From 0464fb56778529e30b0d1cbccdbaa114fa182146 Mon Sep 17 00:00:00 2001 From: ayulockin Date: Tue, 7 May 2024 19:10:37 +0530 Subject: [PATCH 1/6] chore: remove extra logger.info --- src/wandbot/api/routers/chat.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/wandbot/api/routers/chat.py b/src/wandbot/api/routers/chat.py index 2317565..735188d 100644 --- a/src/wandbot/api/routers/chat.py +++ b/src/wandbot/api/routers/chat.py @@ -8,7 +8,6 @@ logger = get_logger(__name__) chat_config = ChatConfig() -logger.info(f"Chat config: {chat_config}") chat: Chat | None = None router = APIRouter( From 7a0c4f09bbef4c32a85d8553adacb462fa0dfdb4 Mon Sep 17 00:00:00 2001 From: ayulockin Date: Wed, 8 May 2024 19:11:34 +0530 Subject: [PATCH 2/6] instrument weave eval --- .../evaluation/weave_eval/log_eval_data.py | 41 ++++++ src/wandbot/evaluation/weave_eval/main.py | 102 ++++++++++++++ .../weave_eval/weave_correctness.py | 127 ++++++++++++++++++ 3 files changed, 270 insertions(+) create mode 100644 src/wandbot/evaluation/weave_eval/log_eval_data.py create mode 100644 src/wandbot/evaluation/weave_eval/main.py create mode 100644 src/wandbot/evaluation/weave_eval/weave_correctness.py diff --git a/src/wandbot/evaluation/weave_eval/log_eval_data.py b/src/wandbot/evaluation/weave_eval/log_eval_data.py new file mode 100644 index 0000000..a4615d4 --- /dev/null +++ b/src/wandbot/evaluation/weave_eval/log_eval_data.py @@ -0,0 +1,41 @@ +import os +os.environ["WANDB_ENTITY"] = "wandbot" + +import wandb +import weave +import pandas as pd +from weave import Dataset + +from wandbot.evaluation.config import EvalConfig + +config = EvalConfig() + +wandb_project = config.wandb_project +wandb_entity = config.wandb_entity + +eval_artifact = wandb.Api().artifact(config.eval_artifact) +eval_artifact_dir = eval_artifact.download(root=config.eval_artifact_root) + +df = pd.read_json( + f"{eval_artifact_dir}/{config.eval_annotations_file}", + lines=True, + orient="records", +) +df.insert(0, "id", df.index) + +correct_df = df[ + (df["is_wandb_query"] == "YES") & (df["correctness"] == "correct") +] + +data_rows = correct_df.to_dict('records') + +weave.init(wandb_project) + +# Create a dataset +dataset = Dataset( + name='wandbot_eval_data', + rows=data_rows, +) + +# Publish the dataset +weave.publish(dataset) diff --git a/src/wandbot/evaluation/weave_eval/main.py b/src/wandbot/evaluation/weave_eval/main.py new file mode 100644 index 0000000..df3aa7b --- /dev/null +++ b/src/wandbot/evaluation/weave_eval/main.py @@ -0,0 +1,102 @@ +import os +os.environ["WANDB_ENTITY"] = "wandbot" + +import time +import json +import httpx +import wandb +import weave +import asyncio +import pandas as pd +from weave import Evaluation +from llama_index.llms.openai import OpenAI + +from wandbot.evaluation.config import EvalConfig +from wandbot.utils import get_logger + +from wandbot.evaluation.eval.correctness import ( + CORRECTNESS_EVAL_TEMPLATE, + WandbCorrectnessEvaluator, +) + +logger = get_logger(__name__) +config = EvalConfig() + +correctness_evaluator = WandbCorrectnessEvaluator( + llm=OpenAI(config.eval_judge_model), + eval_template=CORRECTNESS_EVAL_TEMPLATE, +) + +wandb_project = config.wandb_project +wandb_entity = config.wandb_entity + +weave.init("weave_test_eval") + + +@weave.op() +async def get_answer(question: str, application: str = "api-eval") -> str: + url = "http://0.0.0.0:8000/chat/query" + payload = { + "question": question, + "application": application, + "language": "en", + } + async with httpx.AsyncClient(timeout=200.0) as client: + response = await client.post(url, json=payload) + response_json = response.json() + return json.dumps(response_json) + + +@weave.op() +async def get_eval_record(row_str: str) -> str: + row = json.loads(row_str) + response = await get_answer(row["question"]) + response = json.loads(response) + response["ground_truths"] = row["answer"] + response["reference_notes"] = row["notes"] + response["contexts"] = response["source_documents"] + response = json.dumps(response) + return response + + +@weave.op() +def parse_answer_eval(metric: str, row): + print("result is: ", row.get("passing"), type(row.get("passing"))) + return { + f"{metric}_result": True if row.get("passing") is True else False, + } + + +@weave.op() +async def get_answer_correctness(model_output: str) -> str: + row = json.loads(model_output) + result = await correctness_evaluator.aevaluate( + query=row["question"], + response=row["answer"], + reference=row["ground_truths"], + contexts=row["contexts"], + reference_notes=row["reference_notes"], + ) + # result = parse_answer_eval("answer_correctness", result.dict()) + # result = json.dumps(result) + return {"answer_correctness": result.dict()["passing"]} + # return result + + +dataset_ref = weave.ref( + "weave:///wandbot/wandbot-eval/object/wandbot_eval_data:eCQQ0GjM077wi4ykTWYhLPRpuGIaXbMwUGEB7IyHlFU" +).get() +question_rows = dataset_ref.rows +question_rows = [ + { + "row_str": json.dumps(row) + } for row in question_rows +] +logger.info("Number of evaluation samples: %s", len(question_rows)) + +evaluation = Evaluation( + dataset=question_rows, scorers=[get_answer_correctness] +) + +if __name__ == "__main__": + asyncio.run(evaluation.evaluate(get_eval_record)) diff --git a/src/wandbot/evaluation/weave_eval/weave_correctness.py b/src/wandbot/evaluation/weave_eval/weave_correctness.py new file mode 100644 index 0000000..093a51c --- /dev/null +++ b/src/wandbot/evaluation/weave_eval/weave_correctness.py @@ -0,0 +1,127 @@ +import asyncio +from typing import Any, Optional, Sequence + +import regex as re +from llama_index.core.evaluation import CorrectnessEvaluator, EvaluationResult + +from wandbot.evaluation.eval.utils import ( + make_eval_template, + safe_parse_eval_response, +) + +import wandb +import weave + +SYSTEM_TEMPLATE = """You are a Weight & Biases support expert tasked with evaluating the correctness of answers to questions asked by users to a a technical support chatbot. + +You are given the following information: +- a user query, +- the documentation used to generate the answer +- a reference answer +- the reason why the reference answer is correct, and +- a generated answer. + + +Your job is to judge the relevance and correctness of the generated answer. +- Consider whether the answer addresses all aspects of the question. +- The generated answer must provide only correct information according to the documentation. +- Compare the generated answer to the reference answer for completeness and correctness. +- Output a score and a decision that represents a holistic evaluation of the generated answer. +- You must return your response only in the below mentioned format. Do not return answers in any other format. + +Follow these guidelines for scoring: +- Your score has to be between 1 and 3, where 1 is the worst and 3 is the best. +- If the generated answer is not correct in comparison to the reference, you should give a score of 1. +- If the generated answer is correct in comparison to the reference but contains mistakes, you should give a score of 2. +- If the generated answer is correct in comparision to the reference and completely answer's the user's query, you should give a score of 3. + +Output your final verdict by strictly following JSON format: +{{ + "reason": <>, + "score": <>, + "decision": <> + +}} + +Example Response 1: +{{ + "reason": "The generated answer has the exact details as the reference answer and completely answer's the user's query.", + "score": 3, + "decision": "correct" +}} + +Example Response 2: +{{ + "reason": "The generated answer doesn't match the reference answer, and deviates from the documentation provided", + "score": 1, + "decision": "incorrect" +}} + +Example Response 3: +{{ + "reason": "The generated answer follows the same steps as the reference answer. However, it includes assumptions about methods that are not mentioned in the documentation.", + "score": 2, + "decision": "incorrect" +}} +""" + + +USER_TEMPLATE = """ +## User Query +{query} + +## Documentation +{context_str} + +## Reference Answer +{reference_answer} + +## Reference Correctness Reason +{reference_notes} + +## Generated Answer +{generated_answer} +""" + +CORRECTNESS_EVAL_TEMPLATE = make_eval_template(SYSTEM_TEMPLATE, USER_TEMPLATE) + + +class WandbCorrectnessEvaluator(CorrectnessEvaluator): + @weave.op() + async def aevaluate( + self, + query: Optional[str] = None, + response: Optional[str] = None, + contexts: Optional[Sequence[str]] = None, + reference: Optional[str] = None, + sleep_time_in_seconds: int = 0, + **kwargs: Any, + ) -> EvaluationResult: + await asyncio.sleep(sleep_time_in_seconds) + + if query is None or response is None or reference is None: + print(query, response, reference, flush=True) + raise ValueError("query, response, and reference must be provided") + + eval_response = await self._llm.apredict( + prompt=self._eval_template, + query=query, + generated_answer=response, + reference_answer=reference, + context_str=re.sub( + "\n+", "\n", "\n---\n".join(contexts) if contexts else "" + ), + reference_notes=kwargs.get("reference_notes", ""), + ) + + passing, reasoning, score = await safe_parse_eval_response( + eval_response, "correct" + ) + + return EvaluationResult( + query=query, + response=response, + passing=passing, + score=score, + feedback=reasoning, + ) From 90c5c106c4947d927d083a5ef19bd7d3465d5ed5 Mon Sep 17 00:00:00 2001 From: ayulockin Date: Wed, 8 May 2024 21:07:48 +0530 Subject: [PATCH 3/6] chore: improve for better UI rendering --- src/wandbot/evaluation/weave_eval/main.py | 61 ++++++++++++----------- 1 file changed, 31 insertions(+), 30 deletions(-) diff --git a/src/wandbot/evaluation/weave_eval/main.py b/src/wandbot/evaluation/weave_eval/main.py index df3aa7b..0e485e9 100644 --- a/src/wandbot/evaluation/weave_eval/main.py +++ b/src/wandbot/evaluation/weave_eval/main.py @@ -1,13 +1,10 @@ import os os.environ["WANDB_ENTITY"] = "wandbot" -import time import json import httpx -import wandb import weave import asyncio -import pandas as pd from weave import Evaluation from llama_index.llms.openai import OpenAI @@ -30,7 +27,7 @@ wandb_project = config.wandb_project wandb_entity = config.wandb_entity -weave.init("weave_test_eval") +weave.init(f"{wandb_entity}/{wandb_project}") @weave.op() @@ -48,39 +45,41 @@ async def get_answer(question: str, application: str = "api-eval") -> str: @weave.op() -async def get_eval_record(row_str: str) -> str: - row = json.loads(row_str) - response = await get_answer(row["question"]) +async def get_eval_record( + question: str, + ground_truth: str, +) -> dict: + response = await get_answer(question) response = json.loads(response) - response["ground_truths"] = row["answer"] - response["reference_notes"] = row["notes"] - response["contexts"] = response["source_documents"] - response = json.dumps(response) - return response - - -@weave.op() -def parse_answer_eval(metric: str, row): - print("result is: ", row.get("passing"), type(row.get("passing"))) return { - f"{metric}_result": True if row.get("passing") is True else False, + "system_prompt": response["system_prompt"], + "generated_answer": response["answer"], + "retrieved_contexts": response["source_documents"], + "model": response["model"], + "total_tokens": response["total_tokens"], + "prompt_tokens": response["prompt_tokens"], + "completion_tokens": response["completion_tokens"], + "time_taken": response["time_taken"], } @weave.op() -async def get_answer_correctness(model_output: str) -> str: - row = json.loads(model_output) +async def get_answer_correctness( + question: str, + ground_truth: str, + notes: str, + model_output: dict +) -> dict: result = await correctness_evaluator.aevaluate( - query=row["question"], - response=row["answer"], - reference=row["ground_truths"], - contexts=row["contexts"], - reference_notes=row["reference_notes"], + query=question, + response=model_output["generated_answer"], + reference=ground_truth, + contexts=model_output["retrieved_contexts"], + reference_notes=notes, ) - # result = parse_answer_eval("answer_correctness", result.dict()) - # result = json.dumps(result) - return {"answer_correctness": result.dict()["passing"]} - # return result + return { + "answer_correctness": result.dict()["passing"] + } dataset_ref = weave.ref( @@ -89,7 +88,9 @@ async def get_answer_correctness(model_output: str) -> str: question_rows = dataset_ref.rows question_rows = [ { - "row_str": json.dumps(row) + "question": row["question"], + "ground_truth": row["answer"], + "notes": row["notes"], } for row in question_rows ] logger.info("Number of evaluation samples: %s", len(question_rows)) From 17884a738a11626386096e293acdabf379f39b6b Mon Sep 17 00:00:00 2001 From: ayulockin Date: Thu, 9 May 2024 12:26:27 +0530 Subject: [PATCH 4/6] add weave Model as evaluator --- src/wandbot/evaluation/weave_eval/main.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/src/wandbot/evaluation/weave_eval/main.py b/src/wandbot/evaluation/weave_eval/main.py index 0e485e9..d4ab641 100644 --- a/src/wandbot/evaluation/weave_eval/main.py +++ b/src/wandbot/evaluation/weave_eval/main.py @@ -6,6 +6,7 @@ import weave import asyncio from weave import Evaluation +from weave import Model from llama_index.llms.openai import OpenAI from wandbot.evaluation.config import EvalConfig @@ -47,7 +48,6 @@ async def get_answer(question: str, application: str = "api-eval") -> str: @weave.op() async def get_eval_record( question: str, - ground_truth: str, ) -> dict: response = await get_answer(question) response = json.loads(response) @@ -63,6 +63,16 @@ async def get_eval_record( } +class EvaluatorModel(Model): + eval_judge_model: str = config.eval_judge_model + + @weave.op() + async def predict(self, question: str) -> dict: + # Model logic goes here + prediction = await get_eval_record(question) + return prediction + + @weave.op() async def get_answer_correctness( question: str, @@ -100,4 +110,4 @@ async def get_answer_correctness( ) if __name__ == "__main__": - asyncio.run(evaluation.evaluate(get_eval_record)) + asyncio.run(evaluation.evaluate(EvaluatorModel())) From a14d1d3d82fa39eb92328fa5e191b468230e3173 Mon Sep 17 00:00:00 2001 From: ayulockin Date: Thu, 9 May 2024 16:20:14 +0530 Subject: [PATCH 5/6] add litellm embedding and evaluated --- poetry.lock | 3111 ++++++------------------- pyproject.toml | 6 +- src/wandbot/evaluation/config.py | 2 +- src/wandbot/ingestion/utils.py | 48 +- src/wandbot/ingestion/vectorstores.py | 6 +- 5 files changed, 716 insertions(+), 2457 deletions(-) diff --git a/poetry.lock b/poetry.lock index 06c7aa7..6fbd947 100644 --- a/poetry.lock +++ b/poetry.lock @@ -195,93 +195,6 @@ files = [ {file = "appdirs-1.4.4.tar.gz", hash = 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"sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"}, ] [package.dependencies] @@ -7936,12 +6145,12 @@ multidict = ">=4.0" [[package]] name = "zenpy" -version = "2.0.47" +version = "2.0.48" description = "Python wrapper for the Zendesk API" optional = false python-versions = "*" files = [ - {file = "zenpy-2.0.47.tar.gz", hash = "sha256:1aeb66690ce8fa1af1fde4d9b496ff226cd1371086631304a6a3f6a10273ff4b"}, + {file = "zenpy-2.0.48.tar.gz", hash = "sha256:799cab168719bbb8487ef875ff2ffe15ed05346954d7dea71b3546c73c1d0e1a"}, ] [package.dependencies] @@ -7969,4 +6178,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.10.0,<3.12" -content-hash = "47b8b0bd93dc35b827b988abac327df444584863a1e16736f798c61d4a65122d" +content-hash = "05e83cf0d9a0600df3aa30ee96a6f188ab592271d6d45f11f80fab1d2a29bf2b" diff --git a/pyproject.toml b/pyproject.toml index ef86126..511643a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ include = ["src/**/*", "LICENSE", "README.md"] [tool.poetry.dependencies] python = ">=3.10.0,<3.12" numpy = "^1.26.1" -wandb = "<=0.16.1" +wandb = "<=0.17.0" tiktoken = "^0.5.1" pandas = "^2.1.2" unstructured = "^0.12.3" @@ -31,9 +31,9 @@ markdownify = "^0.11.6" uvicorn = "^0.24.0" zenpy = "^2.0.46" openai = "^1.3.2" -weave = "^0.31.0" +weave = "^0.50.0" colorlog = "^6.8.0" -litellm = "^1.15.1" +litellm = "^1.31.6" google-cloud-bigquery = "^3.14.1" db-dtypes = "^1.2.0" python-frontmatter = "^1.1.0" diff --git a/src/wandbot/evaluation/config.py b/src/wandbot/evaluation/config.py index fafb0ab..3630b62 100644 --- a/src/wandbot/evaluation/config.py +++ b/src/wandbot/evaluation/config.py @@ -33,4 +33,4 @@ class EvalConfig(BaseSettings): validation_alias="eval_judge_model", ) wandb_entity: str = Field("wandbot", env="WANDB_ENTITY") - wandb_project: str = Field("wandbot-eval", env="WANDB_PROJECT") + wandb_project: str = Field("wandbot-eval") diff --git a/src/wandbot/ingestion/utils.py b/src/wandbot/ingestion/utils.py index 8f63ff4..cf1eb11 100644 --- a/src/wandbot/ingestion/utils.py +++ b/src/wandbot/ingestion/utils.py @@ -30,7 +30,7 @@ import re import subprocess from pathlib import Path -from typing import Any, Dict, Optional +from typing import Any, Dict, Optional, List import frontmatter import giturlparse @@ -38,6 +38,10 @@ import markdownify from bs4 import BeautifulSoup, Comment from git import Repo + +from litellm import embedding, aembedding +from langchain_core.embeddings import Embeddings + from wandbot.utils import get_logger logger = get_logger(__name__) @@ -291,3 +295,45 @@ def extract_frontmatter(file_path: pathlib.Path) -> Dict[str, Any]: with open(file_path, "r") as f: contents = frontmatter.load(f) return {k: contents[k] for k in contents.keys()} + + +class LiteLLMEmbeddings(Embeddings): + def __init__( + self, + model: str, + dimensions: int | None = None, + input_type: int | None = None, + ): + self.args = dict() + if dimensions is not None and input_type is None: # OpenAI + self.args["model"] = model + self.args["dimensions"] = dimensions + elif input_type is not None and dimensions is None: # Cohere + assert isinstance(input_type, str) + self.args["model"] = model + self.args["input_type"] = input_type + else: # Rest + self.args["model"] = model + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Embed search docs.""" + self.args["input"] = texts + response = embedding( + **self.args, + ) + + embeddings = [] + for embedding_dict in response.data: + embeddings.append(embedding_dict["embedding"]) + + return embeddings + + def embed_query(self, text: str) -> List[float]: + """Embed query text.""" + self.args["input"] = [text] + response = embedding( + **self.args, + ) + + assert len(response.data) == 1 + return response.data[0]["embedding"] diff --git a/src/wandbot/ingestion/vectorstores.py b/src/wandbot/ingestion/vectorstores.py index f3ef115..b43c125 100644 --- a/src/wandbot/ingestion/vectorstores.py +++ b/src/wandbot/ingestion/vectorstores.py @@ -23,6 +23,7 @@ from tqdm import trange from wandbot.ingestion.config import VectorStoreConfig +from wandbot.ingestion.utils import LiteLLMEmbeddings from wandbot.utils import get_logger logger = get_logger(__name__) @@ -60,7 +61,10 @@ def load( artifact_dir: str = artifact.download() # Todo: Change to LiteLLM Embeddings - embedding_fn = OpenAIEmbeddings( + # embedding_fn = OpenAIEmbeddings( + # model=config.embeddings_model, dimensions=config.embedding_dim + # ) + embedding_fn = LiteLLMEmbeddings( model=config.embeddings_model, dimensions=config.embedding_dim ) vectorstore_dir = config.persist_dir From 9320a9c9722a3b65524a375958ce9a542f236a9f Mon Sep 17 00:00:00 2001 From: ayulockin Date: Mon, 13 May 2024 10:05:23 +0100 Subject: [PATCH 6/6] nits --- src/wandbot/chat/chat.py | 16 ++++++++-------- src/wandbot/ingestion/__main__.py | 10 ++++++---- src/wandbot/rag/query_handler.py | 2 +- src/wandbot/rag/response_synthesis.py | 2 +- 4 files changed, 16 insertions(+), 14 deletions(-) diff --git a/src/wandbot/chat/chat.py b/src/wandbot/chat/chat.py index 3faf7b3..cb66d8e 100644 --- a/src/wandbot/chat/chat.py +++ b/src/wandbot/chat/chat.py @@ -26,7 +26,7 @@ """ from typing import List -from weave.monitoring import StreamTable +# from weave.monitoring import StreamTable import wandb from wandbot.chat.config import ChatConfig @@ -66,11 +66,11 @@ def __init__( job_type="chat", ) self.run._label(repo="wandbot") - self.stream_table = StreamTable( - table_name="chat_logs", - project_name=self.config.wandb_project, - entity_name=self.config.wandb_entity, - ) + # self.stream_table = StreamTable( + # table_name="chat_logs", + # project_name=self.config.wandb_project, + # entity_name=self.config.wandb_entity, + # ) self.rag_pipeline = RAGPipeline(vector_store=vector_store) @@ -109,7 +109,7 @@ def __call__(self, chat_request: ChatRequest) -> ChatResponse: } result_dict.update({"application": chat_request.application}) self.run.log(usage_stats) - self.stream_table.log(result_dict) + # self.stream_table.log(result_dict) return ChatResponse(**result_dict) except Exception as e: with Timer() as timer: @@ -131,5 +131,5 @@ def __call__(self, chat_request: ChatRequest) -> ChatResponse: "end_time": timer.stop, } ) - self.stream_table.log(result) + # self.stream_table.log(result) return ChatResponse(**result) diff --git a/src/wandbot/ingestion/__main__.py b/src/wandbot/ingestion/__main__.py index a974346..704a628 100644 --- a/src/wandbot/ingestion/__main__.py +++ b/src/wandbot/ingestion/__main__.py @@ -11,13 +11,15 @@ def main(): project = os.environ.get("WANDB_PROJECT", "wandbot-dev") entity = os.environ.get("WANDB_ENTITY", "wandbot") - raw_artifact = prepare_data.load(project, entity) - preprocessed_artifact = preprocess_data.load(project, entity, raw_artifact) + # raw_artifact = prepare_data.load(project, entity) + raw_artifact = "wandbot/wandbot-dev/raw_dataset:v56" + # preprocessed_artifact = preprocess_data.load(project, entity, raw_artifact) + preprocessed_artifact = "wandbot/wandbot-dev/transformed_data:v23" vectorstore_artifact = vectorstores.load( - project, entity, preprocessed_artifact + project, entity, preprocessed_artifact, "chroma_index" ) - create_ingestion_report(project, entity, raw_artifact, vectorstore_artifact) + # create_ingestion_report(project, entity, raw_artifact, vectorstore_artifact) print(vectorstore_artifact) diff --git a/src/wandbot/rag/query_handler.py b/src/wandbot/rag/query_handler.py index 857c9fb..cdde16b 100644 --- a/src/wandbot/rag/query_handler.py +++ b/src/wandbot/rag/query_handler.py @@ -262,7 +262,7 @@ class QueryEnhancer: def __init__( self, model: str = "gpt-4-0125-preview", - fallback_model: str = "gpt-4-1106-preview", + fallback_model: str = "gpt-4-0125-preview", ): self.model = model # type: ignore self.fallback_model = fallback_model # type: ignore diff --git a/src/wandbot/rag/response_synthesis.py b/src/wandbot/rag/response_synthesis.py index 22729d7..65d9ac1 100644 --- a/src/wandbot/rag/response_synthesis.py +++ b/src/wandbot/rag/response_synthesis.py @@ -118,7 +118,7 @@ class ResponseSynthesizer: def __init__( self, model: str = "gpt-4-0125-preview", - fallback_model: str = "gpt-4-1106-preview", + fallback_model: str = "gpt-4-0125-preview", ): self.model = model # type: ignore self.fallback_model = fallback_model # type: ignore