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Method ov_token_counter

llmware/models.py:4527–4546  ·  view source on GitHub ↗

Called twice in inference generation loop to get the input_token_count and output_token_count. This step can be skipped by setting the OVConfig as follows: `from llmware.configs import OVConfig OVConfig().set_config("get_token_counts", False)` In our testin

(self, text)

Source from the content-addressed store, hash-verified

4525 return img_path
4526
4527 def ov_token_counter(self, text):
4528
4529 """ Called twice in inference generation loop to get the input_token_count and
4530 output_token_count. This step can be skipped by setting the OVConfig as follows:
4531
4532 `from llmware.configs import OVConfig
4533 OVConfig().set_config("get_token_counts", False)`
4534
4535 In our testing, the performance impact is negligible, but may be different in your
4536 environment and use case.
4537
4538 If this is set to False, then no token counts will be provided in the usage totals.
4539 """
4540
4541 if self.tokenizer:
4542 toks = len(self.tokenizer.encode(text))
4543 else:
4544 toks = 0
4545
4546 return toks
4547
4548 def prompt_engineer(self, query, context, inference_dict):
4549

Callers 3

inferenceMethod · 0.95
function_callMethod · 0.95
streamMethod · 0.95

Calls 1

encodeMethod · 0.80

Tested by

no test coverage detected