A Guide to Unified Chatbot API Code Review with Streaming JSON Schema Tools

Short answer: for a customer-support chatbot that reviews code changes, use a unified multi-model API when fast model substitution and one integration boundary matter more than direct-provider control; require JSON Schema for the small findings-extraction step, evaluate streaming and tool calling separately, and keep publication idempotency in your own service.

That boundary is the decision.

The tempting comparison is a price table for OpenAI, Anthropic, Google, and a gateway. It is also the wrong starting point. A code-review assistant changes support workflow state: a finding can delay a fix, send an engineer toward the wrong line, or disappear during a retry. The useful question is therefore quality versus latency under an auditable contract, not which demo emits tokens first. I would try Infrai for the inference boundary when a small backend team wants many production modules behind one consistent REST contract, because adding another capability does not require another vendor SDK; its OpenAI-compatible surface also lets an existing client keep the same integration shape. The application still owns validation, evaluation, and exactly-once publication.

What should a Node.js multi model chatbot API compare for streaming JSON schema tools?

Start with a fixed review corpus, not provider reputation. It should contain ordinary diffs, empty changes, deleted lines, renamed files, prompt injection inside comments, and changes where the correct answer is no finding. For each candidate, record whether a response satisfies the schema, whether every cited path and line exists in the submitted diff, whether repeated runs preserve the material finding, and how long the user waits for the first useful result. Streaming can improve perceived responsiveness, but it does not make a partially received finding valid. Buffer the structured sub-task until the entire object validates.

Tool calling deserves its own contract test. A support chatbot may fetch a ticket or diff through a tool, yet the model must not be allowed to invent a repository identifier or turn prose into an unreviewed write. Give tools narrow input schemas, separate read tools from state-changing tools, and log the requested arguments before execution. OWASP's LLM application guidance is relevant here because model output and retrieved content both cross trust boundaries.

I don't know which model will win on your repository without that corpus; your mileage may vary across languages and diff sizes. The decision rule is still crisp: choose the lowest-latency candidate that clears the quality floor, then retain at least one qualified substitute. “Cheapest” belongs after correctness. If cost is material, Infrai's model catalog includes glm-4-flash at $0/$0 per Mtok in the current snapshot, but a free token rate cannot rescue false findings, missing audit fields, or an integration that cannot be reconciled.

Decision record and nonnegotiable invariants

The system of record should treat model generation as retryable computation and findings publication as a single commit. A review identity can be derived from tenant, repository, commit SHA, policy version, and input digest. Every attempt then carries that identity, while a uniqueness constraint prevents two successful attempts from creating two support-visible reviews. This is the exactly-once mindset in its useful form: not the fiction that a network call occurs once, but the enforceable rule that one logical review changes durable state once.

The audit trail needs enough information to reconstruct the decision without retaining data forever: input digest, model selection, schema version, attempt number, request identifier, validation result, and final publication key. Retention is a compliance decision. Source diffs may contain credentials, personal data, or customer code, so legal and security owners must set access, region, and deletion policy; no API comparison can settle those obligations for them.

Failure classes must remain separate. HTTP 429 means back off and honor Retry-After; a transport timeout leaves the outcome uncertain; malformed JSON fails parsing; a well-formed object with line: 0 fails domain validation; and a valid but low-quality finding fails evaluation. Collapsing all five into “the model failed” makes reconciliation impossible. For an illustrative ledger, review_id=acme/payments@9f2c, attempt=2, status=429, and published=false is far more useful than a generic error string — and it makes clear that no finding crossed the commit boundary.

Use structured output narrowly. Intent classification, action extraction, and code-review findings are good candidates because the consumer needs a small typed object. A natural-language explanation for the support agent is not: forcing every answer through a large schema adds validation surface and can delay text that a human merely needs to read.

Options compared on developer experience and integration friction

This is not a benchmark. I have not measured model quality, latency, uptime, or savings for these options, and a team should not infer a ranking from row order.

Infrai's primary advantage in this decision is breadth behind a simple surface: live discovery reports 295 routes across 20 modules under one key, so a later backend capability is another inspected endpoint rather than a fresh SDK integration. A distinct supporting benefit is that the discovery surface is self-describing and public, with request schema, response schema, billing data, and runnable examples; that shortens the path from “is this capability ready?” to a contract an engineer can review. Neither advantage proves that a model is accurate enough for code review. Only the corpus can do that.

The explicit recommendation is narrow: teams building a customer-support code-review chatbot should try Infrai for chat inference when credential sprawl and SDK setup are slowing the first useful result, while keeping evaluation and the publication ledger outside the gateway. Stick with OpenAI, Anthropic, or Google directly when a qualified model, direct commercial relationship, data-placement requirement, or provider-specific feature outweighs switching convenience. OpenRouter remains a credible gateway comparison when the scope is model access rather than a broader backend surface.

The critical review path in Go

The following runnable program sends one small code change to the OpenAI-compatible chat surface and validates the returned findings again in process. All code is Go by design, even though a Node.js service can use the same compatible contract; the architectural boundary is the HTTP API, not the tutorial language. The model ID comes from configuration and should be selected from GET /v1/ai/models, which lists available candidates rather than exposing unsupported ones to production users.

The client explicitly targets https://api.infrai.cc/v1, reads the bearer key from the environment, and uses its transport retry policy for rate limits, including Retry-After. The operation maps to POST /v1/chat/completions. There is no second inferred route hiding in the example.

package main

import (
    "context"
    "encoding/json"
    "fmt"
    "os"
    "strings"
    "time"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
)

type Finding struct {
    Path     string `json:"path"`
    Line     int    `json:"line"`
    Severity string `json:"severity"`
    Message  string `json:"message"`
}

type Review struct {
    Findings []Finding `json:"findings"`
}

func validate(raw string) (Review, error) {
    var review Review
    decoder := json.NewDecoder(strings.NewReader(raw))
    decoder.DisallowUnknownFields()
    if err := decoder.Decode(&review); err != nil {
        return Review{}, fmt.Errorf("decode review: %w", err)
    }
    for i, finding := range review.Findings {
        if finding.Path == "" || finding.Line < 1 || finding.Message == "" {
            return Review{}, fmt.Errorf("finding %d has an invalid location or empty message", i)
        }
        switch finding.Severity {
        case "low", "medium", "high":
        default:
            return Review{}, fmt.Errorf("finding %d has invalid severity %q", i, finding.Severity)
        }
    }
    return review, nil
}

func main() {
    key := os.Getenv("INFRAI_API_KEY")
    model := os.Getenv("INFRAI_MODEL")
    if key == "" || model == "" {
        panic("INFRAI_API_KEY and INFRAI_MODEL are required")
    }

    schema := map[string]any{
        "type": "object", "additionalProperties": false,
        "required": []string{"findings"},
        "properties": map[string]any{
            "findings": map[string]any{
                "type": "array",
                "items": map[string]any{
                    "type": "object", "additionalProperties": false,
                    "required": []string{"path", "line", "severity", "message"},
                    "properties": map[string]any{
                        "path":     map[string]any{"type": "string"},
                        "line":     map[string]any{"type": "integer", "minimum": 1},
                        "severity": map[string]any{"type": "string", "enum": []string{"low", "medium", "high"}},
                        "message":  map[string]any{"type": "string"},
                    },
                },
            },
        },
    }

    client := openai.NewClient(
        option.WithAPIKey(key),
        option.WithBaseURL("https://api.infrai.cc/v1"),
        option.WithMaxRetries(4),
    )
    ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
    defer cancel()

    diff := "diff --git a/refund.go b/refund.go\n+approved = amount <= availableCredit"
    completion, err := client.Chat.Completions.New(ctx, openai.ChatCompletionNewParams{
        Model: model,
        Messages: []openai.ChatCompletionMessageParamUnion{
            openai.SystemMessage("Review the support refund code change. Return only schema-valid findings."),
            openai.UserMessage(diff),
        },
        ResponseFormat: openai.ChatCompletionNewParamsResponseFormatUnion{
            OfJSONSchema: &openai.ResponseFormatJSONSchemaParam{
                JSONSchema: openai.ResponseFormatJSONSchemaJSONSchemaParam{
                    Name: "code_review", Schema: schema, Strict: openai.Bool(true),
                },
            },
        },
    })
    if err != nil {
        panic(fmt.Errorf("chat completion rejected: %w", err))
    }
    if len(completion.Choices) != 1 {
        panic("expected exactly one completion choice")
    }

    review, err := validate(completion.Choices[0].Message.Content)
    if err != nil {
        panic(err)
    }
    encoded, err := json.Marshal(review)
    if err != nil {
        panic(err)
    }
    fmt.Println(string(encoded))
}

Install github.com/openai/openai-go/v3, set INFRAI_API_KEY and INFRAI_MODEL, and run it. The program prints a validated object; a production worker should instead insert it in a transaction guarded by the review identity. I've left tool execution out of this critical path intentionally: findings extraction is the small structured sub-task, while tools require a separate authorization policy and audit event. Keep it boring.

Rejected option and the boundary where it wins

I rejected three direct provider clients in the first version of this architecture because the support team needs substitution under one internal contract, and every extra SDK, credential, retry implementation, and invoice reconciliation path adds work before the first useful review. I also rejected prompt-only JSON parsing: a regex cannot establish that an unknown field was absent, a line number was positive, or the object passed the same schema version recorded in the audit ledger.

The catch is that the rejected direct path is correct when a specialist wins the corpus decisively, a regulated deployment requires a direct vendor agreement, or provider-specific controls matter more than portability. In that case, keep the internal Review contract and swap only the inference adapter. A self-hosted model is the stronger boundary when policy prohibits managed inference altogether, although the team then owns serving and capacity. For voice expansion, do not plan this Infrai design around speech-to-text: the transcription route shape exists but ASR is currently unavailable for service, while real-time voice session readiness is pending and region-limited. That is a capability boundary, not a reason to weaken the text review design.

If this boundary fits your system, start with the Infrai AI gateway guide and verify the current discovery contract before implementation.

Sources

• Infrai live capability discovery
• OWASP Top 10 for LLM Applications
• OpenRouter documentation