Principle:Langchain ai Langchain Provider API Invocation
| Knowledge Sources | |
|---|---|
| Domains | NLP, API_Integration |
| Last Updated | 2026-02-11 00:00 GMT |
Overview
The core execution step that translates LangChain message objects into provider-specific API requests and sends them to the LLM service.
Description
Provider API invocation is the central responsibility of each partner integration. It bridges the gap between LangChain's unified message format (BaseMessage objects) and the provider's specific API contract (e.g., OpenAI's chat completions endpoint, Anthropic's messages endpoint).
The process involves:
- Message conversion: Translating HumanMessage, AIMessage, SystemMessage, ToolMessage into provider-specific dictionaries
- Parameter mapping: Converting LangChain parameters (temperature, max_tokens, stop) to provider-specific keys
- HTTP call: Sending the request via the provider's SDK client
- Error handling: Catching provider-specific exceptions and translating them to LangChain errors
Usage
This principle is implemented by every partner integration's _generate() method. It is called automatically during the invocation pipeline after input preparation, caching, and rate limiting.
Theoretical Basis
The provider invocation follows the Adapter Pattern:
# Abstract algorithm (not real code)
def _generate(messages, **kwargs):
# 1. Convert LangChain messages to provider format
provider_messages = convert_messages(messages)
# 2. Build provider request payload
payload = build_payload(provider_messages, **kwargs)
# 3. Call provider API
raw_response = provider_client.create(**payload)
# 4. Convert response back to LangChain format
return create_chat_result(raw_response)
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