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/README.md

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+# SQL with LLaMA2
+
+This template allows you to chat with a SQL database in natural language using LLaMA2.
+
+It is configured to use [Replicate](https://python.langchain.com/docs/integrations/llms/replicate).
+
+But, it can be adapted to any API that support LLaMA2, including [Fireworks](https://python.langchain.com/docs/integrations/chat/fireworks) and others.
+
+See related templates `sql-ollama` and `sql-llamacpp` for private, local chat with SQL.
+
+## Installation
+```bash
+# from inside your LangServe instance
+poe add sql-llama2
+```

templates/sql-llama2/pyproject.toml

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+[tool.poetry]
+name = "sql-llama2"
+version = "0.1.0"
+description = ""
+authors = ["Lance Martin <lance@langchain.dev>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.313, <0.1"
+replicate = ">=0.15.4"
+
+[tool.langserve]
+export_module = "sql_llama2"
+export_attr = "chain"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

templates/sql-llama2/sql_llama2/__init__.py

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+from llama2.chain import chain
+
+__all__ = ["chain"]

templates/sql-llama2/sql_llama2/chain.py

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+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
+)