Setup Guide

Installation

Install the LangChain CLI if you haven’t yet

pip install -U langchain-cli

Configuration

:warning: This app must be configured in order to work properly. There are a few steps required:

  1. Add API-keys (e.g. OPENAI-API-KEY) to the config file

  2. Add connectors: So far, it is possible to connect to a pygeoapi instance and then index collections of the instance. To add a pygeoapi instance, An entry is necessary in the config file like: "pygeoapi_instances":  ["https://api.weather.gc.ca/",  ...]

  3. Index collections of pygeoapi instance: To enable contextual search, you need to first index the collections from the specified pygeoapi instance. This is done using a local vector store called (ChromaDB). Once the collections are indexed, the retrieval module performs a semantic search on the indexed metadata. To start the indexing process, the app provides an endpoint: GET /fetch_documents. By calling this endpoint, the app synchronizes with the pygeoapi instances specified in the config file. A record manager is used to prevent duplicate entries in the index and to remove indexed documents that are no longer available from the pygeoapi server.

Launch Application

# Go to the directory
cd search-app/server

# launch application
langchain serve --port=8000

API Endpoints

  • Once the server is launched, a Swagger documentation is generated on <server-ip>:8000/docs.

  • :warning: Due to the Pydantic version used by Langserve, OpenAPI docs for invoke, batch, stream, stream_log endpoints will not be generated. API endpoints and playground should work as expected. If you need to see the docs, you can downgrade to Pydantic 1. For example, pip install pydantic==1.10.13. See this issue for details.

  • The endpoints not included in the Swagger docs are:

    • /data/invoke => Conversational module (search for data with chat)

      curl -X POST http://<server-ip>:8000/data/invoke -H "Content-Type: application/json" -d '{"input": "Your query here"}'

    • /retrieve_pygeoapi/invoke => Execute retrieval module without chat functionality

      curl -X POST http://<server-ip>:8000/retrieve_pygeoapi/invoke -H "Content-Type: application/json" -d '{"input": "Your query here"}'

Adding packages

# adding packages from 
# https://github.com/langchain-ai/langchain/tree/master/templates
langchain app add $PROJECT_NAME

# adding custom GitHub repo packages
langchain app add --repo $OWNER/$REPO
# or with whole git string (supports other git providers):
# langchain app add git+https://github.com/hwchase17/chain-of-verification

# with a custom api mount point (defaults to `/{package_name}`)
langchain app add $PROJECT_NAME --api_path=/my/custom/path/rag

Note: you remove packages by their api path

langchain app remove my/custom/path/rag

Running in Docker

This project folder includes a Dockerfile that allows you to easily build and host your LangServe app.

Building the Image

To build the image, you simply:

docker build . -t my-langserve-app

If you tag your image with something other than my-langserve-app, note it for use in the next step.

Running the Image Locally

To run the image, you’ll need to include any environment variables necessary for your application.

In the below example, we inject the OPENAI_API_KEY environment variable with the value set in my local environment ($OPENAI_API_KEY)

We also expose port 8080 with the -p 8080:8080 option.

docker run -e OPENAI_API_KEY=$OPENAI_API_KEY -p 8080:8080 my-langserve-app