mirror of
https://github.com/hwchase17/langchain
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84 lines
2.6 KiB
Markdown
84 lines
2.6 KiB
Markdown
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# neo4j-vector-memory
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This template allows you to integrate an LLM with a vector-based retrieval system using Neo4j as the vector store.
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Additionally, it uses the graph capabilities of the Neo4j database to store and retrieve the dialogue history of a specific user's session.
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Having the dialogue history stored as a graph allows for seamless conversational flows but also gives you the ability to analyze user behavior and text chunk retrieval through graph analytics.
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## Environment Setup
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You need to define 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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## Populating with data
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If you want to populate the DB with some example data, you can run `python ingest.py`.
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The script process and stores sections of the text from the file `dune.txt` into a Neo4j graph database.
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Additionally, a vector index named `dune` is created for efficient querying of these embeddings.
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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-vector-memory
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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-vector-memory
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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_vector_memory import chain as neo4j_vector_memory_chain
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add_routes(app, neo4j_vector_memory_chain, path="/neo4j-vector-memory")
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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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LangSmith is currently in private beta, you can sign up [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-vector-memory/playground](http://127.0.0.1:8000/neo4j-parent/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-vector-memory")
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```
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