mirror of
https://github.com/hwchase17/langchain.git
synced 2026-06-09 10:17:00 +00:00
0d6f915442
fix: Fix LLM mimicking Unicode responses due to forced Unicode
conversion of non-ASCII characters.
- **Description:** This PR fixes an issue where the LLM would mimic
Unicode responses due to forced Unicode conversion of non-ASCII
characters in tool calls. The fix involves disabling the `ensure_ascii`
flag in `json.dumps()` when converting tool calls to OpenAI format.
- **Issue:** Fixes ↓↓↓
input：
```json
{'role': 'assistant', 'tool_calls': [{'type': 'function', 'id': 'call_nv9trcehdpihr21zj9po19vq', 'function': {'name': 'create_customer', 'arguments': '{"customer_name": "你好啊集团"}'}}]}
```
output:
```json
{'role': 'assistant', 'tool_calls': [{'type': 'function', 'id': 'call_nv9trcehdpihr21zj9po19vq', 'function': {'name': 'create_customer', 'arguments': '{"customer_name": "\\u4f60\\u597d\\u554a\\u96c6\\u56e2"}'}}]}
```
then:
llm will mimic outputting unicode. Unicode's vast number of symbols can
lengthen LLM responses, leading to slower performance.
<img width="686" height="277" alt="image"
src="https://github.com/user-attachments/assets/28f3b007-3964-4455-bee2-68f86ac1906d"
/>
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
langchain-anthropic
This package contains the LangChain integration for Anthropic's generative models.
Installation
pip install -U langchain-anthropic
Chat Models
Anthropic recommends using their chat models over text completions.
You can see their recommended models in the Anthropic docs.
To use, you should have an Anthropic API key configured. Initialize the model as:
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import AIMessage, HumanMessage
model = ChatAnthropic(model="claude-3-opus-20240229", temperature=0, max_tokens=1024)
Define the input message
message = HumanMessage(content="What is the capital of France?")
Generate a response using the model
response = model.invoke([message])
For a more detailed walkthrough see here.
LLMs (Legacy)
You can use the Claude 2 models for text completions.
from langchain_anthropic import AnthropicLLM
model = AnthropicLLM(model="claude-2.1", temperature=0, max_tokens=1024)
response = model.invoke("The best restaurant in San Francisco is: ")