forked from phoenix/litellm-mirror
feat - arize ai open inference types
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Ishaan Jaff
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68f8fe87e4
1 changed files with 286 additions and 0 deletions
286
litellm/integrations/_types/open_inference.py
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286
litellm/integrations/_types/open_inference.py
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from enum import Enum
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class SpanAttributes:
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OUTPUT_VALUE = "output.value"
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OUTPUT_MIME_TYPE = "output.mime_type"
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"""
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The type of output.value. If unspecified, the type is plain text by default.
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If type is JSON, the value is a string representing a JSON object.
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"""
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INPUT_VALUE = "input.value"
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INPUT_MIME_TYPE = "input.mime_type"
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"""
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The type of input.value. If unspecified, the type is plain text by default.
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If type is JSON, the value is a string representing a JSON object.
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"""
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EMBEDDING_EMBEDDINGS = "embedding.embeddings"
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"""
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A list of objects containing embedding data, including the vector and represented piece of text.
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"""
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EMBEDDING_MODEL_NAME = "embedding.model_name"
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"""
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The name of the embedding model.
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"""
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LLM_FUNCTION_CALL = "llm.function_call"
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"""
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For models and APIs that support function calling. Records attributes such as the function
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name and arguments to the called function.
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"""
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LLM_INVOCATION_PARAMETERS = "llm.invocation_parameters"
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"""
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Invocation parameters passed to the LLM or API, such as the model name, temperature, etc.
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"""
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LLM_INPUT_MESSAGES = "llm.input_messages"
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"""
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Messages provided to a chat API.
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"""
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LLM_OUTPUT_MESSAGES = "llm.output_messages"
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"""
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Messages received from a chat API.
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"""
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LLM_MODEL_NAME = "llm.model_name"
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"""
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The name of the model being used.
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"""
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LLM_PROMPTS = "llm.prompts"
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"""
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Prompts provided to a completions API.
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"""
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LLM_PROMPT_TEMPLATE = "llm.prompt_template.template"
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"""
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The prompt template as a Python f-string.
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"""
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LLM_PROMPT_TEMPLATE_VARIABLES = "llm.prompt_template.variables"
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"""
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A list of input variables to the prompt template.
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"""
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LLM_PROMPT_TEMPLATE_VERSION = "llm.prompt_template.version"
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"""
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The version of the prompt template being used.
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"""
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LLM_TOKEN_COUNT_PROMPT = "llm.token_count.prompt"
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"""
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Number of tokens in the prompt.
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"""
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LLM_TOKEN_COUNT_COMPLETION = "llm.token_count.completion"
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"""
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Number of tokens in the completion.
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"""
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LLM_TOKEN_COUNT_TOTAL = "llm.token_count.total"
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"""
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Total number of tokens, including both prompt and completion.
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"""
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TOOL_NAME = "tool.name"
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"""
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Name of the tool being used.
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"""
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TOOL_DESCRIPTION = "tool.description"
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"""
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Description of the tool's purpose, typically used to select the tool.
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"""
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TOOL_PARAMETERS = "tool.parameters"
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"""
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Parameters of the tool represented a dictionary JSON string, e.g.
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see https://platform.openai.com/docs/guides/gpt/function-calling
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"""
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RETRIEVAL_DOCUMENTS = "retrieval.documents"
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METADATA = "metadata"
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"""
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Metadata attributes are used to store user-defined key-value pairs.
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For example, LangChain uses metadata to store user-defined attributes for a chain.
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"""
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TAG_TAGS = "tag.tags"
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"""
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Custom categorical tags for the span.
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"""
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OPENINFERENCE_SPAN_KIND = "openinference.span.kind"
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SESSION_ID = "session.id"
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"""
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The id of the session
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"""
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USER_ID = "user.id"
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"""
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The id of the user
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"""
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class MessageAttributes:
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"""
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Attributes for a message sent to or from an LLM
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"""
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MESSAGE_ROLE = "message.role"
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"""
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The role of the message, such as "user", "agent", "function".
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"""
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MESSAGE_CONTENT = "message.content"
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"""
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The content of the message to or from the llm, must be a string.
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"""
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MESSAGE_CONTENTS = "message.contents"
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"""
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The message contents to the llm, it is an array of
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`message_content` prefixed attributes.
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"""
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MESSAGE_NAME = "message.name"
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"""
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The name of the message, often used to identify the function
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that was used to generate the message.
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"""
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MESSAGE_TOOL_CALLS = "message.tool_calls"
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"""
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The tool calls generated by the model, such as function calls.
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"""
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MESSAGE_FUNCTION_CALL_NAME = "message.function_call_name"
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"""
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The function name that is a part of the message list.
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This is populated for role 'function' or 'agent' as a mechanism to identify
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the function that was called during the execution of a tool.
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"""
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MESSAGE_FUNCTION_CALL_ARGUMENTS_JSON = "message.function_call_arguments_json"
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"""
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The JSON string representing the arguments passed to the function
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during a function call.
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"""
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class MessageContentAttributes:
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"""
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Attributes for the contents of user messages sent to an LLM.
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"""
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MESSAGE_CONTENT_TYPE = "message_content.type"
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"""
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The type of the content, such as "text" or "image".
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"""
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MESSAGE_CONTENT_TEXT = "message_content.text"
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"""
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The text content of the message, if the type is "text".
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"""
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MESSAGE_CONTENT_IMAGE = "message_content.image"
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"""
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The image content of the message, if the type is "image".
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An image can be made available to the model by passing a link to
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the image or by passing the base64 encoded image directly in the
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request.
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"""
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class ImageAttributes:
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"""
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Attributes for images
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"""
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IMAGE_URL = "image.url"
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"""
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An http or base64 image url
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"""
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class DocumentAttributes:
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"""
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Attributes for a document.
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"""
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DOCUMENT_ID = "document.id"
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"""
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The id of the document.
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"""
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DOCUMENT_SCORE = "document.score"
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"""
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The score of the document
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"""
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DOCUMENT_CONTENT = "document.content"
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"""
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The content of the document.
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"""
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DOCUMENT_METADATA = "document.metadata"
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"""
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The metadata of the document represented as a dictionary
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JSON string, e.g. `"{ 'title': 'foo' }"`
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"""
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class RerankerAttributes:
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"""
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Attributes for a reranker
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"""
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RERANKER_INPUT_DOCUMENTS = "reranker.input_documents"
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"""
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List of documents as input to the reranker
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"""
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RERANKER_OUTPUT_DOCUMENTS = "reranker.output_documents"
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"""
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List of documents as output from the reranker
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"""
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RERANKER_QUERY = "reranker.query"
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"""
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Query string for the reranker
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"""
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RERANKER_MODEL_NAME = "reranker.model_name"
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"""
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Model name of the reranker
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"""
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RERANKER_TOP_K = "reranker.top_k"
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"""
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Top K parameter of the reranker
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"""
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class EmbeddingAttributes:
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"""
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Attributes for an embedding
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"""
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EMBEDDING_TEXT = "embedding.text"
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"""
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The text represented by the embedding.
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"""
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EMBEDDING_VECTOR = "embedding.vector"
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"""
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The embedding vector.
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"""
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class ToolCallAttributes:
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"""
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Attributes for a tool call
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"""
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TOOL_CALL_FUNCTION_NAME = "tool_call.function.name"
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"""
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The name of function that is being called during a tool call.
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"""
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TOOL_CALL_FUNCTION_ARGUMENTS_JSON = "tool_call.function.arguments"
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"""
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The JSON string representing the arguments passed to the function
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during a tool call.
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"""
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class OpenInferenceSpanKindValues(Enum):
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TOOL = "TOOL"
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CHAIN = "CHAIN"
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LLM = "LLM"
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RETRIEVER = "RETRIEVER"
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EMBEDDING = "EMBEDDING"
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AGENT = "AGENT"
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RERANKER = "RERANKER"
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UNKNOWN = "UNKNOWN"
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GUARDRAIL = "GUARDRAIL"
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EVALUATOR = "EVALUATOR"
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class OpenInferenceMimeTypeValues(Enum):
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TEXT = "text/plain"
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JSON = "application/json"
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