Delete old code

letta/local_llm/groq/api.py

@@ -1,97 +0,0 @@
-from typing import Tuple
-from urllib.parse import urljoin
-
-from letta.local_llm.settings.settings import get_completions_settings
-from letta.local_llm.utils import post_json_auth_request
-from letta.utils import count_tokens
-
-API_CHAT_SUFFIX = "/v1/chat/completions"
-# LMSTUDIO_API_COMPLETIONS_SUFFIX = "/v1/completions"
-
-
-def get_groq_completion(endpoint: str, auth_type: str, auth_key: str, model: str, prompt: str, context_window: int) -> Tuple[str, dict]:
-    """TODO no support for function calling OR raw completions, so we need to route the request into /chat/completions instead"""
-    from letta.utils import printd
-
-    prompt_tokens = count_tokens(prompt)
-    if prompt_tokens > context_window:
-        raise Exception(f"Request exceeds maximum context length ({prompt_tokens} > {context_window} tokens)")
-
-    settings = get_completions_settings()
-    settings.update(
-        {
-            # see https://console.groq.com/docs/text-chat, supports:
-            # "temperature": ,
-            # "max_tokens": ,
-            # "top_p",
-            # "stream",
-            # "stop",
-            # Groq only allows 4 stop tokens
-            "stop": [
-                "\nUSER",
-                "\nASSISTANT",
-                "\nFUNCTION",
-                # "\nFUNCTION RETURN",
-                # "<|im_start|>",
-                # "<|im_end|>",
-                # "<|im_sep|>",
-                # # airoboros specific
-                # "\n### ",
-                # # '\n' +
-                # # '</s>',
-                # # '<|',
-                # "\n#",
-                # # "\n\n\n",
-                # # prevent chaining function calls / multi json objects / run-on generations
-                # # NOTE: this requires the ability to patch the extra '}}' back into the prompt
-                "  }\n}\n",
-            ]
-        }
-    )
-
-    URI = urljoin(endpoint.strip("/") + "/", API_CHAT_SUFFIX.strip("/"))
-
-    # Settings for the generation, includes the prompt + stop tokens, max length, etc
-    request = settings
-    request["model"] = model
-    request["max_tokens"] = context_window
-    # NOTE: Hack for chat/completion-only endpoints: put the entire completion string inside the first message
-    message_structure = [{"role": "user", "content": prompt}]
-    request["messages"] = message_structure
-
-    if not endpoint.startswith(("http://", "https://")):
-        raise ValueError(f"Provided OPENAI_API_BASE value ({endpoint}) must begin with http:// or https://")
-
-    try:
-        response = post_json_auth_request(uri=URI, json_payload=request, auth_type=auth_type, auth_key=auth_key)
-        if response.status_code == 200:
-            result_full = response.json()
-            printd(f"JSON API response:\n{result_full}")
-            result = result_full["choices"][0]["message"]["content"]
-            usage = result_full.get("usage", None)
-        else:
-            # Example error: msg={"error":"Context length exceeded. Tokens in context: 8000, Context length: 8000"}
-            if "context length" in str(response.text).lower():
-                # "exceeds context length" is what appears in the LM Studio error message
-                # raise an alternate exception that matches OpenAI's message, which is "maximum context length"
-                raise Exception(f"Request exceeds maximum context length (code={response.status_code}, msg={response.text}, URI={URI})")
-            else:
-                raise Exception(
-                    f"API call got non-200 response code (code={response.status_code}, msg={response.text}) for address: {URI}."
-                    + f" Make sure that the inference server is running and reachable at {URI}."
-                )
-    except:
-        # TODO handle gracefully
-        raise
-
-    # Pass usage statistics back to main thread
-    # These are used to compute memory warning messages
-    completion_tokens = usage.get("completion_tokens", None) if usage is not None else None
-    total_tokens = prompt_tokens + completion_tokens if completion_tokens is not None else None
-    usage = {
-        "prompt_tokens": prompt_tokens,  # can grab from usage dict, but it's usually wrong (set to 0)
-        "completion_tokens": completion_tokens,
-        "total_tokens": total_tokens,
-    }
-
-    return result, usage