mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
84 lines
2.2 KiB
Python
84 lines
2.2 KiB
Python
from pathlib import Path
|
|
|
|
from langchain.llms import Replicate
|
|
from langchain.prompts import ChatPromptTemplate
|
|
from langchain.pydantic_v1 import BaseModel
|
|
from langchain.schema.output_parser import StrOutputParser
|
|
from langchain.schema.runnable import RunnablePassthrough
|
|
from langchain.utilities import SQLDatabase
|
|
|
|
# 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" # noqa: E501
|
|
llm = Replicate(
|
|
model=replicate_id,
|
|
model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1},
|
|
)
|
|
|
|
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:""" # noqa: E501
|
|
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}""" # noqa: E501
|
|
|
|
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),
|
|
]
|
|
)
|
|
|
|
|
|
# Supply the input types to the prompt
|
|
class InputType(BaseModel):
|
|
question: str
|
|
|
|
|
|
chain = (
|
|
RunnablePassthrough.assign(query=sql_response).with_types(input_type=InputType)
|
|
| RunnablePassthrough.assign(
|
|
schema=get_schema,
|
|
response=lambda x: db.run(x["query"]),
|
|
)
|
|
| prompt_response
|
|
| llm
|
|
)
|