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# rag-chroma-private
This template performs RAG with no reliance on external APIs.
It utilizes Ollama the LLM, GPT4All for embeddings, and Chroma for the vectorstore.
The vectorstore is created in `chain.py` and by default indexes a [popular blog posts on Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) for question-answering.
## Environment Setup
To set up the environment, you need to download Ollama.
Follow the instructions [here](https://python.langchain.com/docs/integrations/chat/ollama).
You can choose the desired LLM with Ollama.
This template uses `llama2:7b-chat`, which can be accessed using `ollama pull llama2:7b-chat`.
There are many other options available [here](https://ollama.ai/library).
This package also uses [GPT4All](https://python.langchain.com/docs/integrations/text_embedding/gpt4all) embeddings.
## Usage
To use this package, you should first have the LangChain CLI installed:
```shell
pip install -U langchain-cli
```
To create a new LangChain project and install this as the only package, you can do:
```shell
langchain app new my-app --package rag-chroma-private
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add rag-chroma-private
```
And add the following code to your `server.py` file:
```python
from rag_chroma_private import chain as rag_chroma_private_chain
add_routes(app, rag_chroma_private_chain, path="/rag-chroma-private")
```
(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/rag-chroma-private/playground](http://127.0.0.1:8000/rag-chroma-private/playground)
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-chroma-private")
```
The package will create and add documents to the vector database in `chain.py`. By default, it will load a popular blog post on agents. However, you can choose from a large number of document loaders [here](https://python.langchain.com/docs/integrations/document_loaders).
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