[Python] Output consistently returns “I don’t know the answer” even when the context isn’t...

I’ve been working on a project integrating LangChain, Google Generative AI (Gemini-1.5-pro), and Neo4j to build a Q&A system that answers questions by querying a Neo4j graph database.

My setup is based on the GraphCypherQAChain from LangChain, but I'm hitting a snag where the LLM consistently returns “I don’t know the answer” even when the context isn’t empty and data seems to exist.

Moreover, it works fine in Python but does not work in JS.

// Neo4j credentials
const url = "";
const username = "neo4j";
const password = "";

// Initialize the model
const model = new ChatGoogleGenerativeAI({
model: "gemini-1.5-pro",
apiKey: "",
temperature: 0,
safetySettings: [
{
category: HarmCategory.HARM_CATEGORY_HARASSMENT,
threshold: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
},
],
verbose: true,
});

async function main() {
try {
const graph = await Neo4jGraph.initialize({
url,
username,
password,
});
const chain = GraphCypherQAChain.fromLLM({
llm: model,
graph: graph,
});

const res = await chain.invoke({
question: "What areas have the highest population density",
});

// Log the result of the query
console.log(res);
} catch (error) {
console.error(
"Error during graph initialization or query execution:",
error,
);
}
}

main();


Output:

{ result: "I don't know the answer. \n" }
​

Update:


Working Python code:

import getpass
import os
from langchain_community.graphs import Neo4jGraph
from langchain.chains import GraphCypherQAChain
from langchain_google_genai import (
ChatGoogleGenerativeAI,
HarmBlockThreshold,
HarmCategory,
)

if "GOOGLE_API_KEY" not in os.environ:
os.environ["GOOGLE_API_KEY"] = ""

os.environ["NEO4J_URI"] = ""
os.environ["NEO4J_USERNAME"] = "neo4j"
os.environ["NEO4J_PASSWORD"] = ""
graph = Neo4jGraph()

graph.refresh_schema()
print(graph.schema)

llm = ChatGoogleGenerativeAI(
allow_dangerous_requests=True,
model="gemini-1.5-pro",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
verbose=True,
safety_settings={
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
},

)
chain = GraphCypherQAChain.from_llm(graph=graph, llm=llm, verbose=True, allow_dangerous_requests=True)
response = chain.invoke({"query": "What areas have the highest population density"})
print(response)


Output:

Entering new GraphCypherQAChain chain...
Generated Cypher:
cypher
MATCH (l:Location)-[r:HAS_UNIQUE_POPULATION_DENSITY]->>(pdensityData) RETURN l.name, pd.population_density ORDER BY pd.population_density DESC LIMIT 10

Full Context:
[{'l.name': 'Tower Hamlets', 'pd.population_density': 15702.9}, {'l.name': 'Islington', 'pd.population_density': 14574.9}, {'l.name': 'Hackney', 'pd.population_density': 13593.3}, {'l.name': 'Lambeth', 'pd.population_density': 11839.1}, {'l.name': 'Kensington and Chelsea', 'pd.population_density': 11816.5}, {'l.name': 'Hammersmith and Fulham', 'pd.population_density': 11161.1}, {'l.name': 'Southwark', 'pd.population_density': 10659.0}, {'l.name': 'Newham', 'pd.population_density': 9690.1}, {'l.name': 'Camden', 'pd.population_density': 9640.9}, {'l.name': 'Wandsworth', 'pd.population_density': 9560.0}]

Finished chain.
{'query': 'What areas have the highest population density', 'result': 'Tower Hamlets, Islington, Hackney, Lambeth, Kensington and Chelsea, Hammersmith and Fulham, Southwark, Newham, Camden and Wandsworth have the highest population density. \n'}

Process finished with exit code 0

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