()
mock_client.answer.create.return_value = AnswerResponse(
id="some_id",
answer="some answer",
answer_in_context=False,
)
return mock_client

108
libs/partners/ai21/tests/unit_tests/test_contextual_answers.py
Unescape Escape View File

@ -0,0 +1,108 @@
from unittest.mock import Mock
import pytest
from langchain_core.documents import Document
from langchain_ai21 import AI21ContextualAnswers
from langchain_ai21.contextual_answers import ContextualAnswerInput
from tests.unit_tests.conftest import DUMMY_API_KEY
@pytest.mark.parametrize(
ids=[
"when_no_context__should_raise_exception",
"when_no_question__should_raise_exception",
"when_question_is_an_empty_string__should_raise_exception",
"when_context_is_an_empty_string__should_raise_exception",
"when_context_is_an_empty_list",
],
argnames="input",
argvalues=[
({"question": "What is the capital of France?"}),
({"context": "Paris is the capital of France"}),
({"question": "", "context": "Paris is the capital of France"}),
({"context": "", "question": "some question?"}),
({"context": [], "question": "What is the capital of France?"}),
],
)
def test_invoke__on_bad_input(
input: ContextualAnswerInput,
mock_client_with_contextual_answers: Mock,
) -> None:
tsm = AI21ContextualAnswers(
api_key=DUMMY_API_KEY, client=mock_client_with_contextual_answers
)
with pytest.raises(ValueError) as error:
tsm.invoke(input)
assert (
error.value.args[0]
== f"Input must contain a 'context' and 'question' fields. Got {input}"
)
@pytest.mark.parametrize(
ids=[
"when_context_is_not_str_or_list_of_docs_or_str",
],
argnames="input",
argvalues=[
({"context": 1242, "question": "What is the capital of France?"}),
],
)
def test_invoke__on_context_bad_input(
input: ContextualAnswerInput, mock_client_with_contextual_answers: Mock
) -> None:
tsm = AI21ContextualAnswers(
api_key=DUMMY_API_KEY,
client=mock_client_with_contextual_answers,
)
with pytest.raises(ValueError) as error:
tsm.invoke(input)
assert (
error.value.args[0] == f"Expected input to be a list of strings or Documents."
f" Received {type(input)}"
)
@pytest.mark.parametrize(
ids=[
"when_context_is_a_list_of_strings",
"when_context_is_a_list_of_documents",
"when_context_is_a_string",
],
argnames="input",
argvalues=[
(
{
"context": ["Paris is the capital of france"],
"question": "What is the capital of France?",
}
),
(
{
"context": [Document(page_content="Paris is the capital of france")],
"question": "What is the capital of France?",
}
),
(
{
"context": "Paris is the capital of france",
"question": "What is the capital of France?",
}
),
],
)
def test_invoke__on_good_input(
input: ContextualAnswerInput, mock_client_with_contextual_answers: Mock
) -> None:
tsm = AI21ContextualAnswers(
api_key=DUMMY_API_KEY,
client=mock_client_with_contextual_answers,
)
response = tsm.invoke(input)
assert isinstance(response, str)

1
libs/partners/ai21/tests/unit_tests/test_imports.py
Unescape Escape View File

@ -4,6 +4,7 @@ EXPECTED_ALL = [
"AI21LLM",
"ChatAI21",
"AI21Embeddings",
"AI21ContextualAnswers",
]

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