examples: add update index example, update README

examples/query_data/README.md

@@ -4,11 +4,17 @@ This example makes use of [Llama-Index](https://gpt-index.readthedocs.io/en/stab
 
 It loosely follows [the quickstart](https://gpt-index.readthedocs.io/en/stable/guides/primer/usage_pattern.html).
 
+Summary of the steps:
+
+- prepare the dataset (and store it into `data`)
+- prepare a vector index database to run queries on
+- run queries
+
 ## Requirements
 
-For this in order to work, you will need a model compatible with the `llama.cpp` backend. This is will not work with gpt4all.
+For this in order to work, you will need LocalAI and a model compatible with the `llama.cpp` backend. This is will not work with gpt4all, however you can mix models (use a llama.cpp one to build the index database, and gpt4all to query it).
 
-The example uses `WizardLM`. Edit the config files in `models/` accordingly to specify the model you use (change `HERE`).
+The example uses `WizardLM` for both embeddings and Q&A. Edit the config files in `models/` accordingly to specify the model you use (change `HERE` in the configuration files).
 
 You will also need a training data set. Copy that over `data`.
 
@@ -28,7 +34,9 @@ cd LocalAI/examples/query_data
 docker-compose up -d --build
 ```
 
-### Create a storage:
+### Create a storage
+
+In this step we will create a local vector database from our document set, so later we can ask questions on it with the LLM.
 
 ```bash
 export OPENAI_API_BASE=http://localhost:8080/v1
@@ -41,9 +49,22 @@ After it finishes, a directory "storage" will be created with the vector index d
 
 ## Query
 
+We can now query the dataset. 
+
 ```bash
 export OPENAI_API_BASE=http://localhost:8080/v1
 export OPENAI_API_KEY=sk-
 
 python query.py
+```
+
+## Update
+
+To update our vector database, run `update.py`
+
+```bash
+export OPENAI_API_BASE=http://localhost:8080/v1
+export OPENAI_API_KEY=sk-
+
+python update.py
 ```