Merge pull request #391 from jis478/main

huggingface model support added

pyproject.toml

@@ -32,7 +32,7 @@ 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", "mistralai", "chromadb", "anthropic", "zhipuai", "marqo", "google-generativeai", "google-cloud-aiplatform", "qdrant-client", "fastembed", "ollama", "httpx", "opensearch-py", "opensearch-dsl"]
+all = ["psycopg2-binary", "db-dtypes", "PyMySQL", "google-cloud-bigquery", "snowflake-connector-python", "duckdb", "openai", "mistralai", "chromadb", "anthropic", "zhipuai", "marqo", "google-generativeai", "google-cloud-aiplatform", "qdrant-client", "fastembed", "ollama", "httpx", "opensearch-py", "opensearch-dsl", "transformers"]
 test = ["tox"]
 chromadb = ["chromadb"]
 openai = ["openai"]
@@ -45,3 +45,4 @@ ollama = ["ollama", "httpx"]
 qdrant = ["qdrant-client"]
 vllm = ["vllm"]
 opensearch = ["opensearch-py", "opensearch-dsl"]
+hf = ["transformers"]

src/vanna/hf/__init__.py

@@ -0,0 +1 @@
+from .hf import Hf

src/vanna/hf/hf.py

@@ -0,0 +1,79 @@
+import re
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+from ..base import VannaBase
+
+
+class Hf(VannaBase):
+    def __init__(self, config=None):
+        model_name = self.config.get(
+            "model_name", None
+        )  # e.g. meta-llama/Meta-Llama-3-8B-Instruct
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype="auto",
+            device_map="auto",
+        )
+
+    def system_message(self, message: str) -> any:
+        return {"role": "system", "content": message}
+
+    def user_message(self, message: str) -> any:
+        return {"role": "user", "content": message}
+
+    def assistant_message(self, message: str) -> any:
+        return {"role": "assistant", "content": message}
+
+    def extract_sql_query(self, text):
+        """
+        Extracts the first SQL statement after the word 'select', ignoring case,
+        matches until the first semicolon, three backticks, or the end of the string,
+        and removes three backticks if they exist in the extracted string.
+
+        Args:
+        - text (str): The string to search within for an SQL statement.
+
+        Returns:
+        - str: The first SQL statement found, with three backticks removed, or an empty string if no match is found.
+        """
+        # Regular expression to find 'select' (ignoring case) and capture until ';', '```', or end of string
+        pattern = re.compile(r"select.*?(?:;|```|$)", re.IGNORECASE | re.DOTALL)
+
+        match = pattern.search(text)
+        if match:
+            # Remove three backticks from the matched string if they exist
+            return match.group(0).replace("```", "")
+        else:
+            return text
+
+    def generate_sql(self, question: str, **kwargs) -> str:
+        # Use the super generate_sql
+        sql = super().generate_sql(question, **kwargs)
+
+        # Replace "\_" with "_"
+        sql = sql.replace("\\_", "_")
+
+        sql = sql.replace("\\", "")
+
+        return self.extract_sql_query(sql)
+
+    def submit_prompt(self, prompt, **kwargs) -> str:
+
+        input_ids = self.tokenizer.apply_chat_template(
+            prompt, add_generation_prompt=True, return_tensors="pt"
+        ).to(self.model.device)
+
+        outputs = self.model.generate(
+            input_ids,
+            max_new_tokens=512,
+            eos_token_id=self.tokenizer.eos_token_id,
+            do_sample=True,
+            temperature=1,
+            top_p=0.9,
+        )
+        response = outputs[0][input_ids.shape[-1] :]
+        response = self.tokenizer.decode(response, skip_special_tokens=True)
+        self.log(response)
+
+        return response

tests/test_imports.py

@@ -4,6 +4,7 @@ def test_regular_imports():
     from vanna.anthropic.anthropic_chat import Anthropic_Chat
     from vanna.base.base import VannaBase
     from vanna.chromadb.chromadb_vector import ChromaDB_VectorStore
+    from vanna.hf.hf import Hf
     from vanna.local import LocalContext_OpenAI
     from vanna.marqo.marqo import Marqo_VectorStore
     from vanna.mistral.mistral import Mistral
@@ -20,6 +21,7 @@ def test_shortcut_imports():
     from vanna.anthropic import Anthropic_Chat
     from vanna.base import VannaBase
     from vanna.chromadb import ChromaDB_VectorStore
+    from vanna.hf import Hf
     from vanna.marqo import Marqo_VectorStore
     from vanna.mistral import Mistral
     from vanna.ollama import Ollama