Initial skeleton to support local LLM #4

sidekick/configs/.env.toml

@@ -1,6 +1,6 @@
 [OPENAI]
 OPENAI_API_KEY = ""
-MODEL_NAME = "gpt-3.5-turbo-0301" # Others: e.g. gpt-4, gpt-4-32k, text-davinci-003
+MODEL_NAME = "nsql" # Others: e.g. gpt-4, gpt-4-32k, text-davinci-003
 
 [LOCAL_DB_CONFIG]
 HOST_NAME = "localhost"
@@ -13,4 +13,4 @@ PORT = "5432"
 LOG-LEVEL = "INFO"
 
 [DB-DIALECT]
-DB_TYPE = "postgresql"
+DB_TYPE = "SQLite" # postgresql

sidekick/configs/prompt_template.py

@@ -59,3 +59,20 @@
                 Query:\n {qry_txt}
                 """,
 }
+
+NSQL_QUERY_PROMPT = """
+For SQL TABLE '{table_name}' sample question/answer pairs,\n({sample_queries})
+
+CREATE TABLE '{table_name}'({data_info}
+)
+
+Table '{table_name}' has sample values ({data_info_detailed})
+
+
+
+-- Using valid {_dialect}, answer the following questions with the information for '{table_name}' provided above; for final SQL only use values from the question.
+
+
+-- Using reference for TABLES '{table_name}' {context}; {question_txt}?
+
+SELECT"""

sidekick/db_config.py

@@ -101,18 +101,6 @@ def create_table(self, schema_info_path=None, schema_info=None):
                 # If schema information is not provided, extract from the template.
                 self.schema_info = """,\n""".join(self._extract_schema_info(schema_info_path)).strip()
                 logger.debug(f"Schema info used for creating table:\n {self.schema_info}")
-            # self.schema_info = """
-            #     id uuid PRIMARY KEY,
-            #     ts TIMESTAMP WITH TIME ZONE NOT NULL,
-            #     kind TEXT NOT NULL, -- or int?,
-            #     user_id TEXT,
-            #     user_name TEXT,
-            #     resource_type TEXT NOT NULL, -- or int?,
-            #     resource_id  TEXT,
-            #     stream TEXT NOT NULL,
-            #     source TEXT NOT NULL,
-            #     payload jsonb NOT NULL
-            # """
         create_syntax = f"""
                 CREATE TABLE IF NOT EXISTS {self.table_name} (
                     {self.schema_info}

sidekick/query.py

@@ -7,13 +7,17 @@
 import openai
 import sqlglot
 import toml
+import torch
 from langchain import OpenAI
-from llama_index import GPTSimpleVectorIndex, GPTSQLStructStoreIndex, LLMPredictor, ServiceContext, SQLDatabase
+from llama_index import (GPTSimpleVectorIndex, GPTSQLStructStoreIndex,
+                         LLMPredictor, ServiceContext, SQLDatabase)
 from llama_index.indices.struct_store import SQLContextContainerBuilder
-from sidekick.configs.prompt_template import DEBUGGING_PROMPT, QUERY_PROMPT, TASK_PROMPT
+from sidekick.configs.prompt_template import (DEBUGGING_PROMPT, QUERY_PROMPT,
+                                              TASK_PROMPT)
 from sidekick.logger import logger
-from sidekick.utils import csv_parser, filter_samples, remove_duplicates
+from sidekick.utils import filter_samples, read_sample_pairs, remove_duplicates
 from sqlalchemy import create_engine
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 
 def _check_file_info(file_path: str):
@@ -63,7 +67,7 @@ def update_context_queries(self):
         new_context_queries = []
         if self.sample_queries_path is not None and Path(self.sample_queries_path).exists():
             logger.info(f"Using samples from path {self.sample_queries_path}")
-            new_context_queries = csv_parser(self.sample_queries_path)
+            new_context_queries = read_sample_pairs(self.sample_queries_path, "gpt")
             # cache the samples for future use
             with open(f"{self.path}/var/lib/tmp/data/queries_cache.json", "w") as f:
                 json.dump(new_context_queries, f, indent=2)
@@ -191,51 +195,105 @@ def generate_tasks(self, table_names: list, input_question: str):
         except Exception as se:
             raise se
 
-    def generate_sql(
-        self, table_name: list, input_question: str, _dialect: str = "postgres", model_name: str = "gpt-3.5-turbo-0301"
-    ):
-        _tasks = self.task_formatter(self._tasks)
+    def generate_sql(self, table_name: list, input_question: str, _dialect: str = "postgres", model_name: str = "nsql"):
         context_file = f"{self.path}/var/lib/tmp/data/context.json"
         additional_context = json.load(open(context_file, "r")) if Path(context_file).exists() else {}
-
         context_queries = self.content_queries
-        # TODO: The need to pass data info again could be eliminated if Task generation becomes more consistent and accurate.
-        query_str = QUERY_PROMPT.format(
-            _dialect=_dialect,
-            _data_info=self._data_info,
-            _question=input_question,
-            _table_name=table_name,
-            _sample_queries=context_queries,
-            _tasks=_tasks,
-        )
 
-        table_context_dict = {str(table_name[0]).lower(): str(additional_context).lower()}
-        self.context_builder = SQLContextContainerBuilder(self.sql_database, context_dict=table_context_dict)
+        if model_name != "nsql":
+            _tasks = self.task_formatter(self._tasks)
 
-        table_schema_index = self.build_index(persist=False)
-        self.context_builder.query_index_for_context(table_schema_index, query_str, store_context_str=True)
-        context_container = self.context_builder.build_context_container()
+            # TODO: The need to pass data info again could be eliminated if Task generation becomes more consistent and accurate.
+            query_str = QUERY_PROMPT.format(
+                _dialect=_dialect,
+                _data_info=self._data_info,
+                _question=input_question,
+                _table_name=table_name,
+                _sample_queries=context_queries,
+                _tasks=_tasks,
+            )
 
-        # Reference: https://github.com/jerryjliu/llama_index/issues/987
-        llm_predictor_gpt3 = LLMPredictor(llm=OpenAI(temperature=0.5, model_name=model_name))
-        service_context_gpt3 = ServiceContext.from_defaults(llm_predictor=llm_predictor_gpt3, chunk_size_limit=512)
+            table_context_dict = {str(table_name[0]).lower(): str(additional_context).lower()}
+            self.context_builder = SQLContextContainerBuilder(self.sql_database, context_dict=table_context_dict)
 
-        index = GPTSQLStructStoreIndex(
-            [], sql_database=self.sql_database, table_name=table_name, service_context=service_context_gpt3
-        )
-        res = self.generate_response(context_container, sql_index=index, input_prompt=query_str)
-        try:
-            # Check if `SQL` is formatted ---> ``` SQL_text ```
-            if "```" in str(res):
-                res = (
-                    str(res).split("```", 1)[1].split(";", 1)[0].strip().replace("```", "").replace("sql\n", "").strip()
-                )
-            else:
-                res = str(res).split("Explanation:", 1)[0].strip()
-            sqlglot.transpile(res)
-        except (sqlglot.errors.ParseError, ValueError, RuntimeError) as e:
-            logger.info("We did the best we could, there might be still be some error:\n")
-            logger.info(f"Realized query so far:\n {res}")
+            table_schema_index = self.build_index(persist=False)
+            self.context_builder.query_index_for_context(table_schema_index, query_str, store_context_str=True)
+            context_container = self.context_builder.build_context_container()
+
+            # Reference: https://github.com/jerryjliu/llama_index/issues/987
+            llm_predictor_gpt3 = LLMPredictor(llm=OpenAI(temperature=0.5, model_name=model_name))
+            service_context_gpt3 = ServiceContext.from_defaults(llm_predictor=llm_predictor_gpt3, chunk_size_limit=512)
+
+            index = GPTSQLStructStoreIndex(
+                [], sql_database=self.sql_database, table_name=table_name, service_context=service_context_gpt3
+            )
+            res = self.generate_response(context_container, sql_index=index, input_prompt=query_str)
+            try:
+                # Check if `SQL` is formatted ---> ``` SQL_text ```
+                if "```" in str(res):
+                    res = (
+                        str(res)
+                        .split("```", 1)[1]
+                        .split(";", 1)[0]
+                        .strip()
+                        .replace("```", "")
+                        .replace("sql\n", "")
+                        .strip()
+                    )
+                else:
+                    res = str(res).split("Explanation:", 1)[0].strip()
+                sqlglot.transpile(res)
+            except (sqlglot.errors.ParseError, ValueError, RuntimeError) as e:
+                logger.info("We did the best we could, there might be still be some error:\n")
+                logger.info(f"Realized query so far:\n {res}")
+        else:
+            # Load h2oGPT.NSQL model
+            tokenizer = AutoTokenizer.from_pretrained("NumbersStation/nsql-6B")
+            model = AutoModelForCausalLM.from_pretrained("NumbersStation/nsql-6B")
+
+            data_samples = context_queries
+
+            _context = {
+            "if patterns like 'current time' or 'now' occurs in question": "always use NOW() - INTERVAL",
+            "if patterns like 'total number', or 'List' occurs in question": "always use DISTINCT",
+            }
+
+            filtered_context = filter_samples(input_question, probable_qs=list(_context.keys()),
+                                model_path='', threshold=0.845)
+
+            print(f"Filter Context: {filtered_context}")
+
+            contextual_context = []
+            for _item in filtered_context:
+                _val = _context.get(_item, None)
+                if _val:
+                    contextual_context.append(f"{_item}: {_val}")
+
+            print("Filtering Question/Query pairs")
+            _samples = filter_samples(input_question, probable_qs=sample_pairs,
+                                model_path=local_model_path, threshold=0.90)
+
+            # If QnA pairs > 5, we keep only 5 of them for focused context
+            if len(_samples) > 5:
+                _samples = _samples[0:5][::-1]
+            qna_samples = '\n'.join(_samples)
+
+            contextual_context_val = ', '.join(contextual_context)
+
+            if len(_samples) > 2:
+                # Check for the columns in the QnA samples provided, if exists keep them
+                context_columns = [_c for _c in column_names if _c.lower() in qna_samples.lower()]
+                if len(context_columns) > 0:
+                    contextual_data_samples = [_d for _cc in context_columns for _d in data_samples_list if _cc.lower() in _d.lower()]
+                    data_samples = contextual_data_samples
+            relevant_columns = context_columns if len(context_columns) > 0 else column_names
+            _data_info = ', '.join(relevant_columns)
+
+            query = prompt_template.format(table_name=_table_name, data_info=_data_info, data_info_detailed=data_samples,
+                                        sample_queries=qna_samples, context=contextual_context_val,
+                                        question_txt=input_question)
+
+            input_ids = tokenizer(query, return_tensors="pt").input_ids
         return res
 
     def task_formatter(self, input_task: str):

sidekick/utils.py

@@ -95,18 +95,29 @@ def setup_dir(base_path: str):
             p.mkdir(parents=True, exist_ok=True)
 
 
-def csv_parser(input_path: str):
+def read_sample_pairs(input_path: str, model_name: str = "nsql"):
     df = pd.read_csv(input_path)
     df = df.dropna()
     df = df.drop_duplicates()
     df = df.reset_index(drop=True)
 
-    # Convert frame to below format
-    # [
-    # "# query": ""
-    # "# answer": ""
-    # ]
-    res = df.apply(lambda row: f"# query: {row['query']}\n# answer: {row['answer']}", axis=1).to_list()
+    # NSQL format
+    if model_name != 'nsql':
+        # Open AI format
+            # Convert frame to below format
+            # [
+            # "# query": ""
+            # "# answer": ""
+            # ]
+        res = df.apply(lambda row: f"# query: {row['query']}\n# answer: {row['answer']}", axis=1).to_list()
+    else:
+        # Convert frame to below format
+            # [
+            # "Question": <question_text>
+            # "Answer":
+            # <response_text>
+            # ]
+        res = df.apply(lambda row: f"Question: {row['query']}\nAnswer:\n{row['answer']}", axis=1).to_list()
     return res