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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
83 lines
2.5 KiB
Markdown
83 lines
2.5 KiB
Markdown
# Neo4j AuraDB - generation
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This template pairs LLM-based knowledge graph extraction with `Neo4j AuraDB`,
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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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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-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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