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179 lines
5.4 KiB
Markdown
179 lines
5.4 KiB
Markdown
9 months ago
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# mongo-parent-document-retrieval
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This template performs RAG using MongoDB and OpenAI.
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It does a more advanced form of RAG called Parent-Document Retrieval.
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In this form of retrieval, a large document is first split into medium sized chunks.
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From there, those medium size chunks are split into small chunks.
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Embeddings are created for the small chunks.
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When a query comes in, an embedding is created for that query and compared to the small chunks.
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But rather than passing the small chunks directly to the LLM for generation, the medium-sized chunks
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from whence the smaller chunks came are passed.
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This helps enable finer-grained search, but then passing of larger context (which can be useful during generation).
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## Environment Setup
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You should export two environment variables, one being your MongoDB URI, the other being your OpenAI API KEY.
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If you do not have a MongoDB URI, see the `Setup Mongo` section at the bottom for instructions on how to do so.
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```shell
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export MONGO_URI=...
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export OPENAI_API_KEY=...
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```
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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 mongo-parent-document-retrieval
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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 mongo-parent-document-retrieval
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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 mongo_parent_document_retrieval import chain as mongo_parent_document_retrieval_chain
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add_routes(app, mongo_parent_document_retrieval_chain, path="/mongo-parent-document-retrieval")
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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 DO NOT already have a Mongo Search Index you want to connect to, see `MongoDB Setup` section below before proceeding.
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Note that because Parent Document Retrieval uses a different indexing strategy, it's likely you will want to run this new setup.
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If you DO have a MongoDB Search index you want to connect to, edit the connection details in `mongo_parent_document_retrieval/chain.py`
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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/mongo-parent-document-retrieval/playground](http://127.0.0.1:8000/mongo-parent-document-retrieval/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/mongo-parent-document-retrieval")
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```
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For additional context, please refer to [this notebook](https://colab.research.google.com/drive/1cr2HBAHyBmwKUerJq2if0JaNhy-hIq7I#scrollTo=TZp7_CBfxTOB).
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## MongoDB Setup
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Use this step if you need to setup your MongoDB account and ingest data.
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We will first follow the standard MongoDB Atlas setup instructions [here](https://www.mongodb.com/docs/atlas/getting-started/).
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1. Create an account (if not already done)
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2. Create a new project (if not already done)
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3. Locate your MongoDB URI.
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This can be done by going to the deployement overview page and connecting to you database
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![connect.png](_images/connect.png)
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We then look at the drivers available
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![driver.png](_images/driver.png)
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Among which we will see our URI listed
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![uri.png](_images/uri.png)
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Let's then set that as an environment variable locally:
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```shell
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export MONGO_URI=...
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```
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4. Let's also set an environment variable for OpenAI (which we will use as an LLM)
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```shell
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export OPENAI_API_KEY=...
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```
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5. Let's now ingest some data! We can do that by moving into this directory and running the code in `ingest.py`, eg:
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```shell
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python ingest.py
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```
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Note that you can (and should!) change this to ingest data of your choice
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6. We now need to set up a vector index on our data.
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We can first connect to the cluster where our database lives
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![cluster.png](_images%2Fcluster.png)
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We can then navigate to where all our collections are listed
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![collections.png](_images%2Fcollections.png)
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We can then find the collection we want and look at the search indexes for that collection
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![search-indexes.png](_images%2Fsearch-indexes.png)
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That should likely be empty, and we want to create a new one:
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![create.png](_images%2Fcreate.png)
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We will use the JSON editor to create it
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![json_editor.png](_images%2Fjson_editor.png)
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And we will paste the following JSON in:
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```text
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{
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"mappings": {
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"dynamic": true,
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"fields": {
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"doc_level": [
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{
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"type": "token"
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}
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],
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"embedding": {
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"dimensions": 1536,
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"similarity": "cosine",
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"type": "knnVector"
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}
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}
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}
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}
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```
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![json.png](_images%2Fjson.png)
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From there, hit "Next" and then "Create Search Index". It will take a little bit but you should then have an index over your data!
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