mirror of
https://github.com/hwchase17/langchain
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95 lines
3.4 KiB
Markdown
95 lines
3.4 KiB
Markdown
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# neo4j-cypher-memory
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This template allows you to have conversations with a Neo4j graph database in natural language, using an OpenAI LLM.
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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.
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Additionally, it features a conversational memory module that stores the dialogue history in the Neo4j graph database.
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The conversation memory is uniquely maintained for each user session, ensuring personalized interactions.
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To facilitate this, please supply both the `user_id` and `session_id` when using the conversation chain.
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## Environment Setup
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Define the following environment variables:
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```
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OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
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NEO4J_URI=<YOUR_NEO4J_URI>
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NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
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NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
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```
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## Neo4j database setup
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There are a number of ways to set up a Neo4j database.
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### Neo4j Aura
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Neo4j AuraDB is a fully managed cloud graph database service.
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Create a free instance on [Neo4j Aura](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve).
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When you initiate a free database instance, you'll receive credentials to access the database.
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## Populating with data
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If you want to populate the DB with some example data, you can run `python ingest.py`.
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This script will populate the database with sample movie data.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package neo4j-cypher-memory
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add neo4j-cypher-memory
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```
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And add the following code to your `server.py` file:
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```python
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from neo4j_cypher_memory import chain as neo4j_cypher_memory_chain
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add_routes(app, neo4j_cypher_memory_chain, path="/neo4j-cypher-memory")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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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)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/neo4j-cypher-memory")
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```
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