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Feature: SQL Template Matcher #5

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17 changes: 16 additions & 1 deletion larch/schema.py
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
#!/usr/bin/env python3


from typing import List, Optional
from typing import List, Optional, Dict, Any

from instructor import OpenAISchema
from pydantic import BaseModel, Field, create_model
Expand Down Expand Up @@ -79,3 +79,18 @@ def dict_flattened(self, **kwargs):
flat_dict.update(flat_dict.pop("assessment_metadata", {}))

return flat_dict


class SQLTemplate(BaseModel):
"""
SQLTemplate represents a SQL template for a given query pattern.

E.g.:
query_pattern: "When did <mission_name> launch?"
sql_template: "SELECT date_operational FROM <table_name> WHERE mission_name ILIKE '%<mission_name>%';"

"""
query_pattern: str
sql_template: str
description: Optional[str] = None
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also let's add intent: Optional[str] = None field as well in case we want the intent detection somewhere in future.

extras: Optional[Dict[str, Any]] = None
76 changes: 76 additions & 0 deletions larch/search/template_matcher.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,76 @@
from abc import ABC, abstractmethod

from typing import Any, List, Optional

from ..schema import SQLTemplate

class SQLTemplateMatcher(ABC):
"""
SQLTemplateMatcher is a base class for all SQL based template matchers.
"""
def __init__(self, debug: bool = False) -> None:
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self.debug=debug

@abstractmethod
def match(self, query: str, top_k=1, **kwargs) -> List[str]:
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Let's return List[SQLTemplate] instead of List[str].

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While the fuzzy matcher might be able to provide response as List[SQLTemplate], it may not be that efficient to do with LLM based matcher. If the number of templates is huge, getting the list of sql templates with pattern and entity substituted sql query might require prompting LLM to provide a list of matching queries. I've not experimented on LLM part so I can't fully support the statement above.

I'll put more context once I get to know how it performs.

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I mean the return list should still be a subset of all the templates, cut-off by threshold or top_k. So, the correct return type should which SQLTemplate objects are returned. Hence List[SQLTemplate] makes more sense as it gives us idea about what sort of query and intents are also being matched for input query.

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Re: With llm-based, even if the llm just gives us the SQL query, we can ideally reverse map the original sqltemplate object as well. I think the result that LLM returns could infact be enforced by in-context prompting with SQL templates. Nevertheless, let's just stick with List[SQLTemplate] as return type because we're technically just selecting/matching the input templates.

"""
Match the given query against the templates.

Args:
query: The query to match against the templates.
top_k: The number of top-k templates to return. Defaults to 1.
Returns:
A list of top-k templates that match the query with entity substitution.
"""
raise NotImplementedError()


def __call__(self, *args: Any, **kwds: Any) -> List[str]:
return self.match(*args, **kwds)


class FuzzySQLTemplateMatcher(SQLTemplateMatcher):
"""
FuzzySQLTemplateMatcher is a SQL based template matcher that uses fuzzy matching.
Given a query, it will use rule-based matching to find best matching template
and return the template(s) with entity substitution.

Args:
templates: A list of SQL templates.
similarity_threshold: The similarity threshold to be used for fuzzy matching.
"""
def __init__(self, templates: List[SQLTemplate],
similarity_threshold: float = 0.4,
debug: bool = False) -> None:
super().__init__(debug = debug)

self.templates = templates
self.similarity_threshold = similarity_threshold

def match(self, query: str, top_k=1, **kwargs) -> List[str]:
pass


class LLMBasedSQLTemplateMatcher(SQLTemplateMatcher):
"""
LLMBasedSQLTemplateMatcher uses LLM to find the best matching template.
Given a query, it will extract the key entities and use LLM to find best SQL template
and generates a subsituted SQL query.

Args:
templates: A list of SQL templates.
ddl_schema: The DDL schema for available tables.
similarity_threshold: The similarity threshold to be used for fuzzy matching.
"""
def __init__(self, templates: List[SQLTemplate],
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ddl_schema: Optional[str] = None,
similarity_threshold: float = 0.4,
debug: bool = False) -> None:
super().__init__(debug = debug)

self.templates = templates
self.ddl_schema = ddl_schema
self.similarity_threshold = similarity_threshold

def match(self, query: str, top_k = 1, **kwargs) -> List[str]:
pass