diff --git a/docs/docs/integrations/llms/ai21.ipynb b/docs/docs/integrations/llms/ai21.ipynb index 29e698e7d8..9db93083d5 100644 --- a/docs/docs/integrations/llms/ai21.ipynb +++ b/docs/docs/integrations/llms/ai21.ipynb @@ -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": { diff --git a/libs/partners/ai21/README.md b/libs/partners/ai21/README.md index 7b563969e9..d4001791be 100644 --- a/libs/partners/ai21/README.md +++ b/libs/partners/ai21/README.md @@ -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"}, +) +``` \ No newline at end of file diff --git a/libs/partners/ai21/langchain_ai21/__init__.py b/libs/partners/ai21/langchain_ai21/__init__.py index 253de3778c..ca8ae8e220 100644 --- a/libs/partners/ai21/langchain_ai21/__init__.py +++ b/libs/partners/ai21/langchain_ai21/__init__.py @@ -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", ] diff --git a/libs/partners/ai21/langchain_ai21/contextual_answers.py b/libs/partners/ai21/langchain_ai21/contextual_answers.py new file mode 100644 index 0000000000..264f45460a --- /dev/null +++ b/libs/partners/ai21/langchain_ai21/contextual_answers.py @@ -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) diff --git a/libs/partners/ai21/poetry.lock b/libs/partners/ai21/poetry.lock index 49a59858f7..bb20fb898d 100644 --- a/libs/partners/ai21/poetry.lock +++ b/libs/partners/ai21/poetry.lock @@ -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 = [ - {file = "ai21-2.0.0-py3-none-any.whl", hash = "sha256:0f40b0fe9254b1ddface81681aa18b74635dbf318ca1aeab38c31b500ecd1fe5"}, - {file = "ai21-2.0.0.tar.gz", hash = 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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"] diff --git a/libs/partners/ai21/tests/integration_tests/test_contextual_answers.py b/libs/partners/ai21/tests/integration_tests/test_contextual_answers.py new file mode 100644 index 0000000000..862d8d5d94 --- /dev/null +++ b/libs/partners/ai21/tests/integration_tests/test_contextual_answers.py @@ -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 diff --git a/libs/partners/ai21/tests/unit_tests/conftest.py b/libs/partners/ai21/tests/unit_tests/conftest.py index 43545eaf02..ba5ee070d5 100644 --- a/libs/partners/ai21/tests/unit_tests/conftest.py +++ b/libs/partners/ai21/tests/unit_tests/conftest.py @@ -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 diff --git a/libs/partners/ai21/tests/unit_tests/test_contextual_answers.py b/libs/partners/ai21/tests/unit_tests/test_contextual_answers.py new file mode 100644 index 0000000000..df2599ccfc --- /dev/null +++ b/libs/partners/ai21/tests/unit_tests/test_contextual_answers.py @@ -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) diff --git a/libs/partners/ai21/tests/unit_tests/test_imports.py b/libs/partners/ai21/tests/unit_tests/test_imports.py index 28b11651a7..92577219c3 100644 --- a/libs/partners/ai21/tests/unit_tests/test_imports.py +++ b/libs/partners/ai21/tests/unit_tests/test_imports.py @@ -4,6 +4,7 @@ EXPECTED_ALL = [ "AI21LLM", "ChatAI21", "AI21Embeddings", + "AI21ContextualAnswers", ]