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hypermodeinc · Nov 21, 2024 · ea69aa7 · ea69aa7
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diff --git a/README.md b/README.md
@@ -17,3 +17,4 @@ The following recipes have associated recorded content:
 | [modushack-data-models](modushack-data-models/) | [ModusHack: Working With Data & AI Models livestream](https://www.youtube.com/watch?v=gB-v7YWwkCw&list=PLzOEKEHv-5e3zgRGzDysyUm8KQklHQQgi&index=3) |
 | [modus-press](modus-press/)                     | Coming soon                                                                                                                                        |
 | [dgraph-101](dgraph-101/)                     | Coming soon                                                                                                                                        |
+| [function-calling](function-calling/)                     | Coming soon                                                                                                                                        |
diff --git a/function-calling/README.md b/function-calling/README.md
@@ -1,5 +1,17 @@
 # Function Calling With Modus
 
+LLM apis such as OpenAI, have a feature called **function calling** or **tool use**. With this feature the LLM response to a chat message could be a request to invoke a function, usually in order to collect information necessary to generate a response.
+
+This project demonstrates how to setup function calling within [Modus](https://docs.hypermode.com/modus), the open source framework for building intelligent APIs. 
+
+The example implements a function `askQuestionToWarehouse` accepting an query in natural language about prices or stock of goods in the warehouse.
+
+The API uses 2 tools available for the LLM
+- get_product_types: provide the list of product types we have in the warehouse
+- get_product_info: return an info (qty or price) about one product type 
+
+
+
 ## Get started
 
 1- Set your credentials
@@ -20,43 +32,47 @@ modus dev
 From a GraphQL client (Postman),
 Introspect the GraphQL endpoint `http://localhost:8686/graphql`
 Invoke the operation  `askQuestionToWarehouse`
-Experiment with some queries to see the function tools at work.
 
-The operation replies with an array of strings showing the steps used to produce a response along with the final response.
+```graphql
+# example query using tool calling
+
+query AskQuestion {
+    askQuestionToWarehouse(
+        question: "What is the most expensive product?") {
+            response
+            logs
+        }
+}
+
+```
+The operation returns the final response and an array of strings showing showing the tool calls and messages exchanged with the LLM API.
+
+Experiment with some queries to see the function calling at work.
+
+
 
 ```text
 # example of questions
 What can you do for me?
 what fo we have in the warehouse?
 How many shoes  in stock?
 How many shoes and hats do we have in stock?
-What is the most expensive product in stock?
 what is the price of a desks?
+What is the most expensive product in stock?
 
 ```
 
 
 
-## About
-
-LLM apis such as OpenAI, have a feature called **function calling** or **tool use**. With this feature the LLM response to a chat message could be a request to invoke a function, usually in order to collect information necessary to generate a response.
-
-This project demonstrates how to setup function calling within Modus when using openAI chat/completion API or any models supporting the same API.
-
-
-In our case, we are implementing a function `askQuestionToWarehouse` accepting an query in natural language about prices or stock of goods in the warehouse.
-
-We are declaring 2 tools available for the LLM
-- getProductList: provide the list of product types we have in the warehouse
-- 
+## Details
 
 The logic is as follow:
 
 - Instruct the LLM to use function calls (tools) with the correct parameters to get the data necessary to reply to the provided question.
 
-- Execute the identified function calls in Modus to build an additional context.
+- Execute the identified function calls in Modus to build an additional context (tool messages)
 
-- Re-invoke the LLM API  with the additional context.
+- Re-invoke the LLM API  with the additional tool messages.
 
 Return the generated responses based on the data retrieved by the function calls.