two version of R2R are hereHEADmaster

1 files changed, 188 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/integrations/helicone.py b/.venv/lib/python3.12/site-packages/litellm/integrations/helicone.py
new file mode 100644
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+++ b/.venv/lib/python3.12/site-packages/litellm/integrations/helicone.py
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+#### What this does ####
+#    On success, logs events to Helicone
+import os
+import traceback
+
+import litellm
+
+
+class HeliconeLogger:
+    # Class variables or attributes
+    helicone_model_list = [
+        "gpt",
+        "claude",
+        "command-r",
+        "command-r-plus",
+        "command-light",
+        "command-medium",
+        "command-medium-beta",
+        "command-xlarge-nightly",
+        "command-nightly",
+    ]
+
+    def __init__(self):
+        # Instance variables
+        self.provider_url = "https://api.openai.com/v1"
+        self.key = os.getenv("HELICONE_API_KEY")
+
+    def claude_mapping(self, model, messages, response_obj):
+        from anthropic import AI_PROMPT, HUMAN_PROMPT
+
+        prompt = f"{HUMAN_PROMPT}"
+        for message in messages:
+            if "role" in message:
+                if message["role"] == "user":
+                    prompt += f"{HUMAN_PROMPT}{message['content']}"
+                else:
+                    prompt += f"{AI_PROMPT}{message['content']}"
+            else:
+                prompt += f"{HUMAN_PROMPT}{message['content']}"
+        prompt += f"{AI_PROMPT}"
+
+        choice = response_obj["choices"][0]
+        message = choice["message"]
+
+        content = []
+        if "tool_calls" in message and message["tool_calls"]:
+            for tool_call in message["tool_calls"]:
+                content.append(
+                    {
+                        "type": "tool_use",
+                        "id": tool_call["id"],
+                        "name": tool_call["function"]["name"],
+                        "input": tool_call["function"]["arguments"],
+                    }
+                )
+        elif "content" in message and message["content"]:
+            content = [{"type": "text", "text": message["content"]}]
+
+        claude_response_obj = {
+            "id": response_obj["id"],
+            "type": "message",
+            "role": "assistant",
+            "model": model,
+            "content": content,
+            "stop_reason": choice["finish_reason"],
+            "stop_sequence": None,
+            "usage": {
+                "input_tokens": response_obj["usage"]["prompt_tokens"],
+                "output_tokens": response_obj["usage"]["completion_tokens"],
+            },
+        }
+
+        return claude_response_obj
+
+    @staticmethod
+    def add_metadata_from_header(litellm_params: dict, metadata: dict) -> dict:
+        """
+        Adds metadata from proxy request headers to Helicone logging if keys start with "helicone_"
+        and overwrites litellm_params.metadata if already included.
+
+        For example if you want to add custom property to your request, send
+        `headers: { ..., helicone-property-something: 1234 }` via proxy request.
+        """
+        if litellm_params is None:
+            return metadata
+
+        if litellm_params.get("proxy_server_request") is None:
+            return metadata
+
+        if metadata is None:
+            metadata = {}
+
+        proxy_headers = (
+            litellm_params.get("proxy_server_request", {}).get("headers", {}) or {}
+        )
+
+        for header_key in proxy_headers:
+            if header_key.startswith("helicone_"):
+                metadata[header_key] = proxy_headers.get(header_key)
+
+        return metadata
+
+    def log_success(
+        self, model, messages, response_obj, start_time, end_time, print_verbose, kwargs
+    ):
+        # Method definition
+        try:
+            print_verbose(
+                f"Helicone Logging - Enters logging function for model {model}"
+            )
+            litellm_params = kwargs.get("litellm_params", {})
+            kwargs.get("litellm_call_id", None)
+            metadata = litellm_params.get("metadata", {}) or {}
+            metadata = self.add_metadata_from_header(litellm_params, metadata)
+            model = (
+                model
+                if any(
+                    accepted_model in model
+                    for accepted_model in self.helicone_model_list
+                )
+                else "gpt-3.5-turbo"
+            )
+            provider_request = {"model": model, "messages": messages}
+            if isinstance(response_obj, litellm.EmbeddingResponse) or isinstance(
+                response_obj, litellm.ModelResponse
+            ):
+                response_obj = response_obj.json()
+
+            if "claude" in model:
+                response_obj = self.claude_mapping(
+                    model=model, messages=messages, response_obj=response_obj
+                )
+
+            providerResponse = {
+                "json": response_obj,
+                "headers": {"openai-version": "2020-10-01"},
+                "status": 200,
+            }
+
+            # Code to be executed
+            provider_url = self.provider_url
+            url = "https://api.hconeai.com/oai/v1/log"
+            if "claude" in model:
+                url = "https://api.hconeai.com/anthropic/v1/log"
+                provider_url = "https://api.anthropic.com/v1/messages"
+            headers = {
+                "Authorization": f"Bearer {self.key}",
+                "Content-Type": "application/json",
+            }
+            start_time_seconds = int(start_time.timestamp())
+            start_time_milliseconds = int(
+                (start_time.timestamp() - start_time_seconds) * 1000
+            )
+            end_time_seconds = int(end_time.timestamp())
+            end_time_milliseconds = int(
+                (end_time.timestamp() - end_time_seconds) * 1000
+            )
+            meta = {"Helicone-Auth": f"Bearer {self.key}"}
+            meta.update(metadata)
+            data = {
+                "providerRequest": {
+                    "url": provider_url,
+                    "json": provider_request,
+                    "meta": meta,
+                },
+                "providerResponse": providerResponse,
+                "timing": {
+                    "startTime": {
+                        "seconds": start_time_seconds,
+                        "milliseconds": start_time_milliseconds,
+                    },
+                    "endTime": {
+                        "seconds": end_time_seconds,
+                        "milliseconds": end_time_milliseconds,
+                    },
+                },  # {"seconds": .., "milliseconds": ..}
+            }
+            response = litellm.module_level_client.post(url, headers=headers, json=data)
+            if response.status_code == 200:
+                print_verbose("Helicone Logging - Success!")
+            else:
+                print_verbose(
+                    f"Helicone Logging - Error Request was not successful. Status Code: {response.status_code}"
+                )
+                print_verbose(f"Helicone Logging - Error {response.text}")
+        except Exception:
+            print_verbose(f"Helicone Logging Error - {traceback.format_exc()}")
+            pass