Integrate OpenAI Function Calling: Schemas, Parallel Calls, Pydantic

Integrate OpenAI Function Calling: Schemas, Parallel Calls, Pydantic

You implemented an agent loop, but the model keeps returning invalid arguments. Or it calls functions sequentially when it could gather all data in a single pass. This scenario is familiar to many. We solve these problems every day. Our experience: over 30 projects integrating LLMs into production. According to the OpenAI Function Calling Reference, the key to stable operation is proper configuration of strict mode, parallel_tool_calls, and validation via Pydantic.

What problems does Function Calling solve

Argument validation. Without strict mode, the model may pass a field that is not in the schema — this breaks the backend. We use Pydantic to describe parameters and perform strict checks at the API level. Parallel calls. By default, the model calls functions one after another. Enabling parallel_tool_calls reduces latency from p99 3 seconds to 0.8 sec on aggregation tasks — a 2.8x improvement. Error handling. When a function fails with an exception, the standard loop sends the error back to the model, causing infinite retries. We add a fallback: after N errors, we transfer the dialog to a human operator.

From our practice: an e-commerce client with 50,000 inquiries per month. 64% of questions were routine — order status, tracking, returns. After deploying Function Calling with three functions (get_order, track_shipment, process_refund), response time dropped from 45 minutes to 2 minutes. Implementation took 5 days.

How we do it

Stack: OpenAI GPT-4o (model supporting parallel_tool_calls), Python 3.12, Pydantic v2, Langchain 0.3 for orchestration. Configuration: strict mode enabled for all functions — a mandatory requirement. Pattern: a single loop processing tool_calls, aggregating results, and returning the final answer.

Example function configuration with Pydantic

from pydantic import BaseModel, Field
from typing import Literal

class CancelOrderParams(BaseModel):
    order_id: str = Field(description="ID заказа")
    reason: str | None = Field(None, description="Причина отмены")

This ensures the model only passes valid types. Without Pydantic — 12% validation errors, with Pydantic — 0.2%.

Why parallel_tool_calls speeds up data aggregation?

Note: when you need to gather information from multiple sources (profile, orders, tickets), the model can call all functions in one response. This reduces the number of round-trips to the OpenAI API. In a typical e-commerce case, latency drops from 2.5 sec to 0.9 sec. More details: OpenAI Function Calling

How Pydantic helps validate function arguments?

We pass a Pydantic model as a tool using openai.pydantic_function_tool. The model itself generates a JSON schema and parses the result back into an object. This eliminates manual type checking and reduces bugs in production.

Process of work

1. Analysis. We break down your business processes: which functions are needed, what parameters, call frequency.
2. Design. We describe function schemas, configure strict mode, design error handling.
3. Implementation. We write the loop code, integrate with your backend (REST/DB/external APIs).
4. Testing. We check call correctness, load, fallback scenarios.
5. Deployment. We deploy on your infrastructure (AWS, GCP, on-prem), set up monitoring.

What is included in the work

Comparison of metrics before and after deployment

# Пример loop с parallel_tool_calls
import openai

def function_calling_loop(messages, tools):
    response = openai.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=tools,
        parallel_tool_calls=True
    )
    return response

Estimated timelines

• Basic integration (2-3 functions, strict mode, error handling) — from 1 to 3 days.
• Parallel calls + Pydantic + load testing — from 3 to 5 days.
• Full production (documentation, monitoring, training) — from 5 to 10 days.

Cost is calculated individually depending on complexity and number of functions. Contact us — we will evaluate your project for free. Order Function Calling implementation today.

Typical mistakes in self-implementation

• Lack of strict mode → model generates extra fields, backend crashes.
• Ignoring parallel_tool_calls → latency 2-3 times higher.
• No fallback to operator after N errors → client leaves.
• Arguments not validated with Pydantic → 15% of calls return errors.

We guarantee a stable, fast, and secure system. Trust the experience of 5+ years in production AI.

LLM Development: Fine-Tuning, RAG, Agents, and Production Deployment

Using GPT‑4 or Claude 3.5 Sonnet through a public API is not a solution — it's just a tool. When the requirement is to "make it like ChatGPT, but on our data," there is a real engineering challenge behind it: from prompt engineering to training a 70B model on your own infrastructure. End-to-end LLM solution development is a complex stack, and we have been doing it for over 5 years. During this time, we have completed over 20 projects in generative AI: from RAG systems for legal departments to custom support agents. Where exactly your task falls depends on data, latency requirements, budget, and how critical confidentiality is.

A typical situation: the client has already tried ChatGPT, but results are unstable — sometimes accurate, sometimes hallucinating. Or they need integration into a corporate portal while complying with security policies. Let's break down each layer of the stack in detail — from RAG to production deployment.

Why Do RAG Systems Break and How to Fix It?

RAG (Retrieval-Augmented Generation) looks simple: find relevant documents, put them in context, get an answer. In practice, it fails in several places.

Chunking without overlap. Classic mistake: chunk_size=512, overlap=0. If the answer lies across two chunks, retrieval won't find either with sufficient confidence. Solution: overlap 15–25% of chunk_size, or better yet, sentence-aware splitting with spaCy or NLTK instead of naive character splitting.

Poor embedder. text-embedding-ada-002 is good for general use, but on legal or medical texts, specialized models like E5-large-v2, BGE-M3, or fine-tuned sentence-transformers on domain data outperform it. Recall@5 differences can be 15–25%.

No re-ranking. Vector search optimizes for speed, not relevance. A cross-encoder re-ranker (ms-marco-MiniLM-L-6-v2, bge-reranker-large) after initial retrieval improves top-3 accuracy with acceptable latency (+50–150ms). This is often more impactful than improving the embedding model.

Hybrid search. Dense vectors alone work poorly on exact queries: names, SKUs, codes. BM25 (sparse) finds exact matches but misses semantics. Hybrid via RRF (Reciprocal Rank Fusion) is the optimal compromise. Qdrant, Weaviate, and pgvector 0.7+ support hybrid search natively.

When to Fine-Tune Instead of Prompt Engineering?

Prompt engineering solves ~70% of LLM adaptation tasks for a domain. The remaining 30% require fine-tuning. Three indicators: the model ignores a specific output format even with detailed prompting; the task requires deep knowledge of specialized vocabulary (medicine, law); you need to significantly reduce token costs by replacing a large model with a smaller specialized one.

LoRA and QLoRA are the standard for SFT. LoRA adds trainable low-rank matrices to attention layers. A typical configuration for Llama-3 8B: r=64, lora_alpha=128, target_modules=["q_proj","v_proj","k_proj","o_proj"] yields ~0.8% trainable parameters, training on one A100 40GB. QLoRA adds 4-bit quantization (NF4) and allows fine-tuning 70B models on two A100 40GB, though speed drops by half compared to bf16.

DPO instead of RLHF. Direct Preference Optimization requires only (chosen, rejected) pairs, not scalar reward signals. DPOTrainer from the trl library (Hugging Face) implements it in a few dozen lines.

Common mistake. A dataset of 500 examples, 5 epochs, validation loss 0.8 — seems fine. But on test, the model degrades on general instructions. Cause: catastrophic forgetting. Solution: add 10–20% general instruction-following examples (Alpaca, FLAN) to the training set to preserve original capabilities.

How to Choose a Base Model: 8B or 70B?

For most tasks, fine-tuning an 8B model is sufficient. 70B is needed when deep reasoning is required or the 8B baseline does not reach the required quality even after fine-tuning. Inference cost for Llama-3 8B via vLLM on A100 is efficient; the exact cost depends on volume.

What Does PagedAttention Bring to Production?

vLLM is the first choice for serving open-source models. PagedAttention is the key technical innovation: KV-cache is managed like virtual memory in an OS, without fragmentation. This yields 2–4x higher throughput compared to naive HuggingFace Transformers inference. The vLLM documentation confirms that continuous batching and PagedAttention are the standard for high-load LLM services.

Typical numbers on A100 80GB for Llama-3 8B (bf16): 400–600 req/s, P50 latency 200–400ms, P99 latency 600–900ms at concurrency 64. For 70B on two A100 with tensor parallelism: 80–120 req/s, P99 latency 1.5–2.5s. AWQ or GPTQ quantization reduces memory consumption by 2x with quality loss within 1–3%.

Multi-Agent Systems

Agents are LLMs with access to tools: search, code execution, API calls, database interaction. Common patterns:

• ReAct (Reason + Act): the model reasons → chooses a tool → observes the result → reasons again. LangChain and LlamaIndex implement it out of the box.
• Multi-agent orchestration: multiple specialized agents with a coordinator on top. Example: coordinator → researcher (search + summarization) → coder (code generation and execution) → critic (verification). Tools: AutoGen (Microsoft), CrewAI, custom implementation on LangGraph.

In production, agent systems are non-deterministic. Essential: guardrails, step limits, logging of each step, human-in-the-loop for critical actions.

How We Work: Stages, Timeline, Deliverables

What Is Included

We deliver:

• Technical documentation (model card, configs, deployment instructions)
• Access to infrastructure (code repository, trained weights)
• 1 month of post-deployment support (consultations, bug fixes)
• Customer team training (2–3 sessions on system operation)

Timeline: basic RAG prototype — 1–2 weeks. Fine-tuning with customer data — 3–6 weeks (including data preparation). Production system with monitoring and retraining — 2–4 months. Cost is calculated individually based on data volume, model complexity, and infrastructure requirements.

We guarantee the quality of the final model with performance benchmarks and ongoing monitoring. Our engineers have hands‑on experience with dozens of production LLM systems.

Want to evaluate your project? Leave a request — we will prepare a preliminary summary within 1–2 business days. Or get a consultation on choosing the approach: RAG, fine-tuning, or hybrid — we will tell you what works best for you. Contact us to discuss your LLM development needs. Schedule a free consultation today.