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https://github.com/hwchase17/langchain
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Add successful request count to OpenAI callback (#2128)
I've found it useful to track the number of successful requests to OpenAI. This gives me a better sense of the efficiency of my prompts and helps compare map_reduce/refine on a cheaper model vs. stuffing on a more expensive model with higher capacity.
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@ -45,7 +45,8 @@
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"text": [
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"Total Tokens: 42\n",
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"Prompt Tokens: 4\n",
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"Completion Tokens: 38\n"
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"Completion Tokens: 38\n",
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"Successful Requests: 1\n"
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]
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}
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],
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@ -54,7 +55,8 @@
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" result = llm(\"Tell me a joke\")\n",
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" print(f\"Total Tokens: {cb.total_tokens}\")\n",
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" print(f\"Prompt Tokens: {cb.prompt_tokens}\")\n",
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" print(f\"Completion Tokens: {cb.completion_tokens}\")"
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" print(f\"Completion Tokens: {cb.completion_tokens}\")\n",
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" print(f\"Successful Requests: {cb.successful_requests}\")"
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]
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},
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{
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@ -11,6 +11,7 @@ class OpenAICallbackHandler(BaseCallbackHandler):
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total_tokens: int = 0
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prompt_tokens: int = 0
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completion_tokens: int = 0
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successful_requests: int = 0
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@property
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def always_verbose(self) -> bool:
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@ -30,6 +31,7 @@ class OpenAICallbackHandler(BaseCallbackHandler):
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def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
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"""Collect token usage."""
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if response.llm_output is not None:
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self.successful_requests += 1
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if "token_usage" in response.llm_output:
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token_usage = response.llm_output["token_usage"]
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if "total_tokens" in token_usage:
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