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Neo4j conversation cypher template (#12927)
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Adding custom graph memory to Cypher chain

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
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tomasonjo and efriis committed Nov 7, 2023
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92 changes: 92 additions & 0 deletions templates/neo4j-cypher-memory/README.md
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# neo4j-cypher-memory

This template allows you to have conversations with a Neo4j graph database in natural language, using an OpenAI LLM.
It transforms a natural language question into a Cypher query (used to fetch data from Neo4j databases), executes the query, and provides a natural language response based on the query results.
Additionally, it features a conversational memory module that stores the dialogue history in the Neo4j graph database.
The conversation memory is uniquely maintained for each user session, ensuring personalized interactions.
To facilitate this, please supply both the `user_id` and `session_id` when using the conversation chain.

## Environment Setup

Define the following environment variables:

```
OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
NEO4J_URI=<YOUR_NEO4J_URI>
NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
```

## Neo4j database setup

There are a number of ways to set up a Neo4j database.

### Neo4j Aura

Neo4j AuraDB is a fully managed cloud graph database service.
Create a free instance on [Neo4j Aura](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve).
When you initiate a free database instance, you'll receive credentials to access the database.

## Populating with data

If you want to populate the DB with some example data, you can run `python ingest.py`.
This script will populate the database with sample movie data.

## Usage

To use this package, you should first have the LangChain CLI installed:

```shell
pip install -U langchain-cli
```

To create a new LangChain project and install this as the only package, you can do:

```shell
langchain app new my-app --package neo4j-cypher-memory
```

If you want to add this to an existing project, you can just run:

```shell
langchain app add neo4j-cypher-memory
```

And add the following code to your `server.py` file:
```python
from neo4j_cypher_memory import chain as neo4j_cypher_memory_chain

add_routes(app, neo4j_cypher_memory_chain, path="/neo4j-cypher-memory")
```

(Optional) Let's now configure LangSmith.
LangSmith will help us trace, monitor and debug LangChain applications.
LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
If you don't have access, you can skip this section

```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
```

If you are inside this directory, then you can spin up a LangServe instance directly by:

```shell
langchain serve
```

This will start the FastAPI app with a server is running locally at
[http://localhost:8000](http://localhost:8000)

We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
We can access the playground at [http://127.0.0.1:8000/neo4j_cypher_memory/playground](http://127.0.0.1:8000/neo4j_cypher/playground)

We can access the template from code with:

```python
from langserve.client import RemoteRunnable

runnable = RemoteRunnable("http://localhost:8000/neo4j-cypher-memory")
```
16 changes: 16 additions & 0 deletions templates/neo4j-cypher-memory/ingest.py
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from langchain.graphs import Neo4jGraph

graph = Neo4jGraph()

graph.query(
"""
MERGE (m:Movie {name:"Top Gun"})
WITH m
UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor
MERGE (a:Actor {name:actor})
MERGE (a)-[:ACTED_IN]->(m)
WITH a
WHERE a.name = "Tom Cruise"
MERGE (a)-[:ACTED_IN]->(:Movie {name:"Mission Impossible"})
"""
)
23 changes: 23 additions & 0 deletions templates/neo4j-cypher-memory/main.py
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from neo4j_cypher_memory.chain import chain

if __name__ == "__main__":
original_query = "Who played in Top Gun?"
print(
chain.invoke(
{
"question": original_query,
"user_id": "user_123",
"session_id": "session_1",
}
)
)
follow_up_query = "Did they play in any other movies?"
print(
chain.invoke(
{
"question": follow_up_query,
"user_id": "user_123",
"session_id": "session_1",
}
)
)
3 changes: 3 additions & 0 deletions templates/neo4j-cypher-memory/neo4j_cypher_memory/__init__.py
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from neo4j_cypher_memory.chain import chain

__all__ = ["chain"]
146 changes: 146 additions & 0 deletions templates/neo4j-cypher-memory/neo4j_cypher_memory/chain.py
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from typing import Any, Dict, List

from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
from langchain.chat_models import ChatOpenAI
from langchain.graphs import Neo4jGraph
from langchain.memory import ChatMessageHistory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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)


def convert_messages(input: List[Dict[str, Any]]) -> ChatMessageHistory:
history = ChatMessageHistory()
for item in input:
history.add_user_message(item["result"]["question"])
history.add_ai_message(item["result"]["answer"])
return history


def get_history(input: Dict[str, Any]) -> ChatMessageHistory:
input.pop("question")
# Lookback conversation window
window = 3
data = graph.query(
"""
MATCH (u:User {id:$user_id})-[:HAS_SESSION]->(s:Session {id:$session_id}),
(s)-[:LAST_MESSAGE]->(last_message)
MATCH p=(last_message)<-[:NEXT*0.."""
+ str(window)
+ """]-()
WITH p, length(p) AS length
ORDER BY length DESC LIMIT 1
UNWIND reverse(nodes(p)) AS node
MATCH (node)-[:HAS_ANSWER]->(answer)
RETURN {question:node.text, answer:answer.text} AS result
""",
params=input,
)
history = convert_messages(data)
return history.messages


def save_history(input):
input.pop("response")
# store history to database
graph.query(
"""MERGE (u:User {id: $user_id})
WITH u
OPTIONAL MATCH (u)-[:HAS_SESSION]->(s:Session{id: $session_id}),
(s)-[l:LAST_MESSAGE]->(last_message)
FOREACH (_ IN CASE WHEN last_message IS NULL THEN [1] ELSE [] END |
CREATE (u)-[:HAS_SESSION]->(s1:Session {id:$session_id}),
(s1)-[:LAST_MESSAGE]->(q:Question {text:$question, cypher:$query, date:datetime()}),
(q)-[:HAS_ANSWER]->(:Answer {text:$output}))
FOREACH (_ IN CASE WHEN last_message IS NOT NULL THEN [1] ELSE [] END |
CREATE (last_message)-[:NEXT]->(q:Question
{text:$question, cypher:$query, date:datetime()}),
(q)-[:HAS_ANSWER]->(:Answer {text:$output}),
(s)-[:LAST_MESSAGE]->(q)
DELETE l) """,
params=input,
)

# Return LLM response to the chain
return input["output"]


# Generate Cypher statement based on natural language input
cypher_template = """This is important for my career.
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.",
),
MessagesPlaceholder(variable_name="history"),
("human", cypher_template),
]
)

cypher_response = (
RunnablePassthrough.assign(schema=lambda _: graph.get_schema, history=get_history)
| 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"])),
)
| RunnablePassthrough.assign(
output=response_prompt | qa_llm | StrOutputParser(),
)
| save_history
)

# Add typing for input


class Question(BaseModel):
question: str
user_id: str
session_id: str


chain = chain.with_types(input_type=Question)
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