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from langchain_community.vectorstores import Neo4jVector
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
retrieval_query = """
MATCH (node)-[:HAS_PARENT]->(parent)
WITH parent, max(score) AS score // deduplicate parents
RETURN parent.text AS text, score, {} AS metadata
"""
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
vectorstore = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
index_name="retrieval",
node_label="Child",
embedding_node_property="embedding",
retrieval_query=retrieval_query,
)
retriever = vectorstore.as_retriever()
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI()
chain = (
RunnableParallel(
{"context": retriever | format_docs, "question": RunnablePassthrough()}
)
| prompt
| model
| StrOutputParser()
)
# Add typing for input
class Question(BaseModel):
__root__: str
chain = chain.with_types(input_type=Question)
|