phoenix/litellm
1
0
Fork 0
forked from phoenix/litellm-mirror
Code Pull requests Activity

fix(bedrock.py): fix output format for cohere embeddings

Browse source
This commit is contained in:
Krrish Dholakia 2023-12-06 22:44:01 -08:00
parent 7ddc0dad24
commit c1e95740b0
4 changed files with 40 additions and 14 deletions
Download patch file Download diff file Expand all files Collapse all files

7
litellm/llms/bedrock.py
View file

@ -552,8 +552,8 @@ def _embedding_func_single(
## FORMAT EMBEDDING INPUT ##
provider = model.split(".")[0]
inference_params = copy.deepcopy(optional_params)
input = input.replace(os.linesep, " ")
if provider == "amazon":
input = input.replace(os.linesep, " ")
data = {"inputText": input, **inference_params}
# data = json.dumps(data)
elif provider == "cohere":
@ -590,7 +590,10 @@ def _embedding_func_single(
original_response=response_body,
)
if provider == "cohere":
return response_body.get("embeddings")
response = response_body.get("embeddings")
# flatten list
response = [item for sublist in response for item in sublist]
return response
elif provider == "amazon":
return response_body.get("embedding")
except Exception as e:

16
litellm/main.py
View file

@ -1775,16 +1775,20 @@ def embedding(
rpm = kwargs.pop("rpm", None)
tpm = kwargs.pop("tpm", None)
aembedding = kwargs.pop("aembedding", None)
openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "request_timeout", "api_base", "api_version", "api_key", "deployment_id", "organization", "base_url", "default_headers", "timeout", "response_format", "seed", "tools", "tool_choice", "max_retries", "encoding_format"]
litellm_params = ["metadata", "acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "fallbacks", "azure", "headers", "model_list", "num_retries", "context_window_fallback_dict", "roles", "final_prompt_value", "bos_token", "eos_token", "request_timeout", "complete_response", "self", "client", "rpm", "tpm", "input_cost_per_token", "output_cost_per_token", "hf_model_name"]
default_params = openai_params + litellm_params
non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider
optional_params = {}
for param in kwargs:
if param != "metadata": # filter out metadata from optional_params
for param in non_default_params:
optional_params[param] = kwargs[param]
model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base, api_key=api_key)
try:
response = None
logging = litellm_logging_obj
logging.update_environment_variables(model=model, user="", optional_params={}, litellm_params={"timeout": timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn})
logging.update_environment_variables(model=model, user="", optional_params=optional_params, litellm_params={"timeout": timeout, "azure": azure, "litellm_call_id": litellm_call_id, "logger_fn": logger_fn})
if azure == True or custom_llm_provider == "azure":
# azure configs
api_type = get_secret("AZURE_API_TYPE") or "azure"
@ -1903,7 +1907,7 @@ def embedding(
input=input,
encoding=encoding,
logging_obj=logging,
optional_params=kwargs,
optional_params=optional_params,
model_response= EmbeddingResponse()
)
elif custom_llm_provider == "sagemaker":
@ -1912,7 +1916,7 @@ def embedding(
input=input,
encoding=encoding,
logging_obj=logging,
optional_params=kwargs,
optional_params=optional_params,
model_response= EmbeddingResponse(),
print_verbose=print_verbose
)

18
litellm/proxy/proxy_server.py
View file

@ -989,6 +989,7 @@ async def embeddings(request: Request, user_api_key_dict: UserAPIKeyAuth = Depen
body = await request.body()
data = orjson.loads(body)
data["user"] = user_api_key_dict.user_id
data["model"] = (
general_settings.get("embedding_model", None) # server default
@ -1001,9 +1002,24 @@ async def embeddings(request: Request, user_api_key_dict: UserAPIKeyAuth = Depen
data["metadata"]["user_api_key"] = user_api_key_dict.api_key
else:
data["metadata"] = {"user_api_key": user_api_key_dict.api_key}
router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else []
print(f"received data: {data['input']}")
if "input" in data and isinstance(data['input'], list) and isinstance(data['input'][0], list) and isinstance(data['input'][0][0], int): # check if array of tokens passed in
# check if non-openai/azure model called - e.g. for langchain integration
if data["model"] in router_model_names:
for m in llm_model_list:
if m["model_name"] == data["model"] and (m["litellm_params"]["model"] in litellm.open_ai_embedding_models
or m["litellm_params"]["model"].startswith("azure/")):
pass
else:
# non-openai/azure embedding model called with token input
input_list = []
for i in data["input"]:
input_list.append(litellm.decode(model="gpt-3.5-turbo", tokens=i))
data["input"] = input_list
break
## ROUTE TO CORRECT ENDPOINT ##
router_model_names = [m["model_name"] for m in llm_model_list] if llm_model_list is not None else []
if llm_router is not None and data["model"] in router_model_names: # model in router model list
response = await llm_router.aembedding(**data)
elif llm_router is not None and data["model"] in llm_router.deployment_names: # model in router deployments, calling a specific deployment on the router

11
litellm/tests/test_embedding.py
View file

@ -161,20 +161,23 @@ def test_bedrock_embedding_titan():
print(f"response:", response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
test_bedrock_embedding_titan()
# test_bedrock_embedding_titan()
def test_bedrock_embedding_cohere():
try:
# litellm.set_verbose=True
litellm.set_verbose=False
response = embedding(
model="cohere.embed-multilingual-v3", input=["good morning from litellm, attempting to embed data", "lets test a second string for good measure"],
aws_region_name="os.environ/AWS_REGION_NAME_2"
)
assert isinstance(response['data'][0]['embedding'], list), "Expected response to be a list"
print(f"type of first embedding:", type(response['data'][0]['embedding'][0]))
assert all(isinstance(x, float) for x in response['data'][0]['embedding']), "Expected response to be a list of floats"
# print(f"response:", response)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_bedrock_embedding_cohere()
test_bedrock_embedding_cohere()
# comment out hf tests - since hf endpoints are unstable
def test_hf_embedding():
@ -234,7 +237,7 @@ def test_sagemaker_embeddings():
print(f"response: {response}")
except Exception as e:
pytest.fail(f"Error occurred: {e}")
test_sagemaker_embeddings()
# test_sagemaker_embeddings()
# def local_proxy_embeddings():
# litellm.set_verbose=True
# response = embedding(