diff --git a/.gitignore b/.gitignore
index ea459c6e9..f62facef6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,6 +27,7 @@ wren-ai-service/demo/custom_dataset
 wren-ai-service/demo/.env
 wren-ai-service/tools/dev/etc/**
 .deepeval-cache.json
+.deepeval_telemtry.txt
 docker/config.yaml
 
 # python
diff --git a/wren-ai-service/eval/.env.example b/wren-ai-service/eval/.env.example
deleted file mode 100644
index 6e08f65f1..000000000
--- a/wren-ai-service/eval/.env.example
+++ /dev/null
@@ -1,12 +0,0 @@
-LANGFUSE_PROJECT_ID=
-OPENAI_API_KEY=
-WREN_IBIS_ENDPOINT=http://localhost:8000
-WREN_ENGINE_ENDPOINT=http://localhost:8080
-WREN_IBIS_TIMEOUT=10
-BATCH_SIZE=4
-BATCH_INTERVAL=1
-
-DATA_SOURCE=bigquery
-bigquery.project-id=
-bigquery.dataset-id=
-bigquery.credentials-key=
\ No newline at end of file
diff --git a/wren-ai-service/eval/.gitignore b/wren-ai-service/eval/.gitignore
index 2eea525d8..65cbde796 100644
--- a/wren-ai-service/eval/.gitignore
+++ b/wren-ai-service/eval/.gitignore
@@ -1 +1,2 @@
-.env
\ No newline at end of file
+.env
+config.yaml
\ No newline at end of file
diff --git a/wren-ai-service/eval/__init__.py b/wren-ai-service/eval/__init__.py
index e69de29bb..e40df2f40 100644
--- a/wren-ai-service/eval/__init__.py
+++ b/wren-ai-service/eval/__init__.py
@@ -0,0 +1,21 @@
+from pydantic import Field, SecretStr
+
+from src.config import Settings
+
+
+class EvalSettings(Settings):
+    langfuse_project_id: str = ""
+    batch_size: int = 4
+    batch_interval: int = 1
+    datasource: str = "bigquery"
+    config_path: str = "eval/config.yaml"
+    openai_api_key: SecretStr = Field(alias="LLM_OPENAI_API_KEY")
+
+    @property
+    def langfuse_url(self) -> str:
+        if not self.langfuse_project_id:
+            return ""
+        return f"{self.langfuse_host.rstrip('/')}/project/{self.langfuse_project_id}"
+
+    def get_openai_api_key(self) -> str:
+        return self.openai_api_key.get_secret_value()
diff --git a/wren-ai-service/eval/data_curation/app.py b/wren-ai-service/eval/data_curation/app.py
index 5c3dd0f14..59ab61c6c 100644
--- a/wren-ai-service/eval/data_curation/app.py
+++ b/wren-ai-service/eval/data_curation/app.py
@@ -1,8 +1,10 @@
 import asyncio
 import os
 import re
+import sys
 import uuid
 from datetime import datetime
+from pathlib import Path
 
 import orjson
 import pandas as pd
@@ -10,6 +12,16 @@
 import tomlkit
 from openai import AsyncClient
 from streamlit_tags import st_tags
+
+sys.path.append(f"{Path().parent.resolve()}")
+from eval import EvalSettings
+from eval.utils import (
+    get_documents_given_contexts,
+    get_eval_dataset_in_toml_string,
+    get_openai_client,
+    prepare_duckdb_init_sql,
+    prepare_duckdb_session_sql,
+)
 from utils import (
     DATA_SOURCES,
     WREN_ENGINE_ENDPOINT,
@@ -21,22 +33,14 @@
     prettify_sql,
 )
 
-from eval.utils import (
-    get_documents_given_contexts,
-    get_eval_dataset_in_toml_string,
-    get_openai_client,
-    prepare_duckdb_init_sql,
-    prepare_duckdb_session_sql,
-)
-
 st.set_page_config(layout="wide")
 st.title("WrenAI Data Curation App")
 
 
 LLM_OPTIONS = ["gpt-4o-mini", "gpt-4o"]
 
-llm_client = get_openai_client()
-
+settings = EvalSettings()
+llm_client = get_openai_client(api_key=settings.get_openai_api_key())
 
 # session states
 if "llm_model" not in st.session_state:
@@ -63,9 +67,9 @@
 def on_change_upload_eval_dataset():
     doc = tomlkit.parse(st.session_state.uploaded_eval_file.getvalue().decode("utf-8"))
 
-    assert (
-        doc["mdl"] == st.session_state["mdl_json"]
-    ), "The model in the uploaded dataset is different from the deployed model"
+    assert doc["mdl"] == st.session_state["mdl_json"], (
+        "The model in the uploaded dataset is different from the deployed model"
+    )
     st.session_state["candidate_dataset"] = doc["eval_dataset"]
 
 
@@ -93,7 +97,7 @@ def on_click_setup_uploaded_file():
     uploaded_file = st.session_state.get("uploaded_mdl_file")
     if uploaded_file:
         match = re.match(
-            r".+_(" + "|".join(DATA_SOURCES) + r")_mdl\.json$",
+            r".+_(" + "|".join(DATA_SOURCES) + r")(_.+)?_mdl\.json$",
             uploaded_file.name,
         )
         if not match:
@@ -126,7 +130,7 @@ def on_click_setup_uploaded_file():
 
 
 def on_change_llm_model():
-    st.toast(f"Switching LLM model to {st.session_state["select_llm_model"]}")
+    st.toast(f"Switching LLM model to {st.session_state['select_llm_model']}")
     st.session_state["llm_model"] = st.session_state["select_llm_model"]
 
 
@@ -338,7 +342,7 @@ def on_click_remove_candidate_dataset_button(i: int):
                         )
                     else:
                         st.error(
-                            f"SQL is invalid: {st.session_state["llm_question_sql_pairs"][i]["error"]}"
+                            f"SQL is invalid: {st.session_state['llm_question_sql_pairs'][i]['error']}"
                         )
 
                     st.button(
@@ -417,7 +421,7 @@ def on_click_remove_candidate_dataset_button(i: int):
                     )
                 else:
                     st.error(
-                        f"SQL is invalid: {st.session_state.get("user_question_sql_pair", {}).get('error', '')}"
+                        f"SQL is invalid: {st.session_state.get('user_question_sql_pair', {}).get('error', '')}"
                     )
 
                 st.button(
@@ -475,7 +479,7 @@ def on_click_remove_candidate_dataset_button(i: int):
         with st.popover("Save as Evaluation Dataset", use_container_width=True):
             file_name = st.text_input(
                 "File Name",
-                f'eval_dataset_{datetime.today().strftime("%Y_%m_%d")}.toml',
+                f"eval_dataset_{datetime.today().strftime('%Y_%m_%d')}.toml",
                 key="eval_dataset_file_name",
             )
             download_btn = st.download_button(
diff --git a/wren-ai-service/eval/evaluation.py b/wren-ai-service/eval/evaluation.py
index acf3f76e9..5254a9038 100644
--- a/wren-ai-service/eval/evaluation.py
+++ b/wren-ai-service/eval/evaluation.py
@@ -1,19 +1,11 @@
 import argparse
-import os
 import sys
 from pathlib import Path
 from typing import Tuple
 
-import dotenv
 from deepeval import evaluate
 from deepeval.evaluate import TestResult
 from deepeval.test_case import LLMTestCase
-from dspy_modules.prompt_optimizer import (
-    build_optimizing_module,
-    configure_llm_provider,
-    optimizer_parameters,
-    prepare_dataset,
-)
 from langfuse import Langfuse
 from langfuse.decorators import langfuse_context, observe
 
@@ -21,7 +13,9 @@
 import traceback
 
 import eval.pipelines as pipelines
-from eval.utils import parse_toml, trace_metadata
+import src.providers as provider
+from eval import EvalSettings
+from eval.utils import engine_config, parse_toml, trace_metadata
 from src import utils
 
 
@@ -88,7 +82,7 @@ def eval(self, meta: dict, predictions: list) -> None:
 
             try:
                 test_case = LLMTestCase(**formatter(prediction, meta))
-                result = evaluate([test_case], self._metrics, ignore_errors=True)[0]
+                result = evaluate([test_case], self._metrics, ignore_errors=True).test_results[0]
                 self._score_metrics(test_case, result)
                 [metric.collect(test_case, result) for metric in self._post_metrics]
             except Exception:
@@ -149,31 +143,33 @@ def _average_score(self, meta: dict) -> None:
 if __name__ == "__main__":
     args = parse_args()
 
-    dotenv.load_dotenv()
-    utils.load_env_vars()
+    settings = EvalSettings()
+    pipe_components = provider.generate_components(settings.components)
+    utils.init_langfuse(settings)
 
     predicted_file = parse_toml(f"outputs/predictions/{args.file}")
     meta = predicted_file["meta"]
     predictions = predicted_file["predictions"]
 
     dataset = parse_toml(meta["evaluation_dataset"])
-    metrics = pipelines.metrics_initiator(
-        meta["pipeline"], dataset["mdl"], args.semantics
-    )
+    engine_info = engine_config(dataset["mdl"], pipe_components)
+    metrics = pipelines.metrics_initiator(meta["pipeline"], engine_info, args.semantics)
 
     evaluator = Evaluator(**metrics)
-    if args.training_dataset:
-        optimizer_parameters["evaluator"] = evaluator
-        optimizer_parameters["metrics"] = metrics
-        optimizer_parameters["meta"] = meta
-        optimizer_parameters["predictions"] = predictions
-        configure_llm_provider(
-            os.getenv("GENERATION_MODEL"), os.getenv("LLM_OPENAI_API_KEY")
-        )
-        trainset, devset = prepare_dataset(args.training_dataset)
-        build_optimizing_module(trainset)
-    else:
-        evaluator.eval(meta, predictions)
+    evaluator.eval(meta, predictions)
+    # if args.training_dataset:
+    #     # todo: for now comment dspy related code
+    #     optimizer_parameters["evaluator"] = evaluator
+    #     optimizer_parameters["metrics"] = metrics
+    #     optimizer_parameters["meta"] = meta
+    #     optimizer_parameters["predictions"] = predictions
+    #     configure_llm_provider(
+    #         os.getenv("GENERATION_MODEL"), os.getenv("LLM_OPENAI_API_KEY")
+    #     )
+    #     trainset, devset = prepare_dataset(args.training_dataset)
+    #     build_optimizing_module(trainset)
+    # else:
+    #     evaluator.eval(meta, predictions)
 
     langfuse_context.flush()
 
diff --git a/wren-ai-service/eval/metrics/accuracy.py b/wren-ai-service/eval/metrics/accuracy.py
index c325a145d..4c37a0c3e 100644
--- a/wren-ai-service/eval/metrics/accuracy.py
+++ b/wren-ai-service/eval/metrics/accuracy.py
@@ -12,12 +12,12 @@
 
 
 class AccuracyMetric(BaseMetric):
-    def __init__(self, engine_config: dict, enable_semantics_comparison: bool = False):
+    def __init__(self, engine_info: dict, enable_semantics_comparison: bool = False):
         self.threshold = 0
         self.score = 0
-        self._engine_config = engine_config
-        self._enable_semantics_comparison = enable_semantics_comparison
-        if self._enable_semantics_comparison:
+        self.engine_info = engine_info
+        self.enable_semantics_comparison = enable_semantics_comparison
+        if self.enable_semantics_comparison:
             self._openai_client = get_openai_client()
 
     def measure(self, test_case: LLMTestCase):
@@ -82,7 +82,7 @@ def _rewrite_sql(self, sql: str) -> str:
         return sql
 
     async def _retrieve_data(self, sql: str) -> pd.DataFrame:
-        response = await get_data_from_wren_engine(sql=sql, **self._engine_config)
+        response = await get_data_from_wren_engine(sql=sql, **self.engine_info)
 
         df = pd.DataFrame(**response)
         sorted_columns = sorted(df.columns)
@@ -145,7 +145,7 @@ async def a_measure(self, test_case: LLMTestCase, *args, **kwargs):
 
             self.score = self._count_partial_matches(expected_dataset, actual_dataset)
             # use llm to check sql semantics
-            if self.score == 0 and self._enable_semantics_comparison:
+            if self.score == 0 and self.enable_semantics_comparison:
                 # TODO: we may need to upload the sql semantics result to langfuse
                 print(f"before _check_sql_semantics: {self.score}")
                 print(f"expected sql: {rewritten_expected_output}")
diff --git a/wren-ai-service/eval/metrics/answer_relevancy.py b/wren-ai-service/eval/metrics/answer_relevancy.py
index 33e6c9f6f..c1e16258e 100644
--- a/wren-ai-service/eval/metrics/answer_relevancy.py
+++ b/wren-ai-service/eval/metrics/answer_relevancy.py
@@ -7,21 +7,21 @@
 
 
 class AnswerRelevancyMetric(BaseMetric):
-    def __init__(self, engine_config: dict):
+    def __init__(self, engine_info: dict):
         self.threshold = 0
         self.score = 0
-        self._engine_config = engine_config
+        self.engine_info = engine_info
 
     def measure(self, test_case: LLMTestCase):
         return asyncio.run(self.a_measure(test_case))
 
     async def a_measure(self, test_case: LLMTestCase, *args, **kwargs):
         actual_units = await get_contexts_from_sql(
-            sql=test_case.actual_output, **self._engine_config
+            sql=test_case.actual_output, **self.engine_info
         )
 
         expected_units = await get_contexts_from_sql(
-            sql=test_case.expected_output, **self._engine_config
+            sql=test_case.expected_output, **self.engine_info
         )
 
         intersection = set(actual_units) & set(expected_units)
diff --git a/wren-ai-service/eval/metrics/context_recall.py b/wren-ai-service/eval/metrics/context_recall.py
index 9dff4bb0b..07cb9f544 100644
--- a/wren-ai-service/eval/metrics/context_recall.py
+++ b/wren-ai-service/eval/metrics/context_recall.py
@@ -7,17 +7,17 @@
 
 
 class ContextualRecallMetric(BaseMetric):
-    def __init__(self, engine_config: dict):
+    def __init__(self, engine_info: dict):
         self.threshold = 0
         self.score = 0
-        self._engine_config = engine_config
+        self.engine_info = engine_info
 
     def measure(self, test_case: LLMTestCase):
         return asyncio.run(self.a_measure(test_case))
 
     async def a_measure(self, test_case: LLMTestCase, *args, **kwargs):
         expected_units = await get_contexts_from_sql(
-            sql=test_case.expected_output, **self._engine_config
+            sql=test_case.expected_output, **self.engine_info
         )
 
         intersection = set(test_case.retrieval_context) & set(expected_units)
diff --git a/wren-ai-service/eval/metrics/faithfulness.py b/wren-ai-service/eval/metrics/faithfulness.py
index 6ea7a7197..ff17e0eaf 100644
--- a/wren-ai-service/eval/metrics/faithfulness.py
+++ b/wren-ai-service/eval/metrics/faithfulness.py
@@ -7,17 +7,17 @@
 
 
 class FaithfulnessMetric(BaseMetric):
-    def __init__(self, engine_config: dict):
+    def __init__(self, engine_info: dict):
         self.threshold = 0
         self.score = 0
-        self._engine_config = engine_config
+        self.engine_info = engine_info
 
     def measure(self, test_case: LLMTestCase):
         return asyncio.run(self.a_measure(test_case))
 
     async def a_measure(self, test_case: LLMTestCase, *args, **kwargs):
         actual_units = await get_contexts_from_sql(
-            sql=test_case.actual_output, **self._engine_config
+            sql=test_case.actual_output, **self.engine_info
         )
         intersection = set(actual_units) & set(test_case.retrieval_context)
         self.score = len(intersection) / len(actual_units)
diff --git a/wren-ai-service/eval/pipelines.py b/wren-ai-service/eval/pipelines.py
index d7a09f106..6aa292bfd 100644
--- a/wren-ai-service/eval/pipelines.py
+++ b/wren-ai-service/eval/pipelines.py
@@ -1,5 +1,4 @@
 import asyncio
-import os
 import re
 import sys
 from abc import abstractmethod
@@ -13,6 +12,7 @@
 
 sys.path.append(f"{Path().parent.resolve()}")
 
+from eval import EvalSettings
 from eval.metrics import (
     AccuracyMetric,
     AccuracyMultiCandidateMetric,
@@ -20,8 +20,6 @@
     ContextualPrecisionMetric,
     ContextualRecallMetric,
     ContextualRelevancyMetric,
-    ExactMatchAccuracy,
-    ExecutionAccuracy,
     FaithfulnessMetric,
 )
 from eval.utils import (
@@ -29,14 +27,10 @@
     get_contexts_from_sql,
     trace_metadata,
 )
-from src.core.engine import Engine
-from src.core.provider import DocumentStoreProvider, EmbedderProvider, LLMProvider
-from src.pipelines.generation import sql_generation
-from src.pipelines.indexing import indexing
-from src.pipelines.retrieval import retrieval
+from src.pipelines import generation, indexing, retrieval
 
 
-def deploy_model(mdl: str, pipe: indexing.Indexing) -> None:
+def deploy_model(mdl: str, pipe: indexing.DBSchema) -> None:
     async def wrapper():
         await pipe.run(orjson.dumps(mdl).decode())
 
@@ -118,8 +112,7 @@ async def wrapper(batch: list):
         return [prediction for batch in batches for prediction in batch]
 
     @abstractmethod
-    def _process(self, prediction: dict, **_) -> dict:
-        ...
+    def _process(self, prediction: dict, **_) -> dict: ...
 
     async def _flat(self, prediction: dict, **_) -> dict:
         """
@@ -176,26 +169,23 @@ def __init__(
         self,
         meta: dict,
         mdl: dict,
-        llm_provider: LLMProvider,
-        embedder_provider: EmbedderProvider,
-        document_store_provider: DocumentStoreProvider,
+        pipe_components: dict,
+        settings: EvalSettings,
         **kwargs,
     ):
         super().__init__(meta)
 
-        document_store_provider.get_store(recreate_index=True)
-        _indexing = indexing.Indexing(
-            embedder_provider=embedder_provider,
-            document_store_provider=document_store_provider,
+        _indexing = indexing.DBSchema(
+            **pipe_components["db_schema_indexing"],
+            column_batch_size=settings.column_indexing_batch_size,
         )
         deploy_model(mdl, _indexing)
 
         self._retrieval = retrieval.Retrieval(
-            llm_provider=llm_provider,
-            embedder_provider=embedder_provider,
-            document_store_provider=document_store_provider,
-            table_retrieval_size=meta["table_retrieval_size"],
-            table_column_retrieval_size=meta["table_column_retrieval_size"],
+            **pipe_components["db_schema_retrieval"],
+            table_retrieval_size=settings.table_retrieval_size,
+            table_column_retrieval_size=settings.table_column_retrieval_size,
+            allow_using_db_schemas_without_pruning=settings.allow_using_db_schemas_without_pruning,
         )
 
     async def _process(self, prediction: dict, **_) -> dict:
@@ -211,10 +201,10 @@ async def __call__(self, query: str, **_):
         return [prediction, await self.flat(prediction.copy())]
 
     @staticmethod
-    def mertics(config: dict) -> dict:
+    def metrics(engine_info: dict) -> dict:
         return {
             "metrics": [
-                ContextualRecallMetric(config),
+                ContextualRecallMetric(engine_info=engine_info),
                 ContextualRelevancyMetric(),
                 ContextualPrecisionMetric(),
             ]
@@ -226,21 +216,21 @@ def __init__(
         self,
         meta: dict,
         mdl: dict,
-        llm_provider: LLMProvider,
-        engine: Engine,
+        pipe_components: dict,
         **kwargs,
     ):
-        super().__init__(meta, 3)
+        super().__init__(meta)
         self._mdl = mdl
-        self._generation = sql_generation.SQLGeneration(
-            llm_provider=llm_provider,
-            engine=engine,
+        self._generation = generation.SQLGeneration(
+            **pipe_components["sql_generation"],
         )
 
+        self._engine_info = engine_config(mdl, pipe_components)
+
     async def _flat(self, prediction: dict, actual: str) -> dict:
         prediction["actual_output"] = actual
         prediction["actual_output_units"] = await get_contexts_from_sql(
-            sql=actual["sql"], **engine_config(self._mdl)
+            sql=actual["sql"], **self._engine_info
         )
 
         return prediction
@@ -273,16 +263,15 @@ async def __call__(self, query: str, **_):
         ]
 
     @staticmethod
-    def mertics(
-        config: dict, accuracy_config: dict, enable_semantics_comparison: bool
-    ) -> dict:
+    def metrics(engine_info: dict, enable_semantics_comparison: bool) -> dict:
         return {
             "metrics": [
-                AccuracyMetric(accuracy_config, enable_semantics_comparison),
-                AnswerRelevancyMetric(config),
-                FaithfulnessMetric(config),
-                ExactMatchAccuracy(),
-                ExecutionAccuracy(),
+                AccuracyMetric(engine_info=engine_info, enable_semantics_comparison=enable_semantics_comparison),
+                AnswerRelevancyMetric(engine_info=engine_info),
+                FaithfulnessMetric(engine_info=engine_info),
+                # this is for spider dataset, rn we temporarily disable it
+                # ExactMatchAccuracy(),
+                # ExecutionAccuracy(),
             ],
             "post_metrics": [AccuracyMultiCandidateMetric()],
         }
@@ -293,36 +282,34 @@ def __init__(
         self,
         meta: dict,
         mdl: dict,
-        llm_provider: LLMProvider,
-        embedder_provider: EmbedderProvider,
-        document_store_provider: DocumentStoreProvider,
-        engine: Engine,
+        pipe_components: dict,
+        settings: EvalSettings,
         **kwargs,
     ):
-        super().__init__(meta, 3)
+        super().__init__(meta)
 
-        document_store_provider.get_store(recreate_index=True)
-        _indexing = indexing.Indexing(
-            embedder_provider=embedder_provider,
-            document_store_provider=document_store_provider,
+        _indexing = indexing.DBSchema(
+            **pipe_components["db_schema_indexing"],
+            column_batch_size=settings.column_indexing_batch_size,
         )
         deploy_model(mdl, _indexing)
 
-        self._mdl = mdl
         self._retrieval = retrieval.Retrieval(
-            llm_provider=llm_provider,
-            embedder_provider=embedder_provider,
-            document_store_provider=document_store_provider,
+            **pipe_components["db_schema_retrieval"],
+            table_retrieval_size=settings.table_retrieval_size,
+            table_column_retrieval_size=settings.table_column_retrieval_size,
+            allow_using_db_schemas_without_pruning=settings.allow_using_db_schemas_without_pruning,
         )
-        self._generation = sql_generation.SQLGeneration(
-            llm_provider=llm_provider,
-            engine=engine,
+        self._generation = generation.SQLGeneration(
+            **pipe_components["sql_generation"],
         )
 
+        self._engine_info = engine_config(mdl, pipe_components)
+
     async def _flat(self, prediction: dict, actual: str) -> dict:
         prediction["actual_output"] = actual
         prediction["actual_output_units"] = await get_contexts_from_sql(
-            sql=actual["sql"], **engine_config(self._mdl)
+            sql=actual["sql"], **self._engine_info
         )
         return prediction
 
@@ -332,8 +319,7 @@ async def _process(self, prediction: dict, **_) -> dict:
         actual_output = await self._generation.run(
             query=prediction["input"],
             contexts=documents,
-            samples=prediction["samples"],
-            exclude=[],
+            sql_samples=[],
         )
 
         prediction["actual_output"] = actual_output
@@ -355,19 +341,21 @@ async def __call__(self, query: str, **_):
         ]
 
     @staticmethod
-    def mertics(
-        config: dict, accuracy_config: dict, enable_semantics_comparison: bool
-    ) -> dict:
+    def metrics(engine_info: dict, enable_semantics_comparison: bool) -> dict:
         return {
             "metrics": [
-                AccuracyMetric(accuracy_config, enable_semantics_comparison),
-                AnswerRelevancyMetric(config),
-                FaithfulnessMetric(config),
-                ContextualRecallMetric(config),
+                AccuracyMetric(
+                    engine_info=engine_info,
+                    enable_semantics_comparison=enable_semantics_comparison,
+                ),
+                AnswerRelevancyMetric(engine_info=engine_info),
+                FaithfulnessMetric(engine_info=engine_info),
+                ContextualRecallMetric(engine_info=engine_info),
                 ContextualRelevancyMetric(),
                 ContextualPrecisionMetric(),
-                ExactMatchAccuracy(),
-                ExecutionAccuracy(),
+                # this is for spider dataset, rn we temporarily disable it
+                # ExactMatchAccuracy(),
+                # ExecutionAccuracy(),
             ],
             "post_metrics": [AccuracyMultiCandidateMetric()],
         }
@@ -377,9 +365,16 @@ def init(
     name: Literal["retrieval", "generation", "ask"],
     meta: dict,
     mdl: dict,
-    providers: Dict[str, Any],
+    components: Dict[str, Any],
+    settings: EvalSettings,
 ) -> Eval:
-    args = {"meta": meta, "mdl": mdl, **providers}
+    args = {
+        "meta": meta,
+        "mdl": mdl,
+        "pipe_components": components,
+        "settings": settings,
+    }
+
     match name:
         case "retrieval":
             return RetrievalPipeline(**args)
@@ -392,38 +387,14 @@ def init(
 
 
 def metrics_initiator(
-    pipeline: str, mdl: dict, enable_semantics_comparison: bool = True
+    pipeline: str,
+    engine_info: dict,
+    enable_semantics_comparison: bool = True,
 ) -> dict:
-    # todo: refactor configs
-    config = engine_config(mdl)
-    if os.getenv("DATA_SOURCE") == "bigquery":
-        accuracy_config = {
-            "api_endpoint": os.getenv("WREN_IBIS_ENDPOINT"),
-            "data_source": "bigquery",
-            "mdl_json": mdl,
-            "connection_info": {
-                "project_id": os.getenv("bigquery.project-id"),
-                "dataset_id": os.getenv("bigquery.dataset-id"),
-                "credentials": os.getenv("bigquery.credentials-key"),
-            },
-            "timeout": int(os.getenv("WREN_IBIS_TIMEOUT"))
-            if os.getenv("WREN_IBIS_TIMEOUT")
-            else 10,
-            "limit": 10,
-        }
-
-    if os.getenv("DATA_SOURCE") == "duckdb":
-        accuracy_config = config
-        accuracy_config["connection_info"] = None
-
     match pipeline:
         case "retrieval":
-            return RetrievalPipeline.mertics(config)
+            return RetrievalPipeline.metrics(engine_info)
         case "generation":
-            return GenerationPipeline.mertics(
-                config, accuracy_config, enable_semantics_comparison
-            )
+            return GenerationPipeline.metrics(engine_info, enable_semantics_comparison)
         case "ask":
-            return AskPipeline.mertics(
-                config, accuracy_config, enable_semantics_comparison
-            )
+            return AskPipeline.metrics(engine_info, enable_semantics_comparison)
diff --git a/wren-ai-service/eval/prediction.py b/wren-ai-service/eval/prediction.py
index 8735f318f..d35eae199 100644
--- a/wren-ai-service/eval/prediction.py
+++ b/wren-ai-service/eval/prediction.py
@@ -1,14 +1,10 @@
 import argparse
-import base64
-import os
 import sys
 import uuid
 from datetime import datetime
 from pathlib import Path
 from typing import Any, Dict, Tuple
 
-import dotenv
-import orjson
 from git import Repo
 from langfuse.decorators import langfuse_context
 from tomlkit import document, dumps
@@ -17,26 +13,19 @@
 import eval.pipelines as pipelines
 import src.providers as provider
 import src.utils as utils
+from eval import EvalSettings
 from eval.utils import parse_toml
-from src.core.engine import EngineConfig
-from src.core.provider import EmbedderProvider, LLMProvider
 
 
 def generate_meta(
     path: str,
     dataset: dict,
     pipe: str,
-    llm_provider: LLMProvider,
-    embedder_provider: EmbedderProvider,
+    settings: EvalSettings,
     **kwargs,
 ) -> Dict[str, Any]:
-    if langfuse_project_id := os.getenv("LANGFUSE_PROJECT_ID"):
-        langfuse_url = f'{os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com").rstrip('/')}/project/{langfuse_project_id}'
-    else:
-        langfuse_url = ""
-
     return {
-        "langfuse_url": langfuse_url,
+        "langfuse_url": settings.langfuse_url,
         "user_id": "wren-evaluator",  # this property is using for langfuse
         "session_id": f"eval_{pipe}_{uuid.uuid4()}",
         "date": datetime.now(),
@@ -44,18 +33,14 @@ def generate_meta(
         "evaluation_dataset": path,
         "query_count": len(dataset["eval_dataset"]),
         "commit": obtain_commit_hash(),
-        "embedding_model": embedder_provider.get_model(),
-        "generation_model": llm_provider.get_model(),
-        "column_indexing_batch_size": int(os.getenv("COLUMN_INDEXING_BATCH_SIZE"))
-        or 50,
-        "table_retrieval_size": int(os.getenv("TABLE_RETRIEVAL_SIZE")) or 10,
-        "table_column_retrieval_size": int(os.getenv("TABLE_COLUMN_RETRIEVAL_SIZE"))
-        or 100,
+        "column_indexing_batch_size": settings.column_indexing_batch_size,
+        "table_retrieval_size": settings.table_retrieval_size,
+        "table_column_retrieval_size": settings.table_column_retrieval_size,
         "pipeline": pipe,
-        "batch_size": os.getenv("BATCH_SIZE") or 4,
-        "batch_interval": os.getenv("BATCH_INTERVAL") or 1,
+        "batch_size": settings.batch_size,
+        "batch_interval": settings.batch_interval,
         "catalog": dataset["mdl"]["catalog"],
-        "datasource": os.getenv("DATA_SOURCE"),
+        "datasource": settings.datasource,
     }
 
 
@@ -65,7 +50,7 @@ def write_prediction(
     if Path(dir_path).exists() is False:
         Path(dir_path).mkdir(parents=True, exist_ok=True)
 
-    output_file = f"prediction_{meta['session_id']}_{meta['date'].strftime("%Y_%m_%d_%H%M%S")}.toml"
+    output_file = f"prediction_{meta['session_id']}_{meta['date'].strftime('%Y_%m_%d_%H%M%S')}.toml"
     output_path = f"{dir_path}/{output_file}"
 
     doc = document()
@@ -94,60 +79,7 @@ def obtain_commit_hash() -> str:
     return f"{repo.head.commit}@{branch.name}"
 
 
-def init_providers(mdl: dict) -> dict:
-    engine_config = None
-
-    if os.getenv("DATA_SOURCE") == "bigquery":
-        engine_config = EngineConfig(
-            provider="wren_ibis",
-            config={
-                "source": "bigquery",
-                "manifest": base64.b64encode(orjson.dumps(mdl)).decode(),
-                "connection_info": base64.b64encode(
-                    orjson.dumps(
-                        {
-                            "project_id": os.getenv("bigquery.project-id"),
-                            "dataset_id": os.getenv("bigquery.dataset-id"),
-                            "credentials": os.getenv("bigquery.credentials-key"),
-                        }
-                    )
-                ).decode(),
-            },
-        )
-
-    if os.getenv("DATA_SOURCE") == "duckdb":
-        print("datasource is duckdb")
-        # init duckdb
-        from eval.utils import prepare_duckdb_init_sql, prepare_duckdb_session_sql
-
-        endpoint = os.getenv("WREN_ENGINE_ENDPOINT")
-        os.environ["WREN_ENGINE_MANIFEST"] = base64.b64encode(
-            orjson.dumps(mdl)
-        ).decode()
-
-        prepare_duckdb_session_sql(endpoint)
-        prepare_duckdb_init_sql(endpoint, mdl["catalog"])
-
-        engine_config = EngineConfig(
-            provider="wren_engine",
-            config={
-                "endpoint": endpoint,
-            },
-        )
-
-    if engine_config is None:
-        raise ValueError("Invalid datasource")
-
-    providers = provider.init_providers(engine_config=engine_config)
-    return {
-        "llm_provider": providers[0],
-        "embedder_provider": providers[1],
-        "document_store_provider": providers[2],
-        "engine": providers[3],
-    }
-
-
-def parse_args() -> Tuple[str]:
+def parse_args() -> Tuple[str, str]:
     parser = argparse.ArgumentParser()
     parser.add_argument(
         "--file",
@@ -163,31 +95,27 @@ def parse_args() -> Tuple[str]:
         help="Specify the pipeline that you want to evaluate",
     )
     args = parser.parse_args()
-    return f"eval/dataset/{args.file}", args.pipeline
+    return args.file, args.pipeline
 
 
 if __name__ == "__main__":
     path, pipe_name = parse_args()
 
-    dotenv.load_dotenv()
-    utils.load_env_vars()
-    utils.init_langfuse()
+    settings = EvalSettings()
+    # todo: handle duckdb as datasource
+    pipe_components = provider.generate_components(settings.components)
+    utils.init_langfuse(settings)
 
     dataset = parse_toml(path)
-    providers = init_providers(dataset["mdl"])
 
-    meta = generate_meta(
-        path=path,
-        dataset=dataset,
-        pipe=pipe_name,
-        **providers,
-    )
+    meta = generate_meta(path=path, dataset=dataset, pipe=pipe_name, settings=settings)
 
-    pipe = pipelines.init(
+    pipe: pipelines.Eval = pipelines.init(
         pipe_name,
         meta,
         mdl=dataset["mdl"],
-        providers=providers,
+        components=pipe_components,
+        settings=settings,
     )
 
     predictions = pipe.predict(dataset["eval_dataset"])
diff --git a/wren-ai-service/eval/utils.py b/wren-ai-service/eval/utils.py
index 66be051e3..bf747898d 100644
--- a/wren-ai-service/eval/utils.py
+++ b/wren-ai-service/eval/utils.py
@@ -88,7 +88,7 @@ async def get_contexts_from_sql(
     def _get_contexts_from_sql_analysis_results(sql_analysis_results: list[dict]):
         def _compose_contexts_of_select_type(select_items: list[dict]):
             return [
-                f'{expr_source['sourceDataset']}.{expr_source['sourceColumn']}'
+                f"{expr_source['sourceDataset']}.{expr_source['sourceColumn']}"
                 for select_item in select_items
                 for expr_source in select_item["exprSources"]
             ]
@@ -97,7 +97,7 @@ def _compose_contexts_of_filter_type(filter: dict):
             contexts = []
             if filter["type"] == "EXPR":
                 contexts += [
-                    f'{expr_source["sourceDataset"]}.{expr_source["sourceColumn"]}'
+                    f"{expr_source['sourceDataset']}.{expr_source['sourceColumn']}"
                     for expr_source in filter["exprSources"]
                 ]
             elif filter["type"] in ("AND", "OR"):
@@ -110,7 +110,7 @@ def _compose_contexts_of_groupby_type(groupby_keys: list[list[dict]]):
             contexts = []
             for groupby_key_list in groupby_keys:
                 contexts += [
-                    f'{expr_source["sourceDataset"]}.{expr_source["sourceColumn"]}'
+                    f"{expr_source['sourceDataset']}.{expr_source['sourceColumn']}"
                     for groupby_key in groupby_key_list
                     for expr_source in groupby_key["exprSources"]
                 ]
@@ -118,7 +118,7 @@ def _compose_contexts_of_groupby_type(groupby_keys: list[list[dict]]):
 
         def _compose_contexts_of_sorting_type(sortings: list[dict]):
             return [
-                f'{expr_source["sourceDataset"]}.{expr_source["sourceColumn"]}'
+                f"{expr_source['sourceDataset']}.{expr_source['sourceColumn']}"
                 for sorting in sortings
                 for expr_source in sorting["exprSources"]
             ]
@@ -127,7 +127,7 @@ def _compose_contexts_of_relation_type(relation: dict):
             contexts = []
             if relation["type"] != "TABLE" and relation["type"] != "SUBQUERY":
                 contexts += [
-                    f'{expr_source["sourceDataset"]}.{expr_source["sourceColumn"]}'
+                    f"{expr_source['sourceDataset']}.{expr_source['sourceColumn']}"
                     for expr_source in relation["exprSources"]
                 ]
 
@@ -196,8 +196,6 @@ def trace_metadata(
     return {
         "commit": meta["commit"],
         "dataset_id": meta["dataset_id"],
-        "embedding_model": meta["embedding_model"],
-        "generation_model": meta["generation_model"],
         "column_indexing_batch_size": meta["column_indexing_batch_size"],
         "table_retrieval_size": meta["table_retrieval_size"],
         "table_column_retrieval_size": meta["table_column_retrieval_size"],
@@ -206,11 +204,19 @@ def trace_metadata(
     }
 
 
-def engine_config(mdl: dict) -> dict:
+def engine_config(mdl: dict, pipe_components: dict[str, Any] = {}) -> dict:
+    engine = pipe_components.get("sql_generation", {}).get("engine")
+
+    if engine is None:
+        raise ValueError(
+            "SQL Generation engine not found in pipe_components. Ensure 'sql_generation' key exists and contains 'engine' configuration."
+        )
+
     return {
         "mdl_json": mdl,
-        "data_source": os.getenv("DATA_SOURCE"),
-        "api_endpoint": os.getenv("WREN_ENGINE_ENDPOINT"),
+        "data_source": engine._source,
+        "api_endpoint": engine._endpoint,
+        "connection_info": engine._connection_info,
         "timeout": 10,
     }
 
@@ -233,13 +239,13 @@ def _convert_models_and_relationships(
                         column["properties"]["alias"] = column["properties"].pop(
                             "displayName", ""
                         )
-                        comment = f"-- {orjson.dumps(column['properties']).decode("utf-8")}\n  "
+                        comment = f"-- {orjson.dumps(column['properties']).decode('utf-8')}\n  "
                     else:
                         comment = ""
                     if "isCalculated" in column and column["isCalculated"]:
                         comment = (
                             comment
-                            + f"-- This column is a Calculated Field\n  -- column expression: {column["expression"]}\n  "
+                            + f"-- This column is a Calculated Field\n  -- column expression: {column['expression']}\n  "
                         )
                     column_name = column["name"]
                     column_type = column["type"]
@@ -289,7 +295,7 @@ def _convert_models_and_relationships(
                     "displayName", ""
                 )
                 comment = (
-                    f"\n/* {orjson.dumps(model['properties']).decode("utf-8")} */\n"
+                    f"\n/* {orjson.dumps(model['properties']).decode('utf-8')} */\n"
                 )
             else:
                 comment = ""
@@ -326,11 +332,11 @@ def _convert_metrics(metrics: List[Dict[str, Any]]) -> List[str]:
             for measure in metric["measure"]:
                 column_name = measure["name"]
                 column_type = measure["type"]
-                comment = f"-- This column is a measure\n  -- expression: {measure["expression"]}\n  "
+                comment = f"-- This column is a measure\n  -- expression: {measure['expression']}\n  "
                 column_ddl = f"{comment}{column_name} {column_type}"
                 columns_ddl.append(column_ddl)
 
-            comment = f"\n/* This table is a metric */\n/* Metric Base Object: {metric["baseObject"]} */\n"
+            comment = f"\n/* This table is a metric */\n/* Metric Base Object: {metric['baseObject']} */\n"
             create_table_ddl = (
                 f"{comment}CREATE TABLE {table_name} (\n  "
                 + ",\n  ".join(columns_ddl)
diff --git a/wren-ai-service/poetry.lock b/wren-ai-service/poetry.lock
index ac0dca48e..a11c15692 100644
--- a/wren-ai-service/poetry.lock
+++ b/wren-ai-service/poetry.lock
@@ -27,87 +27,87 @@ files = [
 
 [[package]]
 name = "aiohttp"
-version = "3.11.10"
+version = "3.11.11"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
 files = [
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-    {file = "aiohttp-3.11.10-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:80886dac673ceaef499de2f393fc80bb4481a129e6cb29e624a12e3296cc088f"},
-    {file = "aiohttp-3.11.10-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:61b9bae80ed1f338c42f57c16918853dc51775fb5cb61da70d590de14d8b5fb4"},
-    {file = "aiohttp-3.11.10-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9e2e576caec5c6a6b93f41626c9c02fc87cd91538b81a3670b2e04452a63def6"},
-    {file = "aiohttp-3.11.10-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:02c13415b5732fb6ee7ff64583a5e6ed1c57aa68f17d2bda79c04888dfdc2769"},
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-    {file = "aiohttp-3.11.10-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3bbbfff4c679c64e6e23cb213f57cc2c9165c9a65d63717108a644eb5a7398df"},
-    {file = "aiohttp-3.11.10-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:49c7dbbc1a559ae14fc48387a115b7d4bbc84b4a2c3b9299c31696953c2a5219"},
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-    {file = "aiohttp-3.11.10-cp310-cp310-win_amd64.whl", hash = "sha256:68ff6f48b51bd78ea92b31079817aff539f6c8fc80b6b8d6ca347d7c02384e33"},
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 ]
 
 [package.dependencies]
@@ -210,13 +210,13 @@ files = [
 
 [[package]]
 name = "anyio"
-version = "4.7.0"
+version = "4.8.0"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.dependencies]
@@ -226,7 +226,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -330,32 +330,32 @@ files = [
 
 [[package]]
 name = "boto3"
-version = "1.35.90"
+version = "1.36.1"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
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 jmespath = ">=0.7.1,<2.0.0"
-s3transfer = ">=0.10.0,<0.11.0"
+s3transfer = ">=0.11.0,<0.12.0"
 
 [package.extras]
 crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.35.90"
+version = "1.36.1"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -364,7 +364,7 @@ python-dateutil = ">=2.1,<3.0.0"
 urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}
 
 [package.extras]
-crt = ["awscrt (==0.22.0)"]
+crt = ["awscrt (==0.23.4)"]
 
 [[package]]
 name = "brotli"
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 ]
 
 [package.dependencies]
@@ -872,73 +901,73 @@ test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist"
 
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 ]
 
 [[package]]
@@ -1776,13 +1805,13 @@ examples = ["oauth2"]
 
 [[package]]
 name = "gitdb"
-version = "4.0.11"
+version = "4.0.12"
 description = "Git Object Database"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -1790,20 +1819,20 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.43"
+version = "3.1.44"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
 
 [package.extras]
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+doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
 test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
 
 [[package]]
@@ -2248,13 +2277,13 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.27.0"
+version = "0.27.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2293,13 +2322,13 @@ files = [
 
 [[package]]
 name = "identify"
-version = "2.6.3"
+version = "2.6.5"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.9"
 files = [
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 ]
 
 [package.extras]
@@ -2384,13 +2413,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.30.0"
+version = "8.31.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -2701,141 +2730,107 @@ test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"
 
 [[package]]
 name = "kiwisolver"
-version = "1.4.7"
+version = "1.4.8"
 description = "A fast implementation of the Cassowary constraint solver"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 files = [
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+    {file = "kiwisolver-1.4.8.tar.gz", hash = "sha256:23d5f023bdc8c7e54eb65f03ca5d5bb25b601eac4d7f1a042888a1f45237987e"},
 ]
 
 [[package]]
 name = "langchain"
-version = "0.3.12"
+version = "0.3.14"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain-0.3.12-py3-none-any.whl", hash = "sha256:581ad93a9de12e4b957bc2af9ba8482eb86e3930e84c4ee20ed677da5e2311cd"},
-    {file = "langchain-0.3.12.tar.gz", hash = "sha256:0d8247afbf37beb263b4adc29f7aa8a5ae83c43a6941894e2f9ba39d5c869e3b"},
+    {file = "langchain-0.3.14-py3-none-any.whl", hash = "sha256:5df9031702f7fe6c956e84256b4639a46d5d03a75be1ca4c1bc9479b358061a2"},
+    {file = "langchain-0.3.14.tar.gz", hash = "sha256:4a5ae817b5832fa0e1fcadc5353fbf74bebd2f8e550294d4dc039f651ddcd3d1"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
-langchain-core = ">=0.3.25,<0.4.0"
+langchain-core = ">=0.3.29,<0.4.0"
 langchain-text-splitters = ">=0.3.3,<0.4.0"
 langsmith = ">=0.1.17,<0.3"
 numpy = {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""}
@@ -2847,21 +2842,21 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
 [[package]]
 name = "langchain-community"
-version = "0.3.12"
+version = "0.3.14"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain_community-0.3.12-py3-none-any.whl", hash = "sha256:5a993c931d46dc07fcdfcdfa4d87095c5a15d37ff32b0c16e9ecf6f5caa58c9c"},
-    {file = "langchain_community-0.3.12.tar.gz", hash = "sha256:b4694f34c7214dede03fe5a75e9f335e16bd788dfa6ca279302ad357bf0d0fc4"},
+    {file = "langchain_community-0.3.14-py3-none-any.whl", hash = "sha256:cc02a0abad0551edef3e565dff643386a5b2ee45b933b6d883d4a935b9649f3c"},
+    {file = "langchain_community-0.3.14.tar.gz", hash = "sha256:d8ba0fe2dbb5795bff707684b712baa5ee379227194610af415ccdfdefda0479"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
 httpx-sse = ">=0.4.0,<0.5.0"
-langchain = ">=0.3.12,<0.4.0"
-langchain-core = ">=0.3.25,<0.4.0"
+langchain = ">=0.3.14,<0.4.0"
+langchain-core = ">=0.3.29,<0.4.0"
 langsmith = ">=0.1.125,<0.3"
 numpy = {version = ">=1.26.2,<3", markers = "python_version >= \"3.12\""}
 pydantic-settings = ">=2.4.0,<3.0.0"
@@ -2872,13 +2867,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
 
 [[package]]
 name = "langchain-core"
-version = "0.3.25"
+version = "0.3.30"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain_core-0.3.25-py3-none-any.whl", hash = "sha256:e10581c6c74ba16bdc6fdf16b00cced2aa447cc4024ed19746a1232918edde38"},
-    {file = "langchain_core-0.3.25.tar.gz", hash = "sha256:fdb8df41e5cdd928c0c2551ebbde1cea770ee3c64598395367ad77ddf9acbae7"},
+    {file = "langchain_core-0.3.30-py3-none-any.whl", hash = "sha256:0a4c4e02fac5968b67fbb0142c00c2b976c97e45fce62c7ac9eb1636a6926493"},
+    {file = "langchain_core-0.3.30.tar.gz", hash = "sha256:0f1281b4416977df43baf366633ad18e96c5dcaaeae6fcb8a799f9889c853243"},
 ]
 
 [package.dependencies]
@@ -2895,43 +2890,43 @@ typing-extensions = ">=4.7"
 
 [[package]]
 name = "langchain-openai"
-version = "0.2.12"
+version = "0.3.0"
 description = "An integration package connecting OpenAI and LangChain"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain_openai-0.2.12-py3-none-any.whl", hash = "sha256:916965c45584d9ea565825ad3bb7629b1ff57f12f36d4b937e5b7d65903839d6"},
-    {file = "langchain_openai-0.2.12.tar.gz", hash = "sha256:8b92096623065a2820e89aa5fb0a262fb109d56c346e3b09ba319af424c45cd1"},
+    {file = "langchain_openai-0.3.0-py3-none-any.whl", hash = "sha256:49c921a22d272b04749a61e78bffa83aecdb8840b24b69f2909e115a357a9a5b"},
+    {file = "langchain_openai-0.3.0.tar.gz", hash = "sha256:88d623eeb2aaa1fff65c2b419a4a1cfd37d3a1d504e598b87cf0bc822a3b70d0"},
 ]
 
 [package.dependencies]
-langchain-core = ">=0.3.21,<0.4.0"
-openai = ">=1.55.3,<2.0.0"
+langchain-core = ">=0.3.29,<0.4.0"
+openai = ">=1.58.1,<2.0.0"
 tiktoken = ">=0.7,<1"
 
 [[package]]
 name = "langchain-text-splitters"
-version = "0.3.3"
+version = "0.3.5"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langchain_text_splitters-0.3.3-py3-none-any.whl", hash = "sha256:c2f8650457685072971edc8c52c9f8826496b3307f28004a7fd09eb32d4d819f"},
-    {file = "langchain_text_splitters-0.3.3.tar.gz", hash = "sha256:c596958dcab15fdfe0627fd36ce9d588d0a7e35593af70cd10d0a4a06d69b3ee"},
+    {file = "langchain_text_splitters-0.3.5-py3-none-any.whl", hash = "sha256:8c9b059827438c5fa8f327b4df857e307828a5ec815163c9b5c9569a3e82c8ee"},
+    {file = "langchain_text_splitters-0.3.5.tar.gz", hash = "sha256:11cb7ca3694e5bdd342bc16d3875b7f7381651d4a53cbb91d34f22412ae16443"},
 ]
 
 [package.dependencies]
-langchain-core = ">=0.3.25,<0.4.0"
+langchain-core = ">=0.3.29,<0.4.0"
 
 [[package]]
 name = "langfuse"
-version = "2.57.0"
+version = "2.57.10"
 description = "A client library for accessing langfuse"
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langfuse-2.57.0-py3-none-any.whl", hash = "sha256:cb14b2393b73e5548ddd7e5cf2967210e24706e10b2699a727ece9077e69c990"},
-    {file = "langfuse-2.57.0.tar.gz", hash = "sha256:8aa0b59af12c46417d1c9993f17921a984ec4688c0cd50004ef18647c9c5ae77"},
+    {file = "langfuse-2.57.10-py3-none-any.whl", hash = "sha256:db7e8f7cf8d0204e17074e6729b144e7f9c7198499cd84a824bbc81fb5e37e4a"},
+    {file = "langfuse-2.57.10.tar.gz", hash = "sha256:751dd03271809f4bf50f6e4e0d1138b0e0eb028efefc984fdc6948d2bfddd95d"},
 ]
 
 [package.dependencies]
@@ -2951,13 +2946,13 @@ openai = ["openai (>=0.27.8)"]
 
 [[package]]
 name = "langsmith"
-version = "0.2.3"
+version = "0.2.11"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.9"
 files = [
-    {file = "langsmith-0.2.3-py3-none-any.whl", hash = "sha256:4958b6e918f57fedba6ddc55b8534d1e06478bb44c779aa73713ce898ca6ae87"},
-    {file = "langsmith-0.2.3.tar.gz", hash = "sha256:54c231b07fdeb0f8472925074a0ec0ed2cb654a0437d63c6ccf76a9da635900d"},
+    {file = "langsmith-0.2.11-py3-none-any.whl", hash = "sha256:084cf66a7f093c25e6b30fb4005008ec5fa9843110e2f0b265ce133c6a0225e6"},
+    {file = "langsmith-0.2.11.tar.gz", hash = "sha256:edf070349dbfc63dc4fc30e22533a11d77768e99ef269399b221c48fee25c737"},
 ]
 
 [package.dependencies]
@@ -2971,17 +2966,18 @@ requests = ">=2,<3"
 requests-toolbelt = ">=1.0.0,<2.0.0"
 
 [package.extras]
+compression = ["zstandard (>=0.23.0,<0.24.0)"]
 langsmith-pyo3 = ["langsmith-pyo3 (>=0.1.0rc2,<0.2.0)"]
 
 [[package]]
 name = "lazy-imports"
-version = "0.3.1"
+version = "0.4.0"
 description = "Tool to support lazy imports"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 files = [
-    {file = "lazy_imports-0.3.1-py3-none-any.whl", hash = "sha256:149500cb1015726f5148155989386b30b425dfd7e48fd156385eb1c9e23887dd"},
-    {file = "lazy_imports-0.3.1.tar.gz", hash = "sha256:636624104bf45b09761323e06292927832aca067c3c199e5de483738789aeb21"},
+    {file = "lazy_imports-0.4.0-py3-none-any.whl", hash = "sha256:de0625cbbd781091c46a66003ec3953c87f0130e85a257cd1a24304237e4966b"},
+    {file = "lazy_imports-0.4.0.tar.gz", hash = "sha256:c3d9e4e295e6118588f58c6710d152bb2836e0276f790a120ee4ab608829cece"},
 ]
 
 [package.extras]
@@ -2991,13 +2987,13 @@ testing = ["packaging", "pytest"]
 
 [[package]]
 name = "litellm"
-version = "1.55.3"
+version = "1.58.2"
 description = "Library to easily interface with LLM API providers"
 optional = false
 python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
 files = [
-    {file = "litellm-1.55.3-py3-none-any.whl", hash = "sha256:83e48a6104f2b30edb62f708ca47dfd42f22416ed2ab34702770b3a57d866aa5"},
-    {file = "litellm-1.55.3.tar.gz", hash = "sha256:83f7fd5c7b6eec1d431e786be9d73fbb72f1f1bc9aff333169a4a8eb61e72018"},
+    {file = "litellm-1.58.2-py3-none-any.whl", hash = "sha256:51b14b2f5e30d2d41a76fbf926d7d882f1fddbbfda8812358cb4bb27d0d27692"},
+    {file = "litellm-1.58.2.tar.gz", hash = "sha256:4e1b7191a86970bbacd30e5315d3b6a0f5fc75a99763c9164116de60c6ac0bf3"},
 ]
 
 [package.dependencies]
@@ -3010,23 +3006,22 @@ jsonschema = ">=4.22.0,<5.0.0"
 openai = ">=1.55.3"
 pydantic = ">=2.0.0,<3.0.0"
 python-dotenv = ">=0.2.0"
-requests = ">=2.31.0,<3.0.0"
 tiktoken = ">=0.7.0"
 tokenizers = "*"
 
 [package.extras]
 extra-proxy = ["azure-identity (>=1.15.0,<2.0.0)", "azure-keyvault-secrets (>=4.8.0,<5.0.0)", "google-cloud-kms (>=2.21.3,<3.0.0)", "prisma (==0.11.0)", "resend (>=0.8.0,<0.9.0)"]
-proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "cryptography (>=42.0.5,<43.0.0)", "fastapi (>=0.111.0,<0.112.0)", "fastapi-sso (>=0.10.0,<0.11.0)", "gunicorn (>=22.0.0,<23.0.0)", "orjson (>=3.9.7,<4.0.0)", "pynacl (>=1.5.0,<2.0.0)", "python-multipart (>=0.0.9,<0.0.10)", "pyyaml (>=6.0.1,<7.0.0)", "rq", "uvicorn (>=0.22.0,<0.23.0)"]
+proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "cryptography (>=43.0.1,<44.0.0)", "fastapi (>=0.115.5,<0.116.0)", "fastapi-sso (>=0.16.0,<0.17.0)", "gunicorn (>=22.0.0,<23.0.0)", "orjson (>=3.9.7,<4.0.0)", "pynacl (>=1.5.0,<2.0.0)", "python-multipart (>=0.0.18,<0.0.19)", "pyyaml (>=6.0.1,<7.0.0)", "rq", "uvicorn (>=0.22.0,<0.23.0)", "uvloop (>=0.21.0,<0.22.0)"]
 
 [[package]]
 name = "locust"
-version = "2.32.4"
+version = "2.32.6"
 description = "Developer-friendly load testing framework"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "locust-2.32.4-py3-none-any.whl", hash = "sha256:7c5b8767c0d771b5167d5d6b82878622faead74f394eb9cafe8891d89eb36b97"},
-    {file = "locust-2.32.4.tar.gz", hash = "sha256:fd650cbc40842e721668a8d0f7f8224775432b40c63d0a378546b9a9f54b7559"},
+    {file = "locust-2.32.6-py3-none-any.whl", hash = "sha256:d5c0e4f73134415d250087034431cf3ea42ca695d3dee7f10812287cacb6c4ef"},
+    {file = "locust-2.32.6.tar.gz", hash = "sha256:6600cc308398e724764aacc56ccddf6cfcd0127c4c92dedd5c4979dd37ef5b15"},
 ]
 
 [package.dependencies]
@@ -3172,13 +3167,13 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.23.1"
+version = "3.25.1"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "marshmallow-3.23.1-py3-none-any.whl", hash = "sha256:fece2eb2c941180ea1b7fcbd4a83c51bfdd50093fdd3ad2585ee5e1df2508491"},
-    {file = "marshmallow-3.23.1.tar.gz", hash = "sha256:3a8dfda6edd8dcdbf216c0ede1d1e78d230a6dc9c5a088f58c4083b974a0d468"},
+    {file = "marshmallow-3.25.1-py3-none-any.whl", hash = "sha256:ec5d00d873ce473b7f2ffcb7104286a376c354cab0c2fa12f5573dab03e87210"},
+    {file = "marshmallow-3.25.1.tar.gz", hash = "sha256:f4debda3bb11153d81ac34b0d582bf23053055ee11e791b54b4b35493468040a"},
 ]
 
 [package.dependencies]
@@ -3186,7 +3181,7 @@ packaging = ">=17.0"
 
 [package.extras]
 dev = ["marshmallow[tests]", "pre-commit (>=3.5,<5.0)", "tox"]
-docs = ["alabaster (==1.0.0)", "autodocsumm (==0.2.14)", "sphinx (==8.1.3)", "sphinx-issues (==5.0.0)", "sphinx-version-warning (==1.1.2)"]
+docs = ["autodocsumm (==0.2.14)", "furo (==2024.8.6)", "sphinx (==8.1.3)", "sphinx-copybutton (==0.5.2)", "sphinx-issues (==5.0.0)", "sphinxext-opengraph (==0.9.1)"]
 tests = ["pytest", "simplejson"]
 
 [[package]]
@@ -3284,13 +3279,13 @@ files = [
 
 [[package]]
 name = "more-itertools"
-version = "10.5.0"
+version = "10.6.0"
 description = "More routines for operating on iterables, beyond itertools"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "more-itertools-10.5.0.tar.gz", hash = "sha256:5482bfef7849c25dc3c6dd53a6173ae4795da2a41a80faea6700d9f5846c5da6"},
-    {file = "more_itertools-10.5.0-py3-none-any.whl", hash = "sha256:037b0d3203ce90cca8ab1defbbdac29d5f993fc20131f3664dc8d6acfa872aef"},
+    {file = "more-itertools-10.6.0.tar.gz", hash = "sha256:2cd7fad1009c31cc9fb6a035108509e6547547a7a738374f10bd49a09eb3ee3b"},
+    {file = "more_itertools-10.6.0-py3-none-any.whl", hash = "sha256:6eb054cb4b6db1473f6e15fcc676a08e4732548acd47c708f0e179c2c7c01e89"},
 ]
 
 [[package]]
@@ -3504,23 +3499,29 @@ files = [
 
 [[package]]
 name = "narwhals"
-version = "1.18.4"
+version = "1.22.0"
 description = "Extremely lightweight compatibility layer between dataframe libraries"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "narwhals-1.18.4-py3-none-any.whl", hash = "sha256:c6bb6b6fba59caeab28a7d6ec1e79ab0040c75baef2e4152199ad1a9c266ef96"},
-    {file = "narwhals-1.18.4.tar.gz", hash = "sha256:b1da4e2e4ab185824781760319ac1ec8ee2944a929795064c3a64ffff16b00c4"},
+    {file = "narwhals-1.22.0-py3-none-any.whl", hash = "sha256:5c931bf8696b6dec276f590f1bc5043080606b16ce86d85c9b550312c981970f"},
+    {file = "narwhals-1.22.0.tar.gz", hash = "sha256:8e257c5af70a82382796706f39d681290f2c482812474524087f36cb69f9d2f1"},
 ]
 
 [package.extras]
-cudf = ["cudf (>=23.08.00)"]
-dask = ["dask[dataframe] (>=2024.7)"]
+core = ["duckdb", "pandas", "polars", "pyarrow", "pyarrow-stubs"]
+cudf = ["cudf (>=24.10.0)"]
+dask = ["dask[dataframe] (>=2024.8)"]
+dev = ["covdefaults", "hypothesis", "pre-commit", "pytest", "pytest-cov", "pytest-env", "pytest-randomly", "typing-extensions"]
+docs = ["black", "duckdb", "jinja2", "markdown-exec[ansi]", "mkdocs", "mkdocs-autorefs", "mkdocs-material", "mkdocstrings[python]", "pandas", "polars (>=1.0.0)", "pyarrow"]
+duckdb = ["duckdb (>=1.0)"]
+extra = ["scikit-learn"]
+ibis = ["ibis-framework (>=6.0.0)", "packaging", "pyarrow-hotfix", "rich"]
 modin = ["modin"]
 pandas = ["pandas (>=0.25.3)"]
 polars = ["polars (>=0.20.3)"]
 pyarrow = ["pyarrow (>=11.0.0)"]
-pyspark = ["pyspark (>=3.3.0)"]
+pyspark = ["pyspark (>=3.5.0)"]
 
 [[package]]
 name = "nbformat"
@@ -3685,13 +3686,13 @@ requests = "*"
 
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 [package.dependencies]
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 [[package]]
@@ -4036,93 +4037,89 @@ ptyprocess = ">=0.5"
 
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 mic = ["olefile"]
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 [package.extras]
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 name = "pre-commit"
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 [[package]]
@@ -4414,53 +4412,53 @@ tests = ["pytest"]
 
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 [package.dependencies]
@@ -4722,13 +4720,13 @@ typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pydantic-settings"
-version = "2.7.0"
+version = "2.7.1"
 description = "Settings management using Pydantic"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pydantic_settings-2.7.0-py3-none-any.whl", hash = "sha256:e00c05d5fa6cbbb227c84bd7487c5c1065084119b750df7c8c1a554aed236eb5"},
-    {file = "pydantic_settings-2.7.0.tar.gz", hash = "sha256:ac4bfd4a36831a48dbf8b2d9325425b549a0a6f18cea118436d728eb4f1c4d66"},
+    {file = "pydantic_settings-2.7.1-py3-none-any.whl", hash = "sha256:590be9e6e24d06db33a4262829edef682500ef008565a969c73d39d5f8bfb3fd"},
+    {file = "pydantic_settings-2.7.1.tar.gz", hash = "sha256:10c9caad35e64bfb3c2fbf70a078c0e25cc92499782e5200747f942a065dec93"},
 ]
 
 [package.dependencies]
@@ -4761,13 +4759,13 @@ jupyter = ["ipykernel (>=5.1.2)", "ipython (>=5.8.0)", "ipywidgets (>=7,<8)", "t
 
 [[package]]
 name = "pygments"
-version = "2.18.0"
+version = "2.19.1"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pygments-2.18.0-py3-none-any.whl", hash = "sha256:b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a"},
-    {file = "pygments-2.18.0.tar.gz", hash = "sha256:786ff802f32e91311bff3889f6e9a86e81505fe99f2735bb6d60ae0c5004f199"},
+    {file = "pygments-2.19.1-py3-none-any.whl", hash = "sha256:9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c"},
+    {file = "pygments-2.19.1.tar.gz", hash = "sha256:61c16d2a8576dc0649d9f39e089b5f02bcd27fba10d8fb4dcc28173f7a45151f"},
 ]
 
 [package.extras]
@@ -4775,28 +4773,18 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.2.0"
+version = "3.2.1"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "pyparsing-3.2.0-py3-none-any.whl", hash = "sha256:93d9577b88da0bbea8cc8334ee8b918ed014968fd2ec383e868fb8afb1ccef84"},
-    {file = "pyparsing-3.2.0.tar.gz", hash = "sha256:cbf74e27246d595d9a74b186b810f6fbb86726dbf3b9532efb343f6d7294fe9c"},
+    {file = "pyparsing-3.2.1-py3-none-any.whl", hash = "sha256:506ff4f4386c4cec0590ec19e6302d3aedb992fdc02c761e90416f158dacf8e1"},
+    {file = "pyparsing-3.2.1.tar.gz", hash = "sha256:61980854fd66de3a90028d679a954d5f2623e83144b5afe5ee86f43d762e5f0a"},
 ]
 
 [package.extras]
 diagrams = ["jinja2", "railroad-diagrams"]
 
-[[package]]
-name = "pysbd"
-version = "0.3.4"
-description = "pysbd (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box across many languages."
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "pysbd-0.3.4-py3-none-any.whl", hash = "sha256:cd838939b7b0b185fcf86b0baf6636667dfb6e474743beeff878e9f42e022953"},
-]
-
 [[package]]
 name = "pysocks"
 version = "1.7.1"
@@ -5189,6 +5177,30 @@ urllib3 = ">=1.26.14,<3"
 fastembed = ["fastembed (==0.3.6)"]
 fastembed-gpu = ["fastembed-gpu (==0.3.6)"]
 
+[[package]]
+name = "qdrant-client"
+version = "1.12.2"
+description = "Client library for the Qdrant vector search engine"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "qdrant_client-1.12.2-py3-none-any.whl", hash = "sha256:a0ae500a46a679ff3521ba3f1f1cf3d72b57090a768cec65fc317066bcbac1e6"},
+    {file = "qdrant_client-1.12.2.tar.gz", hash = "sha256:2777e09b3e89bb22bb490384d8b1fa8140f3915287884f18984f7031a346aba5"},
+]
+
+[package.dependencies]
+grpcio = ">=1.41.0"
+grpcio-tools = ">=1.41.0"
+httpx = {version = ">=0.20.0", extras = ["http2"]}
+numpy = {version = ">=1.26", markers = "python_version >= \"3.12\" and python_version < \"3.13\""}
+portalocker = ">=2.7.0,<3.0.0"
+pydantic = ">=1.10.8"
+urllib3 = ">=1.26.14,<3"
+
+[package.extras]
+fastembed = ["fastembed (==0.5.0)"]
+fastembed-gpu = ["fastembed-gpu (==0.5.0)"]
+
 [[package]]
 name = "qdrant-haystack"
 version = "7.0.0"
@@ -5206,18 +5218,19 @@ qdrant-client = ">=1.10.0"
 
 [[package]]
 name = "ragas"
-version = "0.2.8"
+version = "0.2.11"
 description = ""
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "ragas-0.2.8-py3-none-any.whl", hash = "sha256:ae267bbd761de59ed1d912e4423f9b111ba73112c79783863708707ad9d711cd"},
-    {file = "ragas-0.2.8.tar.gz", hash = "sha256:95c9b0f7c9cb9851487b6c40f6a2639fcb18791b435a2c8ceb5d4ddcf45fed12"},
+    {file = "ragas-0.2.11-py3-none-any.whl", hash = "sha256:d79c4805f0551fd98eea0d15de761694cea7617fcb51df33acbb15b14925d3a1"},
+    {file = "ragas-0.2.11.tar.gz", hash = "sha256:70e4a3b7c08abf8459c34ad4884a72ae794f9933a00ac561d314d65ea508d352"},
 ]
 
 [package.dependencies]
 appdirs = "*"
 datasets = "*"
+diskcache = ">=5.6.3"
 langchain = "*"
 langchain-community = "*"
 langchain-core = "*"
@@ -5226,7 +5239,6 @@ nest-asyncio = "*"
 numpy = "*"
 openai = ">1"
 pydantic = ">=2"
-pysbd = ">=0.3.4"
 tiktoken = "*"
 
 [package.extras]
@@ -5237,18 +5249,19 @@ test = ["llama_index", "nbmake", "pytest", "pytest-asyncio", "pytest-xdist[psuti
 
 [[package]]
 name = "referencing"
-version = "0.35.1"
+version = "0.36.1"
 description = "JSON Referencing + Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "referencing-0.35.1-py3-none-any.whl", hash = "sha256:eda6d3234d62814d1c64e305c1331c9a3a6132da475ab6382eaa997b21ee75de"},
-    {file = "referencing-0.35.1.tar.gz", hash = "sha256:25b42124a6c8b632a425174f24087783efb348a6f1e0008e63cd4466fedf703c"},
+    {file = "referencing-0.36.1-py3-none-any.whl", hash = "sha256:363d9c65f080d0d70bc41c721dce3c7f3e77fc09f269cd5c8813da18069a6794"},
+    {file = "referencing-0.36.1.tar.gz", hash = "sha256:ca2e6492769e3602957e9b831b94211599d2aade9477f5d44110d2530cf9aade"},
 ]
 
 [package.dependencies]
 attrs = ">=22.2.0"
 rpds-py = ">=0.7.0"
+typing-extensions = {version = ">=4.4.0", markers = "python_version < \"3.13\""}
 
 [[package]]
 name = "regex"
@@ -5535,30 +5548,30 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "s3transfer"
-version = "0.10.4"
+version = "0.11.1"
 description = "An Amazon S3 Transfer Manager"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "s3transfer-0.10.4-py3-none-any.whl", hash = "sha256:244a76a24355363a68164241438de1b72f8781664920260c48465896b712a41e"},
-    {file = "s3transfer-0.10.4.tar.gz", hash = "sha256:29edc09801743c21eb5ecbc617a152df41d3c287f67b615f73e5f750583666a7"},
+    {file = "s3transfer-0.11.1-py3-none-any.whl", hash = "sha256:8fa0aa48177be1f3425176dfe1ab85dcd3d962df603c3dbfc585e6bf857ef0ff"},
+    {file = "s3transfer-0.11.1.tar.gz", hash = "sha256:3f25c900a367c8b7f7d8f9c34edc87e300bde424f779dc9f0a8ae4f9df9264f6"},
 ]
 
 [package.dependencies]
-botocore = ">=1.33.2,<2.0a.0"
+botocore = ">=1.36.0,<2.0a.0"
 
 [package.extras]
-crt = ["botocore[crt] (>=1.33.2,<2.0a.0)"]
+crt = ["botocore[crt] (>=1.36.0,<2.0a.0)"]
 
 [[package]]
 name = "sentry-sdk"
-version = "2.19.2"
+version = "2.20.0"
 description = "Python client for Sentry (https://sentry.io)"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "sentry_sdk-2.19.2-py2.py3-none-any.whl", hash = "sha256:ebdc08228b4d131128e568d696c210d846e5b9d70aa0327dec6b1272d9d40b84"},
-    {file = "sentry_sdk-2.19.2.tar.gz", hash = "sha256:467df6e126ba242d39952375dd816fbee0f217d119bf454a8ce74cf1e7909e8d"},
+    {file = "sentry_sdk-2.20.0-py2.py3-none-any.whl", hash = "sha256:c359a1edf950eb5e80cffd7d9111f3dbeef57994cb4415df37d39fda2cf22364"},
+    {file = "sentry_sdk-2.20.0.tar.gz", hash = "sha256:afa82713a92facf847df3c6f63cec71eb488d826a50965def3d7722aa6f0fdab"},
 ]
 
 [package.dependencies]
@@ -5603,36 +5616,37 @@ sqlalchemy = ["sqlalchemy (>=1.2)"]
 starlette = ["starlette (>=0.19.1)"]
 starlite = ["starlite (>=1.48)"]
 tornado = ["tornado (>=6)"]
+unleash = ["UnleashClient (>=6.0.1)"]
 
 [[package]]
 name = "setuptools"
-version = "75.6.0"
+version = "75.8.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "setuptools-75.6.0-py3-none-any.whl", hash = "sha256:ce74b49e8f7110f9bf04883b730f4765b774ef3ef28f722cce7c273d253aaf7d"},
-    {file = "setuptools-75.6.0.tar.gz", hash = "sha256:8199222558df7c86216af4f84c30e9b34a61d8ba19366cc914424cdbd28252f6"},
+    {file = "setuptools-75.8.0-py3-none-any.whl", hash = "sha256:e3982f444617239225d675215d51f6ba05f845d4eec313da4418fdbb56fb27e3"},
+    {file = "setuptools-75.8.0.tar.gz", hash = "sha256:c5afc8f407c626b8313a86e10311dd3f661c6cd9c09d4bf8c15c0e11f9f2b0e6"},
 ]
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.7.0)"]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.8.0)"]
 core = ["importlib_metadata (>=6)", "jaraco.collections", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
 enabler = ["pytest-enabler (>=2.2)"]
-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (>=1.12,<1.14)", "pytest-mypy"]
+test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"]
 
 [[package]]
 name = "sf-hamilton"
-version = "1.85.1"
+version = "1.86.1"
 description = "Hamilton, the micro-framework for creating dataframes."
 optional = false
 python-versions = "<4,>=3.8"
 files = [
-    {file = "sf_hamilton-1.85.1-py3-none-any.whl", hash = "sha256:96cd81143f6dc50add253058f6cb534a249298929893a50c041da2d6dad258c5"},
-    {file = "sf_hamilton-1.85.1.tar.gz", hash = "sha256:4b6f600ff5b5dfaa5841878fe8e826e994938ef151df58c15ae74e5806d6542b"},
+    {file = "sf_hamilton-1.86.1-py3-none-any.whl", hash = "sha256:57751476d5bce1f3dbe5e1391cf0d4b90ba6674adb33d913aa18440e2377cd5d"},
+    {file = "sf_hamilton-1.86.1.tar.gz", hash = "sha256:b435ff63b3bb0517161f2708d12dd0a96fdcb8afc38fbbc5caf2bbdaff366c6d"},
 ]
 
 [package.dependencies]
@@ -5693,13 +5707,13 @@ files = [
 
 [[package]]
 name = "smmap"
-version = "5.0.1"
+version = "5.0.2"
 description = "A pure Python implementation of a sliding window memory map manager"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "smmap-5.0.1-py3-none-any.whl", hash = "sha256:e6d8668fa5f93e706934a62d7b4db19c8d9eb8cf2adbb75ef1b675aa332b69da"},
-    {file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"},
+    {file = "smmap-5.0.2-py3-none-any.whl", hash = "sha256:b30115f0def7d7531d22a0fb6502488d879e75b260a9db4d0819cfb25403af5e"},
+    {file = "smmap-5.0.2.tar.gz", hash = "sha256:26ea65a03958fa0c8a1c7e8c7a58fdc77221b8910f6be2131affade476898ad5"},
 ]
 
 [[package]]
@@ -5726,72 +5740,72 @@ files = [
 
 [[package]]
 name = "sqlalchemy"
-version = "2.0.36"
+version = "2.0.37"
 description = "Database Abstraction Library"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "SQLAlchemy-2.0.36-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:59b8f3adb3971929a3e660337f5dacc5942c2cdb760afcabb2614ffbda9f9f72"},
-    {file = "SQLAlchemy-2.0.36-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:37350015056a553e442ff672c2d20e6f4b6d0b2495691fa239d8aa18bb3bc908"},
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-    {file = "SQLAlchemy-2.0.36-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:69f93723edbca7342624d09f6704e7126b152eaed3cdbb634cb657a54332a3c5"},
-    {file = "SQLAlchemy-2.0.36-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:f9511d8dd4a6e9271d07d150fb2f81874a3c8c95e11ff9af3a2dfc35fe42ee44"},
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 [package.extras]
@@ -5931,15 +5945,29 @@ watchdog = {version = ">=2.1.5,<7", markers = "platform_system != \"Darwin\""}
 [package.extras]
 snowflake = ["snowflake-connector-python (>=2.8.0)", "snowflake-snowpark-python[modin] (>=1.17.0)"]
 
+[[package]]
+name = "streamlit-tags"
+version = "1.2.8"
+description = "Tags custom component for Streamlit"
+optional = false
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 [package.dependencies]
@@ -6626,76 +6654,90 @@ watchdog = ["watchdog (>=2.3)"]
 
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 ]
 
 [[package]]
@@ -7020,4 +7062,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.12.*, <4.0"
-content-hash = "080db555e78798d38801a750531ad4d041423b3100e86611b3d79bf6f6ec3f40"
+content-hash = "9c23e950e02c5841fd147964011edcadbee1d38e74a056946862e93adfc90086"
diff --git a/wren-ai-service/pyproject.toml b/wren-ai-service/pyproject.toml
index 52b48cf3e..c283bad43 100644
--- a/wren-ai-service/pyproject.toml
+++ b/wren-ai-service/pyproject.toml
@@ -45,7 +45,6 @@ matplotlib = "^3.9.2"
 sseclient-py = "^1.8.0"
 dspy-ai = "^2.5.26"
 requests = "^2.32.2"
-deepeval = "^1.0.6"
 extra-streamlit-components = "^0.1.71"
 
 [tool.poetry.group.eval.dependencies]
@@ -57,6 +56,8 @@ ipykernel = "^6.29.5"
 itables = "^2.2.1"
 gdown = "^5.2.0"
 nltk = "^3.9.1"
+deepeval = "^1.0.6"
+streamlit-tags = "^1.2.8"
 
 [tool.poetry.group.test.dependencies]
 locust = "^2.32.0"