implement loglikelihood endpoint

CHANGELOG.md

@@ -1,5 +1,12 @@
 # Changelog
 
+## 4.20.0
+
+- [#276] (https://github.com/cohere-ai/cohere-python/pull/276)
+  - Add support for base_model option in create_custom_model
+- [#277] (https://github.com/cohere-ai/cohere-python/pull/277)
+  - Add support for co.loglikelihood endpoint
+
 ## 4.19.1
 
 - [#273] (https://github.com/cohere-ai/cohere-python/pull/273)

cohere/__init__.py

@@ -25,6 +25,7 @@
 CHECK_API_KEY_URL = "check-api-key"
 TOKENIZE_URL = "tokenize"
 DETOKENIZE_URL = "detokenize"
+LOGLIKELIHOOD_URL = "loglikelihood"
 
 CLUSTER_JOBS_URL = "cluster-jobs"
 EMBED_JOBS_URL = "embed-jobs"

cohere/client.py

@@ -25,6 +25,7 @@
     Codebook,
     Detokenization,
     Generations,
+    LogLikelihoods,
     StreamingGenerations,
     Tokens,
 )
@@ -104,6 +105,25 @@ def check_api_key(self) -> Dict[str, bool]:
         """
         return {"valid": is_api_key_valid(self.api_key)}
 
+    def loglikelihood(
+        self,
+        prompt: Optional[str] = None,
+        completion: Optional[str] = None,
+        model: Optional[str] = None,
+    ) -> LogLikelihoods:
+        """Calculates the token log-likelihood for a provided prompt and completion.
+        Using this endpoint instead of co.generate with max_tokens=0 will guarantee that any required tokens such as <EOP_TOKEN>
+        are correctly inserted, and makes it easier to retrieve only the completion log-likelihood.
+
+        Args:
+            prompt (str): The prompt
+            completion (str): (Optional) The completion
+            model (str): (Optional) The model to use for calculating the log-likelihoods
+        """
+        json_body = {"model": model, "prompt": prompt, "completion": completion}
+        response = self._request(cohere.LOGLIKELIHOOD_URL, json=json_body)
+        return LogLikelihoods(response["prompt_tokens"], response["completion_tokens"])
+
     def batch_generate(
         self, prompts: List[str], return_exceptions=False, **kwargs
     ) -> List[Union[Generations, Exception]]:

cohere/client_async.py

@@ -35,6 +35,7 @@
     Generations,
     LabelPrediction,
     Language,
+    LogLikelihoods,
     PreferenceRating,
     Reranking,
     StreamingGenerations,
@@ -143,6 +144,16 @@ async def check_api_key(self) -> Dict[str, bool]:
         """
         return {"valid": is_api_key_valid(self.api_key)}
 
+    async def loglikelihood(
+        self,
+        prompt: Optional[str] = None,
+        completion: Optional[str] = None,
+        model: Optional[str] = None,
+    ) -> LogLikelihoods:
+        json_body = {"model": model, "prompt": prompt, "completion": completion}
+        response = await self._request(cohere.LOGLIKELIHOOD_URL, json=json_body)
+        return LogLikelihoods(response["prompt_tokens"], response["completion_tokens"])
+
     async def batch_generate(
         self, prompts: List[str], return_exceptions=False, **kwargs
     ) -> List[Union[Exception, Generations]]:

cohere/responses/__init__.py

@@ -11,6 +11,7 @@
     PreferenceRating,
 )
 from cohere.responses.generation import Generation, Generations, StreamingGenerations
+from cohere.responses.loglikelihood import LogLikelihoods
 from cohere.responses.rerank import RerankDocument, Reranking, RerankResult
 from cohere.responses.summarize import SummarizeResponse
 from cohere.responses.tokenize import Detokenization, Tokens

cohere/responses/loglikelihood.py

@@ -0,0 +1,15 @@
+from typing import Dict, List, NamedTuple, Optional
+
+TokenLogLikelihood = NamedTuple("TokenLogLikelihood", [("encoded", int), ("decoded", str), ("log_likelihood", float)])
+
+
+class LogLikelihoods:
+    @staticmethod
+    def token_list_from_dict(token_list: Optional[List[Dict]]):
+        if token_list is None:
+            return None
+        return [TokenLogLikelihood(**token) for token in token_list]
+
+    def __init__(self, prompt_tokens: List[TokenLogLikelihood], completion_tokens: List[TokenLogLikelihood]):
+        self.prompt_tokens = self.token_list_from_dict(prompt_tokens)
+        self.completion_tokens = self.token_list_from_dict(completion_tokens)

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "cohere"
-version = "4.19.3"
+version = "4.20.0"
 description = ""
 authors = ["Cohere"]
 readme = "README.md"

tests/async/test_async_loglikelihood.py

@@ -0,0 +1,31 @@
+import pytest
+
+from cohere.responses import LogLikelihoods
+
+TEST_MODEL = "command-light"
+
+
+@pytest.mark.asyncio
+async def test_basic_llh_async(async_client):
+    resp = await async_client.loglikelihood(model=TEST_MODEL, prompt="co:here", completion="co:where?")
+    assert isinstance(resp, LogLikelihoods)
+    assert isinstance(resp.prompt_tokens[0].encoded, int)
+    assert isinstance(resp.prompt_tokens[0].decoded, str)
+    assert isinstance(resp.prompt_tokens[1].log_likelihood, float)
+
+    assert resp.prompt_tokens[0].decoded == "<BOS_TOKEN>"
+    assert resp.prompt_tokens[-1].decoded == "<EOP_TOKEN>"
+
+    assert isinstance(resp.completion_tokens[0].encoded, int)
+    assert isinstance(resp.completion_tokens[0].decoded, str)
+    assert isinstance(resp.completion_tokens[0].log_likelihood, float)
+
+    assert resp.completion_tokens[-1].decoded == "<EOS_TOKEN>"
+
+
+@pytest.mark.asyncio
+async def test_only_prompt_async_llh(async_client):
+    resp = await async_client.loglikelihood(model=TEST_MODEL, prompt="co:here")
+    assert isinstance(resp, LogLikelihoods)
+    assert isinstance(resp.prompt_tokens[0].encoded, int)
+    assert resp.completion_tokens is None

tests/sync/test_loglikelihood.py

@@ -0,0 +1,17 @@
+import unittest
+
+from utils import get_api_key
+
+import cohere
+from cohere.responses import LogLikelihoods
+
+TEST_MODEL = "command-light"
+API_KEY = get_api_key()
+co = cohere.Client(API_KEY)
+
+
+class TestEmbed(unittest.TestCase):
+    def test_basic_llh(self):
+        resp = co.loglikelihood(model=TEST_MODEL, prompt="co:here", completion="co:where?")
+        assert isinstance(resp, LogLikelihoods)
+        assert isinstance(resp.prompt_tokens[0].encoded, int)