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Readme rewrite (#12615)

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Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Erick Friis
2023-10-31 00:06:02 -07:00
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templates/neo4j-generation/README.md
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# Graph Generation Chain for Neo4j Knowledge Graph
# neo4j-generation
Harness the power of natural language understanding of LLMs and convert plain text into structured knowledge graphs with the Graph Generation Chain.
This chain uses OpenAI's LLM to construct a knowledge graph in Neo4j.
Leveraging OpenAI Functions capabilities, the Graph Generation Chain efficiently extracts structured information from text.
The chain has the following input parameters:
The neo4j-generation template is designed to convert plain text into structured knowledge graphs.
* text (str): The input text from which the information will be extracted to construct the graph.
* allowed_nodes (Optional[List[str]]): A list of node labels to guide the extraction process.
If not provided, extraction won't have specific restriction on node labels.
* allowed_relationships (Optional[List[str]]): A list of relationship types to guide the extraction process.
If not provided, extraction won't have specific restriction on relationship types.
By using OpenAI's language model, it can efficiently extract structured information from text and construct a knowledge graph in Neo4j.
Find more details in [this blog post](https://blog.langchain.dev/constructing-knowledge-graphs-from-text-using-openai-functions/).
This package is flexible and allows users to guide the extraction process by specifying a list of node labels and relationship types.
## Neo4j database
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/).
There are a number of ways to set up a Neo4j database.
## Environment Setup
### 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.
## Environment variables
You need to define the following environment variables
You need to set the following environment variables:
```
OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
@@ -34,11 +19,61 @@ NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
```
## Installation
## Usage
To get started with the Graph Generation Chain:
To use this package, you should first have the LangChain CLI installed:
```bash
# from inside your LangServe instance
poe add neo4j-generation
```
```shell
pip install -U "langchain-cli[serve]"
```
To create a new LangChain project and install this as the only package, you can do:
```shell
langchain app new my-app --package neo4j-generation
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add neo4j-generation
```
And add the following code to your `server.py` file:
```python
from neo4j_generation import chain as neo4j_generation_chain
add_routes(app, neo4j_generation_chain, path="/neo4j-generation")
```
(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-generation/playground](http://127.0.0.1:8000/neo4j-generation/playground)
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/neo4j-generation")
```