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-ollama/sql_ollama/__init__.py

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

templates/sql-ollama/sql_ollama/chain.py

@@ -0,0 +1,78 @@
+from langchain.chat_models import ChatOllama
+from langchain.prompts import ChatPromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+
+# Add the LLM downloaded from Ollama
+ollama_llm = "llama2:13b-chat"
+llm = ChatOllama(model=ollama_llm)
+
+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)
+
+# Prompt
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+template = """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."),
+    MessagesPlaceholder(variable_name="history"),
+    ("human", template)
+])
+
+memory = ConversationBufferMemory(return_messages=True)
+
+# Chain to query with memory 
+from langchain.schema.runnable import RunnableLambda
+
+sql_chain = (
+    RunnablePassthrough.assign(
+       schema=get_schema,
+       history=RunnableLambda(lambda x: memory.load_memory_variables(x)["history"])
+    )| prompt
+    | llm.bind(stop=["\nSQLResult:"])
+    | StrOutputParser()
+)
+
+def save(input_output):
+    output = {"output": input_output.pop("output")}
+    memory.save_context(input_output, output)
+    return output['output']
+    
+sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save
+
+# Chain to answer
+template = """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)
+])
+
+chain = (
+    RunnablePassthrough.assign(query=sql_response_memory) 
+    | RunnablePassthrough.assign(
+        schema=get_schema,
+        response=lambda x: db.run(x["query"]),
+    )
+    | prompt_response 
+    | llm
+)