1 files changed, 174 insertions, 0 deletions
diff --git a/server/continuedev/libs/llm/prompts/chat.py b/server/continuedev/libs/llm/prompts/chat.py
new file mode 100644
index 00000000..036f1b1a
--- /dev/null
+++ b/server/continuedev/libs/llm/prompts/chat.py
@@ -0,0 +1,174 @@
+from textwrap import dedent
+from typing import Dict, List
+
+from anthropic import AI_PROMPT, HUMAN_PROMPT
+
+
+def anthropic_template_messages(messages: List[Dict[str, str]]) -> str:
+    prompt = ""
+
+    # Anthropic prompt must start with a Human turn
+    if (
+        len(messages) > 0
+        and messages[0]["role"] != "user"
+        and messages[0]["role"] != "system"
+    ):
+        prompt += f"{HUMAN_PROMPT} Hello."
+    for msg in messages:
+        prompt += f"{HUMAN_PROMPT if (msg['role'] == 'user' or msg['role'] == 'system') else AI_PROMPT} {msg['content']} "
+
+    prompt += AI_PROMPT
+    return prompt
+
+
+def template_alpaca_messages(msgs: List[Dict[str, str]]) -> str:
+    prompt = ""
+
+    if msgs[0]["role"] == "system":
+        prompt += f"{msgs[0]['content']}\n"
+        msgs.pop(0)
+
+    for msg in msgs:
+        prompt += "### Instruction:\n" if msg["role"] == "user" else "### Response:\n"
+        prompt += f"{msg['content']}\n"
+
+    prompt += "### Response:\n"
+
+    return prompt
+
+
+def raw_input_template(msgs: List[Dict[str, str]]) -> str:
+    return msgs[-1]["content"]
+
+
+SQL_CODER_DEFAULT_SCHEMA = """\
+CREATE TABLE products (
+  product_id INTEGER PRIMARY KEY, -- Unique ID for each product
+  name VARCHAR(50), -- Name of the product
+  price DECIMAL(10,2), -- Price of each unit of the product
+  quantity INTEGER  -- Current quantity in stock
+);
+
+CREATE TABLE customers (
+   customer_id INTEGER PRIMARY KEY, -- Unique ID for each customer
+   name VARCHAR(50), -- Name of the customer
+   address VARCHAR(100) -- Mailing address of the customer
+);
+
+CREATE TABLE salespeople (
+  salesperson_id INTEGER PRIMARY KEY, -- Unique ID for each salesperson
+  name VARCHAR(50), -- Name of the salesperson
+  region VARCHAR(50) -- Geographic sales region
+);
+
+CREATE TABLE sales (
+  sale_id INTEGER PRIMARY KEY, -- Unique ID for each sale
+  product_id INTEGER, -- ID of product sold
+  customer_id INTEGER,  -- ID of customer who made purchase
+  salesperson_id INTEGER, -- ID of salesperson who made the sale
+  sale_date DATE, -- Date the sale occurred
+  quantity INTEGER -- Quantity of product sold
+);
+
+CREATE TABLE product_suppliers (
+  supplier_id INTEGER PRIMARY KEY, -- Unique ID for each supplier
+  product_id INTEGER, -- Product ID supplied
+  supply_price DECIMAL(10,2) -- Unit price charged by supplier
+);
+
+-- sales.product_id can be joined with products.product_id
+-- sales.customer_id can be joined with customers.customer_id
+-- sales.salesperson_id can be joined with salespeople.salesperson_id
+-- product_suppliers.product_id can be joined with products.product_id
+"""
+
+
+def _sqlcoder_template_messages(
+    msgs: List[Dict[str, str]], schema: str = SQL_CODER_DEFAULT_SCHEMA
+) -> str:
+    question = msgs[-1]["content"]
+    return f"""\
+Your task is to convert a question into a SQL query, given a Postgres database schema.
+Adhere to these rules:
+- **Deliberately go through the question and database schema word by word** to appropriately answer the question
+- **Use Table Aliases** to prevent ambiguity. For example, `SELECT table1.col1, table2.col1 FROM table1 JOIN table2 ON table1.id = table2.id`.
+- When creating a ratio, always cast the numerator as float
+
+### Input:
+Generate a SQL query that answers the question `{question}`.
+This query will run on a database whose schema is represented in this string:
+{schema}
+
+### Response:
+Based on your instructions, here is the SQL query I have generated to answer the question `{question}`:
+```sql
+"""
+
+
+def sqlcoder_template_messages(schema: str = SQL_CODER_DEFAULT_SCHEMA):
+    if schema == "<MY_DATABASE_SCHEMA>" or schema == "":
+        schema = SQL_CODER_DEFAULT_SCHEMA
+
+    def fn(msgs):
+        return _sqlcoder_template_messages(msgs, schema=schema)
+
+    fn.__name__ = "sqlcoder_template_messages"
+    return fn
+
+
+def llama2_template_messages(msgs: List[Dict[str, str]]) -> str:
+    if len(msgs) == 0:
+        return ""
+
+    if msgs[0]["role"] == "assistant":
+        # These models aren't trained to handle assistant message coming first,
+        # and typically these are just introduction messages from Continue
+        msgs.pop(0)
+
+    prompt = ""
+    has_system = msgs[0]["role"] == "system"
+
+    if has_system and msgs[0]["content"].strip() == "":
+        has_system = False
+        msgs = msgs[1:]
+
+    if has_system:
+        system_message = dedent(
+            f"""\
+                <<SYS>>
+                {msgs[0]["content"]}
+                <</SYS>>
+                
+                """
+        )
+        if len(msgs) > 1:
+            prompt += f"[INST] {system_message}{msgs[1]['content']} [/INST]"
+        else:
+            prompt += f"[INST] {system_message} [/INST]"
+            return
+
+    for i in range(2 if has_system else 0, len(msgs)):
+        if msgs[i]["role"] == "user":
+            prompt += f"[INST] {msgs[i]['content']} [/INST]"
+        else:
+            prompt += msgs[i]["content"] + " "
+
+    return prompt
+
+
+def code_llama_template_messages(msgs: List[Dict[str, str]]) -> str:
+    return f"[INST] {msgs[-1]['content']}\n[/INST]"
+
+
+def extra_space_template_messages(msgs: List[Dict[str, str]]) -> str:
+    return f" {msgs[-1]['content']}"
+
+
+def code_llama_python_template_messages(msgs: List[Dict[str, str]]) -> str:
+    return dedent(
+        f"""\
+        [INST]
+        You are an expert Python programmer and personal assistant, here is your task: {msgs[-1]['content']}
+        Your answer should start with a [PYTHON] tag and end with a [/PYTHON] tag.
+        [/INST]"""
+    )