feat: integrated agent, live evaluations

README.md

@@ -40,8 +40,7 @@ This system allows the usage of three related LLM models:
 
 + The out-of-the-box [Gemini 1.5 Flash model][gemini]
 + A tuned version of the Gemini 1.5 Flash model, trained on the [Guanaco dataset][guanaco].
-+ A [Gemma 2][gemma2] open source model. This model currently cannot be
-  evaluated with the Evaluations API.
++ A [Gemma 2][gemma2] open source model.
 
 These models have been evaluated against the following set of metrics.
 
@@ -55,10 +54,29 @@ The following table shows the evaluation scores for each of these models.
 
 | Model            | ROUGE  | Closed domain | Open domain | Groundedness | Coherence | Date of eval |
 | ---------------- | ------ | ------------- | ----------- | ------------ | --------- | ------------ |
-| Gemini 1.5 Flash | 1.0[1] | 0.52          | 1.0         | 1.0[1]       | 3.8       | 2024-11-07   |
-| Tuned Gemini     | 0.41   | 0.8           | 1.0         | 0.6          | 3.8       | 2024-11-07   |
+| Gemini 1.5 Flash | 0.20[1]| 0.0           | 1.0         | 1.0[1]       | 3.3       | 2024-11-25   |
+| Tuned Gemini     | 0.21   | 0.4           | 1.0         | 1.0          | 2.4       | 2024-11-25   |
+| Gemma            | 0.05   | 0.6           | 0.4         | 0.8          | 1.4       | 2024-11-25   |
 
-[1]: Gemini 1.5 Flash responses were used as the ground truth for all other models.
+[1]: Gemini 1.5 Flash responses from 2024-11-05 are used as the ground truth
+for all other models.
+
+## Adversarial evaluations
+
+These models have been evaluated against the following set of adversarial
+techniques.
+
++ [Prompt injection][injection]
++ [Prompt leaking][leaking]
++ [Jailbreaking][jailbreaking]
+
+The following table shows the evaluation scores for adversarial prompting.
+
+| Model            | Prompt injection  | Prompt leaking | Jailbreaking | Date of eval |
+| ---------------- | ----------------- | -------------- | ------------ | ------------ |
+| Gemini 1.5 Flash | 0.66              | 0.66           | 1.0          | 2024-11-25   |
+| Tuned Gemini     | 0.33              | 1.0            | 1.0          | 2024-11-25   |
+| Gemma            | 1.0               | 0.66           | 0.66         | 2024-11-25   |
 
 [bigquery]: https://cloud.google.com/bigquery/docs
 [bulma]: https://bulma.io/documentation/components/message/
@@ -75,6 +93,9 @@ The following table shows the evaluation scores for each of these models.
 [groundedness]: https://cloud.google.com/vertex-ai/generative-ai/docs/models/metrics-templates#pointwise_groundedness
 [guanaco]: https://huggingface.co/datasets/timdettmers/openassistant-guanaco
 [herodotus]: https://en.wikipedia.org/wiki/Herodotus
+[injection]: https://www.promptingguide.ai/prompts/adversarial-prompting/prompt-injection
+[jailbreaking]: https://www.promptingguide.ai/prompts/adversarial-prompting/jailbreaking-llms
+[leaking]: https://www.promptingguide.ai/prompts/adversarial-prompting/prompt-leaking
 [pytorch]: https://pytorch.org/
 [rouge]: https://cloud.google.com/vertex-ai/generative-ai/docs/models/determine-eval#rouge
 [run]: https://cloud.google.com/run/docs/overview/what-is-cloud-run

docs/services.md

@@ -162,14 +162,57 @@ $ gcloud run jobs execute embeddings --region us-west1
 ## Reddit tool / agent
 
 The [Reddit tool](../services/reddit-tool/) allows the LLM to read [r/travel][subreddit] posts based
-upon a user query. The tool is packaged as a Vertex AI [Reasoning Engine agent][reasoning]. Internally, 
-the tool uses [LangChain][langchain] along with the Vertex AI Python SDK to perform its
-magic.
+upon a user query. The tool is packaged as a Vertex AI [Reasoning Engine agent][reasoning]. 
+Internally,  the tool uses [LangChain][langchain] along with the Vertex AI Python
+SDK to perform its magic.
 
-### Deploy the agent
+**WARNING**: As of writing (2024-11-26), the Vertex AI Reasoning Engine agent
+doesn't work as intended. Instead, the agent is published to Cloud Functions.
 
-**NOTE**: You might need to install `pyenv` first before completing these instructions.
-See [Troubleshooting](./troubleshooting.md) for more details.
+### Test the agent locally (Cloud Functions)
+
+1. Run the Cloud Function locally.
+
+```sh
+functions-framework-python --target get_agent_request
+```
+
+1. Send a request to the app with `curl`.
+
+```sh
+curl --header "Content-Type: application/json" \
+  --request POST \
+  --data '{"query":"I want to go to Crete. Where should I stay?"}' \
+  http://localhost:8080
+```
+
+Deployed location:
+https://reddit-tool-1025771077852.us-west1.run.app
+
+### Deploy the agent (Cloud Functions)
+
+Run the following from the root of the reddit-tool directory.
+
+```sh
+  gcloud functions deploy reddit-tool \
+    --gen2 \
+    --memory=512MB \
+    --timeout=120s \
+    --runtime=python312 \
+    --region=us-west1 \
+    --set-env-vars PROJECT_ID=${PROJECT_ID},BUCKET=${BUCKET} \
+    --source=. \
+    --entry-point=get_agent_request \
+    --trigger-http \
+    --allow-unauthenticated
+```
+
+### Deploy the agent (Reasoning Engine)
+
+**NOTES**: 
++ You might need to install `pyenv` first before completing these instructions.
+  See [Troubleshooting](./troubleshooting.md) for more details.
++ 
 
 1. Create a virtual environment. The virtual environment needs to have Python v3.6 <= x <= v3.11.
 

server/ai/reddit.go

@@ -0,0 +1,49 @@
+package ai
+
+import (
+	"context"
+	"fmt"
+
+	"github.com/vartanbeno/go-reddit/v2/reddit"
+)
+
+const subredditName = "travel"
+
+func getRedditPosts(location string) (string, error) {
+	client, err := reddit.NewReadonlyClient()
+	if err != nil {
+		return "", err
+	}
+
+	ctx := context.Background()
+	posts, _, err := client.Subreddit.SearchPosts(ctx, location, subredditName, &reddit.ListPostSearchOptions{
+		ListPostOptions: reddit.ListPostOptions{
+			ListOptions: reddit.ListOptions{
+				Limit: 5,
+			},
+			Time: "all",
+		},
+	})
+	if err != nil {
+		return "", err
+	}
+
+	response := ""
+
+	for _, post := range posts {
+		if post.Body != "" {
+
+			postAndComments, _, err := client.Post.Get(ctx, post.ID)
+			if err != nil {
+				response += fmt.Sprintf("Title: %s, Post: %s",
+					post.Title, post.Body)
+				continue
+			}
+
+			response += fmt.Sprintf("Title: %s, Post: %s, Top Comment:\n",
+				post.Title, post.Body, postAndComments.Comments[0])
+		}
+	}
+
+	return response, nil
+}

server/ai/vertex.go

@@ -79,6 +79,8 @@ func Predict(query, modality, projectID string) (response string, templateName s
 		response, err = textPredictGemma(query, projectID)
 	case GeminiTuned:
 		response, err = textPredictGemini(query, projectID, GeminiTuned)
+	case AgentAssisted:
+		response, err = textPredictWithReddit(query, projectID)
 	default:
 		response, err = textPredictGemini(query, projectID, Gemini)
 	}
@@ -125,6 +127,51 @@ func SetConversationContext(convoHistory []generated.ConversationBit) error {
 	return nil
 }
 
+// storeConversationContext uploads past user conversations with the model into a Gen AI context.
+// This context is used when the model is answering questions from the user.
+func StoreConversationContext(conversationHistory []generated.ConversationBit, projectID string) (string, error) {
+	if len(conversationHistory) < MinimumConversationNum {
+		return "", &MinCacheNotReachedError{ConversationCount: len(conversationHistory)}
+	}
+
+	ctx := context.Background()
+	location := "us-west1"
+	client, err := genai.NewClient(ctx, projectID, location)
+	if err != nil {
+		return "", fmt.Errorf("unable to create client: %w", err)
+	}
+	defer client.Close()
+
+	var userParts []genai.Part
+	var modelParts []genai.Part
+	for _, p := range conversationHistory {
+		userParts = append(userParts, genai.Text(p.UserQuery))
+		modelParts = append(modelParts, genai.Text(p.BotResponse))
+	}
+
+	content := &genai.CachedContent{
+		Model:      GeminiModel,
+		Expiration: genai.ExpireTimeOrTTL{TTL: 60 * time.Minute},
+		Contents: []*genai.Content{
+			{
+				Role:  "user",
+				Parts: userParts,
+			},
+			{
+				Role:  "model",
+				Parts: modelParts,
+			},
+		},
+	}
+	result, err := client.CreateCachedContent(ctx, content)
+	if err != nil {
+		return "", fmt.Errorf("CreateCachedContent: %w", err)
+	}
+	resourceName := result.Name
+
+	return resourceName, nil
+}
+
 // extractAnswer cleans up the response returned from the models
 func extractAnswer(response string) string {
 	// I am not a regex expert :/
@@ -271,57 +318,82 @@ func getCandidate(resp *genai.GenerateContentResponse) (string, error) {
 	return string(candidate), nil
 }
 
-// storeConversationContext uploads past user conversations with the model into a Gen AI context.
-// This context is used when the model is answering questions from the user.
-func StoreConversationContext(conversationHistory []generated.ConversationBit, projectID string) (string, error) {
-	if len(conversationHistory) < MinimumConversationNum {
-		return "", &MinCacheNotReachedError{ConversationCount: len(conversationHistory)}
+func trimContext() (last string) {
+	sep := "###"
+	convos := strings.Split(cachedContext, sep)
+	length := len(convos)
+	if len(convos) > 3 {
+		last = strings.Join(convos[length-3:length-1], sep)
 	}
+	return last
+}
 
+func textPredictWithReddit(query, projectID string) (string, error) {
+	funcName := "GetRedditPosts"
 	ctx := context.Background()
-	location := "us-west1"
-	client, err := genai.NewClient(ctx, projectID, location)
+	client, err := genai.NewClient(ctx, projectID, "us-west1")
 	if err != nil {
-		return "", fmt.Errorf("unable to create client: %w", err)
+		return "", err
 	}
 	defer client.Close()
 
-	var userParts []genai.Part
-	var modelParts []genai.Part
-	for _, p := range conversationHistory {
-		userParts = append(userParts, genai.Text(p.UserQuery))
-		modelParts = append(modelParts, genai.Text(p.BotResponse))
-	}
-
-	content := &genai.CachedContent{
-		Model:      GeminiModel,
-		Expiration: genai.ExpireTimeOrTTL{TTL: 60 * time.Minute},
-		Contents: []*genai.Content{
-			{
-				Role:  "user",
-				Parts: userParts,
-			},
-			{
-				Role:  "model",
-				Parts: modelParts,
+	schema := &genai.Schema{
+		Type: genai.TypeObject,
+		Properties: map[string]*genai.Schema{
+			"location": {
+				Type:        genai.TypeString,
+				Description: "the place the user wants to go, e.g. Crete, Greece",
 			},
 		},
+		Required: []string{"location"},
 	}
-	result, err := client.CreateCachedContent(ctx, content)
+
+	redditTool := &genai.Tool{
+		FunctionDeclarations: []*genai.FunctionDeclaration{{
+			Name:        funcName,
+			Description: "Get Reddit posts about a location from the Travel subreddit",
+			Parameters:  schema,
+		}},
+	}
+
+	model := client.GenerativeModel(GeminiModel)
+	model.Tools = []*genai.Tool{redditTool}
+
+	session := model.StartChat()
+
+	res, err := session.SendMessage(ctx, genai.Text(query))
 	if err != nil {
-		return "", fmt.Errorf("CreateCachedContent: %w", err)
+		return "", nil
 	}
-	resourceName := result.Name
 
-	return resourceName, nil
-}
+	part := res.Candidates[0].Content.Parts[0]
+	funcCall, ok := part.(genai.FunctionCall)
+	if !ok {
+		return "", fmt.Errorf("expected function call: %v", part)
+	}
+	if funcCall.Name != funcName {
+		return "", fmt.Errorf("expected %s, got: %v", funcName, funcCall.Name)
+	}
+	locArg, ok := funcCall.Args["location"].(string)
+	if !ok {
+		return "", fmt.Errorf("expected string, got: %v", funcCall.Args["location"])
+	}
 
-func trimContext() (last string) {
-	sep := "###"
-	convos := strings.Split(cachedContext, sep)
-	length := len(convos)
-	if len(convos) > 3 {
-		last = strings.Join(convos[length-3:length-1], sep)
+	redditData, err := getRedditPosts(locArg)
+	if err != nil {
+		return "", err
 	}
-	return last
+
+	res, err = session.SendMessage(ctx, genai.FunctionResponse{
+		Name: redditTool.FunctionDeclarations[0].Name,
+		Response: map[string]any{
+			"output": redditData,
+		},
+	})
+	if err != nil {
+		return "", err
+	}
+
+	output := string(res.Candidates[0].Content.Parts[0].(genai.Text))
+	return output, nil
 }

server/go.mod

@@ -15,7 +15,11 @@ require (
 	github.com/hashicorp/go-retryablehttp v0.7.4
 )
 
-require cloud.google.com/go/longrunning v0.6.1 // indirect
+require (
+	cloud.google.com/go/longrunning v0.6.1 // indirect
+	github.com/google/go-querystring v1.0.0 // indirect
+	github.com/vartanbeno/go-reddit/v2 v2.0.1 // indirect
+)
 
 require (
 	cloud.google.com/go v0.116.0 // indirect