reduce number of arguments in openai_completion and openai_completion

src/instructlab/sdg/generate_data.py

@@ -22,8 +22,10 @@
     num_chars_from_tokens,
 )
 from jinja2 import Template
+from openai import OpenAI
 from rouge_score import rouge_scorer
 import click
+import httpx
 import instructlab.utils
 import tqdm
 
@@ -266,18 +268,13 @@ def get_instructions_from_model(
     request_idx,
     instruction_data_pool,
     prompt_template,
-    api_base,
-    api_key,
+    client,
     model_name,
     num_prompt_instructions,
     request_batch_size,
     temperature,
     top_p,
     output_file_discarded,
-    tls_insecure,
-    tls_client_cert,
-    tls_client_key,
-    tls_client_passwd,
 ):
     batch_inputs = []
     for _ in range(request_batch_size):
@@ -306,14 +303,9 @@ def get_instructions_from_model(
     request_start = time.time()
     try:
         results = utils.openai_completion(
-            api_base=api_base,
-            api_key=api_key,
+            client,
             prompts=batch_inputs,
             model_name=model_name,
-            tls_insecure=tls_insecure,
-            tls_client_cert=tls_client_cert,
-            tls_client_key=tls_client_key,
-            tls_client_passwd=tls_client_passwd,
             batch_size=request_batch_size,
             decoding_args=decoding_args,
         )
@@ -473,6 +465,15 @@ def generate_data(
     tls_client_key: Optional[str] = None,
     tls_client_passwd: Optional[str] = None,
 ):
+    cert = tuple(
+        item for item in (tls_client_cert, tls_client_key, tls_client_passwd) if item
+    )
+    client = OpenAI(
+        base_url=api_base,
+        api_key=api_key,
+        http_client=httpx.Client(cert=cert, verify=not tls_insecure),
+    )
+
     seed_instruction_data = []
     machine_seed_instruction_data = []
     generate_start = time.time()
@@ -566,18 +567,13 @@ def generate_data(
             request_idx,
             instruction_data_pool,
             prompt_template,
-            api_base,
-            api_key,
+            client,
             model_name,
             num_prompt_instructions,
             request_batch_size,
             temperature,
             top_p,
             output_file_discarded,
-            tls_insecure,
-            tls_client_cert,
-            tls_client_key,
-            tls_client_passwd,
         )
         total_discarded += discarded
         total = len(instruction_data)

src/instructlab/sdg/utils.py

@@ -13,10 +13,9 @@
 
 # Third Party
 # instructlab - TODO these need to go away, issue #6
-from instructlab.configuration import DEFAULT_API_KEY, DEFAULT_MODEL_OLD
+from instructlab.configuration import DEFAULT_MODEL_OLD
 from instructlab.utils import get_sysprompt
-from openai import OpenAI, OpenAIError
-import httpx
+from openai import OpenAIError
 
 StrOrOpenAIObject = Union[str, object]
 
@@ -40,19 +39,14 @@ class OpenAIDecodingArguments:
 
 
 def openai_completion(
-    api_base,
-    tls_insecure,
-    tls_client_cert,
-    tls_client_key,
-    tls_client_passwd,
+    client,
     prompts: Union[str, Sequence[str], Sequence[dict[str, str]], dict[str, str]],
     decoding_args: OpenAIDecodingArguments,
     model_name="ggml-merlinite-7b-lab-Q4_K_M",
     batch_size=1,
     max_instances=sys.maxsize,
     max_batches=sys.maxsize,
     return_text=False,
-    api_key=DEFAULT_API_KEY,
     **decoding_kwargs,
 ) -> Union[
     Union[StrOrOpenAIObject],
@@ -62,11 +56,6 @@ def openai_completion(
     """Decode with OpenAI API.
 
     Args:
-        api_base: Endpoint URL where model is hosted
-        tls_insecure: Disable TLS verification
-        tls_client_cert: Path to the TLS client certificate to use
-        tls_client_key: Path to the TLS client key to use
-        tls_client_passwd: TLS client certificate password
         prompts: A string or a list of strings to complete. If it is a chat model the strings
             should be formatted as explained here:
             https://github.com/openai/openai-python/blob/main/chatml.md.
@@ -78,7 +67,6 @@ def openai_completion(
         max_instances: Maximum number of prompts to decode.
         max_batches: Maximum number of batches to decode. This will be deprecated in the future.
         return_text: If True, return text instead of full completion object (e.g. includes logprob).
-        api_key: API key API key for API endpoint where model is hosted
         decoding_kwargs: Extra decoding arguments. Pass in `best_of` and `logit_bias` if needed.
 
     Returns:
@@ -116,22 +104,8 @@ def openai_completion(
             **decoding_kwargs,
         }
 
-        if not api_key:
-            # we need to explicitly set non-empty api-key, to ensure generate
-            # connects to our local server
-            api_key = "no_api_key"
-
         # do not pass a lower timeout to this client since generating a dataset takes some time
         # pylint: disable=R0801
-        orig_cert = (tls_client_cert, tls_client_key, tls_client_passwd)
-        cert = tuple(item for item in orig_cert if item)
-        verify = not tls_insecure
-        client = OpenAI(
-            base_url=api_base,
-            api_key=api_key,
-            http_client=httpx.Client(cert=cert, verify=verify),
-        )
-
         # ensure the model specified exists on the server. with backends like vllm, this is crucial.
         model_list = client.models.list().data
         model_ids = []