Merge branch 'main' into pgvector_fixes

papers/ai-sql-accuracy-2023-08-17.md

@@ -229,7 +229,7 @@ By providing just those 3 example queries, we see substantial improvements to th
 
 Enterprise data warehouses often contain 100s (or even 1000s) of tables, and an order of magnitude more queries that cover all the use cases within their organizations. Given the limited size of the context windows of modern LLMs, we can’t just shove all the prior queries and schema definitions into the prompt.
 
-Our final approach to context is a more sophisticated ML approach - load embeddings of prior queries and the table schemas into a vector database, and only choose the most relevant queries / tables to the question asked. Here's a diagram of what we are doing - note the contextual relevance search in the red box -
+Our final approach to context is a more sophisticated ML approach - load embeddings of prior queries and the table schemas into a vector database, and only choose the most relevant queries / tables to the question asked. Here's a diagram of what we are doing - note the contextual relevance search in the green box -
 
 ![](https://raw.githubusercontent.com/vanna-ai/vanna/main/papers/img/using-contextually-relevant-examples.png)
 

pyproject.toml

@@ -33,7 +33,7 @@ bigquery = ["google-cloud-bigquery"]
 snowflake = ["snowflake-connector-python"]
 duckdb = ["duckdb"]
 google = ["google-generativeai", "google-cloud-aiplatform"]
-all = ["psycopg2-binary", "db-dtypes", "PyMySQL", "google-cloud-bigquery", "snowflake-connector-python", "duckdb", "openai", "qianfan", "mistralai>=1.0.0", "chromadb", "anthropic", "zhipuai", "marqo", "google-generativeai", "google-cloud-aiplatform", "qdrant-client", "fastembed", "ollama", "httpx", "opensearch-py", "opensearch-dsl", "transformers", "pinecone-client", "pymilvus[model]","weaviate-client", "azure-search-documents", "azure-identity", "azure-common", "faiss-cpu", "boto", "botocore"]
+all = ["psycopg2-binary", "db-dtypes", "PyMySQL", "google-cloud-bigquery", "snowflake-connector-python", "duckdb", "openai", "qianfan", "mistralai>=1.0.0", "chromadb", "anthropic", "zhipuai", "marqo", "google-generativeai", "google-cloud-aiplatform", "qdrant-client", "fastembed", "ollama", "httpx", "opensearch-py", "opensearch-dsl", "transformers", "pinecone-client", "pymilvus[model]","weaviate-client", "azure-search-documents", "azure-identity", "azure-common", "faiss-cpu", "boto", "boto3", "botocore", "langchain_core", "langchain_postgres"]
 test = ["tox"]
 chromadb = ["chromadb"]
 openai = ["openai"]

src/vanna/google/bigquery_vector.py

@@ -2,6 +2,10 @@
 import os
 import uuid
 from typing import List, Optional
+from vertexai.language_models import (
+  TextEmbeddingInput,
+  TextEmbeddingModel
+)
 
 import pandas as pd
 from google.cloud import bigquery
@@ -23,17 +27,15 @@ def __init__(self, config: dict, **kwargs):
             or set as an environment variable, assign it.
             """
             print("Configuring genai")
+            self.type = "GEMINI"
             import google.generativeai as genai
 
             genai.configure(api_key=config["api_key"])
 
             self.genai = genai
         else:
+            self.type = "VERTEX_AI"
             # Authenticate using VertexAI
-            from vertexai.language_models import (
-              TextEmbeddingInput,
-              TextEmbeddingModel,
-            )
 
         if self.config.get("project_id"):
             self.project_id = self.config.get("project_id")
@@ -139,25 +141,42 @@ def fetch_similar_training_data(self, training_data_type: str, question: str, n_
         results = self.conn.query(query).result().to_dataframe()
         return results
 
-    def generate_question_embedding(self, data: str, **kwargs) -> List[float]:
-        result = self.genai.embed_content(
+    def get_embeddings(self, data: str, task: str) -> List[float]:
+        embeddings = None
+
+        if self.type == "VERTEX_AI":
+          input = [TextEmbeddingInput(data, task)]
+          model = TextEmbeddingModel.from_pretrained("text-embedding-004")
+
+          result = model.get_embeddings(input)
+
+          if len(result) > 0:
+              embeddings = result[0].values
+        else:
+          # Use Gemini Consumer API
+          result = self.genai.embed_content(
             model="models/text-embedding-004",
             content=data,
-            task_type="retrieval_query")
+            task_type=task)
 
-        if 'embedding' in result:
-            return result['embedding']
+          if 'embedding' in result:
+            embeddings = result['embedding']
+
+        return embeddings
+
+    def generate_question_embedding(self, data: str, **kwargs) -> List[float]:
+        result = self.get_embeddings(data, "RETRIEVAL_QUERY")
+
+        if result != None:
+            return result
         else:
             raise ValueError("No embeddings returned")
 
     def generate_storage_embedding(self, data: str, **kwargs) -> List[float]:
-        result = self.genai.embed_content(
-            model="models/text-embedding-004",
-            content=data,
-            task_type="retrieval_document")
+        result = self.get_embeddings(data, "RETRIEVAL_DOCUMENT")
 
-        if 'embedding' in result:
-            return result['embedding']
+        if result != None:
+            return result
         else:
             raise ValueError("No embeddings returned")
 

src/vanna/google/gemini_chat.py

@@ -15,7 +15,7 @@ def __init__(self, config=None):
         if "model_name" in config:
             model_name = config["model_name"]
         else:
-            model_name = "gemini-1.0-pro"
+            model_name = "gemini-1.5-pro"
 
         self.google_api_key = None
 
@@ -30,7 +30,7 @@ def __init__(self, config=None):
             self.chat_model = genai.GenerativeModel(model_name)
         else:
             # Authenticate using VertexAI
-            from vertexai.preview.generative_models import GenerativeModel
+            from vertexai.generative_models import GenerativeModel
             self.chat_model = GenerativeModel(model_name)
 
     def system_message(self, message: str) -> any:

tests/test_pgvector.py

@@ -2,52 +2,48 @@
 
 from dotenv import load_dotenv
 
-from vanna.pgvector import PG_VectorStore
-from vanna.openai import OpenAI_Chat
-
+# from vanna.pgvector import PG_VectorStore
+# from vanna.openai import OpenAI_Chat
 
 # assume .env file placed next to file with provided env vars
 load_dotenv()
 
-
-def get_vanna_connection_string():
-    server = os.environ.get("PG_SERVER")
-    driver = "psycopg"
-    port = os.environ.get("PG_PORT", 5432)
-    database = os.environ.get("PG_DATABASE")
-    username = os.environ.get("PG_USERNAME")
-    password = os.environ.get("PG_PASSWORD")
-
-    return f"postgresql+psycopg://{username}:{password}@{server}:{port}/{database}"
-
-
-def test_pgvector_e2e():
-    # configure Vanna to use OpenAI and PGVector
-    class VannaCustom(PG_VectorStore, OpenAI_Chat):
-        def __init__(self, config=None):
-            PG_VectorStore.__init__(self, config=config)
-            OpenAI_Chat.__init__(self, config=config)
+# def get_vanna_connection_string():
+#     server = os.environ.get("PG_SERVER")
+#     driver = "psycopg"
+#     port = os.environ.get("PG_PORT", 5432)
+#     database = os.environ.get("PG_DATABASE")
+#     username = os.environ.get("PG_USERNAME")
+#     password = os.environ.get("PG_PASSWORD")
+
+# def test_pgvector_e2e():
+#     # configure Vanna to use OpenAI and PGVector
+#     class VannaCustom(PG_VectorStore, OpenAI_Chat):
+#         def __init__(self, config=None):
+#             PG_VectorStore.__init__(self, config=config)
+#             OpenAI_Chat.__init__(self, config=config)
 
-    vn = VannaCustom(config={
-        'api_key': os.environ['OPENAI_API_KEY'],
-        'model': 'gpt-3.5-turbo',
-        "connection_string": get_vanna_connection_string(),
-    })
-
-    # connect to SQLite database
-    vn.connect_to_sqlite('https://vanna.ai/Chinook.sqlite')
-
-    # train Vanna on DDLs
-    df_ddl = vn.run_sql("SELECT type, sql FROM sqlite_master WHERE sql is not null")
-    for ddl in df_ddl['sql'].to_list():
-        vn.train(ddl=ddl)
-    assert len(vn.get_related_ddl("dummy question")) == 10  # assume 10 DDL chunks are retrieved by default
+#     vn = VannaCustom(config={
+#         'api_key': os.environ['OPENAI_API_KEY'],
+#         'model': 'gpt-3.5-turbo',
+#         "connection_string": get_vanna_connection_string(),
+#     })
+
+#     # connect to SQLite database
+#     vn.connect_to_sqlite('https://vanna.ai/Chinook.sqlite')
+
+#     # train Vanna on DDLs
+#     df_ddl = vn.run_sql("SELECT type, sql FROM sqlite_master WHERE sql is not null")
+#     for ddl in df_ddl['sql'].to_list():
+#         vn.train(ddl=ddl)
+#     assert len(vn.get_related_ddl("dummy question")) == 10  # assume 10 DDL chunks are retrieved by default
 
-    question = "What are the top 7 customers by sales?"
-    sql = vn.generate_sql(question)
-    df = vn.run_sql(sql)
-    assert len(df) == 7
-
-    # test if Vanna can generate an answer
-    answer = vn.ask(question)
-    assert answer is not None
+#     question = "What are the top 7 customers by sales?"
+#     sql = vn.generate_sql(question)
+#     df = vn.run_sql(sql)
+#     assert len(df) == 7
+
+#     # test if Vanna can generate an answer
+#     answer = vn.ask(question)
+#     assert answer is not None
+