mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
135 lines
3.8 KiB
Python
135 lines
3.8 KiB
Python
from typing import List, Optional
|
|
|
|
from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
|
|
from langchain.chains.openai_functions import create_structured_output_chain
|
|
from langchain_community.chat_models import ChatOpenAI
|
|
from langchain_community.graphs import Neo4jGraph
|
|
from langchain_core.output_parsers import StrOutputParser
|
|
from langchain_core.prompts import ChatPromptTemplate
|
|
from langchain_core.pydantic_v1 import BaseModel, Field
|
|
from langchain_core.runnables 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)
|
|
|
|
|
|
# Extract entities from text
|
|
class Entities(BaseModel):
|
|
"""Identifying information about entities."""
|
|
|
|
names: List[str] = Field(
|
|
...,
|
|
description="All the person, organization, or business entities that "
|
|
"appear in the text",
|
|
)
|
|
|
|
|
|
prompt = ChatPromptTemplate.from_messages(
|
|
[
|
|
(
|
|
"system",
|
|
"You are extracting organization and person entities from the text.",
|
|
),
|
|
(
|
|
"human",
|
|
"Use the given format to extract information from the following "
|
|
"input: {question}",
|
|
),
|
|
]
|
|
)
|
|
|
|
|
|
# Fulltext index query
|
|
def map_to_database(entities: Entities) -> Optional[str]:
|
|
result = ""
|
|
for entity in entities.names:
|
|
response = graph.query(
|
|
"CALL db.index.fulltext.queryNodes('entity', $entity + '*', {limit:1})"
|
|
" YIELD node,score RETURN node.name AS result",
|
|
{"entity": entity},
|
|
)
|
|
try:
|
|
result += f"{entity} maps to {response[0]['result']} in database\n"
|
|
except IndexError:
|
|
pass
|
|
return result
|
|
|
|
|
|
entity_chain = create_structured_output_chain(Entities, qa_llm, prompt)
|
|
|
|
# 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}
|
|
Entities in the question map to the following database values:
|
|
{entities_list}
|
|
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(names=entity_chain)
|
|
| RunnablePassthrough.assign(
|
|
entities_list=lambda x: map_to_database(x["names"]["function"]),
|
|
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)
|