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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
74 lines
2.3 KiB
Markdown
74 lines
2.3 KiB
Markdown
# Hybrid search - Weaviate
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This template shows you how to use the hybrid search feature in `Weaviate` vector store.
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Hybrid search combines multiple search algorithms to improve the accuracy and relevance of search results.
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`Weaviate` uses both sparse and dense vectors to represent the meaning and context of search queries and documents.
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The results use a combination of `bm25` and `vector search ranking` to return the top results.
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## Configurations
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Connect to your hosted Weaviate Vectorstore by setting a few env variables in `chain.py`:
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* `WEAVIATE_ENVIRONMENT`
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* `WEAVIATE_API_KEY`
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You will also need to set your `OPENAI_API_KEY` to use the OpenAI models.
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## Get Started
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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 hybrid-search-weaviate
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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 hybrid-search-weaviate
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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 hybrid_search_weaviate import chain as hybrid_search_weaviate_chain
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add_routes(app, hybrid_search_weaviate_chain, path="/hybrid-search-weaviate")
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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/hybrid-search-weaviate/playground](http://127.0.0.1:8000/hybrid-search-weaviate/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/hybrid-search-weaviate")
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```
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