Templates (#12294)

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Jacob Lee <jacoblee93@gmail.com>

templates/sql-llama2/sql_llama2/__init__.py

@@ -0,0 +1,3 @@
+from llama2.chain import chain
+
+__all__ = ["chain"]

templates/sql-llama2/sql_llama2/chain.py

@@ -0,0 +1,73 @@
+from langchain.llms import Replicate
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.prompts import ChatPromptTemplate
+
+# make sure to set REPLICATE_API_TOKEN in your environment
+# use llama-2-13b model in replicate
+replicate_id = "meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d"
+llm = Replicate(
+    model=replicate_id,
+    model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1},
+)
+
+from pathlib import Path
+from langchain.utilities import SQLDatabase
+db_path = Path(__file__).parent / "nba_roster.db"
+rel = db_path.relative_to(Path.cwd())
+db_string = f"sqlite:///{rel}"
+db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0)
+
+def get_schema(_):
+    return db.get_table_info()
+
+
+def run_query(query):
+    return db.run(query)
+
+
+template_query = """Based on the table schema below, write a SQL query that would answer the user's question:
+{schema}
+
+Question: {question}
+SQL Query:"""
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "Given an input question, convert it to a SQL query. No pre-amble."),
+        ("human", template_query),
+    ]
+)
+
+sql_response = (
+    RunnablePassthrough.assign(schema=get_schema)
+    | prompt
+    | llm.bind(stop=["\nSQLResult:"])
+    | StrOutputParser()
+)
+
+template_response = """Based on the table schema below, question, sql query, and sql response, write a natural language response:
+{schema}
+
+Question: {question}
+SQL Query: {query}
+SQL Response: {response}"""
+
+prompt_response = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "Given an input question and SQL response, convert it to a natural language answer. No pre-amble.",
+        ),
+        ("human", template_response),
+    ]
+)
+
+chain = (
+    RunnablePassthrough.assign(query=sql_response)
+    | RunnablePassthrough.assign(
+        schema=get_schema,
+        response=lambda x: db.run(x["query"]),
+    )
+    | prompt_response
+    | llm
+)