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Minor updates to READMEs (#12642)

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Lance Martin 2023-10-31 08:34:46 -07:00 committed by GitHub
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templates/neo4j-generation/README.md
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# neo4j-generation
The neo4j-generation template is designed to convert plain text into structured knowledge graphs.
This template pairs LLM-based knowledge graph extraction with Neo4j AuraDB, a fully managed cloud graph database.
By using OpenAI's language model, it can efficiently extract structured information from text and construct a knowledge graph in Neo4j.
You can create a free instance on [Neo4j Aura](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve).
This package is flexible and allows users to guide the extraction process by specifying a list of node labels and relationship types.
When you initiate a free database instance, you'll receive credentials to access the database.
This template is flexible and allows users to guide the extraction process by specifying a list of node labels and relationship types.
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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templates/plate-chain/README.md
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Then add the following code to your `server.py` file:
```python
from plate_chain import chain as plate_chain_chain
from plate_chain import chain as plate_chain
add_routes(app, plate_chain_chain, path="/plate-chain")
add_routes(app, plate_chain, path="/plate-chain")
```
(Optional) For configuring LangSmith, which helps trace, monitor and debug LangChain applications, use the following code:

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templates/rag-semi-structured/README.md
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This template performs RAG on semi-structured data, such as a PDF with text and tables.
See [this cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb) as a reference.
## Environment Setup
Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.