mirror of
https://github.com/hwchase17/langchain
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80 lines
2.7 KiB
Markdown
80 lines
2.7 KiB
Markdown
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# rag-chroma-private
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This template performs RAG with no reliance on external APIs.
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It utilizes Ollama the LLM, GPT4All for embeddings, and Chroma for the vectorstore.
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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.
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## Environment Setup
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To set up the environment, you need to download Ollama.
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Follow the instructions [here](https://python.langchain.com/docs/integrations/chat/ollama).
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You can choose the desired LLM with Ollama.
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This template uses `llama2:7b-chat`, which can be accessed using `ollama pull llama2:7b-chat`.
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There are many other options available [here](https://ollama.ai/library).
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This package also uses [GPT4All](https://python.langchain.com/docs/integrations/text_embedding/gpt4all) 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 rag-chroma-private
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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 rag-chroma-private
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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 rag_chroma_private import chain as rag_chroma_private_chain
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add_routes(app, rag_chroma_private_chain, path="/rag-chroma-private")
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```
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(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 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/rag-chroma-private/playground](http://127.0.0.1:8000/rag-chroma-private/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/rag-chroma-private")
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```
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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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