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chore(wren-ai-service): sql generation improvement (#618)

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* add dspy

* fix typo

* update

* separate sql-summary generation to two pipelines

* add langfuse settings

* disable langfuse by default

* remove dspy
This commit is contained in:
Chih-Yu Yeh
2024-08-26 14:29:20 +08:00
committed by GitHub
parent fee709f0eb
commit bbddff3368
14 changed files with 491 additions and 258 deletions
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deployment/kustomizations/base/cm.yaml
+4
View File
@@ -30,6 +30,10 @@ data:
QDRANT_HOST: "wren-qdrant"
DOCUMENT_STORE_PROVIDER: "qdrant"
# Langfuse: for LLM tracing
LANGFUSE_HOST: "https://cloud.langfuse.com"
LANGFUSE_ENABLE: ""
# Telemetry
POSTHOG_HOST: "https://app.posthog.com"
TELEMETRY_ENABLED: "false"
deployment/kustomizations/base/deploy-wren-ai-service.yaml
+20
View File
@@ -88,5 +88,25 @@ spec:
key: WREN_AI_SERVICE_ENABLE_TIMER
- name: PYTHONUNBUFFERED
value: "1"
- name: LANGFUSE_ENABLE
valueFrom:
configMapKeyRef:
name: wren-config
key: LANGFUSE_ENABLE
- name: LANGFUSE_HOST
valueFrom:
configMapKeyRef:
name: wren-config
key: LANGFUSE_HOST
- name: LANGFUSE_PUBLIC_KEY
valueFrom:
secretKeyRef:
name: wrenai-secrets
key: LANGFUSE_PUBLIC_KEY
- name: LANGFUSE_SECRET_KEY
valueFrom:
secretKeyRef:
name: wrenai-secrets
key: LANGFUSE_SECRET_KEY
ports:
- containerPort: 5555
deployment/kustomizations/examples/secret-wren_example.yaml
+7 -3
View File
@@ -7,10 +7,9 @@ type: Opaque
data:
# LLM_OPENAI_API_KEY and EMBEDDER_OPENAI_API_KEY are REQUIRED: without a valid key the wren-ai-service-deployment pod will not start
LLM_OPENAI_API_KEY: UkVRVUlSRUQ6IHNrLXByb2otYWxsLWFjY2Vzcy1wbGFjZWhvbGRlci00LXdyZW4tYWktc2VydmljZS1kZXBsb3ltZW50
EMBEDDER_OPENAI_API_KEY: UkVRVUlSRUQ6IHNrLXByb2otYWxsLWFjY2Vzcy1wbGFjZWhvbGRlci00LXdyZW4tYWktc2VydmljZS1kZXBsb3ltZW50
PG_USERNAME: cG9zdGdyZXM=
EMBEDDER_OPENAI_API_KEY: UkVRVUlSRUQ6IHNrLXByb2otYWxsLWFjY2Vzcy1wbGFjZWhvbGRlci00LXdyZW4tYWktc2VydmljZS1kZXBsb3ltZW50
#Azure openai env
# Azure openai env
AZURE_CHAT_BASE: bi9h
AZURE_CHAT_KEY: bi9h
AZURE_CHAT_VERSION: bi9h
@@ -19,6 +18,10 @@ data:
AZURE_EMBED_KEY: bi9h
AZURE_EMBED_VERSION: bi9h
# Langfuse: for LLM tracing
LANGFUSE_PUBLIC_KEY: xxxx
LANGFUSE_SECRET_KEY: xxxx
### postgres:// This is the protocol. It tells the system that you’re connecting to a PostgreSQL database.
### postgres:postgres These are the username and password for the database, separated by a colon. In this case, both the username and password are “postgres”.
### @wren-postgresql This is the hostname of the database server. "wren-postgresql" means the database server is running in a Kubernetes cluster and it is named "wren-postgresql" in the *same* namespace. If you are using another namespace you must provide the full hostname, example: `wren-postgresql.wrenai.svc.cluster.local`, "wrenai" is the namespace name, "svc.cluster.local" is the default domain name for Kubernetes services no need to change it.
@@ -27,6 +30,7 @@ data:
### PG_URL: "postgres://postgres:postgres@wren-postgresql:5432/admin_ui"
#Fix
PG_URL: cG9zdGdyZXM6Ly9wb3N0Z3Jlczpwb3N0Z3Jlc0B3cmVuLXBvc3RncmVzcWw6NTQzMi9hZG1pbl91aQo=
PG_USERNAME: cG9zdGdyZXM=
POSTHOG_API_KEY: cGhjX2tleS1wbGFjZWhvbGRlcg==
POSTHOG_HOST: aHR0cHM6Ly9hcHAucG9zdGhvZy5jb20=
wren-ai-service/eval/prediction.py
+1 -3
View File
@@ -16,9 +16,7 @@ from tomlkit import document, dumps
sys.path.append(f"{Path().parent.resolve()}")
import eval.pipelines as pipelines
import src.utils as utils
from eval.utils import (
parse_toml,
)
from eval.utils import parse_toml
from src.core.engine import EngineConfig
from src.core.provider import EmbedderProvider, LLMProvider
wren-ai-service/poetry.lock
Generated
+234 -216
View File
@@ -1750,13 +1750,13 @@ license = ["ukkonen"]
[[package]]
name = "idna"
version = "3.7"
version = "3.8"
description = "Internationalized Domain Names in Applications (IDNA)"
optional = false
python-versions = ">=3.5"
python-versions = ">=3.6"
files = [
{file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
{file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
{file = "idna-3.8-py3-none-any.whl", hash = "sha256:050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac"},
{file = "idna-3.8.tar.gz", hash = "sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603"},
]
[[package]]
@@ -1925,13 +1925,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
[[package]]
name = "langchain-core"
version = "0.2.33"
version = "0.2.35"
description = "Building applications with LLMs through composability"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langchain_core-0.2.33-py3-none-any.whl", hash = "sha256:c8de411336c13fa440b7a52895bfd1c064f04d315344855962988483902cc532"},
{file = "langchain_core-0.2.33.tar.gz", hash = "sha256:dd2659e0a560fc987b210107bf989aa14a6f4b67dd214c13a2c9669036cda975"},
{file = "langchain_core-0.2.35-py3-none-any.whl", hash = "sha256:e994fb8598e919842205824836a584243c1fb7f44268f6f14921c529667c4e6e"},
{file = "langchain_core-0.2.35.tar.gz", hash = "sha256:da8f51355a4a285345c41a3232fd0f1cc3a33726eb7b3557b753ffabfeb68b3f"},
]
[package.dependencies]
@@ -1978,16 +1978,17 @@ langchain-core = ">=0.2.10,<0.3.0"
[[package]]
name = "langfuse"
version = "2.35.0"
version = "2.43.3"
description = "A client library for accessing langfuse"
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langfuse-2.35.0-py3-none-any.whl", hash = "sha256:e9df2474a01f8e167b7b13674c554915415b27064e48ad207054475f7fa8f82d"},
{file = "langfuse-2.35.0.tar.gz", hash = "sha256:b1d4b478233eefbc8a6fc63ca00ca82f6afecf2b0fdc1835ca65e751cf901577"},
{file = "langfuse-2.43.3-py3-none-any.whl", hash = "sha256:62a368009dd26f698905321a52929ab4e75996a871f41db2892beb5257ab69d2"},
{file = "langfuse-2.43.3.tar.gz", hash = "sha256:046d872d0d0053d02816d5e5a610be0e4ae7ebb69e65d979111fc522be965691"},
]
[package.dependencies]
anyio = ">=4.4.0,<5.0.0"
backoff = ">=1.10.0"
httpx = ">=0.15.4,<1.0"
idna = ">=3.7,<4.0"
@@ -2002,16 +2003,17 @@ openai = ["openai (>=0.27.8)"]
[[package]]
name = "langsmith"
version = "0.1.99"
version = "0.1.104"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = "<4.0,>=3.8.1"
files = [
{file = "langsmith-0.1.99-py3-none-any.whl", hash = "sha256:ef8d1d74a2674c514aa429b0171a9fbb661207dc3835142cca0e8f1bf97b26b0"},
{file = "langsmith-0.1.99.tar.gz", hash = "sha256:b5c6a1f158abda61600a4a445081ee848b4a28b758d91f2793dc02aeffafcaf1"},
{file = "langsmith-0.1.104-py3-none-any.whl", hash = "sha256:049cd312952a0db9f5edeed3b9a8616e66ef86e5490c835c8bb054569203b0d0"},
{file = "langsmith-0.1.104.tar.gz", hash = "sha256:7892dfe452d143fba573d7eb28dbff3202d2f2daacab8c7276ffe4a850179d4d"},
]
[package.dependencies]
httpx = ">=0.23.0,<1"
orjson = ">=3.9.14,<4.0.0"
pydantic = [
{version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
@@ -2155,13 +2157,13 @@ files = [
[[package]]
name = "marshmallow"
version = "3.21.3"
version = "3.22.0"
description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
optional = false
python-versions = ">=3.8"
files = [
{file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"},
{file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"},
{file = "marshmallow-3.22.0-py3-none-any.whl", hash = "sha256:71a2dce49ef901c3f97ed296ae5051135fd3febd2bf43afe0ae9a82143a494d9"},
{file = "marshmallow-3.22.0.tar.gz", hash = "sha256:4972f529104a220bb8637d595aa4c9762afbe7f7a77d82dc58c1615d70c5823e"},
]
[package.dependencies]
@@ -2169,7 +2171,7 @@ packaging = ">=17.0"
[package.extras]
dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.3.7)", "sphinx-issues (==4.1.0)", "sphinx-version-warning (==1.1.2)"]
docs = ["alabaster (==1.0.0)", "autodocsumm (==0.2.13)", "sphinx (==8.0.2)", "sphinx-issues (==4.1.0)", "sphinx-version-warning (==1.1.2)"]
tests = ["pytest", "pytz", "simplejson"]
[[package]]
@@ -2406,17 +2408,19 @@ files = [
[[package]]
name = "narwhals"
version = "1.4.2"
version = "1.5.5"
description = "Extremely lightweight compatibility layer between dataframe libraries"
optional = false
python-versions = ">=3.8"
files = [
{file = "narwhals-1.4.2-py3-none-any.whl", hash = "sha256:cee6bb1255f63aa5ea9e268fcda58cb293235494c1dc7eb327d4beb253eaaebf"},
{file = "narwhals-1.4.2.tar.gz", hash = "sha256:b33348b1d187208227f404f1808713f434e401951b780a34719e4eade1fa5df5"},
{file = "narwhals-1.5.5-py3-none-any.whl", hash = "sha256:b0cee9a1cfdf88fa1c85720953946b27e7d56629dd0637021efe708cdb59c891"},
{file = "narwhals-1.5.5.tar.gz", hash = "sha256:2da2f9388f5bfbc11dd6e82fcea1fc15ac2060a2e6a585d7d3a866a73ad2e357"},
]
[package.extras]
cudf = ["cudf (>=23.08.00)"]
dask = ["dask[dataframe] (>=2024.7)"]
modin = ["modin"]
pandas = ["pandas (>=0.25.3)"]
polars = ["polars (>=0.20.3)"]
pyarrow = ["pyarrow (>=11.0.0)"]
@@ -2935,13 +2939,13 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
[[package]]
name = "posthog"
version = "3.5.0"
version = "3.5.2"
description = "Integrate PostHog into any python application."
optional = false
python-versions = "*"
files = [
{file = "posthog-3.5.0-py2.py3-none-any.whl", hash = "sha256:3c672be7ba6f95d555ea207d4486c171d06657eb34b3ce25eb043bfe7b6b5b76"},
{file = "posthog-3.5.0.tar.gz", hash = "sha256:8f7e3b2c6e8714d0c0c542a2109b83a7549f63b7113a133ab2763a89245ef2ef"},
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{file = "posthog-3.5.2.tar.gz", hash = "sha256:a383a80c1f47e0243f5ce359e81e06e2e7b37eb39d1d6f8d01c3e64ed29df2ee"},
]
[package.dependencies]
@@ -3537,120 +3541,120 @@ files = [
[[package]]
name = "pyzmq"
version = "26.1.1"
version = "26.2.0"
description = "Python bindings for 0MQ"
optional = false
python-versions = ">=3.7"
files = [
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@@ -4756,94 +4760,108 @@ anyio = ">=3.0.0"
[[package]]
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version = "13.0"
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[[package]]
name = "werkzeug"
version = "3.0.3"
version = "3.0.4"
description = "The comprehensive WSGI web application library."
optional = false
python-versions = ">=3.8"
files = [
{file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"},
{file = "werkzeug-3.0.3.tar.gz", hash = "sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"},
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]
[package.dependencies]
@@ -5253,4 +5271,4 @@ testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.12.*, <4.0"
content-hash = "d5bdcee844dbb90791d8b405e91bee2194117d8d7cac170e33395ce656900cff"
content-hash = "f12b6a6b5ec35f97aa5ad9f039b8071b30514d29db9a1ae37fecd04c5231dfc6"
wren-ai-service/pyproject.toml
+1 -1
View File
@@ -24,7 +24,7 @@ orjson = "==3.10.3"
sf-hamilton = {version = "==1.69.0", extras = ["visualization"]}
aiohttp = {extras = ["speedups"], version = "==3.10.2"}
ollama-haystack = "==0.0.6"
langfuse = "==2.35.0"
langfuse = "==2.43.3"
ollama = "==0.2.1"
toml = "==0.10.2"
wren-ai-service/src/globals.py
+4
View File
@@ -10,6 +10,7 @@ from src.pipelines.ask import (
)
from src.pipelines.ask import (
historical_question,
sql_summary,
)
from src.pipelines.ask import (
retrieval as ask_retrieval,
@@ -93,6 +94,9 @@ def init_globals(
llm_provider=llm_provider,
engine=engine,
),
"sql_summary": sql_summary.SQLSummary(
llm_provider=llm_provider,
),
},
)
wren-ai-service/src/pipelines/ask/components/post_processors.py
-3
View File
@@ -74,14 +74,12 @@ class GenerationPostProcessor:
valid_generation_results.append(
{
"sql": quoted_sql,
"summary": result["summary"],
}
)
else:
invalid_generation_results.append(
{
"sql": quoted_sql,
"summary": result["summary"],
"type": "DRY_RUN",
"error": error,
}
@@ -90,7 +88,6 @@ class GenerationPostProcessor:
invalid_generation_results.append(
{
"sql": result["sql"],
"summary": result["summary"],
"type": "ADD_QUOTES",
"error": "add_quotes failed",
}
wren-ai-service/src/pipelines/ask/components/prompts.py
+1
View File
@@ -1,5 +1,6 @@
TEXT_TO_SQL_RULES = """
### ALERT ###
- ONLY USE SELECT statements, NO DELETE, UPDATE OR INSERT etc. statements that might change the data in the database.
- DON'T USE "*" in SELECT queries.
- ONLY USE the tables and columns mentioned in the database schema.
- ONLY CHOOSE columns belong to the tables mentioned in the database schema.
wren-ai-service/src/pipelines/ask/followup_generation.py
+12 -21
View File
@@ -46,16 +46,13 @@ User's Question: How do I modify this to only show departments with more than 10
{
"results": [
{
"sql": "SELECT department, COUNT() as employee_count FROM employees GROUP BY department HAVING COUNT() > 10",
"summary": "Modified to show only departments with more than 10 employees."
"sql": "SELECT department, COUNT() as employee_count FROM employees GROUP BY department HAVING COUNT() > 10"
},
{
"sql": "SELECT department FROM employees GROUP BY department HAVING COUNT() > 10",
"summary": "Shows only the names of departments with more than 10 employees."
"sql": "SELECT department FROM employees GROUP BY department HAVING COUNT() > 10"
},
{
"sql": "SELECT department, COUNT() as employee_count FROM employees WHERE department IN (SELECT department FROM employees GROUP BY department HAVING COUNT(*) > 10)",
"summary": "Lists departments and their employee count, including only those with more than 10 employees."
"sql": "SELECT department, COUNT() as employee_count FROM employees WHERE department IN (SELECT department FROM employees GROUP BY department HAVING COUNT(*) > 10)"
}
]
}
@@ -70,16 +67,13 @@ User's Question: Can you adjust this to include the product name as well?
{
"results": [
{
"sql": "SELECT products.name, SUM(sales.sales) as total_sales FROM sales JOIN products ON sales.product_id = products.id GROUP BY products.name",
"summary": "Includes product name with total sales."
"sql": "SELECT products.name, SUM(sales.sales) as total_sales FROM sales JOIN products ON sales.product_id = products.id GROUP BY products.name"
},
{
"sql": "SELECT p.name, s.total_sales FROM (SELECT product_id, SUM(sales) as total_sales FROM sales GROUP BY product_id) s JOIN products p ON s.product_id = p.id",
"summary": "Joins product table to include names in the total sales summary."
"sql": "SELECT p.name, s.total_sales FROM (SELECT product_id, SUM(sales) as total_sales FROM sales GROUP BY product_id) s JOIN products p ON s.product_id = p.id"
},
{
"sql": "SELECT p.name, IFNULL(SUM(s.sales), 0) as total_sales FROM products p LEFT JOIN sales s ON p.id = s.product_id GROUP BY p.name",
"summary": "Includes all products, even those with no sales, showing total sales with product names."
"sql": "SELECT p.name, IFNULL(SUM(s.sales), 0) as total_sales FROM products p LEFT JOIN sales s ON p.id = s.product_id GROUP BY p.name"
}
]
}
@@ -94,16 +88,13 @@ User's Question: What if I want to see the employee names with the highest salar
{
"results": [
{
"sql": "SELECT department_id, employee_name, salary FROM employees WHERE (department_id, salary) IN (SELECT department_id, MAX(salary) FROM employees GROUP BY department_id)",
"summary": "Shows the names of employees who earn the highest salary in their respective departments."
"sql": "SELECT department_id, employee_name, salary FROM employees WHERE (department_id, salary) IN (SELECT department_id, MAX(salary) FROM employees GROUP BY department_id)"
},
{
"sql": "SELECT e.department_id, e.employee_name, e.salary FROM employees e INNER JOIN (SELECT department_id, MAX(salary) as max_salary FROM employees GROUP BY department_id) d ON e.department_id = d.department_id AND e.salary = d.max_salary",
"summary": "Lists employees with the highest salary in each department."
"sql": "SELECT e.department_id, e.employee_name, e.salary FROM employees e INNER JOIN (SELECT department_id, MAX(salary) as max_salary FROM employees GROUP BY department_id) d ON e.department_id = d.department_id AND e.salary = d.max_salary"
},
{
"sql": "WITH MaxSalaries AS (SELECT department_id, MAX(salary) as max_salary FROM employees GROUP BY department_id) SELECT e.department_id, e.employee_name, e.salary FROM employees e JOIN MaxSalaries m ON e.department_id = m.department_id AND e.salary = m.max_salary",
"summary": "Utilizes a CTE to display each department's highest earners along with their names and salaries."
"sql": "WITH MaxSalaries AS (SELECT department_id, MAX(salary) as max_salary FROM employees GROUP BY department_id) SELECT e.department_id, e.employee_name, e.salary FROM employees e JOIN MaxSalaries m ON e.department_id = m.department_id AND e.salary = m.max_salary"
}
]
}
@@ -113,9 +104,9 @@ The final answer must be the JSON format like following:
{
"results": [
{"sql": <SQL_QUERY_STRING_1>, "summary": <SUMMARY_STRING_1>},
{"sql": <SQL_QUERY_STRING2>, "summary": <SUMMARY_STRING_2>},
{"sql": <SQL_QUERY_STRING3>, "summary": <SUMMARY_STRING_3>}
{"sql": <SQL_QUERY_STRING_1>},
{"sql": <SQL_QUERY_STRING_2>},
{"sql": <SQL_QUERY_STRING_3>}
]
}
wren-ai-service/src/pipelines/ask/generation.py
+5 -4
View File
@@ -28,7 +28,7 @@ logger = logging.getLogger("wren-ai-service")
text_to_sql_user_prompt_template = """
### TASK ###
Given a user query that is ambiguous in nature, your task is to interpret the query in various plausible ways and
generate three SQL statements that could potentially answer each interpreted version of the queries and within-10-words summary.
generate three SQL statements that could potentially answer each interpreted version of the queries.
Provide three different interpretations and corresponding SQL queries that reflect these interpretations.
Ensure that your SQL queries are diverse, covering a range of possible meanings behind the ambiguous query.
@@ -68,7 +68,7 @@ Proceed in a similar manner for the other queries.
### EXCLUDED STATEMETS ###
Ensure that the following excluded statements are not used in the generated queries to maintain variety and avoid repetition.
{% for doc in exclude %}
{{ doc.statement}}
{{ doc.statement }}
{% endfor %}
{{ alert }}
@@ -78,8 +78,9 @@ The final answer must be the JSON format like following:
{
"results": [
{"sql": <SQL_QUERY_STRING_1>, "summary": <SUMMARY_STRING_1>},
{"sql": <SQL_QUERY_STRING2>, "summary": <SUMMARY_STRING_2>}
{"sql": <SQL_QUERY_STRING_1>},
{"sql": <SQL_QUERY_STRING_2>},
{"sql": <SQL_QUERY_STRING_3>}
]
}
wren-ai-service/src/pipelines/ask/sql_summary.py
+184
View File
@@ -0,0 +1,184 @@
import logging
import sys
from pathlib import Path
from typing import Any, List
import orjson
from hamilton import base
from hamilton.experimental.h_async import AsyncDriver
from haystack import component
from haystack.components.builders.prompt_builder import PromptBuilder
from langfuse.decorators import observe
from src.core.pipeline import BasicPipeline
from src.core.provider import LLMProvider
from src.utils import async_timer, timer
logger = logging.getLogger("wren-ai-service")
sql_summary_system_prompt = """
### TASK ###
You are a great data analyst. You are now given a task to summarize a list SQL queries in a human-readable format where each summary should be within 10-20 words.
You will be given a list of SQL queries and a user's question.
### OUTPUT FORMAT ###
Please return the result in the following JSON format:
{
"sql_summary_results": [
{"summary": <SQL_QUERY_SUMMARY_1>},
{"summary": <SQL_QUERY_SUMMARY_2>},
{"summary": <SQL_QUERY_SUMMARY_3>}
]
}
"""
sql_summary_user_prompt_template = """
User's Question: {{query}}
SQLs: {{sqls}}
Please think step by step.
"""
@component
class SQLSummaryPostProcessor:
@component.output_types(
sql_summary_results=List[str],
)
def run(self, sqls: List[str], replies: List[str]):
try:
return {
"sql_summary_results": [
{"sql": sql["sql"], "summary": summary["summary"]}
for (sql, summary) in zip(
sqls, orjson.loads(replies[0])["sql_summary_results"]
)
],
}
except Exception as e:
logger.exception(f"Error in SQLSummaryPostProcessor: {e}")
return {
"sql_summary_results": [],
}
## Start of Pipeline
@timer
@observe(capture_input=False)
def prompt(
query: str,
sqls: List[str],
prompt_builder: PromptBuilder,
) -> dict:
logger.debug(f"query: {query}")
logger.debug(f"sqls: {sqls}")
return prompt_builder.run(
query=query,
sqls=sqls,
)
@async_timer
@observe(as_type="generation", capture_input=False)
async def generate(prompt: dict, generator: Any) -> dict:
logger.debug(f"prompt: {orjson.dumps(prompt, option=orjson.OPT_INDENT_2).decode()}")
return await generator.run(prompt=prompt.get("prompt"))
@timer
def post_process(
generate: dict,
sqls: List[str],
post_processor: SQLSummaryPostProcessor,
) -> dict:
logger.debug(
f"generate: {orjson.dumps(generate, option=orjson.OPT_INDENT_2).decode()}"
)
return post_processor.run(sqls, generate.get("replies"))
## End of Pipeline
class SQLSummary(BasicPipeline):
def __init__(
self,
llm_provider: LLMProvider,
):
self.generator = llm_provider.get_generator(
system_prompt=sql_summary_system_prompt
)
self.prompt_builder = PromptBuilder(template=sql_summary_user_prompt_template)
self.post_processor = SQLSummaryPostProcessor()
super().__init__(
AsyncDriver({}, sys.modules[__name__], result_builder=base.DictResult())
)
def visualize(
self,
query: str,
sqls: List[str],
) -> None:
destination = "outputs/pipelines/ask"
if not Path(destination).exists():
Path(destination).mkdir(parents=True, exist_ok=True)
self._pipe.visualize_execution(
["post_process"],
output_file_path=f"{destination}/sql_summary.dot",
inputs={
"query": query,
"sqls": sqls,
"generator": self.generator,
"prompt_builder": self.prompt_builder,
"post_processor": self.post_processor,
},
show_legend=True,
orient="LR",
)
@async_timer
@observe(name="SQL Summary")
async def run(
self,
query: str,
sqls: List[str],
):
logger.info("SQL Summary pipeline is running...")
return await self._pipe.execute(
["post_process"],
inputs={
"query": query,
"sqls": sqls,
"generator": self.generator,
"prompt_builder": self.prompt_builder,
"post_processor": self.post_processor,
},
)
if __name__ == "__main__":
from langfuse.decorators import langfuse_context
from src.core.engine import EngineConfig
from src.core.pipeline import async_validate
from src.utils import init_langfuse, init_providers, load_env_vars
load_env_vars()
init_langfuse()
llm_provider, _, _, _ = init_providers(engine_config=EngineConfig())
pipeline = SQLSummary(
llm_provider=llm_provider,
)
pipeline.visualize("", [])
async_validate(lambda: pipeline.run("", []))
langfuse_context.flush()
wren-ai-service/src/utils.py
+2 -2
View File
@@ -126,11 +126,11 @@ def remove_trailing_slash(endpoint: str) -> str:
def init_langfuse():
enabled = os.getenv("LANGFUSE_ENABLE", "false")
enabled = bool(os.getenv("LANGFUSE_ENABLE", False))
host = os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com")
langfuse_context.configure(
enabled=(True if enabled.lower() == "true" else False),
enabled=enabled,
public_key=os.getenv("LANGFUSE_PUBLIC_KEY", ""),
secret_key=os.getenv("LANGFUSE_SECRET_KEY", ""),
host=host,
wren-ai-service/src/web/v1/services/ask.py
+16 -5
View File
@@ -245,13 +245,24 @@ class AskService:
logger.debug(failed_dry_run_result["error"])
logger.debug("\n\n")
# remove duplicates of valid_generation_results, which consists of a sql and a summary
valid_generation_results = remove_duplicates(valid_generation_results)
logger.debug("After sql correction:")
logger.debug(f"valid_generation_results: {valid_generation_results}")
if not valid_generation_results and not historical_question_result:
valid_sql_summary_results = []
if valid_generation_results:
sql_summary_results = await self._pipelines["sql_summary"].run(
query=ask_request.query,
sqls=valid_generation_results,
)
valid_sql_summary_results = sql_summary_results["post_process"][
"sql_summary_results"
]
# remove duplicates of valid_sql_summary_results, which consists of a sql and a summary
valid_sql_summary_results = remove_duplicates(
valid_sql_summary_results
)
if not valid_sql_summary_results and not historical_question_result:
logger.exception(
f"ask pipeline - NO_RELEVANT_SQL: {ask_request.query}"
)
@@ -277,7 +288,7 @@ class AskService:
for result in historical_question_result
] + [
AskResultResponse.AskResult(**result)
for result in valid_generation_results
for result in valid_sql_summary_results
]
# only return top 3 results, thus remove the rest