cohere[patch]: add cohere as a partner package (#19049)

Description: adds support for langchain_cohere

---------

Co-authored-by: Harry M <127103098+harry-cohere@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>

docs/docs/integrations/chat/cohere.ipynb

@@ -32,7 +32,7 @@
     "The integration lives in the `langchain-community` package. We also need to install the `cohere` package itself. We can install these with:\n",
     "\n",
     "```bash\n",
-    "pip install -U langchain-community cohere\n",
+    "pip install -U langchain-community langchain-cohere\n",
     "```\n",
     "\n",
     "We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
@@ -89,7 +89,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatCohere\n",
+    "from langchain_cohere import ChatCohere\n",
     "from langchain_core.messages import HumanMessage"
    ]
   },

docs/docs/integrations/llms/cohere.ipynb

@@ -24,7 +24,7 @@
     "The integration lives in the `langchain-community` package. We also need to install the `cohere` package itself. We can install these with:\n",
     "\n",
     "```bash\n",
-    "pip install -U langchain-community cohere\n",
+    "pip install -U langchain-community langchain-cohere\n",
     "```\n",
     "\n",
     "We'll also need to get a [Cohere API key](https://cohere.com/) and set the `COHERE_API_KEY` environment variable:"
@@ -39,7 +39,7 @@
    },
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       " ········\n"
@@ -91,7 +91,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_community.llms import Cohere\n",
+    "from langchain_cohere import Cohere\n",
     "from langchain_core.messages import HumanMessage"
    ]
   },
@@ -255,7 +255,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

docs/docs/integrations/retrievers/cohere.ipynb

@@ -32,7 +32,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_community.chat_models import ChatCohere\n",
+    "from langchain_cohere import ChatCohere\n",
     "from langchain_community.retrievers import CohereRagRetriever\n",
     "from langchain_core.documents import Document"
    ]

docs/docs/integrations/text_embedding/cohere.ipynb

@@ -30,7 +30,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.embeddings import CohereEmbeddings"
+    "from langchain_cohere import CohereEmbeddings"
    ]
   },
   {

libs/community/langchain_community/chat_models/cohere.py

@@ -1,5 +1,6 @@
 from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
 
+from langchain_core._api.deprecation import deprecated
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -94,6 +95,9 @@ def get_cohere_chat_request(
     return {k: v for k, v in req.items() if v is not None}
 
 
+@deprecated(
+    since="0.0.30", removal="0.2.0", alternative_import="langchain_cohere.ChatCohere"
+)
 class ChatCohere(BaseChatModel, BaseCohere):
     """`Cohere` chat large language models.
 

libs/community/langchain_community/embeddings/cohere.py

@@ -1,5 +1,6 @@
 from typing import Any, Dict, List, Optional
 
+from langchain_core._api.deprecation import deprecated
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain_core.utils import get_from_dict_or_env
@@ -7,6 +8,11 @@ from langchain_core.utils import get_from_dict_or_env
 from langchain_community.llms.cohere import _create_retry_decorator
 
 
+@deprecated(
+    since="0.0.30",
+    removal="0.2.0",
+    alternative_import="langchain_cohere.CohereEmbeddings",
+)
 class CohereEmbeddings(BaseModel, Embeddings):
     """Cohere embedding models.
 

libs/community/langchain_community/llms/cohere.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import logging
 from typing import Any, Callable, Dict, List, Optional
 
+from langchain_core._api.deprecation import deprecated
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -69,6 +70,9 @@ def acompletion_with_retry(llm: Cohere, **kwargs: Any) -> Any:
     return _completion_with_retry(**kwargs)
 
 
+@deprecated(
+    since="0.0.30", removal="0.2.0", alternative_import="langchain_cohere.BaseCohere"
+)
 class BaseCohere(Serializable):
     """Base class for Cohere models."""
 
@@ -117,6 +121,9 @@ class BaseCohere(Serializable):
         return values
 
 
+@deprecated(
+    since="0.1.14", removal="0.2.0", alternative_import="langchain_cohere.Cohere"
+)
 class Cohere(LLM, BaseCohere):
     """Cohere large language models.
 

libs/community/langchain_community/retrievers/cohere_rag_retriever.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING, Any, Dict, List
 
+from langchain_core._api.deprecation import deprecated
 from langchain_core.callbacks import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
@@ -40,6 +41,11 @@ def _get_docs(response: Any) -> List[Document]:
     return docs
 
 
+@deprecated(
+    since="0.0.30",
+    removal="0.2.0",
+    alternative_import="langchain_cohere.CohereRagRetriever",
+)
 class CohereRagRetriever(BaseRetriever):
     """Cohere Chat API with RAG."""
 

libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 from copy import deepcopy
 from typing import Any, Dict, List, Optional, Sequence, Union
 
+from langchain_core._api.deprecation import deprecated
 from langchain_core.documents import Document
 from langchain_core.pydantic_v1 import Extra, root_validator
 
@@ -11,6 +12,9 @@ from langchain.retrievers.document_compressors.base import BaseDocumentCompresso
 from langchain.utils import get_from_dict_or_env
 
 
+@deprecated(
+    since="0.0.30", removal="0.2.0", alternative_import="langchain_cohere.CohereRerank"
+)
 class CohereRerank(BaseDocumentCompressor):
     """Document compressor that uses `Cohere Rerank API`."""
 

libs/partners/cohere/.gitignore

@@ -0,0 +1 @@
+__pycache__

libs/partners/cohere/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 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.

libs/partners/cohere/Makefile

@@ -0,0 +1,57 @@
+.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/cohere --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
+lint_package: PYTHON_FILES=langchain_cohere
+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_cohere -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'

libs/partners/cohere/README.md

@@ -0,0 +1 @@
+# langchain-cohere

libs/partners/cohere/langchain_cohere/__init__.py

@@ -0,0 +1,12 @@
+from langchain_cohere.chat_models import ChatCohere
+from langchain_cohere.embeddings import CohereEmbeddings
+from langchain_cohere.rag_retrievers import CohereRagRetriever
+from langchain_cohere.rerank import CohereRerank
+
+__all__ = [
+    "ChatCohere",
+    "CohereVectorStore",
+    "CohereEmbeddings",
+    "CohereRagRetriever",
+    "CohereRerank",
+]

libs/partners/cohere/langchain_cohere/chat_models.py

@@ -0,0 +1,249 @@
+from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
+
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.chat_models import (
+    BaseChatModel,
+    agenerate_from_stream,
+    generate_from_stream,
+)
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    ChatMessage,
+    HumanMessage,
+    SystemMessage,
+)
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+
+from langchain_cohere.llms import BaseCohere
+
+
+def get_role(message: BaseMessage) -> str:
+    """Get the role of the message.
+
+    Args:
+        message: The message.
+
+    Returns:
+        The role of the message.
+
+    Raises:
+        ValueError: If the message is of an unknown type.
+    """
+    if isinstance(message, ChatMessage) or isinstance(message, HumanMessage):
+        return "User"
+    elif isinstance(message, AIMessage):
+        return "Chatbot"
+    elif isinstance(message, SystemMessage):
+        return "System"
+    else:
+        raise ValueError(f"Got unknown type {message}")
+
+
+def get_cohere_chat_request(
+    messages: List[BaseMessage],
+    *,
+    connectors: Optional[List[Dict[str, str]]] = None,
+    **kwargs: Any,
+) -> Dict[str, Any]:
+    """Get the request for the Cohere chat API.
+
+    Args:
+        messages: The messages.
+        connectors: The connectors.
+        **kwargs: The keyword arguments.
+
+    Returns:
+        The request for the Cohere chat API.
+    """
+    documents = (
+        None
+        if "source_documents" not in kwargs
+        else [
+            {
+                "snippet": doc.page_content,
+                "id": doc.metadata.get("id") or f"doc-{str(i)}",
+            }
+            for i, doc in enumerate(kwargs["source_documents"])
+        ]
+    )
+    kwargs.pop("source_documents", None)
+    maybe_connectors = connectors if documents is None else None
+
+    # by enabling automatic prompt truncation, the probability of request failure is
+    # reduced with minimal impact on response quality
+    prompt_truncation = (
+        "AUTO" if documents is not None or connectors is not None else None
+    )
+
+    req = {
+        "message": messages[-1].content,
+        "chat_history": [
+            {"role": get_role(x), "message": x.content} for x in messages[:-1]
+        ],
+        "documents": documents,
+        "connectors": maybe_connectors,
+        "prompt_truncation": prompt_truncation,
+        **kwargs,
+    }
+
+    return {k: v for k, v in req.items() if v is not None}
+
+
+class ChatCohere(BaseChatModel, BaseCohere):
+    """`Cohere` chat large language models.
+
+    To use, you should have the ``cohere`` python package installed, and the
+    environment variable ``COHERE_API_KEY`` set with your API key, or pass
+    it as a named parameter to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_cohere import ChatCohere
+            from langchain_core.messages import HumanMessage
+
+            chat = ChatCohere(cohere_api_key="my-api-key")
+
+            messages = [HumanMessage(content="knock knock")]
+            chat.invoke(messages)
+    """
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        allow_population_by_field_name = True
+        arbitrary_types_allowed = True
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of chat model."""
+        return "cohere-chat"
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling Cohere API."""
+        base_params = {
+            "model": self.model,
+            "temperature": self.temperature,
+        }
+        return {k: v for k, v in base_params.items() if v is not None}
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return self._default_params
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
+
+        if hasattr(self.client, "chat_stream"):  # detect and support sdk v5
+            stream = self.client.chat_stream(**request)
+        else:
+            stream = self.client.chat(**request, stream=True)
+
+        for data in stream:
+            if data.event_type == "text-generation":
+                delta = data.text
+                chunk = ChatGenerationChunk(message=AIMessageChunk(content=delta))
+                if run_manager:
+                    run_manager.on_llm_new_token(delta, chunk=chunk)
+                yield chunk
+
+    async def _astream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
+
+        if hasattr(self.async_client, "chat_stream"):  # detect and support sdk v5
+            stream = self.async_client.chat_stream(**request)
+        else:
+            stream = self.async_client.chat(**request, stream=True)
+
+        async for data in stream:
+            if data.event_type == "text-generation":
+                delta = data.text
+                chunk = ChatGenerationChunk(message=AIMessageChunk(content=delta))
+                if run_manager:
+                    await run_manager.on_llm_new_token(delta, chunk=chunk)
+                yield chunk
+
+    def _get_generation_info(self, response: Any) -> Dict[str, Any]:
+        """Get the generation info from cohere API response."""
+        return {
+            "documents": response.documents,
+            "citations": response.citations,
+            "search_results": response.search_results,
+            "search_queries": response.search_queries,
+            "token_count": response.token_count,
+        }
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if self.streaming:
+            stream_iter = self._stream(
+                messages, stop=stop, run_manager=run_manager, **kwargs
+            )
+            return generate_from_stream(stream_iter)
+
+        request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
+        response = self.client.chat(**request)
+
+        message = AIMessage(content=response.text)
+        generation_info = None
+        if hasattr(response, "documents"):
+            generation_info = self._get_generation_info(response)
+        return ChatResult(
+            generations=[
+                ChatGeneration(message=message, generation_info=generation_info)
+            ]
+        )
+
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if self.streaming:
+            stream_iter = self._astream(
+                messages, stop=stop, run_manager=run_manager, **kwargs
+            )
+            return await agenerate_from_stream(stream_iter)
+
+        request = get_cohere_chat_request(messages, **self._default_params, **kwargs)
+        response = self.client.chat(**request)
+
+        message = AIMessage(content=response.text)
+        generation_info = None
+        if hasattr(response, "documents"):
+            generation_info = self._get_generation_info(response)
+        return ChatResult(
+            generations=[
+                ChatGeneration(message=message, generation_info=generation_info)
+            ]
+        )
+
+    def get_num_tokens(self, text: str) -> int:
+        """Calculate number of tokens."""
+        return len(self.client.tokenize(text).tokens)

libs/partners/cohere/langchain_cohere/embeddings.py

@@ -0,0 +1,168 @@
+import typing
+from typing import Any, Dict, List, Optional
+
+import cohere
+from langchain_core.embeddings import Embeddings
+from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
+from langchain_core.utils import get_from_dict_or_env
+
+from .utils import _create_retry_decorator
+
+
+class CohereEmbeddings(BaseModel, Embeddings):
+    """Cohere embedding models.
+
+    To use, you should have the ``cohere`` python package installed, and the
+    environment variable ``COHERE_API_KEY`` set with your API key or pass it
+    as a named parameter to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_cohere import CohereEmbeddings
+            cohere = CohereEmbeddings(
+                model="embed-english-light-v3.0",
+                cohere_api_key="my-api-key"
+            )
+    """
+
+    client: Any  #: :meta private:
+    """Cohere client."""
+    async_client: Any  #: :meta private:
+    """Cohere async client."""
+    model: str = "embed-english-v2.0"
+    """Model name to use."""
+
+    truncate: Optional[str] = None
+    """Truncate embeddings that are too long from start or end ("NONE"|"START"|"END")"""
+
+    cohere_api_key: Optional[str] = None
+
+    max_retries: int = 3
+    """Maximum number of retries to make when generating."""
+    request_timeout: Optional[float] = None
+    """Timeout in seconds for the Cohere API request."""
+    user_agent: str = "langchain"
+    """Identifier for the application making the request."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+        extra = Extra.forbid
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        cohere_api_key = get_from_dict_or_env(
+            values, "cohere_api_key", "COHERE_API_KEY"
+        )
+        request_timeout = values.get("request_timeout")
+
+        client_name = values["user_agent"]
+        values["client"] = cohere.Client(
+            cohere_api_key,
+            timeout=request_timeout,
+            client_name=client_name,
+        )
+        values["async_client"] = cohere.AsyncClient(
+            cohere_api_key,
+            timeout=request_timeout,
+            client_name=client_name,
+        )
+
+        return values
+
+    def embed_with_retry(self, **kwargs: Any) -> Any:
+        """Use tenacity to retry the embed call."""
+        retry_decorator = _create_retry_decorator(self.max_retries)
+
+        @retry_decorator
+        def _embed_with_retry(**kwargs: Any) -> Any:
+            return self.client.embed(**kwargs)
+
+        return _embed_with_retry(**kwargs)
+
+    def aembed_with_retry(self, **kwargs: Any) -> Any:
+        """Use tenacity to retry the embed call."""
+        retry_decorator = _create_retry_decorator(self.max_retries)
+
+        @retry_decorator
+        async def _embed_with_retry(**kwargs: Any) -> Any:
+            return await self.async_client.embed(**kwargs)
+
+        return _embed_with_retry(**kwargs)
+
+    def embed(
+        self,
+        texts: List[str],
+        *,
+        input_type: typing.Optional[cohere.EmbedInputType] = None,
+    ) -> List[List[float]]:
+        embeddings = self.embed_with_retry(
+            model=self.model,
+            texts=texts,
+            input_type=input_type,
+            truncate=self.truncate,
+        ).embeddings
+        return [list(map(float, e)) for e in embeddings]
+
+    async def aembed(
+        self,
+        texts: List[str],
+        *,
+        input_type: typing.Optional[cohere.EmbedInputType] = None,
+    ) -> List[List[float]]:
+        embeddings = (
+            await self.aembed_with_retry(
+                model=self.model,
+                texts=texts,
+                input_type=input_type,
+                truncate=self.truncate,
+            )
+        ).embeddings
+        return [list(map(float, e)) for e in embeddings]
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed a list of document texts.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        return self.embed(texts, input_type="search_document")
+
+    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Async call out to Cohere's embedding endpoint.
+
+        Args:
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        return await self.aembed(texts, input_type="search_document")
+
+    def embed_query(self, text: str) -> List[float]:
+        """Call out to Cohere's embedding endpoint.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        return self.embed([text], input_type="search_query")[0]
+
+    async def aembed_query(self, text: str) -> List[float]:
+        """Async call out to Cohere's embedding endpoint.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embeddings for the text.
+        """
+        return (await self.aembed([text], input_type="search_query"))[0]

libs/partners/cohere/langchain_cohere/llms.py

@@ -0,0 +1,234 @@
+from __future__ import annotations
+
+import logging
+import re
+from typing import Any, Dict, List, Optional
+
+import cohere
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.llms import LLM
+from langchain_core.load.serializable import Serializable
+from langchain_core.pydantic_v1 import Extra, Field, SecretStr, root_validator
+from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+
+from .utils import _create_retry_decorator
+
+
+def enforce_stop_tokens(text: str, stop: List[str]) -> str:
+    """Cut off the text as soon as any stop words occur."""
+    return re.split("|".join(stop), text, maxsplit=1)[0]
+
+
+logger = logging.getLogger(__name__)
+
+
+def completion_with_retry(llm: Cohere, **kwargs: Any) -> Any:
+    """Use tenacity to retry the completion call."""
+    retry_decorator = _create_retry_decorator(llm.max_retries)
+
+    @retry_decorator
+    def _completion_with_retry(**kwargs: Any) -> Any:
+        return llm.client.generate(**kwargs)
+
+    return _completion_with_retry(**kwargs)
+
+
+def acompletion_with_retry(llm: Cohere, **kwargs: Any) -> Any:
+    """Use tenacity to retry the completion call."""
+    retry_decorator = _create_retry_decorator(llm.max_retries)
+
+    @retry_decorator
+    async def _completion_with_retry(**kwargs: Any) -> Any:
+        return await llm.async_client.generate(**kwargs)
+
+    return _completion_with_retry(**kwargs)
+
+
+class BaseCohere(Serializable):
+    """Base class for Cohere models."""
+
+    client: Any = None  #: :meta private:
+    async_client: Any = None  #: :meta private:
+    model: Optional[str] = Field(default=None)
+    """Model name to use."""
+
+    temperature: Optional[float] = None
+    """A non-negative float that tunes the degree of randomness in generation."""
+
+    cohere_api_key: Optional[SecretStr] = None
+    """Cohere API key. If not provided, will be read from the environment variable."""
+
+    stop: Optional[List[str]] = None
+
+    streaming: bool = Field(default=False)
+    """Whether to stream the results."""
+
+    user_agent: str = "langchain"
+    """Identifier for the application making the request."""
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        values["cohere_api_key"] = convert_to_secret_str(
+            get_from_dict_or_env(values, "cohere_api_key", "COHERE_API_KEY")
+        )
+        client_name = values["user_agent"]
+        values["client"] = cohere.Client(
+            api_key=values["cohere_api_key"].get_secret_value(),
+            client_name=client_name,
+        )
+        values["async_client"] = cohere.AsyncClient(
+            api_key=values["cohere_api_key"].get_secret_value(),
+            client_name=client_name,
+        )
+        return values
+
+
+class Cohere(LLM, BaseCohere):
+    """Cohere large language models.
+
+    To use, you should have the ``cohere`` python package installed, and the
+    environment variable ``COHERE_API_KEY`` set with your API key, or pass
+    it as a named parameter to the constructor.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_cohere import Cohere
+
+            cohere = Cohere(cohere_api_key="my-api-key")
+    """
+
+    max_tokens: Optional[int] = None
+    """Denotes the number of tokens to predict per generation."""
+
+    k: Optional[int] = None
+    """Number of most likely tokens to consider at each step."""
+
+    p: Optional[int] = None
+    """Total probability mass of tokens to consider at each step."""
+
+    frequency_penalty: Optional[float] = None
+    """Penalizes repeated tokens according to frequency. Between 0 and 1."""
+
+    presence_penalty: Optional[float] = None
+    """Penalizes repeated tokens. Between 0 and 1."""
+
+    truncate: Optional[str] = None
+    """Specify how the client handles inputs longer than the maximum token
+    length: Truncate from START, END or NONE"""
+
+    max_retries: int = 10
+    """Maximum number of retries to make when generating."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+        extra = Extra.forbid
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Configurable parameters for calling Cohere's generate API."""
+        base_params = {
+            "model": self.model,
+            "temperature": self.temperature,
+            "max_tokens": self.max_tokens,
+            "k": self.k,
+            "p": self.p,
+            "frequency_penalty": self.frequency_penalty,
+            "presence_penalty": self.presence_penalty,
+            "truncate": self.truncate,
+        }
+        return {k: v for k, v in base_params.items() if v is not None}
+
+    @property
+    def lc_secrets(self) -> Dict[str, str]:
+        return {"cohere_api_key": "COHERE_API_KEY"}
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return self._default_params
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "cohere"
+
+    def _invocation_params(self, stop: Optional[List[str]], **kwargs: Any) -> dict:
+        params = self._default_params
+        if self.stop is not None and stop is not None:
+            raise ValueError("`stop` found in both the input and default params.")
+        elif self.stop is not None:
+            params["stop_sequences"] = self.stop
+        else:
+            params["stop_sequences"] = stop
+        return {**params, **kwargs}
+
+    def _process_response(self, response: Any, stop: Optional[List[str]]) -> str:
+        text = response.generations[0].text
+        # If stop tokens are provided, Cohere's endpoint returns them.
+        # In order to make this consistent with other endpoints, we strip them.
+        if stop:
+            text = enforce_stop_tokens(text, stop)
+        return text
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call out to Cohere's generate endpoint.
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+
+                response = cohere("Tell me a joke.")
+        """
+        params = self._invocation_params(stop, **kwargs)
+        response = completion_with_retry(
+            self, model=self.model, prompt=prompt, **params
+        )
+        _stop = params.get("stop_sequences")
+        return self._process_response(response, _stop)
+
+    async def _acall(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Async call out to Cohere's generate endpoint.
+
+        Args:
+            prompt: The prompt to pass into the model.
+            stop: Optional list of stop words to use when generating.
+
+        Returns:
+            The string generated by the model.
+
+        Example:
+            .. code-block:: python
+
+                response = await cohere("Tell me a joke.")
+        """
+        params = self._invocation_params(stop, **kwargs)
+        response = await acompletion_with_retry(
+            self, model=self.model, prompt=prompt, **params
+        )
+        _stop = params.get("stop_sequences")
+        return self._process_response(response, _stop)

libs/partners/cohere/langchain_cohere/rag_retrievers.py

@@ -0,0 +1,92 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Dict, List
+
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForRetrieverRun,
+    CallbackManagerForRetrieverRun,
+)
+from langchain_core.documents import Document
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import HumanMessage
+from langchain_core.pydantic_v1 import Field
+from langchain_core.retrievers import BaseRetriever
+
+if TYPE_CHECKING:
+    from langchain_core.messages import BaseMessage
+
+
+def _get_docs(response: Any) -> List[Document]:
+    docs = (
+        []
+        if "documents" not in response.generation_info
+        else [
+            Document(page_content=doc["snippet"], metadata=doc)
+            for doc in response.generation_info["documents"]
+        ]
+    )
+    docs.append(
+        Document(
+            page_content=response.message.content,
+            metadata={
+                "type": "model_response",
+                "citations": response.generation_info["citations"],
+                "search_results": response.generation_info["search_results"],
+                "search_queries": response.generation_info["search_queries"],
+                "token_count": response.generation_info["token_count"],
+            },
+        )
+    )
+    return docs
+
+
+class CohereRagRetriever(BaseRetriever):
+    """Cohere Chat API with RAG."""
+
+    connectors: List[Dict] = Field(default_factory=lambda: [{"id": "web-search"}])
+    """
+    When specified, the model's reply will be enriched with information found by
+    querying each of the connectors (RAG). These will be returned as langchain
+    documents.
+
+    Currently only accepts {"id": "web-search"}.
+    """
+
+    llm: BaseChatModel
+    """Cohere ChatModel to use."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+        """Allow arbitrary types."""
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
+    ) -> List[Document]:
+        messages: List[List[BaseMessage]] = [[HumanMessage(content=query)]]
+        res = self.llm.generate(
+            messages,
+            connectors=self.connectors,
+            callbacks=run_manager.get_child(),
+            **kwargs,
+        ).generations[0][0]
+        return _get_docs(res)
+
+    async def _aget_relevant_documents(
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+        **kwargs: Any,
+    ) -> List[Document]:
+        messages: List[List[BaseMessage]] = [[HumanMessage(content=query)]]
+        res = (
+            await self.llm.agenerate(
+                messages,
+                connectors=self.connectors,
+                callbacks=run_manager.get_child(),
+                **kwargs,
+            )
+        ).generations[0][0]
+        return _get_docs(res)

libs/partners/cohere/langchain_cohere/rerank.py

@@ -0,0 +1,104 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from typing import Any, Dict, List, Optional, Sequence, Union
+
+import cohere
+from langchain_core.callbacks.manager import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+from langchain_core.pydantic_v1 import Extra, root_validator
+from langchain_core.utils import get_from_dict_or_env
+
+
+class CohereRerank(BaseDocumentCompressor):
+    """Document compressor that uses `Cohere Rerank API`."""
+
+    client: Any = None
+    """Cohere client to use for compressing documents."""
+    top_n: Optional[int] = 3
+    """Number of documents to return."""
+    model: str = "rerank-english-v2.0"
+    """Model to use for reranking."""
+    cohere_api_key: Optional[str] = None
+    """Cohere API key. Must be specified directly or via environment variable 
+        COHERE_API_KEY."""
+    user_agent: str = "langchain"
+    """Identifier for the application making the request."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    @root_validator(pre=True)
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that api key and python package exists in environment."""
+        if not values.get("client"):
+            cohere_api_key = get_from_dict_or_env(
+                values, "cohere_api_key", "COHERE_API_KEY"
+            )
+            client_name = values.get("user_agent", "langchain")
+            values["client"] = cohere.Client(cohere_api_key, client_name=client_name)
+        return values
+
+    def rerank(
+        self,
+        documents: Sequence[Union[str, Document, dict]],
+        query: str,
+        *,
+        model: Optional[str] = None,
+        top_n: Optional[int] = -1,
+        max_chunks_per_doc: Optional[int] = None,
+    ) -> List[Dict[str, Any]]:
+        """Returns an ordered list of documents ordered by their relevance to the provided query.
+
+        Args:
+            query: The query to use for reranking.
+            documents: A sequence of documents to rerank.
+            model: The model to use for re-ranking. Default to self.model.
+            top_n : The number of results to return. If None returns all results.
+                Defaults to self.top_n.
+            max_chunks_per_doc : The maximum number of chunks derived from a document.
+        """  # noqa: E501
+        if len(documents) == 0:  # to avoid empty api call
+            return []
+        docs = [
+            doc.page_content if isinstance(doc, Document) else doc for doc in documents
+        ]
+        model = model or self.model
+        top_n = top_n if (top_n is None or top_n > 0) else self.top_n
+        results = self.client.rerank(
+            query, docs, model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc
+        )
+        result_dicts = []
+        for res in results:
+            result_dicts.append(
+                {"index": res.index, "relevance_score": res.relevance_score}
+            )
+        return result_dicts
+
+    def compress_documents(
+        self,
+        documents: Sequence[Document],
+        query: str,
+        callbacks: Optional[Callbacks] = None,
+    ) -> Sequence[Document]:
+        """
+        Compress documents using Cohere's rerank API.
+
+        Args:
+            documents: A sequence of documents to compress.
+            query: The query to use for compressing the documents.
+            callbacks: Callbacks to run during the compression process.
+
+        Returns:
+            A sequence of compressed documents.
+        """
+        compressed = []
+        for res in self.rerank(documents, query):
+            doc = documents[res["index"]]
+            doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
+            doc_copy.metadata["relevance_score"] = res["relevance_score"]
+            compressed.append(doc_copy)
+        return compressed

libs/partners/cohere/langchain_cohere/utils.py

@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+import logging
+from typing import Any, Callable
+
+import cohere
+from tenacity import (
+    before_sleep_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _create_retry_decorator(max_retries: int) -> Callable[[Any], Any]:
+    # support v4 and v5
+    retry_conditions = (
+        retry_if_exception_type(cohere.error.CohereError)
+        if hasattr(cohere, "error")
+        else retry_if_exception_type(Exception)
+    )
+
+    min_seconds = 4
+    max_seconds = 10
+    # Wait 2^x * 1 second between each retry starting with
+    # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
+    return retry(
+        reraise=True,
+        stop=stop_after_attempt(max_retries),
+        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
+        retry=retry_conditions,
+        before_sleep=before_sleep_log(logger, logging.WARNING),
+    )

libs/partners/cohere/poetry.lock

@@ -0,0 +1,948 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.6.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
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+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
+
+[[package]]
+name = "anyio"
+version = "4.3.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
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+
+[package.extras]
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+trio = ["trio (>=0.23)"]
+
+[[package]]
+name = "certifi"
+version = "2024.2.2"
+description = "Python package for providing Mozilla's CA Bundle."
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+python-versions = ">=3.6"
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+
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+
+[metadata]
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libs/partners/cohere/pyproject.toml

@@ -0,0 +1,92 @@
+[tool.poetry]
+name = "langchain-cohere"
+version = "0.1.0rc0"
+description = "An integration package connecting Cohere and LangChain"
+authors = []
+readme = "README.md"
+repository = "https://github.com/langchain-ai/langchain"
+license = "MIT"
+
+[tool.poetry.urls]
+"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/cohere"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain-core = ">=0.0.12"
+cohere = "^5.1.1"
+
+[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",
+]
+asyncio_mode = "auto"

libs/partners/cohere/scripts/check_imports.py

@@ -0,0 +1,17 @@
+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)

libs/partners/cohere/scripts/check_pydantic.sh

@@ -0,0 +1,27 @@
+#!/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

libs/partners/cohere/scripts/lint_imports.sh

@@ -0,0 +1,17 @@
+#!/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

libs/partners/cohere/tests/integration_tests/test_chat_models.py

@@ -0,0 +1,63 @@
+"""Test ChatCohere chat model."""
+from langchain_cohere import ChatCohere
+
+
+def test_stream() -> None:
+    """Test streaming tokens from OpenAI."""
+    llm = ChatCohere()
+
+    for token in llm.stream("I'm Pickle Rick"):
+        assert isinstance(token.content, str)
+
+
+async def test_astream() -> None:
+    """Test streaming tokens from OpenAI."""
+    llm = ChatCohere()
+
+    async for token in llm.astream("I'm Pickle Rick"):
+        assert isinstance(token.content, str)
+
+
+async def test_abatch() -> None:
+    """Test streaming tokens from ChatCohere."""
+    llm = ChatCohere()
+
+    result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
+    for token in result:
+        assert isinstance(token.content, str)
+
+
+async def test_abatch_tags() -> None:
+    """Test batch tokens from ChatCohere."""
+    llm = ChatCohere()
+
+    result = await llm.abatch(
+        ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
+    )
+    for token in result:
+        assert isinstance(token.content, str)
+
+
+def test_batch() -> None:
+    """Test batch tokens from ChatCohere."""
+    llm = ChatCohere()
+
+    result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
+    for token in result:
+        assert isinstance(token.content, str)
+
+
+async def test_ainvoke() -> None:
+    """Test invoke tokens from ChatCohere."""
+    llm = ChatCohere()
+
+    result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
+    assert isinstance(result.content, str)
+
+
+def test_invoke() -> None:
+    """Test invoke tokens from ChatCohere."""
+    llm = ChatCohere()
+
+    result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
+    assert isinstance(result.content, str)

libs/partners/cohere/tests/integration_tests/test_compile.py

@@ -0,0 +1,7 @@
+import pytest
+
+
+@pytest.mark.compile
+def test_placeholder() -> None:
+    """Used for compiling integration tests without running any real tests."""
+    pass

libs/partners/cohere/tests/integration_tests/test_embeddings.py

@@ -0,0 +1,19 @@
+"""Test Cohere embeddings."""
+from langchain_cohere import CohereEmbeddings
+
+
+def test_langchain_cohere_embedding_documents() -> None:
+    """Test cohere embeddings."""
+    documents = ["foo bar"]
+    embedding = CohereEmbeddings()
+    output = embedding.embed_documents(documents)
+    assert len(output) == 1
+    assert len(output[0]) > 0
+
+
+def test_langchain_cohere_embedding_query() -> None:
+    """Test cohere embeddings."""
+    document = "foo bar"
+    embedding = CohereEmbeddings()
+    output = embedding.embed_query(document)
+    assert len(output) > 0

libs/partners/cohere/tests/unit_tests/test_chat_models.py

@@ -0,0 +1,30 @@
+"""Test chat model integration."""
+import typing
+
+import pytest
+
+from langchain_cohere.chat_models import ChatCohere
+
+
+def test_initialization() -> None:
+    """Test chat model initialization."""
+    ChatCohere(cohere_api_key="test")
+
+
+@pytest.mark.parametrize(
+    "chat_cohere,expected",
+    [
+        pytest.param(ChatCohere(cohere_api_key="test"), {}, id="defaults"),
+        pytest.param(
+            ChatCohere(cohere_api_key="test", model="foo", temperature=1.0),
+            {
+                "model": "foo",
+                "temperature": 1.0,
+            },
+            id="values are set",
+        ),
+    ],
+)
+def test_default_params(chat_cohere: ChatCohere, expected: typing.Dict) -> None:
+    actual = chat_cohere._default_params
+    assert expected == actual

libs/partners/cohere/tests/unit_tests/test_embeddings.py

@@ -0,0 +1,9 @@
+"""Test embedding model integration."""
+
+
+from langchain_cohere.embeddings import CohereEmbeddings
+
+
+def test_initialization() -> None:
+    """Test embedding model initialization."""
+    CohereEmbeddings(cohere_api_key="test")

libs/partners/cohere/tests/unit_tests/test_imports.py

@@ -0,0 +1,13 @@
+from langchain_cohere import __all__
+
+EXPECTED_ALL = [
+    "ChatCohere",
+    "CohereVectorStore",
+    "CohereEmbeddings",
+    "CohereRagRetriever",
+    "CohereRerank",
+]
+
+
+def test_all_imports() -> None:
+    assert sorted(EXPECTED_ALL) == sorted(__all__)

libs/partners/cohere/tests/unit_tests/test_llms.py

@@ -0,0 +1,61 @@
+"""Test Cohere API wrapper."""
+import typing
+
+import pytest
+from langchain_core.pydantic_v1 import SecretStr
+
+from langchain_cohere.llms import BaseCohere, Cohere
+
+
+def test_cohere_api_key(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Test that cohere api key is a secret key."""
+    # test initialization from init
+    assert isinstance(BaseCohere(cohere_api_key="1").cohere_api_key, SecretStr)
+
+    # test initialization from env variable
+    monkeypatch.setenv("COHERE_API_KEY", "secret-api-key")
+    assert isinstance(BaseCohere().cohere_api_key, SecretStr)
+
+
+@pytest.mark.parametrize(
+    "cohere,expected",
+    [
+        pytest.param(Cohere(cohere_api_key="test"), {}, id="defaults"),
+        pytest.param(
+            Cohere(
+                # the following are arbitrary testing values which shouldn't be used:
+                cohere_api_key="test",
+                model="foo",
+                temperature=0.1,
+                max_tokens=2,
+                k=3,
+                p=4,
+                frequency_penalty=0.5,
+                presence_penalty=0.6,
+                truncate="START",
+            ),
+            {
+                "model": "foo",
+                "temperature": 0.1,
+                "max_tokens": 2,
+                "k": 3,
+                "p": 4,
+                "frequency_penalty": 0.5,
+                "presence_penalty": 0.6,
+                "truncate": "START",
+            },
+            id="with values set",
+        ),
+    ],
+)
+def test_default_params(cohere: Cohere, expected: typing.Dict) -> None:
+    actual = cohere._default_params
+    assert expected == actual
+
+
+# def test_saving_loading_llm(tmp_path: Path) -> None:
+#     """Test saving/loading an Cohere LLM."""
+#     llm = BaseCohere(max_tokens=10)
+#     llm.save(file_path=tmp_path / "cohere.yaml")
+#     loaded_llm = load_llm(tmp_path / "cohere.yaml")
+#     assert_llm_equality(llm, loaded_llm)

libs/partners/cohere/tests/unit_tests/test_rag_retrievers.py

@@ -0,0 +1,10 @@
+"""Test rag retriever integration."""
+
+
+from langchain_cohere.chat_models import ChatCohere
+from langchain_cohere.rag_retrievers import CohereRagRetriever
+
+
+def test_initialization() -> None:
+    """Test chat model initialization."""
+    CohereRagRetriever(llm=ChatCohere(cohere_api_key="test"))

libs/partners/cohere/tests/unit_tests/test_rerank.py

@@ -0,0 +1,8 @@
+"""Test chat model integration."""
+
+from langchain_cohere import CohereRerank
+
+
+def test_initialization() -> None:
+    """Test chat model initialization."""
+    CohereRerank(cohere_api_key="test")