mirror of
https://github.com/hwchase17/langchain
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83 lines
2.5 KiB
Markdown
83 lines
2.5 KiB
Markdown
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# neo4j-generation
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This template pairs LLM-based knowledge graph extraction with Neo4j AuraDB, a fully managed cloud graph database.
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You can 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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This template is flexible and allows users to guide the extraction process by specifying a list of node labels and relationship types.
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For more details on the functionality and capabilities of this package, please refer to [this blog post](https://blog.langchain.dev/constructing-knowledge-graphs-from-text-using-openai-functions/).
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## Environment Setup
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You need to set 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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## 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-generation
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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-generation
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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_generation.chain import chain as neo4j_generation_chain
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add_routes(app, neo4j_generation_chain, path="/neo4j-generation")
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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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LangSmith is currently in private beta, you can sign up [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-generation/playground](http://127.0.0.1:8000/neo4j-generation/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-generation")
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```
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