# propositional-retrieval This template demonstrates the multi-vector indexing strategy proposed by Chen, et. al.'s [Dense X Retrieval: What Retrieval Granularity Should We Use?](https://arxiv.org/abs/2312.06648). The prompt, which you can [try out on the hub](https://smith.langchain.com/hub/wfh/proposal-indexing), directs an LLM to generate de-contextualized "propositions" which can be vectorized to increase the retrieval accuracy. You can see the full definition in `proposal_chain.py`. ![Diagram illustrating the multi-vector indexing strategy for information retrieval, showing the process from Wikipedia data through a Proposition-izer to FactoidWiki, and the retrieval of information units for a QA model.](https://github.com/langchain-ai/langchain/raw/master/templates/propositional-retrieval/_images/retriever_diagram.png "Retriever Diagram") ## Storage For this demo, we index a simple academic paper using the RecursiveUrlLoader, and store all retriever information locally (using chroma and a bytestore stored on the local filesystem). You can modify the storage layer in `storage.py`. ## Environment Setup Set the `OPENAI_API_KEY` environment variable to access `gpt-3.5` and the OpenAI Embeddings classes. ## Indexing Create the index by running the following: ```python poetry install poetry run python propositional_retrieval/ingest.py ``` ## 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 propositional-retrieval ``` If you want to add this to an existing project, you can just run: ```shell langchain app add propositional-retrieval ``` And add the following code to your `server.py` file: ```python from propositional_retrieval import chain add_routes(app, chain, path="/propositional-retrieval") ``` (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= export LANGCHAIN_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/propositional-retrieval/playground](http://127.0.0.1:8000/propositional-retrieval/playground) We can access the template from code with: ```python from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/propositional-retrieval") ```