480626dc99
…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ``` |
||
|---|---|---|
| .. | ||
| _images | ||
| propositional_retrieval | ||
| tests | ||
| .gitignore | ||
| LICENSE | ||
| poetry.lock | ||
| propositional_retrieval.ipynb | ||
| pyproject.toml | ||
| README.md | ||
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?. The prompt, which you can try out on the hub, 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.
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:
poetry install
poetry run python propositional_retrieval/ingest.py
Usage
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package propositional-retrieval
If you want to add this to an existing project, you can just run:
langchain app add propositional-retrieval
And add the following code to your server.py file:
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. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
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:
langchain serve
This will start the FastAPI app with a server is running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/propositional-retrieval/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/propositional-retrieval")