From 471ceed05ba1409a1f0bc6ec7ce867a2d46a5982 Mon Sep 17 00:00:00 2001
From: fern-api <115122769+fern-api[bot]@users.noreply.github.com>
Date: Fri, 15 Nov 2024 17:45:36 +0000
Subject: [PATCH] SDK regeneration
---
poetry.lock | 659 +++++++++---------
pyproject.toml | 2 +-
reference.md | 49 +-
src/cohere/__init__.py | 2 +
src/cohere/base_client.py | 44 +-
src/cohere/core/client_wrapper.py | 2 +-
src/cohere/types/__init__.py | 10 +
src/cohere/types/chat_content_delta_event.py | 2 +
src/cohere/types/chat_response.py | 4 +-
src/cohere/types/json_response_format.py | 2 +-
src/cohere/types/json_response_format_v2.py | 2 +-
src/cohere/types/logprob_item.py | 31 +
src/cohere/types/single_generation.py | 2 +-
src/cohere/types/streamed_chat_response_v2.py | 2 +
src/cohere/v2/client.py | 42 +-
15 files changed, 482 insertions(+), 373 deletions(-)
create mode 100644 src/cohere/types/logprob_item.py
diff --git a/poetry.lock b/poetry.lock
index fd2e611ae..5b7a6d730 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,13 +2,13 @@
[[package]]
name = "alembic"
-version = "1.13.3"
+version = "1.14.0"
description = "A database migration tool for SQLAlchemy."
optional = true
python-versions = ">=3.8"
files = [
- {file = "alembic-1.13.3-py3-none-any.whl", hash = "sha256:908e905976d15235fae59c9ac42c4c5b75cfcefe3d27c0fbf7ae15a37715d80e"},
- {file = "alembic-1.13.3.tar.gz", hash = "sha256:203503117415561e203aa14541740643a611f641517f0209fcae63e9fa09f1a2"},
+ {file = "alembic-1.14.0-py3-none-any.whl", hash = "sha256:99bd884ca390466db5e27ffccff1d179ec5c05c965cfefc0607e69f9e411cb25"},
+ {file = "alembic-1.14.0.tar.gz", hash = "sha256:b00892b53b3642d0b8dbedba234dbf1924b69be83a9a769d5a624b01094e304b"},
]
[package.dependencies]
@@ -89,17 +89,17 @@ files = [
[[package]]
name = "boto3"
-version = "1.35.51"
+version = "1.35.62"
description = "The AWS SDK for Python"
optional = true
python-versions = ">=3.8"
files = [
- {file = "boto3-1.35.51-py3-none-any.whl", hash = "sha256:c922f6a18958af9d8af0489d6d8503b517029d8159b26aa4859a8294561c72e9"},
- {file = "boto3-1.35.51.tar.gz", hash = "sha256:a57c6c7012ecb40c43e565a6f7a891f39efa990ff933eab63cd456f7501c2731"},
+ {file = "boto3-1.35.62-py3-none-any.whl", hash = "sha256:e6574047701ab009c2b2bb17b530a3a2fb34de8698b77f8bbb34dd0c9286c117"},
+ {file = "boto3-1.35.62.tar.gz", hash = "sha256:f80eefe7506aa01799b1027d03eddfd3c4a60548d6db5c32f139e1dec9f3f4f5"},
]
[package.dependencies]
-botocore = ">=1.35.51,<1.36.0"
+botocore = ">=1.35.62,<1.36.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.10.0,<0.11.0"
@@ -108,13 +108,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
-version = "1.35.51"
+version = "1.35.62"
description = "Low-level, data-driven core of boto 3."
optional = true
python-versions = ">=3.8"
files = [
- {file = "botocore-1.35.51-py3-none-any.whl", hash = "sha256:4d65b00111bd12b98e9f920ecab602cf619cc6a6d0be6e5dd53f517e4b92901c"},
- {file = "botocore-1.35.51.tar.gz", hash = "sha256:a9b3d1da76b3e896ad74605c01d88f596324a3337393d4bfbfa0d6c35822ca9c"},
+ {file = "botocore-1.35.62-py3-none-any.whl", hash = "sha256:4c3960a33289371d96eba5116364c41e6b848b5afbed3a43f5d8c7ba36f55e1d"},
+ {file = "botocore-1.35.62.tar.gz", hash = "sha256:9df762294d5c727d9ea1c48b98579729a0ba40fd317c3262a6b8d8e12fb67489"},
]
[package.dependencies]
@@ -450,13 +450,13 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
[[package]]
name = "databricks-sdk"
-version = "0.36.0"
+version = "0.37.0"
description = "Databricks SDK for Python (Beta)"
optional = true
python-versions = ">=3.7"
files = [
- {file = "databricks_sdk-0.36.0-py3-none-any.whl", hash = "sha256:e6105a2752c7980de35f7c7e3c4d63389c0763c9ef7bf7e2813e464acef907e9"},
- {file = "databricks_sdk-0.36.0.tar.gz", hash = "sha256:d8c46348cbd3e0b56991a6b7a59d7a6e0437947f6387bef832e6fe092e2dd427"},
+ {file = "databricks_sdk-0.37.0-py3-none-any.whl", hash = "sha256:8fc333af657cbc4e46264560af20f0afb9510e25dea272cd84f665d586f83494"},
+ {file = "databricks_sdk-0.37.0.tar.gz", hash = "sha256:92c3159729e136ed8cd1630153855fb3b3afb23172d293a48a2dff55f960bd6b"},
]
[package.dependencies]
@@ -470,20 +470,20 @@ openai = ["httpx", "langchain-openai", "openai"]
[[package]]
name = "deprecated"
-version = "1.2.14"
+version = "1.2.15"
description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
optional = true
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
files = [
- {file = "Deprecated-1.2.14-py2.py3-none-any.whl", hash = "sha256:6fac8b097794a90302bdbb17b9b815e732d3c4720583ff1b198499d78470466c"},
- {file = "Deprecated-1.2.14.tar.gz", hash = "sha256:e5323eb936458dccc2582dc6f9c322c852a775a27065ff2b0c4970b9d53d01b3"},
+ {file = "Deprecated-1.2.15-py2.py3-none-any.whl", hash = "sha256:353bc4a8ac4bfc96800ddab349d89c25dec1079f65fd53acdcc1e0b975b21320"},
+ {file = "deprecated-1.2.15.tar.gz", hash = "sha256:683e561a90de76239796e6b6feac66b99030d2dd3fcf61ef996330f14bbb9b0d"},
]
[package.dependencies]
wrapt = ">=1.10,<2"
[package.extras]
-dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
+dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "jinja2 (>=3.0.3,<3.1.0)", "setuptools", "sphinx (<2)", "tox"]
[[package]]
name = "dill"
@@ -623,59 +623,61 @@ dotenv = ["python-dotenv"]
[[package]]
name = "fonttools"
-version = "4.54.1"
+version = "4.55.0"
description = "Tools to manipulate font files"
optional = true
python-versions = ">=3.8"
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+ {file = "tokenizers-0.20.3.tar.gz", hash = "sha256:2278b34c5d0dd78e087e1ca7f9b1dcbf129d80211afa645f214bd6e051037539"},
]
[package.dependencies]
@@ -3219,24 +3233,24 @@ testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
[[package]]
name = "tomli"
-version = "2.0.2"
+version = "2.1.0"
description = "A lil' TOML parser"
optional = false
python-versions = ">=3.8"
files = [
- {file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"},
- {file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"},
+ {file = "tomli-2.1.0-py3-none-any.whl", hash = "sha256:a5c57c3d1c56f5ccdf89f6523458f60ef716e210fc47c4cfb188c5ba473e0391"},
+ {file = "tomli-2.1.0.tar.gz", hash = "sha256:3f646cae2aec94e17d04973e4249548320197cfabdf130015d023de4b74d8ab8"},
]
[[package]]
name = "tqdm"
-version = "4.66.6"
+version = "4.67.0"
description = "Fast, Extensible Progress Meter"
optional = false
python-versions = ">=3.7"
files = [
- {file = "tqdm-4.66.6-py3-none-any.whl", hash = "sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63"},
- {file = "tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090"},
+ {file = "tqdm-4.67.0-py3-none-any.whl", hash = "sha256:0cd8af9d56911acab92182e88d763100d4788bdf421d251616040cc4d44863be"},
+ {file = "tqdm-4.67.0.tar.gz", hash = "sha256:fe5a6f95e6fe0b9755e9469b77b9c3cf850048224ecaa8293d7d2d31f97d869a"},
]
[package.dependencies]
@@ -3244,6 +3258,7 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
[package.extras]
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+discord = ["requests"]
notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"]
telegram = ["requests"]
diff --git a/pyproject.toml b/pyproject.toml
index 0664f1e8e..7f406ed2a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "cohere"
-version = "5.11.3"
+version = "5.9.4"
description = ""
readme = "README.md"
authors = []
diff --git a/reference.md b/reference.md
index 3721861a7..797a2fb2b 100644
--- a/reference.md
+++ b/reference.md
@@ -530,12 +530,12 @@ Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private D
**safety_mode:** `typing.Optional[ChatStreamRequestSafetyMode]`
-Used to select the [safety instruction](/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
+Used to select the [safety instruction](https://docs.cohere.com/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
When `NONE` is specified, the safety instruction will be omitted.
Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters.
-**Note**: This parameter is only compatible with models [Command R 08-2024](/docs/command-r#august-2024-release), [Command R+ 08-2024](/docs/command-r-plus#august-2024-release) and newer.
+**Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/docs/command-r-plus#august-2024-release) and newer.
Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
@@ -1015,12 +1015,12 @@ Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private D
**safety_mode:** `typing.Optional[ChatRequestSafetyMode]`
-Used to select the [safety instruction](/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
+Used to select the [safety instruction](https://docs.cohere.com/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
When `NONE` is specified, the safety instruction will be omitted.
Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters.
-**Note**: This parameter is only compatible with models [Command R 08-2024](/docs/command-r#august-2024-release), [Command R+ 08-2024](/docs/command-r-plus#august-2024-release) and newer.
+**Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/docs/command-r-plus#august-2024-release) and newer.
Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
@@ -1056,7 +1056,7 @@ Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private D
-This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
+This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](https://docs.cohere.com/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
Generates realistic text conditioned on a given input.
@@ -1129,7 +1129,7 @@ Note: The prompt will be pre-processed and modified before reaching the model.
**model:** `typing.Optional[str]`
The identifier of the model to generate with. Currently available models are `command` (default), `command-nightly` (experimental), `command-light`, and `command-light-nightly` (experimental).
-Smaller, "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+Smaller, "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
@@ -1329,7 +1329,7 @@ If `ALL` is selected, the token likelihoods will be provided both for the prompt
-This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
+This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](https://docs.cohere.com/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
Generates realistic text conditioned on a given input.
@@ -1385,7 +1385,7 @@ Note: The prompt will be pre-processed and modified before reaching the model.
**model:** `typing.Optional[str]`
The identifier of the model to generate with. Currently available models are `command` (default), `command-nightly` (experimental), `command-light`, and `command-light-nightly` (experimental).
-Smaller, "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+Smaller, "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
@@ -1588,7 +1588,7 @@ This endpoint returns text and image embeddings. An embedding is a list of float
Embeddings can be used to create classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page.
-If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](/docs/semantic-search).
+If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](https://docs.cohere.com/docs/semantic-search).
@@ -1649,7 +1649,7 @@ The image must be a valid [data URI](https://developer.mozilla.org/en-US/docs/We
Defaults to embed-english-v2.0
-The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
Available models and corresponding embedding dimensions:
@@ -1976,7 +1976,7 @@ If `NONE` is selected, when the input exceeds the maximum input token length an
-This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
+This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](https://docs.cohere.com/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
Generates a summary in English for a given text.
@@ -2380,6 +2380,7 @@ response = client.v2.chat_stream(
k=1.1,
p=1.1,
return_prompt=True,
+ logprobs=True,
)
for chunk in response:
yield chunk
@@ -2545,7 +2546,7 @@ Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty
**k:** `typing.Optional[float]`
-Ensures only the top `k` most likely tokens are considered for generation at each step.
+Ensures that only the top `k` most likely tokens are considered for generation at each step. When `k` is set to `0`, k-sampling is disabled.
Defaults to `0`, min value of `0`, max value of `500`.
@@ -2569,6 +2570,15 @@ Defaults to `0.75`. min value of `0.01`, max value of `0.99`.
**return_prompt:** `typing.Optional[bool]` — Whether to return the prompt in the response.
+
+
+
+
+-
+
+**logprobs:** `typing.Optional[bool]` — Whether to return the log probabilities of the generated tokens. Defaults to false.
+
+
@@ -2793,7 +2803,7 @@ Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty
**k:** `typing.Optional[float]`
-Ensures only the top `k` most likely tokens are considered for generation at each step.
+Ensures that only the top `k` most likely tokens are considered for generation at each step. When `k` is set to `0`, k-sampling is disabled.
Defaults to `0`, min value of `0`, max value of `500`.
@@ -2817,6 +2827,15 @@ Defaults to `0.75`. min value of `0.01`, max value of `0.99`.
**return_prompt:** `typing.Optional[bool]` — Whether to return the prompt in the response.
+
+
+
+
+-
+
+**logprobs:** `typing.Optional[bool]` — Whether to return the log probabilities of the generated tokens. Defaults to false.
+
+
@@ -2851,7 +2870,7 @@ This endpoint returns text embeddings. An embedding is a list of floating point
Embeddings can be used to create text classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page.
-If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](/docs/semantic-search).
+If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](https://docs.cohere.com/docs/semantic-search).
@@ -2896,7 +2915,7 @@ client.v2.embed(
Defaults to embed-english-v2.0
-The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
Available models and corresponding embedding dimensions:
diff --git a/src/cohere/__init__.py b/src/cohere/__init__.py
index d68434c9f..87f49bd4f 100644
--- a/src/cohere/__init__.py
+++ b/src/cohere/__init__.py
@@ -149,6 +149,7 @@
ListConnectorsResponse,
ListEmbedJobResponse,
ListModelsResponse,
+ LogprobItem,
Message,
MessageEndStreamedChatResponseV2,
MessageStartStreamedChatResponseV2,
@@ -436,6 +437,7 @@
"ListConnectorsResponse",
"ListEmbedJobResponse",
"ListModelsResponse",
+ "LogprobItem",
"Message",
"MessageEndStreamedChatResponseV2",
"MessageStartStreamedChatResponseV2",
diff --git a/src/cohere/base_client.py b/src/cohere/base_client.py
index f23a777a4..b18ccb2f3 100644
--- a/src/cohere/base_client.py
+++ b/src/cohere/base_client.py
@@ -416,12 +416,12 @@ def chat_stream(
response_format : typing.Optional[ResponseFormat]
safety_mode : typing.Optional[ChatStreamRequestSafetyMode]
- Used to select the [safety instruction](/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
+ Used to select the [safety instruction](https://docs.cohere.com/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
When `NONE` is specified, the safety instruction will be omitted.
Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters.
- **Note**: This parameter is only compatible with models [Command R 08-2024](/docs/command-r#august-2024-release), [Command R+ 08-2024](/docs/command-r-plus#august-2024-release) and newer.
+ **Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/docs/command-r-plus#august-2024-release) and newer.
Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
@@ -967,12 +967,12 @@ def chat(
response_format : typing.Optional[ResponseFormat]
safety_mode : typing.Optional[ChatRequestSafetyMode]
- Used to select the [safety instruction](/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
+ Used to select the [safety instruction](https://docs.cohere.com/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
When `NONE` is specified, the safety instruction will be omitted.
Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters.
- **Note**: This parameter is only compatible with models [Command R 08-2024](/docs/command-r#august-2024-release), [Command R+ 08-2024](/docs/command-r-plus#august-2024-release) and newer.
+ **Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/docs/command-r-plus#august-2024-release) and newer.
Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
@@ -1195,7 +1195,7 @@ def generate_stream(
) -> typing.Iterator[GenerateStreamedResponse]:
"""
- This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
+ This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](https://docs.cohere.com/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
Generates realistic text conditioned on a given input.
@@ -1208,7 +1208,7 @@ def generate_stream(
model : typing.Optional[str]
The identifier of the model to generate with. Currently available models are `command` (default), `command-nightly` (experimental), `command-light`, and `command-light-nightly` (experimental).
- Smaller, "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+ Smaller, "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
num_generations : typing.Optional[int]
The maximum number of generations that will be returned. Defaults to `1`, min value of `1`, max value of `5`.
@@ -1504,7 +1504,7 @@ def generate(
) -> Generation:
"""
- This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
+ This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](https://docs.cohere.com/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
Generates realistic text conditioned on a given input.
@@ -1517,7 +1517,7 @@ def generate(
model : typing.Optional[str]
The identifier of the model to generate with. Currently available models are `command` (default), `command-nightly` (experimental), `command-light`, and `command-light-nightly` (experimental).
- Smaller, "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+ Smaller, "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
num_generations : typing.Optional[int]
The maximum number of generations that will be returned. Defaults to `1`, min value of `1`, max value of `5`.
@@ -1781,7 +1781,7 @@ def embed(
Embeddings can be used to create classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page.
- If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](/docs/semantic-search).
+ If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](https://docs.cohere.com/docs/semantic-search).
Parameters
----------
@@ -1796,7 +1796,7 @@ def embed(
model : typing.Optional[str]
Defaults to embed-english-v2.0
- The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+ The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
Available models and corresponding embedding dimensions:
@@ -2398,7 +2398,7 @@ def summarize(
) -> SummarizeResponse:
"""
- This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
+ This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](https://docs.cohere.com/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
Generates a summary in English for a given text.
@@ -3411,12 +3411,12 @@ async def chat_stream(
response_format : typing.Optional[ResponseFormat]
safety_mode : typing.Optional[ChatStreamRequestSafetyMode]
- Used to select the [safety instruction](/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
+ Used to select the [safety instruction](https://docs.cohere.com/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
When `NONE` is specified, the safety instruction will be omitted.
Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters.
- **Note**: This parameter is only compatible with models [Command R 08-2024](/docs/command-r#august-2024-release), [Command R+ 08-2024](/docs/command-r-plus#august-2024-release) and newer.
+ **Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/docs/command-r-plus#august-2024-release) and newer.
Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
@@ -3970,12 +3970,12 @@ async def chat(
response_format : typing.Optional[ResponseFormat]
safety_mode : typing.Optional[ChatRequestSafetyMode]
- Used to select the [safety instruction](/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
+ Used to select the [safety instruction](https://docs.cohere.com/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`.
When `NONE` is specified, the safety instruction will be omitted.
Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters.
- **Note**: This parameter is only compatible with models [Command R 08-2024](/docs/command-r#august-2024-release), [Command R+ 08-2024](/docs/command-r-plus#august-2024-release) and newer.
+ **Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/docs/command-r-plus#august-2024-release) and newer.
Compatible Deployments: Cohere Platform, Azure, AWS Sagemaker/Bedrock, Private Deployments
@@ -4206,7 +4206,7 @@ async def generate_stream(
) -> typing.AsyncIterator[GenerateStreamedResponse]:
"""
- This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
+ This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](https://docs.cohere.com/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
Generates realistic text conditioned on a given input.
@@ -4219,7 +4219,7 @@ async def generate_stream(
model : typing.Optional[str]
The identifier of the model to generate with. Currently available models are `command` (default), `command-nightly` (experimental), `command-light`, and `command-light-nightly` (experimental).
- Smaller, "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+ Smaller, "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
num_generations : typing.Optional[int]
The maximum number of generations that will be returned. Defaults to `1`, min value of `1`, max value of `5`.
@@ -4523,7 +4523,7 @@ async def generate(
) -> Generation:
"""
- This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
+ This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](https://docs.cohere.com/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
Generates realistic text conditioned on a given input.
@@ -4536,7 +4536,7 @@ async def generate(
model : typing.Optional[str]
The identifier of the model to generate with. Currently available models are `command` (default), `command-nightly` (experimental), `command-light`, and `command-light-nightly` (experimental).
- Smaller, "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+ Smaller, "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
num_generations : typing.Optional[int]
The maximum number of generations that will be returned. Defaults to `1`, min value of `1`, max value of `5`.
@@ -4808,7 +4808,7 @@ async def embed(
Embeddings can be used to create classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page.
- If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](/docs/semantic-search).
+ If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](https://docs.cohere.com/docs/semantic-search).
Parameters
----------
@@ -4823,7 +4823,7 @@ async def embed(
model : typing.Optional[str]
Defaults to embed-english-v2.0
- The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+ The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
Available models and corresponding embedding dimensions:
@@ -5449,7 +5449,7 @@ async def summarize(
) -> SummarizeResponse:
"""
- This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
+ This API is marked as "Legacy" and is no longer maintained. Follow the [migration guide](https://docs.cohere.com/docs/migrating-from-cogenerate-to-cochat) to start using the Chat API.
Generates a summary in English for a given text.
diff --git a/src/cohere/core/client_wrapper.py b/src/cohere/core/client_wrapper.py
index bb3170c91..7d323ab41 100644
--- a/src/cohere/core/client_wrapper.py
+++ b/src/cohere/core/client_wrapper.py
@@ -24,7 +24,7 @@ def get_headers(self) -> typing.Dict[str, str]:
headers: typing.Dict[str, str] = {
"X-Fern-Language": "Python",
"X-Fern-SDK-Name": "cohere",
- "X-Fern-SDK-Version": "5.11.3",
+ "X-Fern-SDK-Version": "5.9.4",
}
if self._client_name is not None:
headers["X-Client-Name"] = self._client_name
diff --git a/src/cohere/types/__init__.py b/src/cohere/types/__init__.py
index 8643f70d4..981c7f41f 100644
--- a/src/cohere/types/__init__.py
+++ b/src/cohere/types/__init__.py
@@ -111,6 +111,7 @@
from .detokenize_response import DetokenizeResponse
from .document import Document
from .document_content import DocumentContent
+from .document_source import DocumentSource
from .embed_by_type_response import EmbedByTypeResponse
from .embed_by_type_response_embeddings import EmbedByTypeResponseEmbeddings
from .embed_floats_response import EmbedFloatsResponse
@@ -149,6 +150,7 @@
from .list_connectors_response import ListConnectorsResponse
from .list_embed_job_response import ListEmbedJobResponse
from .list_models_response import ListModelsResponse
+from .logprob_item import LogprobItem
from .message import ChatbotMessage, Message, SystemMessage, ToolMessage, UserMessage
from .metrics import Metrics
from .metrics_embed_data import MetricsEmbedData
@@ -199,8 +201,12 @@
from .summarize_request_format import SummarizeRequestFormat
from .summarize_request_length import SummarizeRequestLength
from .summarize_response import SummarizeResponse
+from .system_message import SystemMessage
from .system_message_content import SystemMessageContent
from .system_message_content_item import SystemMessageContentItem, TextSystemMessageContentItem
+from .text_content import TextContent
+from .text_response_format import TextResponseFormat
+from .text_response_format_v2 import TextResponseFormatV2
from .tokenize_response import TokenizeResponse
from .too_many_requests_error_body import TooManyRequestsErrorBody
from .tool import Tool
@@ -209,10 +215,12 @@
from .tool_call_v2 import ToolCallV2
from .tool_call_v2function import ToolCallV2Function
from .tool_content import DocumentToolContent, TextToolContent, ToolContent
+from .tool_message import ToolMessage
from .tool_message_v2 import ToolMessageV2
from .tool_message_v2content import ToolMessageV2Content
from .tool_parameter_definitions_value import ToolParameterDefinitionsValue
from .tool_result import ToolResult
+from .tool_source import ToolSource
from .tool_v2 import ToolV2
from .tool_v2function import ToolV2Function
from .unprocessable_entity_error_body import UnprocessableEntityErrorBody
@@ -220,6 +228,7 @@
from .usage import Usage
from .usage_billed_units import UsageBilledUnits
from .usage_tokens import UsageTokens
+from .user_message import UserMessage
from .user_message_content import UserMessageContent
__all__ = [
@@ -371,6 +380,7 @@
"ListConnectorsResponse",
"ListEmbedJobResponse",
"ListModelsResponse",
+ "LogprobItem",
"Message",
"MessageEndStreamedChatResponseV2",
"MessageStartStreamedChatResponseV2",
diff --git a/src/cohere/types/chat_content_delta_event.py b/src/cohere/types/chat_content_delta_event.py
index 6954ebea9..3d57b9b2b 100644
--- a/src/cohere/types/chat_content_delta_event.py
+++ b/src/cohere/types/chat_content_delta_event.py
@@ -3,6 +3,7 @@
from .chat_stream_event_type import ChatStreamEventType
import typing
from .chat_content_delta_event_delta import ChatContentDeltaEventDelta
+from .logprob_item import LogprobItem
from ..core.pydantic_utilities import IS_PYDANTIC_V2
import pydantic
@@ -14,6 +15,7 @@ class ChatContentDeltaEvent(ChatStreamEventType):
index: typing.Optional[int] = None
delta: typing.Optional[ChatContentDeltaEventDelta] = None
+ logprobs: typing.Optional[LogprobItem] = None
if IS_PYDANTIC_V2:
model_config: typing.ClassVar[pydantic.ConfigDict] = pydantic.ConfigDict(extra="allow") # type: ignore # Pydantic v2
diff --git a/src/cohere/types/chat_response.py b/src/cohere/types/chat_response.py
index ca40f48a4..483cbade6 100644
--- a/src/cohere/types/chat_response.py
+++ b/src/cohere/types/chat_response.py
@@ -6,6 +6,7 @@
import typing
from .assistant_message_response import AssistantMessageResponse
from .usage import Usage
+from .logprob_item import LogprobItem
from ..core.pydantic_utilities import IS_PYDANTIC_V2
@@ -21,8 +22,9 @@ class ChatResponse(UncheckedBaseModel):
The prompt that was used. Only present when `return_prompt` in the request is set to true.
"""
- message: typing.Optional[AssistantMessageResponse] = None
+ message: AssistantMessageResponse
usage: typing.Optional[Usage] = None
+ logprobs: typing.Optional[typing.List[LogprobItem]] = None
if IS_PYDANTIC_V2:
model_config: typing.ClassVar[pydantic.ConfigDict] = pydantic.ConfigDict(extra="allow") # type: ignore # Pydantic v2
diff --git a/src/cohere/types/json_response_format.py b/src/cohere/types/json_response_format.py
index 325a118b2..d12edb8ef 100644
--- a/src/cohere/types/json_response_format.py
+++ b/src/cohere/types/json_response_format.py
@@ -13,7 +13,7 @@ class JsonResponseFormat(UncheckedBaseModel):
typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]], FieldMetadata(alias="schema")
] = pydantic.Field(default=None)
"""
- A JSON schema object that the output will adhere to. There are some restrictions we have on the schema, refer to [our guide](/docs/structured-outputs-json#schema-constraints) for more information.
+ A JSON schema object that the output will adhere to. There are some restrictions we have on the schema, refer to [our guide](https://docs.cohere.com/docs/structured-outputs-json#schema-constraints) for more information.
Example (required name and age object):
```json
diff --git a/src/cohere/types/json_response_format_v2.py b/src/cohere/types/json_response_format_v2.py
index c728cb3d8..42721388e 100644
--- a/src/cohere/types/json_response_format_v2.py
+++ b/src/cohere/types/json_response_format_v2.py
@@ -9,7 +9,7 @@
class JsonResponseFormatV2(UncheckedBaseModel):
json_schema: typing.Optional[typing.Dict[str, typing.Optional[typing.Any]]] = pydantic.Field(default=None)
"""
- A [JSON schema](https://json-schema.org/overview/what-is-jsonschema) object that the output will adhere to. There are some restrictions we have on the schema, refer to [our guide](/docs/structured-outputs-json#schema-constraints) for more information.
+ A [JSON schema](https://json-schema.org/overview/what-is-jsonschema) object that the output will adhere to. There are some restrictions we have on the schema, refer to [our guide](https://docs.cohere.com/docs/structured-outputs-json#schema-constraints) for more information.
Example (required name and age object):
```json
diff --git a/src/cohere/types/logprob_item.py b/src/cohere/types/logprob_item.py
new file mode 100644
index 000000000..0178b0944
--- /dev/null
+++ b/src/cohere/types/logprob_item.py
@@ -0,0 +1,31 @@
+# This file was auto-generated by Fern from our API Definition.
+
+from ..core.unchecked_base_model import UncheckedBaseModel
+import typing
+import pydantic
+from ..core.pydantic_utilities import IS_PYDANTIC_V2
+
+
+class LogprobItem(UncheckedBaseModel):
+ text: typing.Optional[str] = pydantic.Field(default=None)
+ """
+ The text chunk for which the log probabilities was calculated.
+ """
+
+ token_ids: typing.List[int] = pydantic.Field()
+ """
+ The token ids of each token used to construct the text chunk.
+ """
+
+ logprobs: typing.Optional[typing.List[float]] = pydantic.Field(default=None)
+ """
+ The log probability of each token used to construct the text chunk.
+ """
+
+ if IS_PYDANTIC_V2:
+ model_config: typing.ClassVar[pydantic.ConfigDict] = pydantic.ConfigDict(extra="allow") # type: ignore # Pydantic v2
+ else:
+
+ class Config:
+ smart_union = True
+ extra = pydantic.Extra.allow
diff --git a/src/cohere/types/single_generation.py b/src/cohere/types/single_generation.py
index 2a53f1a5d..077a8e7b3 100644
--- a/src/cohere/types/single_generation.py
+++ b/src/cohere/types/single_generation.py
@@ -18,7 +18,7 @@ class SingleGeneration(UncheckedBaseModel):
likelihood: typing.Optional[float] = None
token_likelihoods: typing.Optional[typing.List[SingleGenerationTokenLikelihoodsItem]] = pydantic.Field(default=None)
"""
- Only returned if `return_likelihoods` is set to `GENERATION` or `ALL`. The likelihood refers to the average log-likelihood of the entire specified string, which is useful for [evaluating the performance of your model](likelihood-eval), especially if you've created a [custom model](/docs/training-custom-models). Individual token likelihoods provide the log-likelihood of each token. The first token will not have a likelihood.
+ Only returned if `return_likelihoods` is set to `GENERATION` or `ALL`. The likelihood refers to the average log-likelihood of the entire specified string, which is useful for [evaluating the performance of your model](likelihood-eval), especially if you've created a [custom model](https://docs.cohere.com/docs/training-custom-models). Individual token likelihoods provide the log-likelihood of each token. The first token will not have a likelihood.
"""
if IS_PYDANTIC_V2:
diff --git a/src/cohere/types/streamed_chat_response_v2.py b/src/cohere/types/streamed_chat_response_v2.py
index cb224b8c1..6c1e111b8 100644
--- a/src/cohere/types/streamed_chat_response_v2.py
+++ b/src/cohere/types/streamed_chat_response_v2.py
@@ -8,6 +8,7 @@
import pydantic
from .chat_content_start_event_delta import ChatContentStartEventDelta
from .chat_content_delta_event_delta import ChatContentDeltaEventDelta
+from .logprob_item import LogprobItem
from .chat_tool_plan_delta_event_delta import ChatToolPlanDeltaEventDelta
from .chat_tool_call_start_event_delta import ChatToolCallStartEventDelta
from .chat_tool_call_delta_event_delta import ChatToolCallDeltaEventDelta
@@ -61,6 +62,7 @@ class ContentDeltaStreamedChatResponseV2(UncheckedBaseModel):
type: typing.Literal["content-delta"] = "content-delta"
index: typing.Optional[int] = None
delta: typing.Optional[ChatContentDeltaEventDelta] = None
+ logprobs: typing.Optional[LogprobItem] = None
if IS_PYDANTIC_V2:
model_config: typing.ClassVar[pydantic.ConfigDict] = pydantic.ConfigDict(extra="allow") # type: ignore # Pydantic v2
diff --git a/src/cohere/v2/client.py b/src/cohere/v2/client.py
index 1adddc1a5..58059a61d 100644
--- a/src/cohere/v2/client.py
+++ b/src/cohere/v2/client.py
@@ -70,6 +70,7 @@ def chat_stream(
k: typing.Optional[float] = OMIT,
p: typing.Optional[float] = OMIT,
return_prompt: typing.Optional[bool] = OMIT,
+ logprobs: typing.Optional[bool] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> typing.Iterator[StreamedChatResponseV2]:
"""
@@ -143,7 +144,7 @@ def chat_stream(
k : typing.Optional[float]
- Ensures only the top `k` most likely tokens are considered for generation at each step.
+ Ensures that only the top `k` most likely tokens are considered for generation at each step. When `k` is set to `0`, k-sampling is disabled.
Defaults to `0`, min value of `0`, max value of `500`.
@@ -155,6 +156,10 @@ def chat_stream(
return_prompt : typing.Optional[bool]
Whether to return the prompt in the response.
+ logprobs : typing.Optional[bool]
+ Whether to return the log probabilities of the generated tokens. Defaults to false.
+
+
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
@@ -209,6 +214,7 @@ def chat_stream(
k=1.1,
p=1.1,
return_prompt=True,
+ logprobs=True,
)
for chunk in response:
yield chunk
@@ -243,6 +249,7 @@ def chat_stream(
"k": k,
"p": p,
"return_prompt": return_prompt,
+ "logprobs": logprobs,
"stream": True,
},
request_options=request_options,
@@ -398,6 +405,7 @@ def chat(
k: typing.Optional[float] = OMIT,
p: typing.Optional[float] = OMIT,
return_prompt: typing.Optional[bool] = OMIT,
+ logprobs: typing.Optional[bool] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> ChatResponse:
"""
@@ -471,7 +479,7 @@ def chat(
k : typing.Optional[float]
- Ensures only the top `k` most likely tokens are considered for generation at each step.
+ Ensures that only the top `k` most likely tokens are considered for generation at each step. When `k` is set to `0`, k-sampling is disabled.
Defaults to `0`, min value of `0`, max value of `500`.
@@ -483,6 +491,10 @@ def chat(
return_prompt : typing.Optional[bool]
Whether to return the prompt in the response.
+ logprobs : typing.Optional[bool]
+ Whether to return the log probabilities of the generated tokens. Defaults to false.
+
+
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
@@ -539,6 +551,7 @@ def chat(
"k": k,
"p": p,
"return_prompt": return_prompt,
+ "logprobs": logprobs,
"stream": False,
},
request_options=request_options,
@@ -684,14 +697,14 @@ def embed(
Embeddings can be used to create text classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page.
- If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](/docs/semantic-search).
+ If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](https://docs.cohere.com/docs/semantic-search).
Parameters
----------
model : str
Defaults to embed-english-v2.0
- The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+ The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
Available models and corresponding embedding dimensions:
@@ -1121,6 +1134,7 @@ async def chat_stream(
k: typing.Optional[float] = OMIT,
p: typing.Optional[float] = OMIT,
return_prompt: typing.Optional[bool] = OMIT,
+ logprobs: typing.Optional[bool] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> typing.AsyncIterator[StreamedChatResponseV2]:
"""
@@ -1194,7 +1208,7 @@ async def chat_stream(
k : typing.Optional[float]
- Ensures only the top `k` most likely tokens are considered for generation at each step.
+ Ensures that only the top `k` most likely tokens are considered for generation at each step. When `k` is set to `0`, k-sampling is disabled.
Defaults to `0`, min value of `0`, max value of `500`.
@@ -1206,6 +1220,10 @@ async def chat_stream(
return_prompt : typing.Optional[bool]
Whether to return the prompt in the response.
+ logprobs : typing.Optional[bool]
+ Whether to return the log probabilities of the generated tokens. Defaults to false.
+
+
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
@@ -1265,6 +1283,7 @@ async def main() -> None:
k=1.1,
p=1.1,
return_prompt=True,
+ logprobs=True,
)
async for chunk in response:
yield chunk
@@ -1302,6 +1321,7 @@ async def main() -> None:
"k": k,
"p": p,
"return_prompt": return_prompt,
+ "logprobs": logprobs,
"stream": True,
},
request_options=request_options,
@@ -1457,6 +1477,7 @@ async def chat(
k: typing.Optional[float] = OMIT,
p: typing.Optional[float] = OMIT,
return_prompt: typing.Optional[bool] = OMIT,
+ logprobs: typing.Optional[bool] = OMIT,
request_options: typing.Optional[RequestOptions] = None,
) -> ChatResponse:
"""
@@ -1530,7 +1551,7 @@ async def chat(
k : typing.Optional[float]
- Ensures only the top `k` most likely tokens are considered for generation at each step.
+ Ensures that only the top `k` most likely tokens are considered for generation at each step. When `k` is set to `0`, k-sampling is disabled.
Defaults to `0`, min value of `0`, max value of `500`.
@@ -1542,6 +1563,10 @@ async def chat(
return_prompt : typing.Optional[bool]
Whether to return the prompt in the response.
+ logprobs : typing.Optional[bool]
+ Whether to return the log probabilities of the generated tokens. Defaults to false.
+
+
request_options : typing.Optional[RequestOptions]
Request-specific configuration.
@@ -1606,6 +1631,7 @@ async def main() -> None:
"k": k,
"p": p,
"return_prompt": return_prompt,
+ "logprobs": logprobs,
"stream": False,
},
request_options=request_options,
@@ -1751,14 +1777,14 @@ async def embed(
Embeddings can be used to create text classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page.
- If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](/docs/semantic-search).
+ If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](https://docs.cohere.com/docs/semantic-search).
Parameters
----------
model : str
Defaults to embed-english-v2.0
- The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](/docs/training-custom-models) can also be supplied with their full ID.
+ The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID.
Available models and corresponding embedding dimensions: