mirror of
https://github.com/hwchase17/langchain
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a1fae1fddd
Co-authored-by: Lance Martin <lance@langchain.dev> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
89 lines
3.2 KiB
Markdown
89 lines
3.2 KiB
Markdown
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# cassandra-entomology-rag
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This template will perform RAG using Astra DB and Apache Cassandra®.
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## Environment Setup
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For the setup, you will require:
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- an [Astra](https://astra.datastax.com) Vector Database. You must have a [Database Administrator token](https://awesome-astra.github.io/docs/pages/astra/create-token/#c-procedure), specifically the string starting with `AstraCS:...`.
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- [Database ID](https://awesome-astra.github.io/docs/pages/astra/faq/#where-should-i-find-a-database-identifier).
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- an **OpenAI API Key**. (More info [here](https://cassio.org/start_here/#llm-access))
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You may also use a regular Cassandra cluster. In this case, provide the `USE_CASSANDRA_CLUSTER` entry as shown in `.env.template` and the subsequent environment variables to specify how to connect to it.
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The connection parameters and secrets must be provided through environment variables. Refer to `.env.template` for the required variables.
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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[serve]"
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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 cassandra-entomology-rag
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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 cassandra-entomology-rag
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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 cassandra_entomology_rag import chain as cassandra_entomology_rag_chain
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add_routes(app, cassandra_entomology_rag_chain, path="/cassandra-entomology-rag")
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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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LangSmith is currently in private beta, you can sign up [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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To populate the vector store, ensure that you have set all the environment variables, then from this directory, execute the following just once:
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```shell
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poetry run bash -c "cd [...]/cassandra_entomology_rag; python setup.py"
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```
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The output will be something like `Done (29 lines inserted).`.
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Note: In a full application, the vector store might be populated in other ways. This step is to pre-populate the vector store with some rows for the demo RAG chains to work sensibly.
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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/cassandra-entomology-rag/playground](http://127.0.0.1:8000/cassandra-entomology-rag/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/cassandra-entomology-rag")
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```
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## Reference
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Stand-alone repo with LangServe chain: [here](https://github.com/hemidactylus/langserve_cassandra_entomology_rag).
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