mirror of
https://github.com/hwchase17/langchain
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82 lines
2.9 KiB
Markdown
82 lines
2.9 KiB
Markdown
# propositional-retrieval
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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`.
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## Storage
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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`.
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access `gpt-3.5` and the OpenAI Embeddings classes.
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## Indexing
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Create the index by running the following:
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```python
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poetry install
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poetry run python propositional_retrieval/ingest.py
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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 propositional-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 propositional-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 propositional_retrieval import chain
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add_routes(app, chain, path="/propositional-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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You can sign up for LangSmith [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/propositional-retrieval/playground](http://127.0.0.1:8000/propositional-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/propositional-retrieval")
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```
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