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…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 ``` |
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|---|---|---|
| .. | ||
| elastic_query_generator | ||
| tests | ||
| ingest.py | ||
| main.py | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
elastic-query-generator
This template allows interacting with Elasticsearch analytics databases in natural language using LLMs.
It builds search queries via the Elasticsearch DSL API (filters and aggregations).
Environment Setup
Set the OPENAI_API_KEY environment variable to access the OpenAI models.
Installing Elasticsearch
There are a number of ways to run Elasticsearch. However, one recommended way is through Elastic Cloud.
Create a free trial account on Elastic Cloud.
With a deployment, update the connection string.
Password and connection (elasticsearch url) can be found on the deployment console.
Note that the Elasticsearch client must have permissions for index listing, mapping description, and search queries.
Populating with data
If you want to populate the DB with some example info, you can run python ingest.py.
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.
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 elastic-query-generator
If you want to add this to an existing project, you can just run:
langchain app add elastic-query-generator
And add the following code to your server.py file:
from elastic_query_generator.chain import chain as elastic_query_generator_chain
add_routes(app, elastic_query_generator_chain, path="/elastic-query-generator")
(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/elastic-query-generator/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/elastic-query-generator")