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85faf5f474
Co-authored-by: Charles Packer <packercharles@gmail.com> Co-authored-by: Shubham Naik <shubham.naik10@gmail.com> Co-authored-by: Shubham Naik <shub@memgpt.ai>
59 lines
2.4 KiB
Python
59 lines
2.4 KiB
Python
from urllib.parse import urljoin
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from letta.local_llm.settings.settings import get_completions_settings
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from letta.local_llm.utils import count_tokens, post_json_auth_request
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LLAMACPP_API_SUFFIX = "/completion"
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def get_llamacpp_completion(endpoint, auth_type, auth_key, prompt, context_window, grammar=None):
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"""See https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md for instructions on how to run the LLM web server"""
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from letta.utils import printd
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prompt_tokens = count_tokens(prompt)
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if prompt_tokens > context_window:
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raise Exception(f"Request exceeds maximum context length ({prompt_tokens} > {context_window} tokens)")
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# Settings for the generation, includes the prompt + stop tokens, max length, etc
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settings = get_completions_settings()
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request = settings
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request["prompt"] = prompt
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# Set grammar
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if grammar is not None:
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request["grammar"] = grammar
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if not endpoint.startswith(("http://", "https://")):
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raise ValueError(f"Provided OPENAI_API_BASE value ({endpoint}) must begin with http:// or https://")
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try:
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# NOTE: llama.cpp server returns the following when it's out of context
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# curl: (52) Empty reply from server
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URI = urljoin(endpoint.strip("/") + "/", LLAMACPP_API_SUFFIX.strip("/"))
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response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
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if response.status_code == 200:
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result_full = response.json()
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printd(f"JSON API response:\n{result_full}")
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result = result_full["content"]
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else:
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raise Exception(
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f"API call got non-200 response code (code={response.status_code}, msg={response.text}) for address: {URI}."
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+ f" Make sure that the llama.cpp server is running and reachable at {URI}."
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)
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except:
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# TODO handle gracefully
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raise
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# Pass usage statistics back to main thread
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# These are used to compute memory warning messages
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completion_tokens = result_full.get("tokens_predicted", None)
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total_tokens = prompt_tokens + completion_tokens if completion_tokens is not None else None
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usage = {
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"prompt_tokens": prompt_tokens, # can grab from "tokens_evaluated", but it's usually wrong (set to 0)
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"completion_tokens": completion_tokens,
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"total_tokens": total_tokens,
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}
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return result, usage
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