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lg-agent-r
...
brandon/st
| Author | SHA1 | Date | |
|---|---|---|---|
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fc2bcc292f | ||
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ea4feb7b2e |
@@ -12,7 +12,7 @@ dependencies = [
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"pydantic>=2.4.2",
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"openai>=1.13.3",
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"litellm==1.59.8",
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"instructor>=1.3.3",
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"instructor>=1.7.2",
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# Text Processing
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"pdfplumber>=0.11.4",
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"regex>=2024.9.11",
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@@ -7,7 +7,10 @@ import warnings
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from contextlib import contextmanager
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from typing import Any, Dict, List, Optional, Union, cast
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import instructor
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from dotenv import load_dotenv
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from openai.types.chat import ChatCompletionMessageParam
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from pydantic import BaseModel
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", UserWarning)
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@@ -182,34 +185,63 @@ class LLM:
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available_functions: Optional[Dict[str, Any]] = None,
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) -> str:
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"""
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High-level llm call method that:
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1) Accepts either a string or a list of messages
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2) Converts string input to the required message format
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3) Calls litellm.completion
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4) Handles function/tool calls if any
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5) Returns the final text response or tool result
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High-level LLM call method that handles:
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1. Multiple input formats (string or message list)
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2. Structured responses via Instructor integration
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3. Tool/function calling with optional structured output
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4. Callback integration
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Parameters:
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- messages (Union[str, List[Dict[str, str]]]): The input messages for the LLM.
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- If a string is provided, it will be converted into a message list with a single entry.
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- If a list of dictionaries is provided, each dictionary should have 'role' and 'content' keys.
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- tools (Optional[List[dict]]): A list of tool schemas for function calling.
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- callbacks (Optional[List[Any]]): A list of callback functions to be executed.
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- available_functions (Optional[Dict[str, Any]]): A dictionary mapping function names to actual Python functions.
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messages: Input prompt(s) as either:
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- String (converted to single user message)
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- List of message dicts with 'role' and 'content'
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tools: List of tool schemas for function calling
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callbacks: List of callback handlers
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available_functions: Mapping of function names to callables
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response_format: Pydantic model for structured responses
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Returns:
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- str: The final text response from the LLM or the result of a tool function call.
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str: Can be:
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- Plain text response
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- Structured response (if response_format provided)
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- Tool function result (raw or structured)
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Behavior:
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- With response_format and no tools: Direct structured response
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- With tools: Initial LLM call → Tool execution → Optional secondary structured call
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- Without tools/response_format: Standard text completion
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Examples:
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---------
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# Example 1: Using a string input
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response = llm.call("Return the name of a random city in the world.")
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print(response)
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# Basic text completion
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llm.call("Hello world")
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# Example 2: Using a list of messages
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messages = [{"role": "user", "content": "What is the capital of France?"}]
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response = llm.call(messages)
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print(response)
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# Structured response without tools
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class City(BaseModel):
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name: str
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population: int
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response = llm.call(
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"Name a major US city",
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response_format=City
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)
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print(response.name) # Structured access
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# Tool usage with raw output
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llm.call(
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"What's 5 squared?",
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tools=[math_tools],
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available_functions={"square": square_number}
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)
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# Tool usage with structured output
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response = llm.call(
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"Analyze this data",
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tools=[data_tools],
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available_functions={"analyze": analyze_data},
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response_format=AnalysisResult
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)
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print(response.metrics) # Structured access
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"""
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if isinstance(messages, str):
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messages = [{"role": "user", "content": messages}]
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@@ -218,37 +250,54 @@ class LLM:
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if callbacks and len(callbacks) > 0:
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self.set_callbacks(callbacks)
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# Prepare the parameters for the completion call.
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params = {
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"model": self.model,
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"messages": messages,
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"timeout": self.timeout,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"n": self.n,
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"stop": self.stop,
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"max_tokens": self.max_tokens or self.max_completion_tokens,
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"presence_penalty": self.presence_penalty,
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"frequency_penalty": self.frequency_penalty,
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"logit_bias": self.logit_bias,
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"seed": self.seed,
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"logprobs": self.logprobs,
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"top_logprobs": self.top_logprobs,
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"api_base": self.api_base,
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"base_url": self.base_url,
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"api_version": self.api_version,
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"api_key": self.api_key,
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"stream": False,
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"tools": tools,
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**self.additional_params,
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}
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# Remove any keys with None values.
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params = {k: v for k, v in params.items() if v is not None}
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# --- Direct structured response if no tools are provided.
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if self.response_format is not None and (tools is None or len(tools) == 0):
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print("Direct structured response")
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try:
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# Cast messages to required type and remove model param
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params["messages"] = cast(
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List[ChatCompletionMessageParam], messages
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)
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params.pop("model", None)
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client = instructor.from_litellm(litellm.completion)
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response = client.chat.completions.create(**params)
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return response
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except Exception as e:
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logging.error(f"LiteLLM call failed: {str(e)}")
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raise
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# --- Standard flow with potential tool calls.
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try:
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# --- 1) Prepare the parameters for the completion call
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params = {
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"model": self.model,
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"messages": messages,
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"timeout": self.timeout,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"n": self.n,
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"stop": self.stop,
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"max_tokens": self.max_tokens or self.max_completion_tokens,
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"presence_penalty": self.presence_penalty,
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"frequency_penalty": self.frequency_penalty,
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"logit_bias": self.logit_bias,
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"response_format": self.response_format,
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"seed": self.seed,
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"logprobs": self.logprobs,
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"top_logprobs": self.top_logprobs,
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"api_base": self.api_base,
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"base_url": self.base_url,
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"api_version": self.api_version,
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"api_key": self.api_key,
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"stream": False,
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"tools": tools,
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**self.additional_params,
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}
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# Remove None values from params
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params = {k: v for k, v in params.items() if v is not None}
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# --- 2) Make the completion call
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print("NOT DIRECT STRUCTURED RESPONSE")
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response = litellm.completion(**params)
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response_message = cast(Choices, cast(ModelResponse, response).choices)[
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0
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@@ -256,7 +305,6 @@ class LLM:
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text_response = response_message.content or ""
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tool_calls = getattr(response_message, "tool_calls", [])
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# --- 3) Handle callbacks with usage info
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if callbacks and len(callbacks) > 0:
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for callback in callbacks:
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if hasattr(callback, "log_success_event"):
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@@ -269,11 +317,11 @@ class LLM:
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end_time=0,
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)
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# --- 4) If no tool calls, return the text response
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# If no tool call is requested or available_functions is not provided, return the text response.
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if not tool_calls or not available_functions:
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return text_response
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# --- 5) Handle the tool call
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# --- Handle tool calls.
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tool_call = tool_calls[0]
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function_name = tool_call.function.name
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@@ -286,22 +334,40 @@ class LLM:
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fn = available_functions[function_name]
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try:
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# Call the actual tool function
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result = fn(**function_args)
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return result
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except Exception as e:
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logging.error(
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f"Error executing function '{function_name}': {e}"
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)
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return text_response
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# If a structured response is requested, perform a secondary call using the tool result.
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if self.response_format is not None:
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new_params = dict(params)
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# Cast tool result message to required type
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new_params["messages"] = cast(
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List[ChatCompletionMessageParam],
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[{"role": "user", "content": result}],
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)
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new_params.pop("model", None)
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if "tools" in new_params:
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del new_params["tools"]
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try:
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client = instructor.from_litellm(litellm.completion)
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final_response = client.chat.completions.create(
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**new_params, response_model=response_format
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)
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||||
return final_response
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||||
except Exception as e:
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logging.error(f"LiteLLM structured call failed: {e}")
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return result
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else:
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||||
return result
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||||
else:
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logging.warning(
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f"Tool call requested unknown function '{function_name}'"
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||||
)
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return text_response
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except Exception as e:
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if not LLMContextLengthExceededException(
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str(e)
|
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|
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85
uv.lock
generated
85
uv.lock
generated
@@ -736,7 +736,7 @@ requires-dist = [
|
||||
{ name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.32.1" },
|
||||
{ name = "docling", marker = "extra == 'docling'", specifier = ">=2.12.0" },
|
||||
{ name = "fastembed", marker = "extra == 'fastembed'", specifier = ">=0.4.1" },
|
||||
{ name = "instructor", specifier = ">=1.3.3" },
|
||||
{ name = "instructor", specifier = ">=1.7.2" },
|
||||
{ name = "json-repair", specifier = ">=0.25.2" },
|
||||
{ name = "json5", specifier = ">=0.10.0" },
|
||||
{ name = "jsonref", specifier = ">=1.1.0" },
|
||||
@@ -1961,7 +1961,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "instructor"
|
||||
version = "1.6.3"
|
||||
version = "1.7.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "aiohttp" },
|
||||
@@ -1971,13 +1971,14 @@ dependencies = [
|
||||
{ name = "openai" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "pydantic-core" },
|
||||
{ name = "requests" },
|
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{ name = "rich" },
|
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{ name = "tenacity" },
|
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{ name = "typer" },
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]
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[[package]]
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@@ -2028,46 +2029,46 @@ wheels = [
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[[package]]
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{ url = "https://files.pythonhosted.org/packages/f9/e2/253fc1fa59103bb4e3aa0665d6ceb1818df1cd7bf3eb492c4dad229b1cd4/jiter-0.8.2-cp312-cp312-win32.whl", hash = "sha256:7efe4853ecd3d6110301665a5178b9856be7e2a9485f49d91aa4d737ad2ae49e", size = 203375 },
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{ url = "https://files.pythonhosted.org/packages/41/69/6d4bbe66b3b3b4507e47aa1dd5d075919ad242b4b1115b3f80eecd443687/jiter-0.8.2-cp312-cp312-win_amd64.whl", hash = "sha256:83c0efd80b29695058d0fd2fa8a556490dbce9804eac3e281f373bbc99045f6c", size = 204740 },
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]
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||||
|
||||
[[package]]
|
||||
|
||||
Reference in New Issue
Block a user