diff --git a/backend/app/alembic/versions/db9b5413d3ce_add_system_prompt_column_to_fine_tuning_.py b/backend/app/alembic/versions/db9b5413d3ce_add_system_prompt_column_to_fine_tuning_.py
new file mode 100644
index 00000000..a661d86e
--- /dev/null
+++ b/backend/app/alembic/versions/db9b5413d3ce_add_system_prompt_column_to_fine_tuning_.py
@@ -0,0 +1,25 @@
+"""add system prompt column to fine tuning table
+
+Revision ID: db9b5413d3ce
+Revises: e3c74fab4356
+Create Date: 2025-08-06 20:32:32.454567
+
+"""
+from alembic import op
+import sqlalchemy as sa
+import sqlmodel.sql.sqltypes
+
+
+# revision identifiers, used by Alembic.
+revision = "db9b5413d3ce"
+down_revision = "e3c74fab4356"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    op.add_column("fine_tuning", sa.Column("system_prompt", sa.Text(), nullable=False))
+
+
+def downgrade():
+    op.drop_column("fine_tuning", "system_prompt")
diff --git a/backend/app/api/docs/fine_tuning/create.md b/backend/app/api/docs/fine_tuning/create.md
new file mode 100644
index 00000000..bd2376ec
--- /dev/null
+++ b/backend/app/api/docs/fine_tuning/create.md
@@ -0,0 +1,21 @@
+This endpoint initiates the fine-tuning of an OpenAI model using your custom dataset that you would have uploaded using the upload document endpoint. The uploaded dataset must include:
+
+- A column named `query`, `question`, or `message` containing user inputs or messages.
+- A column named `label` indicating whether a given message is a genuine query or not (e.g., casual conversation or small talk).
+
+The split_ratio in the request body determines how your data is divided between training and testing. For example, a split ratio of 0.5 means 50% of your data will be used for training, and the remaining 50% for testing. You can also provide multiple split ratios—for instance, [0.7, 0.9]. This will trigger multiple fine-tuning jobs, one for each ratio, effectively training multiple models on different portions of your dataset. You would also need to specify a base model that you want to finetune.
+
+The system_prompt field specified in the request body allows you to define an initial instruction or context-setting message that will be included in the training data. This message helps the model learn how it is expected to behave when responding to user inputs. It is prepended as the first message in each training example during fine-tuning.
+
+The system handles the fine-tuning process by interacting with OpenAI's APIs under the hood. These include:
+
+- [Openai File create to upload your training and testing files](https://platform.openai.com/docs/api-reference/files/create)
+
+- [Openai Fine Tuning Job create to initiate each fine-tuning job](https://platform.openai.com/docs/api-reference/fine_tuning/create)
+
+If successful, the response will include a message along with a list of fine-tuning jobs that were initiated. Each job object includes:
+
+- id: the internal ID of the fine-tuning job
+- document_id: the ID of the document used for fine-tuning
+- split_ratio: the data split used for that job
+- status: the initial status of the job (usually "pending")
diff --git a/backend/app/api/docs/fine_tuning/retrieve.md b/backend/app/api/docs/fine_tuning/retrieve.md
new file mode 100644
index 00000000..8dd93a84
--- /dev/null
+++ b/backend/app/api/docs/fine_tuning/retrieve.md
@@ -0,0 +1,5 @@
+Refreshes the status of a fine-tuning job by retrieving the latest information from OpenAI.
+If there are any changes in status, fine-tuned model, or error message, the local job record is updated accordingly.
+Returns the latest state of the job.
+
+OpenAI’s job status is retrieved using their [Fine-tuning Job Retrieve API](https://platform.openai.com/docs/api-reference/fine_tuning/retrieve).
diff --git a/backend/app/api/main.py b/backend/app/api/main.py
index df0b1016..85ce1cf6 100644
--- a/backend/app/api/main.py
+++ b/backend/app/api/main.py
@@ -17,6 +17,7 @@
     utils,
     onboarding,
     credentials,
+    fine_tuning,
 )
 from app.core.config import settings
 
@@ -36,6 +37,8 @@
 api_router.include_router(threads.router)
 api_router.include_router(users.router)
 api_router.include_router(utils.router)
+api_router.include_router(fine_tuning.router)
+
 
 if settings.ENVIRONMENT == "local":
     api_router.include_router(private.router)
diff --git a/backend/app/api/routes/fine_tuning.py b/backend/app/api/routes/fine_tuning.py
new file mode 100644
index 00000000..d2ee7085
--- /dev/null
+++ b/backend/app/api/routes/fine_tuning.py
@@ -0,0 +1,308 @@
+from typing import Optional
+import logging
+import time
+from uuid import UUID
+
+import openai
+from openai import OpenAI
+from sqlmodel import Session
+from fastapi import APIRouter, HTTPException, BackgroundTasks
+
+from app.models import (
+    FineTuningJobCreate,
+    FineTuningJobPublic,
+    FineTuningUpdate,
+    FineTuningStatus,
+)
+from app.core.cloud import AmazonCloudStorage
+from app.crud.document import DocumentCrud
+from app.utils import get_openai_client, APIResponse, mask_string, load_description
+from app.crud import (
+    create_fine_tuning_job,
+    fetch_by_id,
+    update_finetune_job,
+    fetch_by_document_id,
+)
+from app.api.deps import CurrentUserOrgProject, SessionDep
+from app.core.finetune.preprocessing import DataPreprocessor
+
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/fine_tuning", tags=["fine_tuning"])
+
+
+OPENAI_TO_INTERNAL_STATUS = {
+    "validating_files": FineTuningStatus.running,
+    "queued": FineTuningStatus.running,
+    "running": FineTuningStatus.running,
+    "succeeded": FineTuningStatus.completed,
+    "failed": FineTuningStatus.failed,
+}
+
+
+def handle_openai_error(e: openai.OpenAIError) -> str:
+    """Extract error message from OpenAI error."""
+    if isinstance(e.body, dict) and "message" in e.body:
+        return e.body["message"]
+    return str(e)
+
+
+def process_fine_tuning_job(
+    job_id: int,
+    ratio: float,
+    session: Session,
+    current_user: CurrentUserOrgProject,
+    request: FineTuningJobCreate,
+    client: OpenAI,
+):
+    start_time = time.time()
+    project_id = current_user.project_id
+    fine_tune = None
+
+    logger.info(
+        f"[process_fine_tuning_job]Starting fine-tuning job processing | job_id={job_id}, project_id={project_id}|"
+    )
+
+    try:
+        fine_tune = fetch_by_id(session, job_id, project_id)
+
+        storage = AmazonCloudStorage(current_user)
+        document_crud = DocumentCrud(session=session, owner_id=current_user.id)
+        document = document_crud.read_one(request.document_id)
+        preprocessor = DataPreprocessor(document, storage, ratio, request.system_prompt)
+        result = preprocessor.process()
+        train_path = result["train_file"]
+        test_path = result["test_file"]
+
+        try:
+            with open(train_path, "rb") as train_f:
+                uploaded_train = client.files.create(file=train_f, purpose="fine-tune")
+            with open(test_path, "rb") as test_f:
+                uploaded_test = client.files.create(file=test_f, purpose="fine-tune")
+
+            logger.info(
+                f"[process_fine_tuning_job] Files uploaded to OpenAI successfully | "
+                f"job_id={job_id}, project_id={project_id}|"
+            )
+        except openai.OpenAIError as e:
+            error_msg = handle_openai_error(e)
+            logger.error(
+                f"[process_fine_tuning_job] Failed to upload to OpenAI: {error_msg} | "
+                f"job_id={job_id}, project_id={project_id}|"
+            )
+            update_finetune_job(
+                session=session,
+                job=fine_tune,
+                update=FineTuningUpdate(
+                    status=FineTuningStatus.failed,
+                    error_message="Failed during background job processing",
+                ),
+            )
+            return
+        finally:
+            preprocessor.cleanup()
+
+        training_file_id = uploaded_train.id
+        testing_file_id = uploaded_test.id
+
+        try:
+            job = client.fine_tuning.jobs.create(
+                training_file=training_file_id, model=request.base_model
+            )
+            logger.info(
+                f"[process_fine_tuning_job] OpenAI fine-tuning job created | "
+                f"provider_job_id={mask_string(job.id)}, job_id={job_id}, project_id={project_id}|"
+            )
+        except openai.OpenAIError as e:
+            error_msg = handle_openai_error(e)
+            logger.error(
+                f"[process_fine_tuning_job] Failed to create OpenAI fine-tuning job: {error_msg} | "
+                f"job_id={job_id}, project_id={project_id}|"
+            )
+            update_finetune_job(
+                session=session,
+                job=fine_tune,
+                update=FineTuningUpdate(
+                    status=FineTuningStatus.failed,
+                    error_message="Failed during background job processing",
+                ),
+            )
+            return
+
+        update_finetune_job(
+            session=session,
+            job=fine_tune,
+            update=FineTuningUpdate(
+                training_file_id=training_file_id,
+                testing_file_id=testing_file_id,
+                split_ratio=ratio,
+                provider_job_id=job.id,
+                status=FineTuningStatus.running,
+            ),
+        )
+
+        end_time = time.time()
+        duration = end_time - start_time
+
+        logger.info(
+            f"[process_fine_tuning_job] Fine-tuning job processed successfully | "
+            f"time_taken={duration:.2f}s, job_id={job_id}, project_id={project_id}|"
+        )
+
+    except Exception as e:
+        logger.error(
+            f"[process_fine_tuning_job] Background job failure: {e} | "
+            f"job_id={job_id}, project_id={project_id}|"
+        )
+        update_finetune_job(
+            session=session,
+            job=fine_tune,
+            update=FineTuningUpdate(
+                status=FineTuningStatus.failed,
+                error_message="Failed during background job processing",
+            ),
+        )
+
+
+@router.post(
+    "/fine-tune",
+    description=load_description("fine_tuning/create.md"),
+    response_model=APIResponse,
+)
+def fine_tune_from_CSV(
+    session: SessionDep,
+    current_user: CurrentUserOrgProject,
+    request: FineTuningJobCreate,
+    background_tasks: BackgroundTasks,
+):
+    client = get_openai_client(
+        session, current_user.organization_id, current_user.project_id
+    )
+    results = []
+
+    for ratio in request.split_ratio:
+        job, created = create_fine_tuning_job(
+            session=session,
+            request=request,
+            split_ratio=ratio,
+            organization_id=current_user.organization_id,
+            project_id=current_user.project_id,
+        )
+        results.append((job, created))
+
+        if created:
+            background_tasks.add_task(
+                process_fine_tuning_job,
+                job.id,
+                ratio,
+                session,
+                current_user,
+                request,
+                client,
+            )
+
+    if not results:
+        logger.error(
+            f"[fine_tune_from_CSV]All fine-tuning job creations failed for document_id={request.document_id}, project_id={current_user.project_id}"
+        )
+        raise HTTPException(
+            status_code=500, detail="Failed to create or fetch any fine-tuning jobs."
+        )
+
+    job_infos = [
+        {
+            "id": job.id,
+            "document_id": job.document_id,
+            "split_ratio": job.split_ratio,
+            "status": job.status,
+        }
+        for job, _ in results
+    ]
+
+    created_count = sum(c for _, c in results)
+    total = len(results)
+    message = (
+        "Fine-tuning job(s) started."
+        if created_count == total
+        else "Active fine-tuning job(s) already exists."
+        if created_count == 0
+        else f"Started {created_count} job(s); {total - created_count} active fine-tuning job(s) already exists."
+    )
+
+    return APIResponse.success_response({"message": message, "jobs": job_infos})
+
+
+@router.get(
+    "/{job_id}/refresh",
+    description=load_description("fine_tuning/retrieve.md"),
+    response_model=APIResponse[FineTuningJobPublic],
+)
+def refresh_fine_tune_status(
+    job_id: int, session: SessionDep, current_user: CurrentUserOrgProject
+):
+    project_id = current_user.project_id
+    job = fetch_by_id(session, job_id, project_id)
+    client = get_openai_client(session, current_user.organization_id, project_id)
+
+    if job.provider_job_id is None:
+        return APIResponse.success_response(job)
+
+    else:
+        try:
+            openai_job = client.fine_tuning.jobs.retrieve(job.provider_job_id)
+        except openai.OpenAIError as e:
+            error_msg = handle_openai_error(e)
+            logger.error(
+                f"[Retrieve_fine_tune_status] Failed to retrieve OpenAI job | "
+                f"provider_job_id={mask_string(job.provider_job_id)}, "
+                f"error={error_msg}, job_id={job_id}, project_id={project_id}"
+            )
+            raise HTTPException(
+                status_code=502, detail=f"OpenAI API error: {error_msg}"
+            )
+
+        mapped_status: Optional[str] = OPENAI_TO_INTERNAL_STATUS.get(
+            getattr(openai_job, "status", None)
+        )
+
+        openai_error = getattr(openai_job, "error", None)
+        openai_error_msg = (
+            getattr(openai_error, "message", None) if openai_error else None
+        )
+
+        update_payload = FineTuningUpdate(
+            status=mapped_status or job.status,
+            fine_tuned_model=getattr(openai_job, "fine_tuned_model", None),
+            error_message=openai_error_msg,
+        )
+
+        if (
+            job.status != update_payload.status
+            or job.fine_tuned_model != update_payload.fine_tuned_model
+            or job.error_message != update_payload.error_message
+        ):
+            job = update_finetune_job(session=session, job=job, update=update_payload)
+
+    return APIResponse.success_response(job)
+
+
+@router.get(
+    "/{document_id}",
+    description="Retrieves all fine-tuning jobs associated with the given document ID for the current project",
+    response_model=APIResponse[list[FineTuningJobPublic]],
+)
+def retrieve_jobs_by_document(
+    document_id: UUID, session: SessionDep, current_user: CurrentUserOrgProject
+):
+    project_id = current_user.project_id
+    jobs = fetch_by_document_id(session, document_id, project_id)
+    if not jobs:
+        logger.warning(
+            f"[retrive_job_by_document]No fine-tuning jobs found for document_id={document_id}, project_id={project_id}"
+        )
+        raise HTTPException(
+            status_code=404,
+            detail="No fine-tuning jobs found for the given document ID",
+        )
+    return APIResponse.success_response(jobs)
diff --git a/backend/app/core/finetune/preprocessing.py b/backend/app/core/finetune/preprocessing.py
new file mode 100644
index 00000000..a936daf7
--- /dev/null
+++ b/backend/app/core/finetune/preprocessing.py
@@ -0,0 +1,110 @@
+import os
+import json
+import uuid
+import tempfile
+import logging
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+logger = logging.getLogger(__name__)
+
+
+class DataPreprocessor:
+    RANDOM_STATE = 42
+
+    def __init__(self, document, storage, split_ratio: float, system_message: str):
+        self.document = document
+        self.storage = storage
+        self.split_ratio = split_ratio
+        self.query_col = None
+        self.label_col = None
+        self.generated_files = []
+
+        self.system_message = {"role": "system", "content": system_message.strip()}
+
+    def _save_to_jsonl(self, data, filename):
+        temp_dir = tempfile.gettempdir()
+        file_path = os.path.join(temp_dir, filename)
+        with open(file_path, "w", encoding="utf-8") as f:
+            for record in data:
+                json.dump(record, f, ensure_ascii=False)
+                f.write("\n")
+        self.generated_files.append(file_path)
+        return file_path
+
+    def cleanup(self):
+        for path in self.generated_files:
+            if os.path.exists(path):
+                os.remove(path)
+        self.generated_files = []
+
+    def _modify_data_format(self, data):
+        modified_data = []
+        for record in data:
+            user_message = {"role": "user", "content": record[self.query_col]}
+            assistant_message = {"role": "assistant", "content": record[self.label_col]}
+            modified_record = {
+                "messages": [self.system_message, user_message, assistant_message]
+            }
+            modified_data.append(modified_record)
+        return modified_data
+
+    def _load_dataframe(self):
+        logger.info(f"Loading CSV from: {self.document.object_store_url}")
+        f_obj = self.storage.stream(self.document.object_store_url)
+        try:
+            f_obj.name = self.document.fname
+            df = pd.read_csv(f_obj)
+            df.columns = [col.strip().lower() for col in df.columns]
+
+            possible_query_columns = ["query", "question", "message"]
+            self.query_col = next(
+                (col for col in possible_query_columns if col in df.columns), None
+            )
+            self.label_col = "label" if "label" in df.columns else None
+
+            if not self.query_col or not self.label_col:
+                logger.error(
+                    f"[DataPreprocessor] Dataset does not contai a 'label' column and one of: {possible_query_columns} "
+                )
+                raise ValueError(
+                    f"CSV must contain a 'label' column and one of: {possible_query_columns}"
+                )
+
+            logger.info(
+                f"[DataPreprocessor]Identified columns - query_col={self.query_col}, label_col={self.label_col}"
+            )
+            return df
+        finally:
+            f_obj.close()
+
+    def process(self):
+        logger.info("Starting data preprocessing")
+        df = self._load_dataframe()
+
+        train_data, test_data = train_test_split(
+            df,
+            test_size=1 - self.split_ratio,
+            stratify=df[self.label_col],
+            random_state=self.RANDOM_STATE,
+        )
+
+        logger.info(
+            f"[DataPreprocessor]Data split complete: train_size={len(train_data)}, test_size={len(test_data)}"
+        )
+
+        train_dict = train_data.to_dict(orient="records")
+        test_dict = test_data.to_dict(orient="records")
+
+        train_jsonl = self._modify_data_format(train_dict)
+        test_jsonl = self._modify_data_format(test_dict)
+
+        train_file = (
+            f"train_data_{int(self.split_ratio * 100)}_{uuid.uuid4().hex}.jsonl"
+        )
+        test_file = f"test_data_{int(self.split_ratio * 100)}_{uuid.uuid4().hex}.jsonl"
+
+        train_path = self._save_to_jsonl(train_jsonl, train_file)
+        test_path = self._save_to_jsonl(test_jsonl, test_file)
+
+        return {"train_file": train_path, "test_file": test_path}
diff --git a/backend/app/crud/__init__.py b/backend/app/crud/__init__.py
index e4b973a0..a7893260 100644
--- a/backend/app/crud/__init__.py
+++ b/backend/app/crud/__init__.py
@@ -64,3 +64,12 @@
     create_conversation,
     delete_conversation,
 )
+
+from .fine_tuning import (
+    create_fine_tuning_job,
+    fetch_by_id,
+    fetch_by_provider_job_id,
+    fetch_by_document_id,
+    update_finetune_job,
+    fetch_active_jobs_by_document_id,
+)
diff --git a/backend/app/crud/fine_tuning.py b/backend/app/crud/fine_tuning.py
new file mode 100644
index 00000000..1890665b
--- /dev/null
+++ b/backend/app/crud/fine_tuning.py
@@ -0,0 +1,176 @@
+from uuid import UUID
+import logging
+from typing import Optional
+
+from sqlmodel import Session, select
+from fastapi import HTTPException
+
+from app.core.util import now
+from app.models import (
+    Fine_Tuning,
+    FineTuningJobCreate,
+    FineTuningUpdate,
+    FineTuningStatus,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def create_fine_tuning_job(
+    session: Session,
+    request: FineTuningJobCreate,
+    split_ratio: float,
+    status: FineTuningStatus = FineTuningStatus.pending,
+    project_id: int = None,
+    organization_id: int = None,
+) -> tuple[Fine_Tuning, bool]:
+    active_jobs = fetch_active_jobs_by_document_id(
+        session=session,
+        document_id=request.document_id,
+        project_id=project_id,
+        split_ratio=split_ratio,
+        base_model=request.base_model,
+    )
+    if active_jobs:
+        existing = active_jobs[0]
+        logger.info(
+            f"[Create_fine_tuning_job]Active fine-tune job exists; returning it. document_id={request.document_id}, split_ratio={split_ratio}, base_model={request.base_model}, project_id={project_id}"
+        )
+        return existing, False
+
+    fine_tune_data = request.model_dump(exclude_unset=True)
+    base_data = {
+        **fine_tune_data,
+        "split_ratio": split_ratio,
+        "project_id": project_id,
+        "organization_id": organization_id,
+        "status": status,
+    }
+
+    fine_tune = Fine_Tuning(**base_data)
+    fine_tune.updated_at = now()
+
+    session.add(fine_tune)
+    session.commit()
+    session.refresh(fine_tune)
+
+    logger.info(
+        f"[Create_fine_tuning_job]Created new fine-tuning job ID={fine_tune.id}, project_id={project_id}"
+    )
+    return fine_tune, True
+
+
+def fetch_by_provider_job_id(
+    session: Session, provider_job_id: str, project_id: int
+) -> Fine_Tuning:
+    job = session.exec(
+        select(Fine_Tuning).where(
+            Fine_Tuning.provider_job_id == provider_job_id,
+            Fine_Tuning.project_id == project_id,
+        )
+    ).one_or_none()
+
+    if job is None:
+        logger.warning(
+            f"Fine-tune job not found for openai_job_id={provider_job_id}, project_id={project_id}"
+        )
+        raise HTTPException(status_code=404, detail="OpenAI fine tuning ID not found")
+
+    return job
+
+
+def fetch_by_id(session: Session, job_id: int, project_id: int) -> Fine_Tuning:
+    job = session.exec(
+        select(Fine_Tuning).where(
+            Fine_Tuning.id == job_id, Fine_Tuning.project_id == project_id
+        )
+    ).one_or_none()
+
+    if job is None:
+        logger.error(
+            f"[fetch_by_id]Fine-tune job not found: job_id={job_id}, project_id={project_id}"
+        )
+        raise HTTPException(status_code=404, detail="Job not found")
+
+    logger.info(
+        f"[fetch_by_id]Fetched fine-tune job ID={job.id}, project_id={project_id}"
+    )
+    return job
+
+
+def fetch_by_document_id(
+    session: Session,
+    document_id: UUID,
+    project_id: int,
+    split_ratio: float = None,
+    base_model: Optional[str] = None,
+) -> list[Fine_Tuning]:
+    query = select(Fine_Tuning).where(
+        Fine_Tuning.document_id == document_id, Fine_Tuning.project_id == project_id
+    )
+
+    if split_ratio is not None:
+        query = query.where(Fine_Tuning.split_ratio == split_ratio)
+    if base_model is not None:
+        query = query.where(Fine_Tuning.base_model == base_model)
+
+    jobs = session.exec(query).all()
+    logger.info(
+        f"[fetch_by_document_id]Found {len(jobs)} fine-tune jobs for document_id={document_id}, project_id={project_id}"
+    )
+    return jobs
+
+
+def fetch_active_jobs_by_document_id(
+    session: Session,
+    document_id: UUID,
+    project_id: int,
+    split_ratio: Optional[float] = None,
+    base_model: Optional[str] = None,
+    exclude_job_id: Optional[int] = None,
+) -> list["Fine_Tuning"]:
+    """
+    Return all ACTIVE jobs for the given document & project.
+    Active = status != failed AND is_deleted is false.
+    """
+    stmt = (
+        select(Fine_Tuning)
+        .where(
+            Fine_Tuning.document_id == document_id,
+            Fine_Tuning.project_id == project_id,
+            Fine_Tuning.is_deleted.is_(False),
+            Fine_Tuning.status != FineTuningStatus.failed,
+        )
+        .order_by(Fine_Tuning.inserted_at.desc())
+    )
+
+    if split_ratio is not None:
+        stmt = stmt.where(Fine_Tuning.split_ratio == split_ratio)
+
+    if base_model is not None:
+        stmt = stmt.where(Fine_Tuning.base_model == base_model)
+
+    if exclude_job_id is not None:
+        stmt = stmt.where(Fine_Tuning.id != exclude_job_id)
+
+    return session.exec(stmt).all()
+
+
+def update_finetune_job(
+    session: Session,
+    job: Fine_Tuning,
+    update: FineTuningUpdate,
+) -> Fine_Tuning:
+    for key, value in update.dict(exclude_unset=True).items():
+        setattr(job, key, value)
+
+    job.updated_at = now()
+
+    session.add(job)
+    session.commit()
+    session.refresh(job)
+
+    logger.info(
+        f"[update_finetune_job]Updated fine-tune job ID={job.id}, project_id={job.project_id}"
+    )
+    return job
diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py
index 169267f8..d4291be6 100644
--- a/backend/app/models/__init__.py
+++ b/backend/app/models/__init__.py
@@ -61,6 +61,8 @@
     Fine_Tuning,
     FineTuningJobCreate,
     FineTuningJobPublic,
+    FineTuningUpdate,
+    FineTuningStatus,
 )
 
 from .openai_conversation import (
diff --git a/backend/app/models/fine_tuning.py b/backend/app/models/fine_tuning.py
index 5f522676..813628af 100644
--- a/backend/app/models/fine_tuning.py
+++ b/backend/app/models/fine_tuning.py
@@ -2,6 +2,9 @@
 from uuid import UUID
 from enum import Enum
 from datetime import datetime
+
+from sqlalchemy import Column, Text
+from pydantic import field_validator
 from sqlmodel import SQLModel, Field, Relationship
 
 from app.core.util import now
@@ -20,12 +23,33 @@ class FineTuningJobBase(SQLModel):
     document_id: UUID = Field(foreign_key="document.id", nullable=False)
     training_file_id: Optional[str] = Field(default=None)
     testing_file_id: Optional[str] = Field(default=None)
+    system_prompt: str = Field(sa_column=Column(Text, nullable=False))
 
 
 class FineTuningJobCreate(SQLModel):
+    document_id: UUID
     base_model: str
     split_ratio: list[float]
-    document_id: UUID
+    system_prompt: str
+
+    @field_validator("split_ratio")
+    @classmethod
+    def check_ratios(cls, v):
+        if not v:
+            raise ValueError("split_ratio cannot be empty")
+        for ratio in v:
+            if not (0 < ratio < 1):
+                raise ValueError(
+                    f"Invalid split_ratio: {ratio}. Must be between 0 and 1."
+                )
+        return v
+
+    @field_validator("system_prompt")
+    @classmethod
+    def check_prompt(cls, v):
+        if not v.strip():
+            raise ValueError("system_prompt must be a non-empty string")
+        return v.strip()
 
 
 class Fine_Tuning(FineTuningJobBase, table=True):
diff --git a/backend/app/seed_data/seed_data.json b/backend/app/seed_data/seed_data.json
index 8d4ce12e..3427375b 100644
--- a/backend/app/seed_data/seed_data.json
+++ b/backend/app/seed_data/seed_data.json
@@ -94,5 +94,19 @@
       "project_name": "Dalgo",
       "organization_name": "Project Tech4dev"
     }
-  ]
+  ],
+  "documents": [
+      {
+        "fname": "glific_sample.json",
+        "object_store_url": "s3://test-bucket/glific_sample.json",
+        "project_name": "Glific",
+        "organization_name": "Project Tech4dev"
+      },
+      {
+        "fname": "dalgo_sample.json",
+        "object_store_url": "s3://test-bucket/dalgo_sample.json",
+        "project_name": "Dalgo",
+        "organization_name": "Project Tech4dev"
+      }
+    ]
 }
diff --git a/backend/app/seed_data/seed_data.py b/backend/app/seed_data/seed_data.py
index 2c91d26f..5b629d24 100644
--- a/backend/app/seed_data/seed_data.py
+++ b/backend/app/seed_data/seed_data.py
@@ -10,7 +10,15 @@
 from app.core.db import engine
 from app.core import settings
 from app.core.security import encrypt_api_key, get_password_hash
-from app.models import APIKey, Organization, Project, User, Credential, Assistant
+from app.models import (
+    APIKey,
+    Organization,
+    Project,
+    User,
+    Credential,
+    Assistant,
+    Document,
+)
 
 
 # Pydantic models for data validation
@@ -65,6 +73,13 @@ class AssistantData(BaseModel):
     organization_name: str
 
 
+class DocumentData(BaseModel):
+    fname: str
+    object_store_url: str
+    organization_name: str
+    project_name: str
+
+
 def load_seed_data() -> dict:
     """Load seed data from JSON file."""
     json_path = Path(__file__).parent / "seed_data.json"
@@ -276,6 +291,62 @@ def create_assistant(session: Session, assistant_data_raw: dict) -> Assistant:
         raise
 
 
+def create_document(session: Session, document_data_raw: dict) -> Document:
+    """Create a document from seed data."""
+    try:
+        document_data = DocumentData.model_validate(document_data_raw)
+        logging.info(f"Creating document: {document_data.fname}")
+
+        # Get the organization
+        organization = session.exec(
+            select(Organization).where(
+                Organization.name == document_data.organization_name
+            )
+        ).first()
+
+        if not organization:
+            raise ValueError(
+                f"Organization '{document_data.organization_name}' not found"
+            )
+
+        # Get the project
+        project = session.exec(
+            select(Project).where(
+                Project.name == document_data.project_name,
+                Project.organization_id == organization.id,
+            )
+        ).first()
+        if not project:
+            raise ValueError(
+                f"Project '{document_data.project_name}' not found in organization '{organization.name}'"
+            )
+
+        users = session.exec(
+            select(User)
+            .join(APIKey, APIKey.user_id == User.id)
+            .where(APIKey.organization_id == organization.id)
+        ).all()
+
+        user = users[1]
+        if not user:
+            raise ValueError(f"No user found in organization '{organization.name}'")
+
+        # Create and store document
+        document = Document(
+            fname=document_data.fname,
+            object_store_url=document_data.object_store_url,
+            owner_id=user.id,
+        )
+
+        session.add(document)
+        session.flush()
+        return document
+
+    except Exception as e:
+        logging.error(f"Error creating document: {e}")
+        raise
+
+
 def clear_database(session: Session) -> None:
     """Clear all seeded data from the database."""
     logging.info("Clearing existing data...")
@@ -358,6 +429,12 @@ def seed_database(session: Session) -> None:
             assistants.append(assistant)
             logging.info(f"Created assistant: {assistant.name} (ID: {assistant.id})")
 
+        documents = []
+        for document_data in seed_data.get("documents", []):
+            document = create_document(session, document_data)
+            documents.append(document)
+            logging.info(f"Created document: {document.fname} (ID: {document.id})")
+
         logging.info("Database seeding completed successfully!")
         session.commit()
     except Exception as e:
diff --git a/backend/app/tests/api/routes/test_fine_tuning.py b/backend/app/tests/api/routes/test_fine_tuning.py
new file mode 100644
index 00000000..6920d8aa
--- /dev/null
+++ b/backend/app/tests/api/routes/test_fine_tuning.py
@@ -0,0 +1,250 @@
+import pytest
+from unittest.mock import patch, MagicMock
+from openai import OpenAIError
+
+from app.crud import fetch_by_id
+from app.tests.utils.test_data import create_test_fine_tuning_jobs
+from app.tests.utils.utils import get_user_from_api_key, get_document
+
+
+def create_file_mock(file_type):
+    counter = {"train": 0, "test": 0}
+
+    def _side_effect(file=None, purpose=None):
+        if purpose == "fine-tune":
+            if "train" in file.name:
+                counter["train"] += 1
+                return MagicMock(id=f"file_{counter['train']}")
+            elif "test" in file.name:
+                counter["test"] += 1
+                return MagicMock(id=f"file_{counter['test']}")
+
+    return _side_effect
+
+
+@pytest.mark.usefixtures("client", "db", "user_api_key_header")
+@patch("app.api.routes.fine_tuning.DataPreprocessor")
+@patch("app.api.routes.fine_tuning.get_openai_client")
+class TestCreateFineTuningJobAPI:
+    def test_finetune_from_csv_multiple_split_ratio(
+        self,
+        mock_get_openai_client,
+        mock_preprocessor_cls,
+        client,
+        db,
+        user_api_key_header,
+    ):
+        user = get_user_from_api_key(db, user_api_key_header)
+        document = get_document(db)
+
+        for path in ["/tmp/train.jsonl", "/tmp/test.jsonl"]:
+            with open(path, "w") as f:
+                f.write("{}")
+
+        mock_preprocessor_cls.return_value = MagicMock(
+            process=MagicMock(
+                return_value={
+                    "train_file": "/tmp/train.jsonl",
+                    "test_file": "/tmp/test.jsonl",
+                }
+            )
+        )
+
+        mock_openai = MagicMock()
+        mock_openai.files.create.side_effect = create_file_mock("fine-tune")
+        mock_openai.fine_tuning.jobs.create.side_effect = [
+            MagicMock(id=f"ft_mock_job_{i}", status="running") for i in range(1, 4)
+        ]
+        mock_get_openai_client.return_value = mock_openai
+
+        body = {
+            "document_id": str(document.id),
+            "base_model": "gpt-4",
+            "split_ratio": [0.5, 0.7, 0.9],
+            "system_prompt": "you are a model able to classify",
+        }
+
+        print("autssh=", user_api_key_header)
+        response = client.post(
+            "/api/v1/fine_tuning/fine-tune", json=body, headers=user_api_key_header
+        )
+        assert response.status_code == 200
+
+        json_data = response.json()
+        assert json_data["success"] is True
+        assert json_data["data"]["message"] == "Fine-tuning job(s) started."
+        assert json_data["metadata"] is None
+
+        for job in json_data["data"]["jobs"]:
+            job_id = int(job["id"])
+            job_db = fetch_by_id(db, job_id=job_id, project_id=user.project_id)
+            assert job_db.training_file_id.startswith("file")
+            assert job_db.testing_file_id.startswith("file")
+            assert job_db.provider_job_id.startswith("ft_mock_job")
+            assert job_db.status == "running"
+
+    def test_data_preprocessing_failure(
+        self,
+        mock_get_openai_client,
+        mock_preprocessor_cls,
+        client,
+        db,
+        user_api_key_header,
+    ):
+        user = get_user_from_api_key(db, user_api_key_header)
+        document = get_document(db)
+
+        mock_preprocessor = MagicMock()
+        mock_preprocessor.process.side_effect = ValueError("Invalid format")
+        mock_preprocessor_cls.return_value = mock_preprocessor
+
+        mock_get_openai_client.return_value = MagicMock()
+
+        body = {
+            "document_id": str(document.id),
+            "base_model": "gpt-4",
+            "split_ratio": [0.8],
+            "system_prompt": "you are a model able to classify",
+        }
+
+        response = client.post(
+            "/api/v1/fine_tuning/fine-tune", json=body, headers=user_api_key_header
+        )
+        assert response.status_code == 200
+
+        job_id = response.json()["data"]["jobs"][0]["id"]
+        job = fetch_by_id(db, job_id, user.project_id)
+        assert job.status == "failed"
+
+    def test_openai_job_creation_failure(
+        self,
+        mock_get_openai_client,
+        mock_preprocessor_cls,
+        client,
+        db,
+        user_api_key_header,
+    ):
+        user = get_user_from_api_key(db, user_api_key_header)
+        document = get_document(db)
+
+        mock_preprocessor_cls.return_value = MagicMock(
+            process=MagicMock(
+                return_value={
+                    "train_file": "/tmp/train.jsonl",
+                    "test_file": "/tmp/test.jsonl",
+                }
+            )
+        )
+
+        openai_error = OpenAIError("Job create failed")
+        openai_error.body = {"message": "Job create failed"}
+
+        mock_openai = MagicMock()
+        mock_openai.files.create.side_effect = create_file_mock("fine-tune")
+        mock_openai.fine_tuning.jobs.create.side_effect = openai_error
+        mock_get_openai_client.return_value = mock_openai
+
+        body = {
+            "document_id": str(document.id),
+            "base_model": "gpt-4",
+            "split_ratio": [0.8],
+            "system_prompt": "you are a model able to classify",
+        }
+
+        response = client.post(
+            "/api/v1/fine_tuning/fine-tune", json=body, headers=user_api_key_header
+        )
+        print("resposneee=", response)
+        assert response.status_code == 200
+
+        job_id = response.json()["data"]["jobs"][0]["id"]
+        job = fetch_by_id(db, job_id, user.project_id)
+        assert job.status == "failed"
+
+
+@pytest.mark.usefixtures("client", "db", "user_api_key_header")
+@patch("app.api.routes.fine_tuning.get_openai_client")
+class TestRetriveFineTuningJobAPI:
+    def test_retrieve_fine_tuning_job(
+        self, mock_get_openai_client, client, db, user_api_key_header
+    ):
+        jobs, _ = create_test_fine_tuning_jobs(db, [0.3])
+        job = jobs[0]
+        job.provider_job_id = "ft_mock_job_123"
+        db.flush()
+
+        mock_openai_job = MagicMock(
+            status="succeeded",
+            fine_tuned_model="ft:gpt-4:custom-model",
+            error=None,
+        )
+
+        mock_openai = MagicMock()
+        mock_openai.fine_tuning.jobs.retrieve.return_value = mock_openai_job
+        mock_get_openai_client.return_value = mock_openai
+
+        response = client.get(
+            f"/api/v1/fine_tuning/{job.id}/refresh", headers=user_api_key_header
+        )
+
+        assert response.status_code == 200
+        json_data = response.json()
+
+        assert json_data["data"]["status"] == "completed"
+        assert json_data["data"]["fine_tuned_model"] == "ft:gpt-4:custom-model"
+        assert json_data["data"]["id"] == job.id
+
+    def test_retrieve_fine_tuning_job_failed(
+        self, mock_get_openai_client, client, db, user_api_key_header
+    ):
+        jobs, _ = create_test_fine_tuning_jobs(db, [0.3])
+        job = jobs[0]
+        job.provider_job_id = "ft_mock_job_123"
+        db.flush()
+
+        mock_openai_job = MagicMock(
+            status="failed",
+            fine_tuned_model=None,
+            error=MagicMock(message="Invalid file format for openai fine tuning"),
+        )
+
+        mock_openai = MagicMock()
+        mock_openai.fine_tuning.jobs.retrieve.return_value = mock_openai_job
+        mock_get_openai_client.return_value = mock_openai
+
+        response = client.get(
+            f"/api/v1/fine_tuning/{job.id}/refresh", headers=user_api_key_header
+        )
+
+        assert response.status_code == 200
+        json_data = response.json()
+        assert json_data["data"]["status"] == "failed"
+        assert (
+            json_data["data"]["error_message"]
+            == "Invalid file format for openai fine tuning"
+        )
+        assert json_data["data"]["id"] == job.id
+
+
+@pytest.mark.usefixtures("client", "db", "user_api_key_header")
+class TestFetchJob:
+    def test_fetch_jobs_document(self, client, db, user_api_key_header):
+        jobs, _ = create_test_fine_tuning_jobs(db, [0.3, 0.4])
+        document = get_document(db)
+
+        response = client.get(
+            f"/api/v1/fine_tuning/{document.id}", headers=user_api_key_header
+        )
+        assert response.status_code == 200
+        json_data = response.json()
+
+        assert json_data["success"] is True
+        assert json_data["metadata"] is None
+        assert len(json_data["data"]) == 2
+
+        job_ratios = sorted([job["split_ratio"] for job in json_data["data"]])
+        assert job_ratios == sorted([0.3, 0.4])
+
+        for job in json_data["data"]:
+            assert job["document_id"] == str(document.id)
+            assert job["status"] == "pending"
diff --git a/backend/app/tests/crud/test_fine_tuning.py b/backend/app/tests/crud/test_fine_tuning.py
new file mode 100644
index 00000000..387f6d55
--- /dev/null
+++ b/backend/app/tests/crud/test_fine_tuning.py
@@ -0,0 +1,138 @@
+import pytest
+from sqlmodel import Session
+from fastapi import HTTPException
+
+from app.models import FineTuningUpdate, FineTuningJobCreate, FineTuningStatus
+from app.crud import (
+    create_fine_tuning_job,
+    fetch_by_provider_job_id,
+    fetch_by_id,
+    fetch_by_document_id,
+    update_finetune_job,
+    fetch_active_jobs_by_document_id,
+)
+from app.tests.utils.test_data import create_test_fine_tuning_jobs
+from app.tests.utils.utils import get_project, get_document
+
+
+def test_create_fine_tuning_job(db: Session):
+    project = get_project(db, "Dalgo")
+    document = get_document(db)
+
+    job_request = FineTuningJobCreate(
+        document_id=document.id,
+        base_model="gpt-4",
+        split_ratio=[0.8, 0.2],
+        system_prompt="you are a model able to classify",
+    )
+
+    created_job, created = create_fine_tuning_job(
+        session=db,
+        request=job_request,
+        split_ratio=0.8,
+        project_id=project.id,
+        organization_id=project.organization_id,
+    )
+
+    assert created is True
+    assert created_job.id is not None
+    assert created_job.document_id == document.id
+    assert created_job.split_ratio == 0.8
+    assert created_job.project_id == project.id
+    assert created_job.provider_job_id is None
+
+
+def test_fetch_by_provider_job_id_success(db: Session):
+    jobs, _ = create_test_fine_tuning_jobs(db, ratios=[0.3])
+    job = jobs[0]
+
+    result = fetch_by_provider_job_id(
+        db, provider_job_id=job.provider_job_id, project_id=job.project_id
+    )
+    assert result.id == job.id
+
+
+def test_fetch_by_provider_job_id_not_found(db: Session):
+    with pytest.raises(HTTPException) as exc:
+        fetch_by_provider_job_id(db, "invalid_id", project_id=999)
+    assert exc.value.status_code == 404
+
+
+def test_fetch_by_id_success(db: Session):
+    jobs, _ = create_test_fine_tuning_jobs(db, ratios=[0.3, 0.4])
+    job = jobs[0]
+
+    result = fetch_by_id(db, job_id=job.id, project_id=job.project_id)
+    assert result.id == job.id
+
+
+def test_fetch_by_id_not_found(db: Session):
+    with pytest.raises(HTTPException) as exc:
+        fetch_by_id(db, job_id=9999, project_id=1)
+    assert exc.value.status_code == 404
+
+
+def test_fetch_by_document_id_filters(db: Session):
+    jobs, _ = create_test_fine_tuning_jobs(db, ratios=[0.3, 0.4])
+    job = jobs[0]
+
+    results = fetch_by_document_id(
+        session=db,
+        document_id=job.document_id,
+        project_id=job.project_id,
+        split_ratio=job.split_ratio,
+        base_model=job.base_model,
+    )
+    assert len(results) == 1
+    assert results[0].id == job.id
+
+
+def test_update_finetune_job(db: Session):
+    jobs, _ = create_test_fine_tuning_jobs(db, ratios=[0.3, 0.4])
+    job = jobs[0]
+
+    update = FineTuningUpdate(status="completed", fine_tuned_model="ft:gpt-4:custom")
+    updated_job = update_finetune_job(db, job=job, update=update)
+
+    assert updated_job.status == "completed"
+    assert updated_job.fine_tuned_model == "ft:gpt-4:custom"
+
+
+def test_fetch_active_jobs_by_document_id(db: Session):
+    project = get_project(db, "Dalgo")
+    document = get_document(db)
+
+    job_request = FineTuningJobCreate(
+        document_id=document.id,
+        base_model="gpt-4",
+        split_ratio=[0.3, 0.5, 0.7, 0.9],
+        system_prompt="you are a model good at classification",
+    )
+    active_job, _ = create_fine_tuning_job(
+        db,
+        request=job_request,
+        split_ratio=0.3,
+        project_id=project.id,
+        organization_id=project.organization_id,
+        status=FineTuningStatus.running,
+    )
+
+    failed_job, _ = create_fine_tuning_job(
+        db,
+        request=job_request,
+        split_ratio=0.5,
+        project_id=project.id,
+        organization_id=project.organization_id,
+        status=FineTuningStatus.failed,
+    )
+
+    result = fetch_active_jobs_by_document_id(
+        session=db,
+        document_id=document.id,
+        project_id=project.id,
+    )
+
+    assert len(result) == 1
+    assert result[0].id == active_job.id
+    assert result[0].status == FineTuningStatus.running
+    assert result[0].is_deleted is False
diff --git a/backend/app/tests/utils/test_data.py b/backend/app/tests/utils/test_data.py
index 0d9c5d3b..d5cafc8f 100644
--- a/backend/app/tests/utils/test_data.py
+++ b/backend/app/tests/utils/test_data.py
@@ -4,20 +4,29 @@
     Organization,
     Project,
     APIKey,
+    Document,
     Credential,
     OrganizationCreate,
     ProjectCreate,
     CredsCreate,
+    FineTuningJobCreate,
+    Fine_Tuning,
 )
 from app.crud import (
     create_organization,
     create_project,
     create_api_key,
     set_creds_for_org,
+    create_fine_tuning_job,
 )
 from app.core.providers import Provider
 from app.tests.utils.user import create_random_user
-from app.tests.utils.utils import random_lower_string, generate_random_string
+from app.tests.utils.utils import (
+    random_lower_string,
+    generate_random_string,
+    get_document,
+    get_project,
+)
 
 
 def create_test_organization(db: Session) -> Organization:
@@ -112,3 +121,33 @@ def create_test_credential(db: Session) -> tuple[list[Credential], Project]:
         },
     )
     return set_creds_for_org(session=db, creds_add=creds_data), project
+
+
+def create_test_fine_tuning_jobs(
+    db: Session,
+    ratios: list[float],
+) -> tuple[list[Fine_Tuning], bool]:
+    project = get_project(db, "Dalgo")
+    document = get_document(db)
+    jobs = []
+    any_created = False
+
+    for ratio in ratios:
+        job_request = FineTuningJobCreate(
+            document_id=document.id,
+            base_model="gpt-4",
+            split_ratio=[0.5],
+            system_prompt="str",
+        )
+        job, created = create_fine_tuning_job(
+            session=db,
+            request=job_request,
+            split_ratio=ratio,
+            project_id=project.id,
+            organization_id=project.organization_id,
+        )
+        jobs.append(job)
+        if created:
+            any_created = True
+
+    return jobs, any_created
diff --git a/backend/app/tests/utils/utils.py b/backend/app/tests/utils/utils.py
index 9fb5311f..362b7434 100644
--- a/backend/app/tests/utils/utils.py
+++ b/backend/app/tests/utils/utils.py
@@ -12,7 +12,7 @@
 from app.core.config import settings
 from app.crud.user import get_user_by_email
 from app.crud.api_key import get_api_key_by_value, get_api_key_by_user_id
-from app.models import APIKeyPublic, Project, Assistant, Organization
+from app.models import APIKeyPublic, Project, Assistant, Organization, Document
 
 
 T = TypeVar("T")
@@ -113,6 +113,30 @@ def get_assistant(session: Session, name: str | None = None) -> Assistant:
     return assistant
 
 
+def get_document(session: Session, fname: str | None = None) -> Document:
+    """
+    Retrieve an active document from the database.
+
+    If a document filename is provided, fetch the active document with that name.
+    If no name is provided, fetch any available document.
+    """
+    if fname:
+        statement = (
+            select(Document)
+            .where(Document.fname == fname, Document.deleted_at.is_(None))
+            .limit(1)
+        )
+    else:
+        statement = select(Document).where(Document.deleted_at.is_(None)).limit(1)
+
+    document = session.exec(statement).first()
+
+    if not document:
+        raise ValueError("No active documents found")
+
+    return document
+
+
 def get_organization(session: Session, name: str | None = None) -> Organization:
     """
     Retrieve an active organization from the database.
diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 219c0b03..c4e48a56 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -29,6 +29,8 @@ dependencies = [
     "openai_responses",
     "langfuse>=2.60.3",
     "asgi-correlation-id>=4.3.4",
+    "scikit-learn>=1.7.1",
+    "pandas>=2.3.1",
 ]
 
 [tool.uv]
diff --git a/backend/uv.lock b/backend/uv.lock
index ce719fb4..fa9dd491 100644
--- a/backend/uv.lock
+++ b/backend/uv.lock
@@ -1,8 +1,10 @@
 version = 1
-revision = 2
+revision = 1
 requires-python = ">=3.10, <4.0"
 resolution-markers = [
-    "python_full_version < '3.13'",
+    "python_full_version == '3.12.*'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
     "python_full_version >= '3.13'",
 ]
 
@@ -15,18 +17,18 @@ dependencies = [
     { name = "sqlalchemy" },
     { name = "typing-extensions" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/66/e2/efa88e86029cada2da5941ec664d50d9a3b2a91f5066405c6f90e5016c16/alembic-1.13.2.tar.gz", hash = "sha256:1ff0ae32975f4fd96028c39ed9bb3c867fe3af956bd7bb37343b54c9fe7445ef", size = 1206463 }
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+    { url = "https://files.pythonhosted.org/packages/df/ed/c884465c33c25451e4a5cd4acad154c29e5341e3214e220e7f3478aa4b0d/alembic-1.13.2-py3-none-any.whl", hash = "sha256:6b8733129a6224a9a711e17c99b08462dbf7cc9670ba8f2e2ae9af860ceb1953", size = 232990 },
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 [[package]]
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 version = "0.7.0"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
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+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
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 [[package]]
@@ -39,9 +41,9 @@ dependencies = [
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     { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/78/49/f3f17ec11c4a91fe79275c426658e509b07547f874b14c1a526d86a83fc8/anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb", size = 170983 }
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+    { url = "https://files.pythonhosted.org/packages/9e/ef/7a4f225581a0d7886ea28359179cb861d7fbcdefad29663fc1167b86f69f/anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a", size = 89631 },
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 [[package]]
@@ -62,6 +64,7 @@ dependencies = [
     { name = "moto", extra = ["s3"] },
     { name = "openai" },
     { name = "openai-responses" },
+    { name = "pandas" },
     { name = "passlib", extra = ["bcrypt"] },
     { name = "pre-commit" },
     { name = "psycopg", extra = ["binary"] },
@@ -70,6 +73,7 @@ dependencies = [
     { name = "pyjwt" },
     { name = "pytest" },
     { name = "python-multipart" },
+    { name = "scikit-learn" },
     { name = "sentry-sdk", extra = ["fastapi"] },
     { name = "sqlmodel" },
     { name = "tenacity" },
@@ -100,6 +104,7 @@ requires-dist = [
     { name = "moto", extras = ["s3"], specifier = ">=5.1.1" },
     { name = "openai", specifier = ">=1.67.0" },
     { name = "openai-responses" },
+    { name = "pandas", specifier = ">=2.3.1" },
     { name = "passlib", extras = ["bcrypt"], specifier = ">=1.7.4,<2.0.0" },
     { name = "pre-commit", specifier = ">=3.8.0" },
     { name = "psycopg", extras = ["binary"], specifier = ">=3.1.13,<4.0.0" },
@@ -108,6 +113,7 @@ requires-dist = [
     { name = "pyjwt", specifier = ">=2.8.0,<3.0.0" },
     { name = "pytest", specifier = ">=7.4.4" },
     { name = "python-multipart", specifier = ">=0.0.7,<1.0.0" },
+    { name = "scikit-learn", specifier = ">=1.7.1" },
     { name = "sentry-sdk", extras = ["fastapi"], specifier = ">=1.40.6,<2.0.0" },
     { name = "sqlmodel", specifier = ">=0.0.21,<1.0.0" },
     { name = "tenacity", specifier = ">=8.2.3,<9.0.0" },
@@ -131,37 +137,37 @@ dependencies = [
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