Updated example project to point to ai-examples repo

Author: rjrudin

examples/langchain/.gitignore

@@ -1,70 +1 @@
-# Example langchain retriever
-
-This project demonstrates one approach for implementing a 
-[langchain retriever](https://python.langchain.com/docs/modules/data_connection/)
-that allows for 
-[Retrieval Augmented Generation (RAG)](https://python.langchain.com/docs/use_cases/question_answering/)
-to be supported via MarkLogic and the MarkLogic Python Client. This example uses the same data as in 
-[the langchain RAG quickstart guide](https://python.langchain.com/docs/use_cases/question_answering/quickstart), 
-but with the data having first been loaded into MarkLogic.
-
-**This is only intended as an example** of how easily a langchain retriever can be developed
-using the MarkLogic Python Client. The queries in this example are simple and naturally 
-do not have any knowledge of how your data is modeled in MarkLogic. You are encouraged to use 
-this as an example for developing your own retriever, where you can build a query based on a 
-question submitted to langchain that fully leverages the indexes and data models in your MarkLogic
-application. Additionally, please see the 
-[langchain documentation on splitting text](https://python.langchain.com/docs/modules/data_connection/document_transformers/). You may need to restructure your data so that you have a larger number of 
-smaller documents in your database so that you do not exceed the limit that langchain imposes on how
-much data a retriever can return.
-
-# Setup
-
-To try out this project, use [docker-compose](https://docs.docker.com/compose/) to instantiate a new MarkLogic 
-instance with port 8003 available (you can use your own MarkLogic instance too, just be sure that port 8003
-is available):
-
-    docker-compose up -d --build
-
-Then deploy a small REST API application to MarkLogic, which includes a basic non-admin MarkLogic user 
-named `langchain-user`:
-
-    ./gradlew -i mlDeploy
-
-Next, create a new Python virtual environment - [pyenv](https://github.com/pyenv/pyenv) is recommended for this - 
-and install the 
-[langchain example dependencies](https://python.langchain.com/docs/use_cases/question_answering/quickstart#dependencies),
-along with the MarkLogic Python Client: 
-
-    pip install -U langchain langchain_openai langchain-community langchainhub openai chromadb bs4 marklogic_python_client
-
-Then run the following Python program to load text data from the langchain quickstart guide 
-into two different collections in the `langchain-test-content` database:
-
-    python load_data.py
-
-Create a ".env" file to hold your OpenAI API key:
-
-    echo "OPENAI_API_KEY=<your key here>" > .env
-
-# Testing the retriever
-
-You are now ready to test the example retriever. Run the following to ask a question with the 
-results augmented via the `marklogic_retriever.py` module in this project; you will be 
-prompted for an OpenAI API key when you run this, which you can type or paste in:
-
-    python ask.py "What is task decomposition?" posts
-
-The retriever uses a [cts.similarQuery](https://docs.marklogic.com/cts.similarQuery) to select from the documents 
-loaded via `load_data.py`. It defaults to a page length of 10. You can change this by providing a command line
-argument - e.g.:
-
-    python ask.py "What is task decomposition?" posts 15
-
-Example of a question for the "sotu" (State of the Union speech) collection:
-
-    python ask.py "What are economic sanctions?" sotu 20
-
-To use a word query instead of a similar query, along with a set of drop words, specify "word" as the 4th argument:
-
-    python ask.py "What are economic sanctions?" sotu 20 word
+This example project has been moved to the [MarkLogic AI examples repository](https://github.com/marklogic/marklogic-ai-examples).