411 lines
15 KiB
Python
411 lines
15 KiB
Python
import logging
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import uuid
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from typing import TYPE_CHECKING, Dict, List, Optional, Union, cast
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from llama_index.core.base.llms.types import ChatMessage, MessageRole
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from llama_index.core.base.response.schema import Response, StreamingResponse
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from llama_index.core.instrumentation.event_handlers import BaseEventHandler
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from llama_index.core.instrumentation.events import BaseEvent
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from llama_index.core.instrumentation.events.agent import (
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AgentChatWithStepEndEvent,
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AgentChatWithStepStartEvent,
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AgentRunStepEndEvent,
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AgentRunStepStartEvent,
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)
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from llama_index.core.instrumentation.events.embedding import (
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EmbeddingEndEvent,
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EmbeddingStartEvent,
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)
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from llama_index.core.instrumentation.events.llm import (
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LLMChatEndEvent,
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LLMChatStartEvent,
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)
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from llama_index.core.instrumentation.events.query import QueryEndEvent, QueryStartEvent
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from llama_index.core.instrumentation.events.retrieval import (
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RetrievalEndEvent,
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RetrievalStartEvent,
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)
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from llama_index.core.instrumentation.events.synthesis import SynthesizeEndEvent
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from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode
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from openai.types import CompletionUsage
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from openai.types.chat import ChatCompletion, ChatCompletionChunk
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from pydantic import PrivateAttr
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from literalai.context import active_thread_var
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from literalai.instrumentation.llamaindex.span_handler import LiteralSpanHandler
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from literalai.observability.generation import (
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ChatGeneration,
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GenerationMessage,
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GenerationMessageRole,
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)
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from literalai.observability.step import Step, StepType
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if TYPE_CHECKING:
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from literalai.client import LiteralClient
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def convert_message_role(role: MessageRole) -> GenerationMessageRole:
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mapping = {
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MessageRole.SYSTEM: "system",
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MessageRole.USER: "user",
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MessageRole.ASSISTANT: "assistant",
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MessageRole.FUNCTION: "function",
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MessageRole.TOOL: "tool",
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MessageRole.CHATBOT: "assistant",
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MessageRole.MODEL: "assistant",
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}
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return cast(GenerationMessageRole, mapping.get(role, "user"))
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def extract_query_from_bundle(str_or_query_bundle: Union[str, QueryBundle]):
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if isinstance(str_or_query_bundle, QueryBundle):
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return str_or_query_bundle.query_str
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return str_or_query_bundle
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def extract_document_info(nodes: List[NodeWithScore]):
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if not nodes:
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return []
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return [
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{
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"id_": node.id_,
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"metadata": node.metadata,
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"text": node.get_text(),
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"mimetype": node.node.mimetype,
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"start_char_idx": node.node.start_char_idx,
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"end_char_idx": node.node.end_char_idx,
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"char_length": (
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node.node.end_char_idx - node.node.start_char_idx
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if node.node.end_char_idx is not None
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and node.node.start_char_idx is not None
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else None
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),
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"score": node.get_score(),
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}
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for node in nodes
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if isinstance(node.node, TextNode)
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]
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def build_message_dict(message: ChatMessage):
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message_dict: GenerationMessage = {
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"role": convert_message_role(message.role),
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"content": message.content,
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}
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kwargs = message.additional_kwargs
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if kwargs:
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if kwargs.get("tool_call_id", None):
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message_dict["tool_call_id"] = kwargs.get("tool_call_id")
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if kwargs.get("name", None):
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message_dict["name"] = kwargs.get("name")
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tool_calls = kwargs.get("tool_calls", [])
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if len(tool_calls) > 0:
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message_dict["tool_calls"] = [
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tool_call.to_dict() for tool_call in tool_calls
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]
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return message_dict
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def create_generation(event: LLMChatStartEvent):
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model_dict = event.model_dict
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return ChatGeneration(
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provider=model_dict.get("class_name"),
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model=model_dict.get("model"),
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settings={
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"model": model_dict.get("model"),
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"temperature": model_dict.get("temperature"),
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"max_tokens": model_dict.get("max_tokens"),
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"logprobs": model_dict.get("logprobs"),
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"top_logprobs": model_dict.get("top_logprobs"),
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},
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messages=[build_message_dict(message) for message in event.messages],
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)
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def extract_query(x: Union[str, QueryBundle]):
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return x.query_str if isinstance(x, QueryBundle) else x
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class LiteralEventHandler(BaseEventHandler):
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"""This class handles events coming from LlamaIndex."""
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_client: "LiteralClient" = PrivateAttr()
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_span_handler: "LiteralSpanHandler" = PrivateAttr()
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runs: Dict[str, List[Step]] = {}
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streaming_run_ids: List[str] = []
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_standalone_step_id: Optional[str] = None
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open_runs: List[Step] = []
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class Config:
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arbitrary_types_allowed = True
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def __init__(
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self,
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literal_client: "LiteralClient",
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llama_index_span_handler: "LiteralSpanHandler",
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):
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super().__init__()
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object.__setattr__(self, "_client", literal_client)
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object.__setattr__(self, "_span_handler", llama_index_span_handler)
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def _convert_message(self, message: ChatMessage):
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tool_calls = message.additional_kwargs.get("tool_calls")
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msg: GenerationMessage = {
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"name": getattr(message, "name", None),
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"role": convert_message_role(message.role),
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"content": message.content,
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"tool_calls": (
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[tool_call.to_dict() for tool_call in tool_calls]
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if tool_calls
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else None
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),
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}
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return msg
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def handle(self, event: BaseEvent, **kwargs) -> None:
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"""Logic for handling event."""
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try:
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thread_id = self._span_handler.get_thread_id(event.span_id)
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run_id = self._span_handler.get_run_id(event.span_id)
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# AgentChatWithStep wraps several AgentRunStep events
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# as the agent may want to perform multiple tool calls in a row.
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if isinstance(event, AgentChatWithStepStartEvent) or isinstance(
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event, AgentRunStepStartEvent
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):
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run_name = (
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"Agent Chat"
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if isinstance(event, AgentChatWithStepStartEvent)
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else "Agent Step"
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)
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parent_run_id = None
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if len(self.open_runs) > 0:
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parent_run_id = self.open_runs[-1].id
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agent_run_id = str(uuid.uuid4())
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run = self._client.start_step(
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name=run_name,
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type="run",
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id=agent_run_id,
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parent_id=parent_run_id,
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)
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self.open_runs.append(run)
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if isinstance(event, AgentChatWithStepEndEvent) or isinstance(
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event, AgentRunStepEndEvent
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):
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try:
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step = self.open_runs.pop()
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except IndexError:
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logging.error(
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"[Literal] Error in Llamaindex instrumentation: AgentRunStepEndEvent called without an open run."
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)
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if step:
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step.end()
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if isinstance(event, QueryStartEvent):
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active_thread = active_thread_var.get()
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query = extract_query(event.query)
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if not active_thread or not active_thread.name:
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self._client.api.upsert_thread(id=thread_id, name=query)
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self._client.message(
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name="User query",
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id=str(event.id_),
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type="user_message",
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thread_id=thread_id,
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content=query,
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)
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# Retrieval wraps the Embedding step in LlamaIndex
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if isinstance(event, RetrievalStartEvent):
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run = self._client.start_step(
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name="RAG",
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type="run",
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id=run_id,
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thread_id=thread_id,
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)
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self.store_step(run_id=run_id, step=run)
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retrieval_step = self._client.start_step(
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type="retrieval",
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name="Retrieval",
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parent_id=run_id,
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thread_id=thread_id,
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)
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self.store_step(run_id=run_id, step=retrieval_step)
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if isinstance(event, EmbeddingStartEvent):
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retrieval_step = self.get_first_step_of_type(
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run_id=run_id, step_type="retrieval"
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)
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if run_id and retrieval_step:
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embedding_step = self._client.start_step(
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type="embedding",
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name="Embedding",
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parent_id=retrieval_step.id,
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thread_id=thread_id,
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)
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embedding_step.metadata = event.model_dict
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self.store_step(run_id=run_id, step=embedding_step)
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if isinstance(event, EmbeddingEndEvent):
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embedding_step = self.get_first_step_of_type(
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run_id=run_id, step_type="embedding"
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)
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if run_id and embedding_step:
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embedding_step.input = {"query": event.chunks}
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embedding_step.output = {"embeddings": event.embeddings}
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embedding_step.end()
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if isinstance(event, RetrievalEndEvent):
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retrieval_step = self.get_first_step_of_type(
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run_id=run_id, step_type="retrieval"
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)
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if run_id and retrieval_step:
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retrieved_documents = extract_document_info(event.nodes)
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query = extract_query(event.str_or_query_bundle)
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retrieval_step.input = {"query": query}
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retrieval_step.output = {"retrieved_documents": retrieved_documents}
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retrieval_step.end()
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# Only event where we create LLM steps
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if isinstance(event, LLMChatStartEvent):
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if run_id:
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self._client.step()
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llm_step = self._client.start_step(
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type="llm",
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parent_id=run_id,
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thread_id=thread_id,
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)
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self.store_step(run_id=run_id, step=llm_step)
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llm_step = self.get_first_step_of_type(run_id=run_id, step_type="llm")
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if not run_id and not llm_step:
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self._standalone_step_id = str(uuid.uuid4())
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llm_step = self._client.start_step(
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name=event.model_dict.get("model", "LLM"),
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type="llm",
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id=self._standalone_step_id,
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# Remove thread_id for standalone runs
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)
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self.store_step(run_id=self._standalone_step_id, step=llm_step)
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if llm_step:
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generation = create_generation(event=event)
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llm_step.generation = generation
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llm_step.name = event.model_dict.get("model")
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# Actual creation of the event happens upon ending the event
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if isinstance(event, LLMChatEndEvent):
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llm_step = self.get_first_step_of_type(run_id=run_id, step_type="llm")
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if not llm_step and self._standalone_step_id:
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llm_step = self.get_first_step_of_type(
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run_id=self._standalone_step_id, step_type="llm"
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)
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response = event.response
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if llm_step and response:
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chat_completion = response.raw
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# ChatCompletionChunk needed for chat stream methods
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if isinstance(chat_completion, ChatCompletion) or isinstance(
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chat_completion, ChatCompletionChunk
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):
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usage = chat_completion.usage
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if isinstance(usage, CompletionUsage):
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llm_step.generation.input_token_count = usage.prompt_tokens
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llm_step.generation.output_token_count = (
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usage.completion_tokens
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)
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if self._standalone_step_id:
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llm_step.generation.message_completion = (
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self._convert_message(response.message)
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)
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llm_step.end()
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self._standalone_step_id = None
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if isinstance(event, SynthesizeEndEvent):
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llm_step = self.get_first_step_of_type(run_id=run_id, step_type="llm")
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run = self.get_first_step_of_type(run_id=run_id, step_type="run")
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if llm_step and run:
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synthesized_response = event.response
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text_response = ""
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if isinstance(synthesized_response, StreamingResponse):
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text_response = str(synthesized_response.get_response())
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if isinstance(synthesized_response, Response):
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text_response = str(synthesized_response)
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llm_step.generation.message_completion = {
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"role": "assistant",
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"content": text_response,
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}
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llm_step.end()
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run.end()
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self._client.message(
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type="assistant_message",
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thread_id=thread_id,
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content=text_response,
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)
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if isinstance(event, QueryEndEvent):
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if run_id in self.runs:
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del self.runs[run_id]
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except Exception as e:
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logging.error(
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"[Literal] Error in Llamaindex instrumentation : %s",
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str(e),
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exc_info=True,
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)
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def store_step(self, run_id: str, step: Step):
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if run_id not in self.runs:
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self.runs[run_id] = []
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self.runs[run_id].append(step)
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def get_first_step_of_type(
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self, run_id: Optional[str], step_type: StepType
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) -> Optional[Step]:
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if not run_id:
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return None
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if run_id not in self.runs:
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return None
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for step in self.runs[run_id]:
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if step.type == step_type:
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return step
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return None
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@classmethod
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def class_name(cls) -> str:
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"""Class name."""
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return "LiteralEventHandler"
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