mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
98 lines
3.7 KiB
Markdown
98 lines
3.7 KiB
Markdown
# neo4j-advanced-rag
|
|
|
|
This template allows you to balance precise embeddings and context retention by implementing advanced retrieval strategies.
|
|
|
|
## Strategies
|
|
|
|
1. **Typical RAG**:
|
|
- Traditional method where the exact data indexed is the data retrieved.
|
|
2. **Parent retriever**:
|
|
- Instead of indexing entire documents, data is divided into smaller chunks, referred to as Parent and Child documents.
|
|
- Child documents are indexed for better representation of specific concepts, while parent documents is retrieved to ensure context retention.
|
|
3. **Hypothetical Questions**:
|
|
- Documents are processed to determine potential questions they might answer.
|
|
- These questions are then indexed for better representation of specific concepts, while parent documents are retrieved to ensure context retention.
|
|
4. **Summaries**:
|
|
- Instead of indexing the entire document, a summary of the document is created and indexed.
|
|
- Similarly, the parent document is retrieved in a RAG application.
|
|
|
|
## 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 some example data, you can run `python ingest.py`.
|
|
The script process and stores sections of the text from the file `dune.txt` into a Neo4j graph database.
|
|
First, the text is divided into larger chunks ("parents") and then further subdivided into smaller chunks ("children"), where both parent and child chunks overlap slightly to maintain context.
|
|
After storing these chunks in the database, embeddings for the child nodes are computed using OpenAI's embeddings and stored back in the graph for future retrieval or analysis.
|
|
For every parent node, hypothetical questions and summaries are generated, embedded, and added to the database.
|
|
Additionally, a vector index for each retrieval strategy is created for efficient querying of these embeddings.
|
|
|
|
*Note that ingestion can take a minute or two due to LLMs velocity of generating hypothetical questions and summaries.*
|
|
|
|
## 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-advanced-rag
|
|
```
|
|
|
|
If you want to add this to an existing project, you can just run:
|
|
|
|
```shell
|
|
langchain app add neo4j-advanced-rag
|
|
```
|
|
|
|
And add the following code to your `server.py` file:
|
|
```python
|
|
from neo4j_advanced_rag import chain as neo4j_advanced_chain
|
|
|
|
add_routes(app, neo4j_advanced_chain, path="/neo4j-advanced-rag")
|
|
```
|
|
|
|
(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-advanced-rag/playground](http://127.0.0.1:8000/neo4j-advanced-rag/playground)
|
|
|
|
We can access the template from code with:
|
|
|
|
```python
|
|
from langserve.client import RemoteRunnable
|
|
|
|
runnable = RemoteRunnable("http://localhost:8000/neo4j-advanced-rag")
|
|
```
|