mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-11 05:45:01 +00:00
2445b997ee
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.
94 lines
3.6 KiB
Markdown
94 lines
3.6 KiB
Markdown
# 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.
|
|
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).
|
|
|
|
|
|
|
|
## Tools
|
|
|
|
The agent utilizes several tools to interact with the Neo4j graph database effectively:
|
|
|
|
1. **Information tool**:
|
|
- Retrieves data about movies or individuals, ensuring the agent has access to the latest and most relevant information.
|
|
2. **Recommendation Tool**:
|
|
- Provides movie recommendations based upon user preferences and input.
|
|
3. **Memory Tool**:
|
|
- Stores information about user preferences in the knowledge graph, allowing for a personalized experience over multiple interactions.
|
|
|
|
## Environment Setup
|
|
|
|
You need to define the following environment variables
|
|
|
|
```
|
|
OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
|
|
NEO4J_URI=<YOUR_NEO4J_URI>
|
|
NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
|
|
NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
|
|
```
|
|
|
|
## Populating with data
|
|
|
|
If you want to populate the DB with an example movie dataset, you can run `python ingest.py`.
|
|
The script import information about movies and their rating by users.
|
|
Additionally, the script creates two [fulltext indices](https://neo4j.com/docs/cypher-manual/current/indexes-for-full-text-search/), which are used to map information from user input to the database.
|
|
|
|
## Usage
|
|
|
|
To use this package, you should first have the LangChain CLI installed:
|
|
|
|
```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-semantic-layer
|
|
```
|
|
|
|
If you want to add this to an existing project, you can just run:
|
|
|
|
```shell
|
|
langchain app add neo4j-semantic-layer
|
|
```
|
|
|
|
And add the following code to your `server.py` file:
|
|
```python
|
|
from neo4j_semantic_layer import agent_executor as neo4j_semantic_agent
|
|
|
|
add_routes(app, neo4j_semantic_agent, path="/neo4j-semantic-layer")
|
|
```
|
|
|
|
(Optional) Let's now configure LangSmith.
|
|
LangSmith will help us trace, monitor and debug LangChain applications.
|
|
You can sign up for LangSmith [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-semantic-layer/playground](http://127.0.0.1:8000/neo4j-semantic-layer/playground)
|
|
|
|
We can access the template from code with:
|
|
|
|
```python
|
|
from langserve.client import RemoteRunnable
|
|
|
|
runnable = RemoteRunnable("http://localhost:8000/neo4j-semantic-layer")
|
|
```
|