Merge pull request #748 from wwulfric/main

feat: support pgvecto.rs

src/vanna/pgvector/__init__.py

@@ -1 +1,2 @@
 from .pgvector import PG_VectorStore
+from .pgvecto_rs import PG_Vecto_rsStore

src/vanna/pgvector/pgvecto_rs.py

@@ -0,0 +1,269 @@
+import ast
+import json
+import logging
+import uuid
+
+import pandas as pd
+from langchain_core.documents import Document
+from langchain_community.vectorstores.pgvecto_rs import PGVecto_rs
+from sqlalchemy import create_engine, text
+
+from .. import ValidationError
+from ..base import VannaBase
+from ..types import TrainingPlan, TrainingPlanItem
+from ..utils import deterministic_uuid
+
+
+class PG_Vecto_rsStore(VannaBase):
+  def __init__(self, config=None):
+    if not config or "connection_string" not in config:
+      raise ValueError(
+        "A valid 'config' dictionary with a 'connection_string' is required.")
+
+    VannaBase.__init__(self, config=config)
+
+    if config and "connection_string" in config:
+      self.connection_string = config.get("connection_string")
+      self.n_results = config.get("n_results", 10)
+
+    if config and "embedding_function" in config:
+      self.embedding_function = config.get("embedding_function")
+      self.vector_dimension = config.get("vector_dimension")
+    else:
+      from langchain_huggingface import HuggingFaceEmbeddings
+      self.embedding_function = HuggingFaceEmbeddings(
+        model_name="all-MiniLM-L6-v2")
+      self.vector_dimension = 384
+    self.sql_collection = PGVecto_rs(
+      embedding=self.embedding_function,
+      collection_name="sql",
+      db_url=self.connection_string,
+      dimension=self.vector_dimension,
+    )
+    self.ddl_collection = PGVecto_rs(
+      embedding=self.embedding_function,
+      collection_name="ddl",
+      db_url=self.connection_string,
+      dimension=self.vector_dimension,
+    )
+    self.documentation_collection = PGVecto_rs(
+      embedding=self.embedding_function,
+      collection_name="documentation",
+      db_url=self.connection_string,
+      dimension=self.vector_dimension,
+    )
+
+  def add_question_sql(self, question: str, sql: str, **kwargs) -> str:
+    question_sql_json = json.dumps(
+      {
+        "question": question,
+        "sql": sql,
+      },
+      ensure_ascii=False,
+    )
+    id = deterministic_uuid(question_sql_json) + "-sql"
+    createdat = kwargs.get("createdat")
+    doc = Document(
+      page_content=question_sql_json,
+      metadata={"id": id, "createdat": createdat},
+    )
+    self.sql_collection.add_documents([doc], ids=[doc.metadata["id"]])
+
+    return id
+
+  def add_ddl(self, ddl: str, **kwargs) -> str:
+    _id = deterministic_uuid(ddl) + "-ddl"
+    doc = Document(
+      page_content=ddl,
+      metadata={"id": _id},
+    )
+    self.ddl_collection.add_documents([doc], ids=[doc.metadata["id"]])
+    return _id
+
+  def add_documentation(self, documentation: str, **kwargs) -> str:
+    _id = deterministic_uuid(documentation) + "-doc"
+    doc = Document(
+      page_content=documentation,
+      metadata={"id": _id},
+    )
+    self.documentation_collection.add_documents([doc],
+                                                ids=[doc.metadata["id"]])
+    return _id
+
+  def get_collection(self, collection_name):
+    match collection_name:
+      case "sql":
+        return self.sql_collection
+      case "ddl":
+        return self.ddl_collection
+      case "documentation":
+        return self.documentation_collection
+      case _:
+        raise ValueError("Specified collection does not exist.")
+
+  def get_similar_question_sql(self, question: str, **kwargs) -> list:
+    documents = self.sql_collection.similarity_search(query=question,
+                                                      k=self.n_results)
+    return [ast.literal_eval(document.page_content) for document in documents]
+
+  def get_related_ddl(self, question: str, **kwargs) -> list:
+    documents = self.ddl_collection.similarity_search(query=question,
+                                                      k=self.n_results)
+    return [document.page_content for document in documents]
+
+  def get_related_documentation(self, question: str, **kwargs) -> list:
+    documents = self.documentation_collection.similarity_search(query=question,
+                                                                k=self.n_results)
+    return [document.page_content for document in documents]
+
+  def train(
+    self,
+    question: str | None = None,
+    sql: str | None = None,
+    ddl: str | None = None,
+    documentation: str | None = None,
+    plan: TrainingPlan | None = None,
+    createdat: str | None = None,
+  ):
+    if question and not sql:
+      raise ValidationError("Please provide a SQL query.")
+
+    if documentation:
+      logging.info(f"Adding documentation: {documentation}")
+      return self.add_documentation(documentation)
+
+    if sql and question:
+      return self.add_question_sql(question=question, sql=sql,
+                                   createdat=createdat)
+
+    if ddl:
+      logging.info(f"Adding ddl: {ddl}")
+      return self.add_ddl(ddl)
+
+    if plan:
+      for item in plan._plan:
+        if item.item_type == TrainingPlanItem.ITEM_TYPE_DDL:
+          self.add_ddl(item.item_value)
+        elif item.item_type == TrainingPlanItem.ITEM_TYPE_IS:
+          self.add_documentation(item.item_value)
+        elif item.item_type == TrainingPlanItem.ITEM_TYPE_SQL and item.item_name:
+          self.add_question_sql(question=item.item_name, sql=item.item_value)
+
+  def get_training_data(self, **kwargs) -> pd.DataFrame:
+    # Establishing the connection
+    engine = create_engine(self.connection_string)
+
+    # Querying the 'langchain_pg_embedding' table
+    query_embedding = "SELECT cmetadata, document FROM langchain_pg_embedding"
+    df_embedding = pd.read_sql(query_embedding, engine)
+
+    # List to accumulate the processed rows
+    processed_rows = []
+
+    # Process each row in the DataFrame
+    for _, row in df_embedding.iterrows():
+      custom_id = row["cmetadata"]["id"]
+      document = row["document"]
+      training_data_type = "documentation" if custom_id[
+                                              -3:] == "doc" else custom_id[-3:]
+
+      if training_data_type == "sql":
+        # Convert the document string to a dictionary
+        try:
+          doc_dict = ast.literal_eval(document)
+          question = doc_dict.get("question")
+          content = doc_dict.get("sql")
+        except (ValueError, SyntaxError):
+          logging.info(
+            f"Skipping row with custom_id {custom_id} due to parsing error.")
+          continue
+      elif training_data_type in ["documentation", "ddl"]:
+        question = None  # Default value for question
+        content = document
+      else:
+        # If the suffix is not recognized, skip this row
+        logging.info(
+          f"Skipping row with custom_id {custom_id} due to unrecognized training data type.")
+        continue
+
+      # Append the processed data to the list
+      processed_rows.append(
+        {"id": custom_id, "question": question, "content": content,
+         "training_data_type": training_data_type}
+      )
+
+    # Create a DataFrame from the list of processed rows
+    df_processed = pd.DataFrame(processed_rows)
+
+    return df_processed
+
+  def remove_training_data(self, id: str, **kwargs) -> bool:
+    # Create the database engine
+    engine = create_engine(self.connection_string)
+
+    # SQL DELETE statement
+    delete_statement = text(
+      """
+      DELETE FROM langchain_pg_embedding
+      WHERE cmetadata ->> 'id' = :id
+  """
+    )
+
+    # Connect to the database and execute the delete statement
+    with engine.connect() as connection:
+      # Start a transaction
+      with connection.begin() as transaction:
+        try:
+          result = connection.execute(delete_statement, {"id": id})
+          # Commit the transaction if the delete was successful
+          transaction.commit()
+          # Check if any row was deleted and return True or False accordingly
+          return result.rowcount() > 0
+        except Exception as e:
+          # Rollback the transaction in case of error
+          logging.error(f"An error occurred: {e}")
+          transaction.rollback()
+          return False
+
+  def remove_collection(self, collection_name: str) -> bool:
+    engine = create_engine(self.connection_string)
+
+    # Determine the suffix to look for based on the collection name
+    suffix_map = {"ddl": "ddl", "sql": "sql", "documentation": "doc"}
+    suffix = suffix_map.get(collection_name)
+
+    if not suffix:
+      logging.info(
+        "Invalid collection name. Choose from 'ddl', 'sql', or 'documentation'.")
+      return False
+
+    # SQL query to delete rows based on the condition
+    query = text(
+      f"""
+            DELETE FROM langchain_pg_embedding
+            WHERE cmetadata->>'id' LIKE '%{suffix}'
+        """
+    )
+
+    # Execute the deletion within a transaction block
+    with engine.connect() as connection:
+      with connection.begin() as transaction:
+        try:
+          result = connection.execute(query)
+          transaction.commit()  # Explicitly commit the transaction
+          if result.rowcount() > 0:
+            logging.info(
+              f"Deleted {result.rowcount()} rows from "
+              f"langchain_pg_embedding where collection is {collection_name}."
+            )
+            return True
+          else:
+            logging.info(f"No rows deleted for collection {collection_name}.")
+            return False
+        except Exception as e:
+          logging.error(f"An error occurred: {e}")
+          transaction.rollback()  # Rollback in case of error
+          return False
+
+  def generate_embedding(self, *args, **kwargs):
+    pass

src/vanna/pgvector/pgvector.py

@@ -11,6 +11,7 @@
 from .. import ValidationError
 from ..base import VannaBase
 from ..types import TrainingPlan, TrainingPlanItem
+from ..utils import deterministic_uuid
 
 
 class PG_VectorStore(VannaBase):
@@ -55,7 +56,7 @@ def add_question_sql(self, question: str, sql: str, **kwargs) -> str:
             },
             ensure_ascii=False,
         )
-        id = str(uuid.uuid4()) + "-sql"
+        id = deterministic_uuid(question_sql_json) + "-sql"
         createdat = kwargs.get("createdat")
         doc = Document(
             page_content=question_sql_json,
@@ -66,7 +67,7 @@ def add_question_sql(self, question: str, sql: str, **kwargs) -> str:
         return id
 
     def add_ddl(self, ddl: str, **kwargs) -> str:
-        _id = str(uuid.uuid4()) + "-ddl"
+        _id = deterministic_uuid(ddl) + "-ddl"
         doc = Document(
             page_content=ddl,
             metadata={"id": _id},
@@ -75,7 +76,7 @@ def add_ddl(self, ddl: str, **kwargs) -> str:
         return _id
 
     def add_documentation(self, documentation: str, **kwargs) -> str:
-        _id = str(uuid.uuid4()) + "-doc"
+        _id = deterministic_uuid(documentation) + "-doc"
         doc = Document(
             page_content=documentation,
             metadata={"id": _id},
@@ -94,7 +95,7 @@ def get_collection(self, collection_name):
             case _:
                 raise ValueError("Specified collection does not exist.")
 
-    def get_similar_question_sql(self, question: str) -> list:
+    def get_similar_question_sql(self, question: str, **kwargs) -> list:
         documents = self.sql_collection.similarity_search(query=question, k=self.n_results)
         return [ast.literal_eval(document.page_content) for document in documents]
 
@@ -203,7 +204,7 @@ def remove_training_data(self, id: str, **kwargs) -> bool:
                     # Commit the transaction if the delete was successful
                     transaction.commit()
                     # Check if any row was deleted and return True or False accordingly
-                    return result.rowcount > 0
+                    return result.rowcount() > 0
                 except Exception as e:
                     # Rollback the transaction in case of error
                     logging.error(f"An error occurred: {e}")
@@ -235,9 +236,9 @@ def remove_collection(self, collection_name: str) -> bool:
                 try:
                     result = connection.execute(query)
                     transaction.commit()  # Explicitly commit the transaction
-                    if result.rowcount > 0:
+                    if result.rowcount() > 0:
                         logging.info(
-                            f"Deleted {result.rowcount} rows from "
+                            f"Deleted {result.rowcount()} rows from "
                             f"langchain_pg_embedding where collection is {collection_name}."
                         )
                         return True