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Correction of incorrect words (#23557)

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Thank you for contributing to LangChain!

- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
  - Example: "community: add foobar LLM"


- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
    - **Description:** a description of the change
    - **Issue:** the issue # it fixes, if applicable
    - **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!


- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.


- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
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panwg3 2024-06-27 23:13:15 +08:00 committed by GitHub
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2
templates/neo4j-semantic-layer/README.md
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@ -1,7 +1,7 @@
# neo4j-semantic-layer # neo4j-semantic-layer
This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using OpenAI function calling. This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using OpenAI function calling.
The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph databas based on the user's intent. The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph database based on the user's intent.
Learn more about the semantic layer template in the [corresponding blog post](https://medium.com/towards-data-science/enhancing-interaction-between-language-models-and-graph-databases-via-a-semantic-layer-0a78ad3eba49). Learn more about the semantic layer template in the [corresponding blog post](https://medium.com/towards-data-science/enhancing-interaction-between-language-models-and-graph-databases-via-a-semantic-layer-0a78ad3eba49).
![Diagram illustrating the workflow of the Neo4j semantic layer with an agent interacting with tools like Information, Recommendation, and Memory, connected to a knowledge graph.](https://raw.githubusercontent.com/langchain-ai/langchain/master/templates/neo4j-semantic-layer/static/workflow.png "Neo4j Semantic Layer Workflow Diagram") ![Diagram illustrating the workflow of the Neo4j semantic layer with an agent interacting with tools like Information, Recommendation, and Memory, connected to a knowledge graph.](https://raw.githubusercontent.com/langchain-ai/langchain/master/templates/neo4j-semantic-layer/static/workflow.png "Neo4j Semantic Layer Workflow Diagram")

4
templates/rag-opensearch/README.md
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@ -15,7 +15,7 @@ And optionally set the OpenSearch ones if not using defaults:
- `OPENSEARCH_PASSWORD` - Password for the OpenSearch instance - `OPENSEARCH_PASSWORD` - Password for the OpenSearch instance
- `OPENSEARCH_INDEX_NAME` - Name of the index - `OPENSEARCH_INDEX_NAME` - Name of the index
To run the default OpenSeach instance in docker, you can use the command To run the default OpenSearch instance in docker, you can use the command
```shell ```shell
docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" --name opensearch-node -d opensearchproject/opensearch:latest docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" --name opensearch-node -d opensearchproject/opensearch:latest
``` ```
@ -79,4 +79,4 @@ We can access the template from code with:
from langserve.client import RemoteRunnable from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-opensearch") runnable = RemoteRunnable("http://localhost:8000/rag-opensearch")
``` ```