graph

Q&A chain over a graph database

https://python.langchain.com/v0.1/docs/use_cases/graph/quickstart/ https://langchain-ai.github.io/langgraph/

LangGraph is inspired by Pregel and Apache Beam.

At a high-level, the steps of most graph chains are:

1. Convert question to a graph database query: Model converts user input to a graph database query (e.g. Cypher).
2. Execute graph database query: Execute the graph database query.
3. Answer the question: Model responds to user input using the query results.

%pip install --upgrade --quiet  langchain langchain-community langchain-openai neo4j

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

# Uncomment the below to use LangSmith. Not required.
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
# os.environ["LANGCHAIN_TRACING_V2"] = "true"

os.environ["NEO4J_URI"] = "bolt://localhost:7687"
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = "password"


from langchain_community.graphs import Neo4jGraph

graph = Neo4jGraph()

# Import movie information

movies_query = """
LOAD CSV WITH HEADERS FROM 
'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies_small.csv'
AS row
MERGE (m:Movie {id:row.movieId})
SET m.released = date(row.released),
    m.title = row.title,
    m.imdbRating = toFloat(row.imdbRating)
FOREACH (director in split(row.director, '|') | 
    MERGE (p:Person {name:trim(director)})
    MERGE (p)-[:DIRECTED]->(m))
FOREACH (actor in split(row.actors, '|') | 
    MERGE (p:Person {name:trim(actor)})
    MERGE (p)-[:ACTED_IN]->(m))
FOREACH (genre in split(row.genres, '|') | 
    MERGE (g:Genre {name:trim(genre)})
    MERGE (m)-[:IN_GENRE]->(g))
"""

graph.query(movies_query)


graph.refresh_schema()
print(graph.schema)

# chain
from langchain.chains import GraphCypherQAChain
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
chain = GraphCypherQAChain.from_llm(graph=graph, llm=llm, verbose=True)
response = chain.invoke({"query": "What was the cast of the Casino?"})
response



chain = GraphCypherQAChain.from_llm(
    graph=graph, llm=llm, verbose=True, validate_cypher=True
)
response = chain.invoke({"query": "What was the cast of the Casino?"})
response

"""
{'query': 'What was the cast of the Casino?',
 'result': 'The cast of Casino included Joe Pesci, Robert De Niro, Sharon Stone, and James Woods.'}
"""

# Validating relationship direction
chain = GraphCypherQAChain.from_llm(
    graph=graph, llm=llm, verbose=True, validate_cypher=True
)
response = chain.invoke({"query": "What was the cast of the Casino?"})
response

"""
Generated Cypher:
MATCH (:Movie {title: "Casino"})<-[:ACTED_IN]-(actor:Person)
RETURN actor.name
Full Context:
[{'actor.name': 'Joe Pesci'}, {'actor.name': 'Robert De Niro'}, {'actor.name': 'Sharon Stone'}, {'actor.name': 'James Woods'}]
"""

Constructing knowledge graphs

https://python.langchain.com/v0.1/docs/use_cases/graph/constructing/

main module

from langchain_experimental.graph_transformers import LLMGraphTransformer