mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
84 lines
2.3 KiB
Python
84 lines
2.3 KiB
Python
from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
|
|
from langchain.chat_models import ChatOpenAI
|
|
from langchain.graphs import Neo4jGraph
|
|
from langchain.prompts import ChatPromptTemplate
|
|
from langchain.pydantic_v1 import BaseModel
|
|
from langchain.schema.output_parser import StrOutputParser
|
|
from langchain.schema.runnable import RunnablePassthrough
|
|
|
|
# Connection to Neo4j
|
|
graph = Neo4jGraph()
|
|
|
|
# Cypher validation tool for relationship directions
|
|
corrector_schema = [
|
|
Schema(el["start"], el["type"], el["end"])
|
|
for el in graph.structured_schema.get("relationships")
|
|
]
|
|
cypher_validation = CypherQueryCorrector(corrector_schema)
|
|
|
|
# LLMs
|
|
cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0)
|
|
qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0)
|
|
|
|
# Generate Cypher statement based on natural language input
|
|
cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question:
|
|
{schema}
|
|
|
|
Question: {question}
|
|
Cypher query:""" # noqa: E501
|
|
|
|
cypher_prompt = ChatPromptTemplate.from_messages(
|
|
[
|
|
(
|
|
"system",
|
|
"Given an input question, convert it to a Cypher query. No pre-amble.",
|
|
),
|
|
("human", cypher_template),
|
|
]
|
|
)
|
|
|
|
cypher_response = (
|
|
RunnablePassthrough.assign(
|
|
schema=lambda _: graph.get_schema,
|
|
)
|
|
| cypher_prompt
|
|
| cypher_llm.bind(stop=["\nCypherResult:"])
|
|
| StrOutputParser()
|
|
)
|
|
|
|
# Generate natural language response based on database results
|
|
response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response:
|
|
Question: {question}
|
|
Cypher query: {query}
|
|
Cypher Response: {response}""" # noqa: E501
|
|
|
|
response_prompt = ChatPromptTemplate.from_messages(
|
|
[
|
|
(
|
|
"system",
|
|
"Given an input question and Cypher response, convert it to a "
|
|
"natural language answer. No pre-amble.",
|
|
),
|
|
("human", response_template),
|
|
]
|
|
)
|
|
|
|
chain = (
|
|
RunnablePassthrough.assign(query=cypher_response)
|
|
| RunnablePassthrough.assign(
|
|
response=lambda x: graph.query(cypher_validation(x["query"])),
|
|
)
|
|
| response_prompt
|
|
| qa_llm
|
|
| StrOutputParser()
|
|
)
|
|
|
|
# Add typing for input
|
|
|
|
|
|
class Question(BaseModel):
|
|
question: str
|
|
|
|
|
|
chain = chain.with_types(input_type=Question)
|