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Code Issues Releases Activity

feat(product-assistant): evaluation pipeline (#26179)

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Co-authored-by: Michael Matloka <michael@posthog.com>
Co-authored-by: github-actions <41898282+github-actions[bot]@users.noreply.github.com>
This commit is contained in:
Georgiy Tarasov 2024-11-20 15:44:47 +01:00 committed by GitHub
parent bc98fdab44
commit d836bc860a
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14 changed files with 866 additions and 32 deletions
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8
.gitignore vendored
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@ -69,4 +69,10 @@ plugin-transpiler/dist
*.log
# pyright config (keep this until we have a standardized one)
pyrightconfig.json
.temporal-worker-settings
# Assistant Evaluation with Deepeval
.deepeval
.deepeval-cache.json
.deepeval_telemtry.txt
.temporal-worker-settings
temp_test_run_data.json
.temp-deepeval-cache.json

110
ee/hogai/assistant.py
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@ -1,9 +1,9 @@
from collections.abc import Generator
from typing import Any, Literal, TypedDict, TypeGuard, Union
from collections.abc import Generator, Hashable, Iterator
from typing import Any, Literal, Optional, TypedDict, TypeGuard, Union, cast
from langchain_core.messages import AIMessageChunk
from langfuse.callback import CallbackHandler
from langgraph.graph.state import StateGraph
from langgraph.graph.state import CompiledStateGraph, StateGraph
from pydantic import BaseModel
from sentry_sdk import capture_exception
@ -74,25 +74,49 @@ VISUALIZATION_NODES: dict[AssistantNodeName, type[SchemaGeneratorNode]] = {
}
class Assistant:
class AssistantGraph:
_team: Team
_graph: StateGraph
def __init__(self, team: Team):
self._team = team
self._graph = StateGraph(AssistantState)
self._has_start_node = False
def _compile_graph(self):
def add_edge(self, from_node: AssistantNodeName, to_node: AssistantNodeName):
if from_node == AssistantNodeName.START:
self._has_start_node = True
self._graph.add_edge(from_node, to_node)
return self
def compile(self):
if not self._has_start_node:
raise ValueError("Start node not added to the graph")
return self._graph.compile()
def add_start(self):
return self.add_edge(AssistantNodeName.START, AssistantNodeName.ROUTER)
def add_router(
self,
path_map: Optional[dict[Hashable, AssistantNodeName]] = None,
):
builder = self._graph
path_map = path_map or {
"trends": AssistantNodeName.TRENDS_PLANNER,
"funnel": AssistantNodeName.FUNNEL_PLANNER,
}
router_node = RouterNode(self._team)
builder.add_node(AssistantNodeName.ROUTER, router_node.run)
builder.add_edge(AssistantNodeName.START, AssistantNodeName.ROUTER)
builder.add_conditional_edges(
AssistantNodeName.ROUTER,
router_node.router,
path_map={"trends": AssistantNodeName.TRENDS_PLANNER, "funnel": AssistantNodeName.FUNNEL_PLANNER},
path_map=cast(dict[Hashable, str], path_map),
)
return self
def add_trends_planner(self, next_node: AssistantNodeName = AssistantNodeName.TRENDS_GENERATOR):
builder = self._graph
create_trends_plan_node = TrendsPlannerNode(self._team)
builder.add_node(AssistantNodeName.TRENDS_PLANNER, create_trends_plan_node.run)
@ -111,26 +135,36 @@ class Assistant:
create_trends_plan_tools_node.router,
path_map={
"continue": AssistantNodeName.TRENDS_PLANNER,
"plan_found": AssistantNodeName.TRENDS_GENERATOR,
"plan_found": next_node,
},
)
generate_trends_node = TrendsGeneratorNode(self._team)
builder.add_node(AssistantNodeName.TRENDS_GENERATOR, generate_trends_node.run)
return self
generate_trends_tools_node = TrendsGeneratorToolsNode(self._team)
builder.add_node(AssistantNodeName.TRENDS_GENERATOR_TOOLS, generate_trends_tools_node.run)
def add_trends_generator(self, next_node: AssistantNodeName = AssistantNodeName.SUMMARIZER):
builder = self._graph
trends_generator = TrendsGeneratorNode(self._team)
builder.add_node(AssistantNodeName.TRENDS_GENERATOR, trends_generator.run)
trends_generator_tools = TrendsGeneratorToolsNode(self._team)
builder.add_node(AssistantNodeName.TRENDS_GENERATOR_TOOLS, trends_generator_tools.run)
builder.add_edge(AssistantNodeName.TRENDS_GENERATOR_TOOLS, AssistantNodeName.TRENDS_GENERATOR)
builder.add_conditional_edges(
AssistantNodeName.TRENDS_GENERATOR,
generate_trends_node.router,
trends_generator.router,
path_map={
"tools": AssistantNodeName.TRENDS_GENERATOR_TOOLS,
"next": AssistantNodeName.SUMMARIZER,
"next": next_node,
},
)
return self
def add_funnel_planner(self, next_node: AssistantNodeName = AssistantNodeName.FUNNEL_GENERATOR):
builder = self._graph
funnel_planner = FunnelPlannerNode(self._team)
builder.add_node(AssistantNodeName.FUNNEL_PLANNER, funnel_planner.run)
builder.add_conditional_edges(
@ -148,41 +182,69 @@ class Assistant:
funnel_planner_tools.router,
path_map={
"continue": AssistantNodeName.FUNNEL_PLANNER,
"plan_found": AssistantNodeName.FUNNEL_GENERATOR,
"plan_found": next_node,
},
)
return self
def add_funnel_generator(self, next_node: AssistantNodeName = AssistantNodeName.SUMMARIZER):
builder = self._graph
funnel_generator = FunnelGeneratorNode(self._team)
builder.add_node(AssistantNodeName.FUNNEL_GENERATOR, funnel_generator.run)
funnel_generator_tools_node = FunnelGeneratorToolsNode(self._team)
builder.add_node(AssistantNodeName.FUNNEL_GENERATOR_TOOLS, funnel_generator_tools_node.run)
funnel_generator_tools = FunnelGeneratorToolsNode(self._team)
builder.add_node(AssistantNodeName.FUNNEL_GENERATOR_TOOLS, funnel_generator_tools.run)
builder.add_edge(AssistantNodeName.FUNNEL_GENERATOR_TOOLS, AssistantNodeName.FUNNEL_GENERATOR)
builder.add_conditional_edges(
AssistantNodeName.FUNNEL_GENERATOR,
generate_trends_node.router,
funnel_generator.router,
path_map={
"tools": AssistantNodeName.FUNNEL_GENERATOR_TOOLS,
"next": AssistantNodeName.SUMMARIZER,
"next": next_node,
},
)
return self
def add_summarizer(self, next_node: AssistantNodeName = AssistantNodeName.END):
builder = self._graph
summarizer_node = SummarizerNode(self._team)
builder.add_node(AssistantNodeName.SUMMARIZER, summarizer_node.run)
builder.add_edge(AssistantNodeName.SUMMARIZER, AssistantNodeName.END)
builder.add_edge(AssistantNodeName.SUMMARIZER, next_node)
return self
return builder.compile()
def compile_full_graph(self):
return (
self.add_start()
.add_router()
.add_trends_planner()
.add_trends_generator()
.add_funnel_planner()
.add_funnel_generator()
.add_summarizer()
.compile()
)
class Assistant:
_team: Team
_graph: CompiledStateGraph
def __init__(self, team: Team):
self._team = team
self._graph = AssistantGraph(team).compile_full_graph()
def stream(self, conversation: Conversation) -> Generator[BaseModel, None, None]:
assistant_graph = self._compile_graph()
callbacks = [langfuse_handler] if langfuse_handler else []
messages = [message.root for message in conversation.messages]
chunks = AIMessageChunk(content="")
state: AssistantState = {"messages": messages, "intermediate_steps": None, "plan": None}
generator = assistant_graph.stream(
generator: Iterator[Any] = self._graph.stream(
state,
config={"recursion_limit": 24, "callbacks": callbacks},
stream_mode=["messages", "values", "updates"],

0
ee/hogai/eval/__init__.py Normal file
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179
ee/hogai/eval/test_eval_funnel_planner.py Normal file
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@ -0,0 +1,179 @@
from deepeval import assert_test
from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCase, LLMTestCaseParams
from langgraph.graph.state import CompiledStateGraph
from ee.hogai.assistant import AssistantGraph
from ee.hogai.eval.utils import EvalBaseTest
from ee.hogai.utils import AssistantNodeName
from posthog.schema import HumanMessage
class TestEvalFunnelPlanner(EvalBaseTest):
def _get_plan_correctness_metric(self):
return GEval(
name="Funnel Plan Correctness",
criteria="You will be given expected and actual generated plans to provide a taxonomy to answer a user's question with a funnel insight. Compare the plans to determine whether the taxonomy of the actual plan matches the expected plan. Do not apply general knowledge about funnel insights.",
evaluation_steps=[
"A plan must define at least two series in the sequence, but it is not required to define any filters, exclusion steps, or a breakdown.",
"Compare events, properties, math types, and property values of 'expected output' and 'actual output'.",
"Check if the combination of events, properties, and property values in 'actual output' can answer the user's question according to the 'expected output'.",
# The criteria for aggregations must be more specific because there isn't a way to bypass them.
"Check if the math types in 'actual output' match those in 'expected output.' If the aggregation type is specified by a property, user, or group in 'expected output', the same property, user, or group must be used in 'actual output'.",
"If 'expected output' contains exclusion steps, check if 'actual output' contains those, and heavily penalize if the exclusion steps are not present or different.",
"If 'expected output' contains a breakdown, check if 'actual output' contains a similar breakdown, and heavily penalize if the breakdown is not present or different. Plans may only have one breakdown.",
# We don't want to see in the output unnecessary property filters. The assistant tries to use them all the time.
"Heavily penalize if the 'actual output' contains any excessive output not present in the 'expected output'. For example, the `is set` operator in filters should not be used unless the user explicitly asks for it.",
],
evaluation_params=[
LLMTestCaseParams.INPUT,
LLMTestCaseParams.EXPECTED_OUTPUT,
LLMTestCaseParams.ACTUAL_OUTPUT,
],
threshold=0.7,
)
def _call_node(self, query):
graph: CompiledStateGraph = (
AssistantGraph(self.team)
.add_edge(AssistantNodeName.START, AssistantNodeName.FUNNEL_PLANNER)
.add_funnel_planner(AssistantNodeName.END)
.compile()
)
state = graph.invoke({"messages": [HumanMessage(content=query)]})
return state["plan"]
def test_basic_funnel(self):
query = "what was the conversion from a page view to sign up?"
test_case = LLMTestCase(
input=query,
expected_output="""
Sequence:
1. $pageview
2. signed_up
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_outputs_at_least_two_events(self):
"""
Ambigious query. The funnel must return at least two events.
"""
query = "how many users paid a bill?"
test_case = LLMTestCase(
input=query,
expected_output="""
Sequence:
1. any event
2. upgrade_plan
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_no_excessive_property_filters(self):
query = "Show the user conversion from a sign up to a file download"
test_case = LLMTestCase(
input=query,
expected_output="""
Sequence:
1. signed_up
2. downloaded_file
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_basic_filtering(self):
query = (
"What was the conversion from uploading a file to downloading it from Chrome and Safari in the last 30d?"
)
test_case = LLMTestCase(
input=query,
expected_output="""
Sequence:
1. uploaded_file
- property filter 1:
- entity: event
- property name: $browser
- property type: String
- operator: equals
- property value: Chrome
- property filter 2:
- entity: event
- property name: $browser
- property type: String
- operator: equals
- property value: Safari
2. downloaded_file
- property filter 1:
- entity: event
- property name: $browser
- property type: String
- operator: equals
- property value: Chrome
- property filter 2:
- entity: event
- property name: $browser
- property type: String
- operator: equals
- property value: Safari
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_exclusion_steps(self):
query = "What was the conversion from uploading a file to downloading it in the last 30d excluding users that deleted a file?"
test_case = LLMTestCase(
input=query,
expected_output="""
Sequence:
1. uploaded_file
2. downloaded_file
Exclusions:
- deleted_file
- start index: 0
- end index: 1
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_breakdown(self):
query = "Show a conversion from uploading a file to downloading it segmented by a user's email"
test_case = LLMTestCase(
input=query,
expected_output="""
Sequence:
1. uploaded_file
2. downloaded_file
Breakdown by:
- entity: person
- property name: email
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_needle_in_a_haystack(self):
query = "What was the conversion from a sign up to a paying customer on the personal-pro plan?"
test_case = LLMTestCase(
input=query,
expected_output="""
Sequence:
1. signed_up
2. paid_bill
- property filter 1:
- entity: event
- property name: plan
- property type: String
- operator: equals
- property value: personal/pro
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])

59
ee/hogai/eval/test_eval_router.py Normal file
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@ -0,0 +1,59 @@
from langgraph.graph.state import CompiledStateGraph
from ee.hogai.assistant import AssistantGraph
from ee.hogai.eval.utils import EvalBaseTest
from ee.hogai.utils import AssistantNodeName
from posthog.schema import HumanMessage, RouterMessage
class TestEvalRouter(EvalBaseTest):
def _call_node(self, query: str | list):
graph: CompiledStateGraph = (
AssistantGraph(self.team)
.add_start()
.add_router(path_map={"trends": AssistantNodeName.END, "funnel": AssistantNodeName.END})
.compile()
)
messages = [HumanMessage(content=query)] if isinstance(query, str) else query
state = graph.invoke({"messages": messages})
return state["messages"][-1].content
def test_outputs_basic_trends_insight(self):
query = "Show the $pageview trend"
res = self._call_node(query)
self.assertEqual(res, "trends")
def test_outputs_basic_funnel_insight(self):
query = "What is the conversion rate of users who uploaded a file to users who paid for a plan?"
res = self._call_node(query)
self.assertEqual(res, "funnel")
def test_converts_trends_to_funnel(self):
conversation = [
HumanMessage(content="Show trends of $pageview and $identify"),
RouterMessage(content="trends"),
HumanMessage(content="Convert this insight to a funnel"),
]
res = self._call_node(conversation[:1])
self.assertEqual(res, "trends")
res = self._call_node(conversation)
self.assertEqual(res, "funnel")
def test_converts_funnel_to_trends(self):
conversation = [
HumanMessage(content="What is the conversion from a page view to a sign up?"),
RouterMessage(content="funnel"),
HumanMessage(content="Convert this insight to a trends"),
]
res = self._call_node(conversation[:1])
self.assertEqual(res, "funnel")
res = self._call_node(conversation)
self.assertEqual(res, "trends")
def test_outputs_single_trends_insight(self):
"""
Must display a trends insight because it's not possible to build a funnel with a single series.
"""
query = "how many users upgraded their plan to personal pro?"
res = self._call_node(query)
self.assertEqual(res, "trends")

163
ee/hogai/eval/test_eval_trends_planner.py Normal file
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@ -0,0 +1,163 @@
from deepeval import assert_test
from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCase, LLMTestCaseParams
from langgraph.graph.state import CompiledStateGraph
from ee.hogai.assistant import AssistantGraph
from ee.hogai.eval.utils import EvalBaseTest
from ee.hogai.utils import AssistantNodeName
from posthog.schema import HumanMessage
class TestEvalTrendsPlanner(EvalBaseTest):
def _get_plan_correctness_metric(self):
return GEval(
name="Trends Plan Correctness",
criteria="You will be given expected and actual generated plans to provide a taxonomy to answer a user's question with a trends insight. Compare the plans to determine whether the taxonomy of the actual plan matches the expected plan. Do not apply general knowledge about trends insights.",
evaluation_steps=[
"A plan must define at least one event and a math type, but it is not required to define any filters, breakdowns, or formulas.",
"Compare events, properties, math types, and property values of 'expected output' and 'actual output'.",
"Check if the combination of events, properties, and property values in 'actual output' can answer the user's question according to the 'expected output'.",
# The criteria for aggregations must be more specific because there isn't a way to bypass them.
"Check if the math types in 'actual output' match those in 'expected output'. Math types sometimes are interchangeable, so use your judgement. If the aggregation type is specified by a property, user, or group in 'expected output', the same property, user, or group must be used in 'actual output'.",
"If 'expected output' contains a breakdown, check if 'actual output' contains a similar breakdown, and heavily penalize if the breakdown is not present or different.",
"If 'expected output' contains a formula, check if 'actual output' contains a similar formula, and heavily penalize if the formula is not present or different.",
# We don't want to see in the output unnecessary property filters. The assistant tries to use them all the time.
"Heavily penalize if the 'actual output' contains any excessive output not present in the 'expected output'. For example, the `is set` operator in filters should not be used unless the user explicitly asks for it.",
],
evaluation_params=[
LLMTestCaseParams.INPUT,
LLMTestCaseParams.EXPECTED_OUTPUT,
LLMTestCaseParams.ACTUAL_OUTPUT,
],
threshold=0.7,
)
def _call_node(self, query):
graph: CompiledStateGraph = (
AssistantGraph(self.team)
.add_edge(AssistantNodeName.START, AssistantNodeName.TRENDS_PLANNER)
.add_trends_planner(AssistantNodeName.END)
.compile()
)
state = graph.invoke({"messages": [HumanMessage(content=query)]})
return state["plan"]
def test_no_excessive_property_filters(self):
query = "Show the $pageview trend"
test_case = LLMTestCase(
input=query,
expected_output="""
Events:
- $pageview
- math operation: total count
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_no_excessive_property_filters_for_a_defined_math_type(self):
query = "What is the MAU?"
test_case = LLMTestCase(
input=query,
expected_output="""
Events:
- $pageview
- math operation: unique users
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_basic_filtering(self):
query = "can you compare how many Chrome vs Safari users uploaded a file in the last 30d?"
test_case = LLMTestCase(
input=query,
expected_output="""
Events:
- uploaded_file
- math operation: total count
- property filter 1:
- entity: event
- property name: $browser
- property type: String
- operator: equals
- property value: Chrome
- property filter 2:
- entity: event
- property name: $browser
- property type: String
- operator: equals
- property value: Safari
Breakdown by:
- breakdown 1:
- entity: event
- property name: $browser
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_formula_mode(self):
query = "i want to see a ratio of identify divided by page views"
test_case = LLMTestCase(
input=query,
expected_output="""
Events:
- $identify
- math operation: total count
- $pageview
- math operation: total count
Formula:
`A/B`, where `A` is the total count of `$identify` and `B` is the total count of `$pageview`
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_math_type_by_a_property(self):
query = "what is the average session duration?"
test_case = LLMTestCase(
input=query,
expected_output="""
Events:
- All Events
- math operation: average by `$session_duration`
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_math_type_by_a_user(self):
query = "What is the median page view count for a user?"
test_case = LLMTestCase(
input=query,
expected_output="""
Events:
- $pageview
- math operation: median by users
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])
def test_needle_in_a_haystack(self):
query = "How frequently do people pay for a personal-pro plan?"
test_case = LLMTestCase(
input=query,
expected_output="""
Events:
- paid_bill
- math operation: total count
- property filter 1:
- entity: event
- property name: plan
- property type: String
- operator: contains
- property value: personal/pro
""",
actual_output=self._call_node(query),
)
assert_test(test_case, [self._get_plan_correctness_metric()])

28
ee/hogai/eval/utils.py Normal file
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@ -0,0 +1,28 @@
import datetime as dt
import os
import pytest
from flaky import flaky
from posthog.demo.matrix.manager import MatrixManager
from posthog.tasks.demo_create_data import HedgeboxMatrix
from posthog.test.base import BaseTest
@pytest.mark.skipif(os.environ.get("DEEPEVAL") != "YES", reason="Only runs for the assistant evaluation")
@flaky(max_runs=3, min_passes=1)
class EvalBaseTest(BaseTest):
@classmethod
def setUpTestData(cls):
super().setUpTestData()
matrix = HedgeboxMatrix(
seed="b1ef3c66-5f43-488a-98be-6b46d92fbcef", # this seed generates all events
now=dt.datetime.now(dt.UTC) - dt.timedelta(days=25),
days_past=60,
days_future=30,
n_clusters=60,
group_type_index_offset=0,
)
matrix_manager = MatrixManager(matrix, print_steps=True)
existing_user = cls.team.organization.members.first()
matrix_manager.run_on_team(cls.team, existing_user)

2
ee/hogai/schema_generator/nodes.py
View File

@ -23,7 +23,7 @@ from ee.hogai.schema_generator.prompts import (
QUESTION_PROMPT,
)
from ee.hogai.schema_generator.utils import SchemaGeneratorOutput
from ee.hogai.utils import AssistantState, AssistantNode, filter_visualization_conversation
from ee.hogai.utils import AssistantNode, AssistantState, filter_visualization_conversation
from posthog.models.group_type_mapping import GroupTypeMapping
from posthog.schema import (
FailureMessage,

32
posthog/api/test/__snapshots__/test_decide.ambr
View File

@ -712,6 +712,22 @@
'''
# ---
# name: TestDecide.test_flag_with_behavioural_cohorts.5
'''
SELECT "posthog_group"."id",
"posthog_group"."team_id",
"posthog_group"."group_key",
"posthog_group"."group_type_index",
"posthog_group"."group_properties",
"posthog_group"."created_at",
"posthog_group"."properties_last_updated_at",
"posthog_group"."properties_last_operation",
"posthog_group"."version"
FROM "posthog_group"
WHERE "posthog_group"."team_id" = 99999
LIMIT 21
'''
# ---
# name: TestDecide.test_flag_with_behavioural_cohorts.6
'''
SELECT "posthog_cohort"."id",
"posthog_cohort"."name",
@ -736,6 +752,22 @@
AND "posthog_cohort"."team_id" = 99999)
'''
# ---
# name: TestDecide.test_flag_with_behavioural_cohorts.7
'''
SELECT "posthog_group"."id",
"posthog_group"."team_id",
"posthog_group"."group_key",
"posthog_group"."group_type_index",
"posthog_group"."group_properties",
"posthog_group"."created_at",
"posthog_group"."properties_last_updated_at",
"posthog_group"."properties_last_operation",
"posthog_group"."version"
FROM "posthog_group"
WHERE "posthog_group"."team_id" = 99999
LIMIT 21
'''
# ---
# name: TestDecide.test_flag_with_regular_cohorts
'''
SELECT "posthog_hogfunction"."id",

4
posthog/api/test/test_decide.py
View File

@ -2624,12 +2624,12 @@ class TestDecide(BaseTest, QueryMatchingTest):
created_by=self.user,
)
with self.assertNumQueries(5):
with self.assertNumQueries(6):
response = self._post_decide(api_version=3, distinct_id="example_id_1")
self.assertEqual(response.json()["featureFlags"], {})
self.assertEqual(response.json()["errorsWhileComputingFlags"], True)
with self.assertNumQueries(5):
with self.assertNumQueries(6):
response = self._post_decide(api_version=3, distinct_id="another_id")
self.assertEqual(response.json()["featureFlags"], {})
self.assertEqual(response.json()["errorsWhileComputingFlags"], True)

1
requirements-dev.in
View File

@ -56,3 +56,4 @@ flaky==3.7.0
aioresponses==0.7.6
prance==23.06.21.0
openapi-spec-validator==0.7.1 # Needed for prance as a validation backend
deepeval==1.5.5

304
requirements-dev.txt
View File

@ -4,6 +4,10 @@ aiohttp==3.9.3
# via
# -c requirements.txt
# aioresponses
# datasets
# fsspec
# langchain
# langchain-community
aioresponses==0.7.6
# via -r requirements-dev.in
aiosignal==1.2.0
@ -14,6 +18,13 @@ annotated-types==0.7.0
# via
# -c requirements.txt
# pydantic
anyio==4.6.2.post1
# via
# -c requirements.txt
# httpx
# openai
appdirs==1.4.4
# via ragas
argcomplete==2.0.0
# via datamodel-code-generator
asgiref==3.7.2
@ -45,7 +56,10 @@ botocore-stubs==1.34.84
certifi==2019.11.28
# via
# -c requirements.txt
# httpcore
# httpx
# requests
# sentry-sdk
cffi==1.16.0
# via
# -c requirements.txt
@ -61,6 +75,7 @@ click==8.1.7
# -c requirements.txt
# black
# inline-snapshot
# typer
colorama==0.4.4
# via pytest-watch
coverage==5.5
@ -69,8 +84,26 @@ cryptography==39.0.2
# via
# -c requirements.txt
# types-paramiko
dataclasses-json==0.6.7
# via langchain-community
datamodel-code-generator==0.26.1
# via -r requirements-dev.in
datasets==2.19.1
# via ragas
deepeval==1.5.5
# via -r requirements-dev.in
deprecated==1.2.15
# via
# opentelemetry-api
# opentelemetry-exporter-otlp-proto-grpc
dill==0.3.8
# via
# datasets
# multiprocess
distro==1.9.0
# via
# -c requirements.txt
# openai
django==4.2.15
# via
# -c requirements.txt
@ -93,6 +126,8 @@ dnspython==2.2.1
# email-validator
docopt==0.6.2
# via pytest-watch
docx2txt==0.8
# via deepeval
email-validator==2.0.0.post2
# via pydantic
execnet==2.1.1
@ -103,6 +138,11 @@ faker==17.5.0
# via -r requirements-dev.in
fakeredis==2.23.3
# via -r requirements-dev.in
filelock==3.12.0
# via
# -c requirements.txt
# datasets
# huggingface-hub
flaky==3.7.0
# via -r requirements-dev.in
freezegun==1.2.2
@ -112,16 +152,51 @@ frozenlist==1.4.1
# -c requirements.txt
# aiohttp
# aiosignal
fsspec==2023.10.0
# via
# -c requirements.txt
# datasets
# huggingface-hub
genson==1.2.2
# via datamodel-code-generator
googleapis-common-protos==1.60.0
# via
# -c requirements.txt
# opentelemetry-exporter-otlp-proto-grpc
grpcio==1.63.2
# via
# -c requirements.txt
# deepeval
# opentelemetry-exporter-otlp-proto-grpc
h11==0.13.0
# via
# -c requirements.txt
# httpcore
httpcore==1.0.2
# via
# -c requirements.txt
# httpx
httpx==0.26.0
# via
# -c requirements.txt
# langsmith
# openai
huggingface-hub==0.26.2
# via datasets
icdiff==2.0.5
# via pytest-icdiff
idna==3.10
# via
# -c requirements.txt
# anyio
# email-validator
# httpx
# requests
# yarl
importlib-metadata==7.0.0
# via
# deepeval
# opentelemetry-api
inflect==5.6.2
# via datamodel-code-generator
iniconfig==1.1.1
@ -132,6 +207,18 @@ isort==5.2.2
# via datamodel-code-generator
jinja2==3.1.4
# via datamodel-code-generator
jiter==0.5.0
# via
# -c requirements.txt
# openai
jsonpatch==1.33
# via
# -c requirements.txt
# langchain-core
jsonpointer==3.0.0
# via
# -c requirements.txt
# jsonpatch
jsonschema==4.20.0
# via
# -c requirements.txt
@ -144,6 +231,38 @@ jsonschema-specifications==2023.12.1
# -c requirements.txt
# jsonschema
# openapi-schema-validator
langchain==0.3.3
# via
# -c requirements.txt
# deepeval
# langchain-community
# ragas
langchain-community==0.3.2
# via ragas
langchain-core==0.3.10
# via
# -c requirements.txt
# deepeval
# langchain
# langchain-community
# langchain-openai
# langchain-text-splitters
# ragas
langchain-openai==0.2.2
# via
# -c requirements.txt
# deepeval
# ragas
langchain-text-splitters==0.3.0
# via
# -c requirements.txt
# langchain
langsmith==0.1.132
# via
# -c requirements.txt
# langchain
# langchain-community
# langchain-core
lazy-object-proxy==1.10.0
# via openapi-spec-validator
lupa==2.2
@ -152,6 +271,8 @@ markdown-it-py==3.0.0
# via rich
markupsafe==2.1.5
# via jinja2
marshmallow==3.23.1
# via dataclasses-json
mdurl==0.1.2
# via markdown-it-py
multidict==6.0.2
@ -159,6 +280,8 @@ multidict==6.0.2
# -c requirements.txt
# aiohttp
# yarl
multiprocess==0.70.16
# via datasets
mypy==1.11.1
# via -r requirements-dev.in
mypy-baseline==0.7.0
@ -170,18 +293,66 @@ mypy-extensions==1.0.0
# -r requirements-dev.in
# black
# mypy
# typing-inspect
nest-asyncio==1.6.0
# via ragas
numpy==1.23.3
# via
# -c requirements.txt
# datasets
# langchain
# langchain-community
# pandas
# pyarrow
# ragas
openai==1.51.2
# via
# -c requirements.txt
# langchain-openai
# ragas
openapi-schema-validator==0.6.2
# via openapi-spec-validator
openapi-spec-validator==0.7.1
# via -r requirements-dev.in
opentelemetry-api==1.24.0
# via
# deepeval
# opentelemetry-exporter-otlp-proto-grpc
# opentelemetry-sdk
opentelemetry-exporter-otlp-proto-common==1.24.0
# via opentelemetry-exporter-otlp-proto-grpc
opentelemetry-exporter-otlp-proto-grpc==1.24.0
# via deepeval
opentelemetry-proto==1.24.0
# via
# opentelemetry-exporter-otlp-proto-common
# opentelemetry-exporter-otlp-proto-grpc
opentelemetry-sdk==1.24.0
# via
# deepeval
# opentelemetry-exporter-otlp-proto-grpc
opentelemetry-semantic-conventions==0.45b0
# via opentelemetry-sdk
orjson==3.10.7
# via
# -c requirements.txt
# langsmith
packaging==24.1
# via
# -c requirements.txt
# -r requirements-dev.in
# black
# datamodel-code-generator
# datasets
# huggingface-hub
# langchain-core
# marshmallow
# prance
# pytest
pandas==2.2.0
# via
# -c requirements.txt
# datasets
parameterized==0.9.0
# via -r requirements-dev.in
pathable==0.4.3
@ -196,10 +367,24 @@ pluggy==1.5.0
# via
# -c requirements.txt
# pytest
portalocker==2.10.1
# via deepeval
pprintpp==0.4.0
# via pytest-icdiff
prance==23.6.21.0
# via -r requirements-dev.in
protobuf==4.22.1
# via
# -c requirements.txt
# deepeval
# googleapis-common-protos
# opentelemetry-proto
pyarrow==17.0.0
# via
# -c requirements.txt
# datasets
pyarrow-hotfix==0.6
# via datasets
pycparser==2.20
# via
# -c requirements.txt
@ -208,21 +393,34 @@ pydantic==2.9.2
# via
# -c requirements.txt
# datamodel-code-generator
# deepeval
# langchain
# langchain-core
# langsmith
# openai
# pydantic-settings
# ragas
pydantic-core==2.23.4
# via
# -c requirements.txt
# pydantic
pydantic-settings==2.6.1
# via langchain-community
pygments==2.18.0
# via rich
pysbd==0.3.4
# via ragas
pytest==8.0.2
# via
# -r requirements-dev.in
# deepeval
# pytest-asyncio
# pytest-cov
# pytest-django
# pytest-env
# pytest-icdiff
# pytest-mock
# pytest-repeat
# pytest-split
# pytest-watch
# pytest-xdist
@ -239,24 +437,44 @@ pytest-icdiff==0.6
# via -r requirements-dev.in
pytest-mock==3.11.1
# via -r requirements-dev.in
pytest-repeat==0.9.3
# via deepeval
pytest-split==0.9.0
# via -r requirements-dev.in
pytest-watch==4.2.0
# via -r requirements-dev.in
pytest-xdist==3.6.1
# via -r requirements-dev.in
# via
# -r requirements-dev.in
# deepeval
python-dateutil==2.8.2
# via
# -c requirements.txt
# -r requirements-dev.in
# faker
# freezegun
# pandas
python-dotenv==0.21.0
# via
# -c requirements.txt
# pydantic-settings
pytz==2023.3
# via
# -c requirements.txt
# pandas
pyyaml==6.0.1
# via
# -c requirements.txt
# datamodel-code-generator
# datasets
# huggingface-hub
# jsonschema-path
# langchain
# langchain-community
# langchain-core
# responses
ragas==0.2.5
# via deepeval
redis==4.5.4
# via
# -c requirements.txt
@ -267,19 +485,39 @@ referencing==0.31.1
# jsonschema
# jsonschema-path
# jsonschema-specifications
regex==2023.12.25
# via
# -c requirements.txt
# tiktoken
requests==2.32.0
# via
# -c requirements.txt
# datasets
# deepeval
# djangorestframework-stubs
# fsspec
# huggingface-hub
# jsonschema-path
# langchain
# langchain-community
# langsmith
# prance
# requests-toolbelt
# responses
# tiktoken
requests-toolbelt==1.0.0
# via
# -c requirements.txt
# langsmith
responses==0.23.1
# via -r requirements-dev.in
rfc3339-validator==0.1.4
# via openapi-schema-validator
rich==13.7.1
# via inline-snapshot
# via
# deepeval
# inline-snapshot
# typer
rpds-py==0.16.2
# via
# -c requirements.txt
@ -291,6 +529,12 @@ ruamel-yaml-clib==0.2.8
# via ruamel-yaml
ruff==0.6.1
# via -r requirements-dev.in
sentry-sdk==1.44.1
# via
# -c requirements.txt
# deepeval
shellingham==1.5.4
# via typer
six==1.16.0
# via
# -c requirements.txt
@ -298,20 +542,54 @@ six==1.16.0
# prance
# python-dateutil
# rfc3339-validator
sniffio==1.3.1
# via
# -c requirements.txt
# anyio
# httpx
# openai
sortedcontainers==2.4.0
# via
# -c requirements.txt
# fakeredis
sqlalchemy==2.0.31
# via
# -c requirements.txt
# langchain
# langchain-community
sqlparse==0.4.4
# via
# -c requirements.txt
# django
syrupy==4.6.4
# via -r requirements-dev.in
tabulate==0.9.0
# via deepeval
tenacity==8.4.2
# via
# -c requirements.txt
# deepeval
# langchain
# langchain-community
# langchain-core
tiktoken==0.8.0
# via
# -c requirements.txt
# langchain-openai
# ragas
toml==0.10.2
# via
# coverage
# inline-snapshot
tqdm==4.64.1
# via
# -c requirements.txt
# datasets
# deepeval
# huggingface-hub
# openai
typer==0.13.0
# via deepeval
types-awscrt==0.20.9
# via botocore-stubs
types-freezegun==1.1.10
@ -355,21 +633,43 @@ typing-extensions==4.12.2
# django-stubs
# django-stubs-ext
# djangorestframework-stubs
# huggingface-hub
# inline-snapshot
# langchain-core
# mypy
# mypy-boto3-s3
# openai
# opentelemetry-sdk
# pydantic
# pydantic-core
# sqlalchemy
# typer
# typing-inspect
typing-inspect==0.9.0
# via dataclasses-json
tzdata==2023.3
# via
# -c requirements.txt
# pandas
urllib3==1.26.18
# via
# -c requirements.txt
# requests
# responses
# sentry-sdk
watchdog==2.1.8
# via
# -r requirements-dev.in
# pytest-watch
wrapt==1.15.0
# via
# -c requirements.txt
# deprecated
xxhash==3.5.0
# via datasets
yarl==1.9.4
# via
# -c requirements.txt
# aiohttp
zipp==3.21.0
# via importlib-metadata

2
requirements.in
View File

@ -111,3 +111,5 @@ zxcvbn==4.4.28
zstd==1.5.5.1
xmlsec==1.3.13 # Do not change this version - it will break SAML
lxml==4.9.4 # Do not change this version - it will break SAML
grpcio~=1.63.2 # Version constrained so that `deepeval` can be installed in in dev
tenacity~=8.4.2 # Version constrained so that `deepeval` can be installed in in dev

6
requirements.txt
View File

@ -267,8 +267,9 @@ googleapis-common-protos==1.60.0
# via
# google-api-core
# grpcio-status
grpcio==1.57.0
grpcio==1.63.2
# via
# -r requirements.in
# google-api-core
# grpcio-status
# sqlalchemy-bigquery
@ -702,8 +703,9 @@ structlog==23.2.0
# django-structlog
temporalio==1.7.1
# via -r requirements.in
tenacity==8.2.3
tenacity==8.4.2
# via
# -r requirements.in
# celery-redbeat
# dlt
# langchain