mirror of
https://github.com/hwchase17/langchain
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98 lines
3.8 KiB
Markdown
98 lines
3.8 KiB
Markdown
# neo4j-advanced-rag
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This template allows you to balance precise embeddings and context retention by implementing advanced retrieval strategies.
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## Strategies
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1. **Typical RAG**:
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- Traditional method where the exact data indexed is the data retrieved.
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2. **Parent retriever**:
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- Instead of indexing entire documents, data is divided into smaller chunks, referred to as Parent and Child documents.
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- Child documents are indexed for better representation of specific concepts, while parent documents is retrieved to ensure context retention.
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3. **Hypothetical Questions**:
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- Documents are processed to determine potential questions they might answer.
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- These questions are then indexed for better representation of specific concepts, while parent documents are retrieved to ensure context retention.
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4. **Summaries**:
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- Instead of indexing the entire document, a summary of the document is created and indexed.
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- Similarly, the parent document is retrieved in a RAG application.
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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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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.
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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.
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For every parent node, hypothetical questions and summaries are generated, embedded, and added to the database.
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Additionally, a vector index for each retrieval strategy is created for efficient querying of these embeddings.
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*Note that ingestion can take a minute or two due to LLMs velocity of generating hypothetical questions and summaries.*
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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[serve]"
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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-advanced-rag
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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-advanced-rag
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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_advanced_rag import chain as neo4j_advanced_chain
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add_routes(app, neo4j_advanced_chain, path="/neo4j-advanced-rag")
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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-advanced-rag/playground](http://127.0.0.1:8000/neo4j-advanced-rag/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-advanced-rag")
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```
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