From 30cd9feb01d63fc0c67356d1bf320a4a908f1546 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?C=C3=B4me=20Rodriguez?= Date: Thu, 24 Apr 2025 08:13:08 +0200 Subject: [PATCH 1/4] Add vllm library to synda --- pyproject.toml | 3 +- tests/stubs/vllm_rag/config.yaml | 60 ++++++++++++++++++++++++++++++++ tests/stubs/vllm_rag/source.csv | 4 +++ 3 files changed, 66 insertions(+), 1 deletion(-) create mode 100644 tests/stubs/vllm_rag/config.yaml create mode 100644 tests/stubs/vllm_rag/source.csv diff --git a/pyproject.toml b/pyproject.toml index 4796da6..325282e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,7 +11,7 @@ documentation = "https://github.com/timothepearce/synda" packages = [{include = "synda"}] [tool.poetry.dependencies] -python = "^3.10" +python = ">=3.10, <3.13" pyyaml = "^6.0.2" pydantic = "^2.10.6" pandas = "^2.2.3" @@ -22,6 +22,7 @@ sqlmodel = "^0.0.22" typer = {extras = ["all"], version = "^0.15.1"} scikit-learn = "^1.6.1" openpyxl = "^3.1.5" +vllm = "^0.8.4" pypdf2 = "^3.0.1" [tool.poetry.group.dev.dependencies] diff --git a/tests/stubs/vllm_rag/config.yaml b/tests/stubs/vllm_rag/config.yaml new file mode 100644 index 0000000..cec4339 --- /dev/null +++ b/tests/stubs/vllm_rag/config.yaml @@ -0,0 +1,60 @@ +input: + type: csv + properties: + path: tests/stubs/vllm_rag/source.csv + target_column: content + separator: "\t" + +pipeline: + - type: split + method: chunk + name: chunk_faq + parameters: + size: 1500 + + - type: split + method: separator + name: sentence_chunk_faq + parameters: + separator: . + keep_separator: true + + - type: generation + method: llm + parameters: + provider: vllm + model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 + occurrences: 1 + template: | + Ask a question regarding the sentence about the content. + content: {chunk_faq} + sentence: {sentence_chunk_faq} + + Instructions : + 1. Use english only + 2. Keep it short + + question: + + - type: clean + method: deduplicate-tf-idf + parameters: + strategy: fuzzy + similarity_threshold: 0.6 + keep: first + + - type: ablation + method: llm-judge-binary + parameters: + provider: vllm + model: llama3.2 + consensus: all + criteria: + - Is the question written in english? + - Is the question consistent? + +output: + type: csv + properties: + path: tests/stubs/vllm_rag/output.csv + separator: "\t" diff --git a/tests/stubs/vllm_rag/source.csv b/tests/stubs/vllm_rag/source.csv new file mode 100644 index 0000000..848e7eb --- /dev/null +++ b/tests/stubs/vllm_rag/source.csv @@ -0,0 +1,4 @@ +content +A nebula (Latin for 'cloud, fog';[1] pl.: nebulae, nebulæ, or nebulas[2][3][4][5]) is a distinct luminescent part of interstellar medium, which can consist of ionized, neutral, or molecular hydrogen and also cosmic dust. Nebulae are often star-forming regions, such as in the Pillars of Creation in the Eagle Nebula. In these regions, the formations of gas, dust, and other materials "clump" together to form denser regions, which attract further matter and eventually become dense enough to form stars. The remaining material is then thought to form planets and other planetary system objects. +Most nebulae are of vast size; some are hundreds of light-years in diameter. A nebula that is visible to the human eye from Earth would appear larger, but no brighter, from close by.[6] The Orion Nebula, the brightest nebula in the sky and occupying an area twice the angular diameter of the full Moon, can be viewed with the naked eye but was missed by early astronomers.[7] Although denser than the space surrounding them, most nebulae are far less dense than any vacuum created on Earth (105 to 107 molecules per cubic centimeter) – a nebular cloud the size of the Earth would have a total mass of only a few kilograms. Earth's air has a density of approximately 1019 molecules per cubic centimeter; by contrast, the densest nebulae can have densities of 104 molecules per cubic centimeter. Many nebulae are visible due to fluorescence caused by embedded hot stars, while others are so diffused that they can be detected only with long exposures and special filters. Some nebulae are variably illuminated by T Tauri variable stars. +Originally, the term "nebula" was used to describe any diffused astronomical object, including galaxies beyond the Milky Way. The Andromeda Galaxy, for instance, was once referred to as the Andromeda Nebula (and spiral galaxies in general as "spiral nebulae") before the true nature of galaxies was confirmed in the early 20th century by Vesto Slipher, Edwin Hubble, and others. Edwin Hubble discovered that most nebulae are associated with stars and illuminated by starlight. He also helped categorize nebulae based on the type of light spectra they produced.[8] \ No newline at end of file From 6c1c056b2a5d2540b3652c8c14e92fc6ef04d3e7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?C=C3=B4me=20Rodriguez?= Date: Fri, 25 Apr 2025 00:19:50 +0200 Subject: [PATCH 2/4] WIP Add vllm library to synda --- synda/model/step.py | 6 +- synda/progress_manager.py | 9 ++- synda/utils/batch_completion.py | 94 ++++++++++++++++++++++++++++++++ tests/stubs/vllm_rag/config.yaml | 3 +- 4 files changed, 106 insertions(+), 6 deletions(-) create mode 100644 synda/utils/batch_completion.py diff --git a/synda/model/step.py b/synda/model/step.py index a7bd45e..19b3a7b 100644 --- a/synda/model/step.py +++ b/synda/model/step.py @@ -110,10 +110,10 @@ def save_at_execution_end( return self def save_during_execution( - self, session: Session, input_node: Node, output_node: Node + self, session: Session, input_nodes: list[Node], output_nodes: list[Node] ) -> "Step": - self._create_nodes_with_ancestors(session, [input_node], [output_node]) - self._map_output_nodes_to_step(session, [output_node]) + self._create_nodes_with_ancestors(session, input_nodes, output_nodes) + self._map_output_nodes_to_step(session, output_nodes) return self diff --git a/synda/progress_manager.py b/synda/progress_manager.py index 6aa1d09..79e5742 100644 --- a/synda/progress_manager.py +++ b/synda/progress_manager.py @@ -35,10 +35,15 @@ def __init__(self, executor_type: str): @contextmanager def task( - self, description: str, total: int, completed: int = 0, transient: bool = False + self, description: str, total: int, completed: int = 0, batch_size: int = 1, transient: bool = False ): with self.progress as progress: task_id = progress.add_task( description, total=total, transient=transient, completed=completed ) - yield lambda: progress.advance(task_id) + def safe_advance(): + remaining = total - progress.tasks[task_id].completed + progress.advance(task_id, min(batch_size, int(remaining))) + + yield safe_advance + # yield lambda: progress.advance(task_id, batch_size) diff --git a/synda/utils/batch_completion.py b/synda/utils/batch_completion.py new file mode 100644 index 0000000..dc90c70 --- /dev/null +++ b/synda/utils/batch_completion.py @@ -0,0 +1,94 @@ +""" +This file overwrite the litellm batch_completion function for vllm provider +This overwriting is due to an error coming from "stream" args set to False by default +and not supported by vllm +""" +from typing import List, Optional + +import litellm +from litellm.utils import get_optional_params +from litellm.llms.vllm.completion import handler as vllm_handler + +def vllm_batch_completion( + model: str, + # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create + messages: Optional[List] = None, + functions: Optional[List] = None, + function_call: Optional[str] = None, + temperature: Optional[float] = None, + top_p: Optional[float] = None, + n: Optional[int] = None, + stream: Optional[bool] = None, + stop=None, + max_tokens: Optional[int] = None, + presence_penalty: Optional[float] = None, + frequency_penalty: Optional[float] = None, + logit_bias: Optional[dict] = None, + user: Optional[str] = None, + deployment_id=None, + request_timeout: Optional[int] = None, + timeout: Optional[int] = 600, + max_workers: Optional[int] = 100, + # Optional liteLLM function params + **kwargs, +): + """ + Batch litellm.completion function for a given model. + + Args: + model (str): The model to use for generating completions. + messages (List, optional): List of messages to use as input for generating completions. Defaults to []. + functions (List, optional): List of functions to use as input for generating completions. Defaults to []. + function_call (str, optional): The function call to use as input for generating completions. Defaults to "". + temperature (float, optional): The temperature parameter for generating completions. Defaults to None. + top_p (float, optional): The top-p parameter for generating completions. Defaults to None. + n (int, optional): The number of completions to generate. Defaults to None. + stream (bool, optional): Whether to stream completions or not. Defaults to None. + stop (optional): The stop parameter for generating completions. Defaults to None. + max_tokens (float, optional): The maximum number of tokens to generate. Defaults to None. + presence_penalty (float, optional): The presence penalty for generating completions. Defaults to None. + frequency_penalty (float, optional): The frequency penalty for generating completions. Defaults to None. + logit_bias (dict, optional): The logit bias for generating completions. Defaults to {}. + user (str, optional): The user string for generating completions. Defaults to "". + deployment_id (optional): The deployment ID for generating completions. Defaults to None. + request_timeout (int, optional): The request timeout for generating completions. Defaults to None. + max_workers (int,optional): The maximum number of threads to use for parallel processing. + + Returns: + list: A list of completion results. + """ + args = locals() + if not messages: + messages = [] + batch_messages = messages + completions = [] + model = model + custom_llm_provider = None + if model.split("/", 1)[0] in litellm.provider_list: + custom_llm_provider = model.split("/", 1)[0] + model = model.split("/", 1)[1] + + optional_params = get_optional_params( + functions=functions, + function_call=function_call, + temperature=temperature, + top_p=top_p, + n=n, + stream=stream, + stop=stop, + max_tokens=max_tokens, + presence_penalty=presence_penalty, + frequency_penalty=frequency_penalty, + logit_bias=logit_bias, + user=user, + # params to identify the model + model=model, + custom_llm_provider=custom_llm_provider, + ) + results = vllm_handler.batch_completions( + model=model, + messages=batch_messages, + custom_prompt_dict=litellm.custom_prompt_dict, + optional_params=optional_params, + ) + return results diff --git a/tests/stubs/vllm_rag/config.yaml b/tests/stubs/vllm_rag/config.yaml index cec4339..93c70a6 100644 --- a/tests/stubs/vllm_rag/config.yaml +++ b/tests/stubs/vllm_rag/config.yaml @@ -25,6 +25,7 @@ pipeline: provider: vllm model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 occurrences: 1 + batch_size: 2 template: | Ask a question regarding the sentence about the content. content: {chunk_faq} @@ -46,7 +47,7 @@ pipeline: - type: ablation method: llm-judge-binary parameters: - provider: vllm + provider: ollama model: llama3.2 consensus: all criteria: From 3ab8542804e80a1821f424e65bec95370065264d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?C=C3=B4me=20Rodriguez?= Date: Sat, 3 May 2025 02:50:55 +0200 Subject: [PATCH 3/4] WIP add vLLM inference for GENERATION step --- poetry.lock | 5186 ++++++++++++++----- pyproject.toml | 2 +- synda/pipeline/ablation/llm_judge_binary.py | 10 +- synda/pipeline/generation/llm.py | 91 +- synda/utils/vllm_provider.py | 60 + tests/stubs/vllm_rag/config.yaml | 5 +- 6 files changed, 4077 insertions(+), 1277 deletions(-) create mode 100644 synda/utils/vllm_provider.py diff --git a/poetry.lock b/poetry.lock index b3ef391..03135aa 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,106 +1,104 @@ -# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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= ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] type = ["pytest-mypy"] [metadata] -lock-version = "2.1" -python-versions = "^3.10" -content-hash = "b9de1ed6b6abefca4a3401dc8d69ebebdd44d439a5a605d566b229591c35075d" +lock-version = "2.0" +python-versions = ">=3.10, <3.13" +content-hash = "f076c9f47e664276e2b6c730d93b9325a2d3b3e5a2a30c5fbc056726af59f5bf" diff --git a/pyproject.toml b/pyproject.toml index 325282e..52539c7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,7 @@ sqlmodel = "^0.0.22" typer = {extras = ["all"], version = "^0.15.1"} scikit-learn = "^1.6.1" openpyxl = "^3.1.5" -vllm = "^0.8.4" +vllm = {version = "0.8.4", extras = ["all"]} pypdf2 = "^3.0.1" [tool.poetry.group.dev.dependencies] diff --git a/synda/pipeline/ablation/llm_judge_binary.py b/synda/pipeline/ablation/llm_judge_binary.py index 3ae7ffc..c99db06 100644 --- a/synda/pipeline/ablation/llm_judge_binary.py +++ b/synda/pipeline/ablation/llm_judge_binary.py @@ -1,6 +1,6 @@ import json import asyncio -from typing import Literal, Optional +from typing import Literal from pydantic import BaseModel from sqlmodel import Session @@ -63,11 +63,11 @@ def _execute_sequential(self, pending_nodes: list[Node], processed_nodes: list[N def _execute_batch(self, pending_nodes: list[Node], processed_nodes: list[Node], criteria: list[str]) -> list[Node]: result = processed_nodes or [] - num_batches = (len(pending_nodes) + self.batch_size - 1) // self.batch_size with self.progress.task( " Ablating...", - num_batches, - completed=0, + len(pending_nodes) + len(processed_nodes), + completed=len(processed_nodes), + batch_size=self.batch_size ) as advance_node: loop = asyncio.get_event_loop() for i in range(0, len(pending_nodes), self.batch_size): @@ -136,7 +136,7 @@ def _create_result_node(self, node: Node, judge_answers: list[LLMJudgeCriterionB result_node = Node( parent_node_id=node.id, value=node.value, ablated=ablated ) - self.step_model.save_during_execution(self.session, node, result_node) + self.step_model.save_during_execution(self.session, [node], [result_node]) return result_node def _parse_judge_answer(self, judge_answer_str: str) -> LLMJudgeCriterionBinaryAnswer: diff --git a/synda/pipeline/generation/llm.py b/synda/pipeline/generation/llm.py index e483dc0..4753b03 100644 --- a/synda/pipeline/generation/llm.py +++ b/synda/pipeline/generation/llm.py @@ -1,4 +1,5 @@ import asyncio +import requests from sqlmodel import Session from synda.model.provider import Provider @@ -7,6 +8,7 @@ from synda.pipeline.executor import Executor from synda.model.node import Node from synda.utils.llm_provider import LLMProvider +from synda.utils.vllm_provider import VLLMProvider from synda.utils.prompt_builder import PromptBuilder from synda.progress_manager import ProgressManager @@ -27,15 +29,30 @@ def execute(self, pending_nodes: list[Node], processed_nodes: list[Node]): instruction_mode = self.config.parameters.instruction_mode pending_nodes = self._build_node_occurrences(pending_nodes, occurrences) - result = processed_nodes or [] - if not self.batch or self.batch_size is None: - result = self._execute_sequential(pending_nodes, processed_nodes, template, instruction_sets, instruction_mode) + result = self._execute_sequential( + pending_nodes, processed_nodes, template, instruction_sets, instruction_mode + ) else: - result = self._execute_batch(pending_nodes, processed_nodes, template, instruction_sets, instruction_mode) - + result = self._execute_batch( + pending_nodes=pending_nodes, + processed_nodes=processed_nodes, + template=template, + instruction_sets=instruction_sets, + instruction_mode=instruction_mode, + use_vllm="vllm" in self.provider.name + ) + return result - def _execute_sequential(self, pending_nodes: list[Node], processed_nodes: list[Node], template: str, instruction_sets: list, instruction_mode: str) -> list[Node]: + + def _execute_sequential( + self, + pending_nodes: list[Node], + processed_nodes: list[Node], + template: str, + instruction_sets: list, + instruction_mode: str + ) -> list[Node]: result = processed_nodes or [] with self.progress.task( @@ -53,7 +70,15 @@ def _execute_sequential(self, pending_nodes: list[Node], processed_nodes: list[N return result - def _execute_batch(self, pending_nodes: list[Node], processed_nodes: list[Node], template: str, instruction_sets: list, instruction_mode: str) -> list[Node]: + def _execute_batch( + self, + pending_nodes: list[Node], + processed_nodes: list[Node], + template: str, + instruction_sets: list, + instruction_mode: str, + use_vllm: bool + ) -> list[Node]: result = processed_nodes or [] all_prompts = [] @@ -63,25 +88,27 @@ def _execute_batch(self, pending_nodes: list[Node], processed_nodes: list[Node], for prompt in prompts: all_prompts.append(prompt) node_map[prompt] = node - - num_batches = (len(all_prompts) + self.batch_size - 1) // self.batch_size with self.progress.task( "Generating...", - num_batches, - completed=0, + len(pending_nodes) + len(processed_nodes), + completed=len(processed_nodes), + batch_size=self.batch_size ) as advance: for i in range(0, len(all_prompts), self.batch_size): batch_prompts = all_prompts[i:i + self.batch_size] - loop = asyncio.get_event_loop() - tasks = [ - loop.run_in_executor( - None, - self._call_llm, - prompt - ) - for prompt in batch_prompts - ] - responses = loop.run_until_complete(asyncio.gather(*tasks)) + if use_vllm: + responses = self._call_vllm(prompts=batch_prompts) + else: + loop = asyncio.get_event_loop() + tasks = [ + loop.run_in_executor( + None, + self._call_llm, + prompt + ) + for prompt in batch_prompts + ] + responses = loop.run_until_complete(asyncio.gather(*tasks)) for prompt, response in zip(batch_prompts, responses): node = node_map[prompt] result_node = self._create_result_node(node, response, prompt) @@ -89,6 +116,26 @@ def _execute_batch(self, pending_nodes: list[Node], processed_nodes: list[Node], advance() return result + + def _call_vllm(self, prompts: list[str]): + return VLLMProvider.call( + provider=self.provider.name, + model=self.model, + api_url=self.provider.api_url, + prompts=prompts, + temperature=self.config.parameters.temperature, + ) + + def _hosted_vllm_batch_inference(self, prompts): + llm_answers = requests.post( + self.provider.api_url, + json={ + "model": self.model, + "prompt": prompts, + "temperature": self.config.parameters.temperature, + } + ).json() + return [answer["text"] for answer in llm_answers["choices"]] def _build_prompts(self, node: Node, template: str, instruction_sets: list, instruction_mode: str) -> list[str]: return PromptBuilder.build( @@ -116,7 +163,7 @@ def _create_result_node(self, node: Node, value: str, metadata: str) -> Node: node_metadata=metadata, ancestors={'source': node.id} ) - self.step_model.save_during_execution(self.session, node, result_node) + self.step_model.save_during_execution(self.session, [node], [result_node]) return result_node diff --git a/synda/utils/vllm_provider.py b/synda/utils/vllm_provider.py new file mode 100644 index 0000000..c2f5199 --- /dev/null +++ b/synda/utils/vllm_provider.py @@ -0,0 +1,60 @@ +import logging +import requests + +import vllm + + +logging.getLogger("vllm").setLevel(logging.CRITICAL) + +class VLLMProvider: + @classmethod + def call( + cls, + provider: str, + model: str, + api_url: str, + prompts: list[str], + temperature: float = 1.0, + max_tokens: int | None = None + ) -> list[str]: + if "hosted_vllm" in provider: + if "v1/chat/completions" in api_url: + payload = { + "model": model, + "messages": [ + {"role": "user", "content": prompt} for prompt in prompts + ], + "temperature": temperature, + **({"max_tokens": max_tokens} if max_tokens is not None else {}) + } + elif "v1/completions" in api_url: + payload = { + "model": model, + "prompt": prompts, + "temperature": temperature, + **({"max_tokens": max_tokens} if max_tokens is not None else {}) + } + else: + raise ValueError("vLLM API endpoint not recognized in synda") + return cls._invoke_vllm_rest_api(api_url=api_url, payload=payload) + elif "vllm" in provider: + return cls._invoke_vllm_programmatic_api(prompts=prompts, model=model, temperature=temperature) + raise ValueError( + "To use vLLM for inference, the provider name should contains 'vllm' in its name to use vLLM" + "programmatic API or 'hosted_vllm' to request a vLLM server" + ) + + + @staticmethod + def _invoke_vllm_rest_api(api_url: str, payload: dict[str, str | list | dict]) -> list[str]: + llm_answers = requests.post(url=api_url, json=payload).json() + return [response["text"] for response in llm_answers["choices"]] + + @staticmethod + def _invoke_vllm_programmatic_api( + prompts: list[str], model: str, temperature: float, max_tokens: int | None = None + ) -> list[str]: + llm = vllm.LLM(model=model) + sampling_params = vllm.SamplingParams(temperature=temperature, max_tokens=max_tokens) + llm_answers = llm.generate(prompts=prompts, sampling_params=sampling_params) + return [response.outputs[0].text for response in llm_answers] diff --git a/tests/stubs/vllm_rag/config.yaml b/tests/stubs/vllm_rag/config.yaml index 93c70a6..3d65854 100644 --- a/tests/stubs/vllm_rag/config.yaml +++ b/tests/stubs/vllm_rag/config.yaml @@ -22,10 +22,11 @@ pipeline: - type: generation method: llm parameters: - provider: vllm + provider: hosted_vllm model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 occurrences: 1 batch_size: 2 + batch: true template: | Ask a question regarding the sentence about the content. content: {chunk_faq} @@ -50,6 +51,8 @@ pipeline: provider: ollama model: llama3.2 consensus: all + batch_size: 4 + batch: true criteria: - Is the question written in english? - Is the question consistent? From 0102b3b3f5e7de1a728b9fc319254635199488ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?C=C3=B4me=20Rodriguez?= Date: Sat, 3 May 2025 02:53:11 +0200 Subject: [PATCH 4/4] WIP add vLLM inference for GENERATION step --- synda/pipeline/generation/llm.py | 12 ---- synda/utils/batch_completion.py | 94 -------------------------------- 2 files changed, 106 deletions(-) delete mode 100644 synda/utils/batch_completion.py diff --git a/synda/pipeline/generation/llm.py b/synda/pipeline/generation/llm.py index 4753b03..1e8610f 100644 --- a/synda/pipeline/generation/llm.py +++ b/synda/pipeline/generation/llm.py @@ -1,5 +1,4 @@ import asyncio -import requests from sqlmodel import Session from synda.model.provider import Provider @@ -125,17 +124,6 @@ def _call_vllm(self, prompts: list[str]): prompts=prompts, temperature=self.config.parameters.temperature, ) - - def _hosted_vllm_batch_inference(self, prompts): - llm_answers = requests.post( - self.provider.api_url, - json={ - "model": self.model, - "prompt": prompts, - "temperature": self.config.parameters.temperature, - } - ).json() - return [answer["text"] for answer in llm_answers["choices"]] def _build_prompts(self, node: Node, template: str, instruction_sets: list, instruction_mode: str) -> list[str]: return PromptBuilder.build( diff --git a/synda/utils/batch_completion.py b/synda/utils/batch_completion.py deleted file mode 100644 index dc90c70..0000000 --- a/synda/utils/batch_completion.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -This file overwrite the litellm batch_completion function for vllm provider -This overwriting is due to an error coming from "stream" args set to False by default -and not supported by vllm -""" -from typing import List, Optional - -import litellm -from litellm.utils import get_optional_params -from litellm.llms.vllm.completion import handler as vllm_handler - -def vllm_batch_completion( - model: str, - # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create - messages: Optional[List] = None, - functions: Optional[List] = None, - function_call: Optional[str] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - n: Optional[int] = None, - stream: Optional[bool] = None, - stop=None, - max_tokens: Optional[int] = None, - presence_penalty: Optional[float] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[dict] = None, - user: Optional[str] = None, - deployment_id=None, - request_timeout: Optional[int] = None, - timeout: Optional[int] = 600, - max_workers: Optional[int] = 100, - # Optional liteLLM function params - **kwargs, -): - """ - Batch litellm.completion function for a given model. - - Args: - model (str): The model to use for generating completions. - messages (List, optional): List of messages to use as input for generating completions. Defaults to []. - functions (List, optional): List of functions to use as input for generating completions. Defaults to []. - function_call (str, optional): The function call to use as input for generating completions. Defaults to "". - temperature (float, optional): The temperature parameter for generating completions. Defaults to None. - top_p (float, optional): The top-p parameter for generating completions. Defaults to None. - n (int, optional): The number of completions to generate. Defaults to None. - stream (bool, optional): Whether to stream completions or not. Defaults to None. - stop (optional): The stop parameter for generating completions. Defaults to None. - max_tokens (float, optional): The maximum number of tokens to generate. Defaults to None. - presence_penalty (float, optional): The presence penalty for generating completions. Defaults to None. - frequency_penalty (float, optional): The frequency penalty for generating completions. Defaults to None. - logit_bias (dict, optional): The logit bias for generating completions. Defaults to {}. - user (str, optional): The user string for generating completions. Defaults to "". - deployment_id (optional): The deployment ID for generating completions. Defaults to None. - request_timeout (int, optional): The request timeout for generating completions. Defaults to None. - max_workers (int,optional): The maximum number of threads to use for parallel processing. - - Returns: - list: A list of completion results. - """ - args = locals() - if not messages: - messages = [] - batch_messages = messages - completions = [] - model = model - custom_llm_provider = None - if model.split("/", 1)[0] in litellm.provider_list: - custom_llm_provider = model.split("/", 1)[0] - model = model.split("/", 1)[1] - - optional_params = get_optional_params( - functions=functions, - function_call=function_call, - temperature=temperature, - top_p=top_p, - n=n, - stream=stream, - stop=stop, - max_tokens=max_tokens, - presence_penalty=presence_penalty, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - user=user, - # params to identify the model - model=model, - custom_llm_provider=custom_llm_provider, - ) - results = vllm_handler.batch_completions( - model=model, - messages=batch_messages, - custom_prompt_dict=litellm.custom_prompt_dict, - optional_params=optional_params, - ) - return results