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This PR enables AI Agents to use perplexity AI in order to retrieve data from the web.
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# Perplexity Tool Integration for Agents | ||
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Use perplexity in your AI Agent to enrich it with data from the web. | ||
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Full code example: | ||
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```go | ||
package main | ||
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import ( | ||
"context" | ||
"fmt" | ||
"os" | ||
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"github.com/tmc/langchaingo/agents" | ||
"github.com/tmc/langchaingo/callbacks" | ||
"github.com/tmc/langchaingo/chains" | ||
"github.com/tmc/langchaingo/llms/openai" | ||
"github.com/tmc/langchaingo/tools" | ||
"github.com/tmc/langchaingo/tools/perplexity" | ||
) | ||
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func main() { | ||
if err := run(); err != nil { | ||
fmt.Fprintln(os.Stderr, err) | ||
os.Exit(1) | ||
} | ||
} | ||
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func run() error { | ||
llm, err := openai.New( | ||
openai.WithModel("gpt-4o-mini"), | ||
openai.WithCallback(callbacks.LogHandler{}), | ||
) | ||
if err != nil { | ||
return err | ||
} | ||
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perpl, err := perplexity.NewPerplexity(perplexity.ModelLlamaSonarSmall) | ||
if err != nil { | ||
return err | ||
} | ||
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agentTools := []tools.Tool{ | ||
perpl, | ||
} | ||
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agent := agents.NewOneShotAgent(llm, | ||
agentTools, | ||
agents.WithMaxIterations(2), | ||
) | ||
executor := agents.NewExecutor(agent) | ||
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question := "what's the latest and best LLM on the market at the moment?" | ||
answer, err := chains.Run(context.Background(), executor, question) | ||
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fmt.Println(answer) | ||
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return err | ||
} | ||
``` |
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package perplexity | ||
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import ( | ||
"context" | ||
"fmt" | ||
"os" | ||
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"github.com/tmc/langchaingo/llms" | ||
"github.com/tmc/langchaingo/llms/openai" | ||
) | ||
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type Model string | ||
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// Model pricing overview: https://docs.perplexity.ai/guides/pricing | ||
const ( | ||
ModelLlamaSonarSmall Model = "llama-3.1-sonar-small-128k-online" | ||
ModelLlamaSonarLarge Model = "llama-3.1-sonar-large-128k-online" | ||
ModelLlamaSonarHuge Model = "llama-3.1-sonar-huge-128k-online" | ||
) | ||
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type Perplexity struct { | ||
llm *openai.LLM | ||
} | ||
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func NewPerplexity(model Model) (*Perplexity, error) { | ||
perplexity := &Perplexity{} | ||
var err error | ||
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apiKey := os.Getenv("PERPLEXITY_API_KEY") | ||
if apiKey == "" { | ||
return nil, fmt.Errorf("PERPLEXITY_API_KEY not set") | ||
} | ||
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perplexity.llm, err = openai.New( | ||
openai.WithModel(string(model)), | ||
openai.WithBaseURL("https://api.perplexity.ai"), | ||
openai.WithToken(apiKey), | ||
) | ||
if err != nil { | ||
return nil, err | ||
} | ||
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return perplexity, nil | ||
} | ||
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func (p *Perplexity) Name() string { | ||
return "PerplexityAI" | ||
} | ||
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func (p *Perplexity) Description() string { | ||
return "Perplexity AI has access to a wide range of information, as it functions as an AI-powered search engine that indexes, analyzes, and summarizes content from across the internet." | ||
} | ||
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func (p *Perplexity) Call(ctx context.Context, input string) (string, error) { | ||
content := []llms.MessageContent{ | ||
llms.TextParts(llms.ChatMessageTypeHuman, input), | ||
} | ||
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var generatedText string | ||
_, err := p.llm.GenerateContent(ctx, content, | ||
llms.WithStreamingFunc(func(_ context.Context, chunk []byte) error { | ||
generatedText += string(chunk) | ||
return nil | ||
})) | ||
if err != nil { | ||
return "", err | ||
} | ||
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return generatedText, nil | ||
} |
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package perplexity | ||
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import ( | ||
"context" | ||
"os" | ||
"strings" | ||
"testing" | ||
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"github.com/tmc/langchaingo/agents" | ||
"github.com/tmc/langchaingo/chains" | ||
"github.com/tmc/langchaingo/llms/openai" | ||
"github.com/tmc/langchaingo/tools" | ||
) | ||
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func TestRun(t *testing.T) { | ||
t.Parallel() | ||
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if os.Getenv("PERPLEXITY_API_KEY") == "" { | ||
t.Skip("PERPLEXITY_API_KEY not set") | ||
} | ||
if os.Getenv("OPENAI_API_KEY") == "" { | ||
t.Skip("OPENAI_API_KEY not set") | ||
} | ||
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llm, err := openai.New() | ||
if err != nil { | ||
t.Fatalf("failed to create LLM: %v", err) | ||
} | ||
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perpl, err := NewPerplexity(ModelLlamaSonarSmall) | ||
if err != nil { | ||
t.Fatalf("failed to create Perplexity tool: %v", err) | ||
} | ||
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agentTools := []tools.Tool{ | ||
perpl, | ||
} | ||
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agent := agents.NewOneShotAgent(llm, | ||
agentTools, | ||
agents.WithMaxIterations(1), | ||
) | ||
executor := agents.NewExecutor(agent) | ||
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question := "what is the largest country in the world by total area?" | ||
answer, err := chains.Run(context.Background(), executor, question) | ||
if err != nil { | ||
t.Fatalf("failed to run chains: %v", err) | ||
} | ||
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const expectedAnswer = "Russia" | ||
if !strings.Contains(answer, expectedAnswer) { | ||
t.Errorf("expected answer to contain %q, got %q", expectedAnswer, answer) | ||
} | ||
} |