mirror of
https://github.com/hwchase17/langchain
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87 lines
2.8 KiB
Markdown
87 lines
2.8 KiB
Markdown
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# elastic-query-generator
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This template allows interacting with Elasticsearch analytics databases in natural language using LLMs.
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It builds search queries via the Elasticsearch DSL API (filters and aggregations).
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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### Installing Elasticsearch
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There are a number of ways to run Elasticsearch. However, one recommended way is through Elastic Cloud.
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Create a free trial account on [Elastic Cloud](https://cloud.elastic.co/registration?utm_source=langchain&utm_content=langserve).
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With a deployment, update the connection string.
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Password and connection (elasticsearch url) can be found on the deployment console.
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Note that the Elasticsearch client must have permissions for index listing, mapping description, and search queries.
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### Populating with data
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If you want to populate the DB with some example info, you can run `python ingest.py`.
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This will create a `customers` index. In this package, we specify indexes to generate queries against, and we specify `["customers"]`. This is specific to setting up your Elastic index.
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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 elastic-query-generator
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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 elastic-query-generator
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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 elastic_query_generator.chain import chain as elastic_query_generator_chain
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add_routes(app, elastic_query_generator_chain, path="/elastic-query-generator")
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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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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/elastic-query-generator/playground](http://127.0.0.1:8000/elastic-query-generator/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/elastic-query-generator")
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```
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