217 lines
7.1 KiB
Markdown
217 lines
7.1 KiB
Markdown
---
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sidebar_position: 5
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---
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# Multilingual Support
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Hindsight automatically detects the language of your input and responds in the same language. This means facts, entities, and reflections are preserved in their original language without translation to English.
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## How It Works
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```mermaid
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graph LR
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A[Chinese Input] --> B[Language Detection]
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B --> C[Extract Facts in Chinese]
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C --> D[Chinese Entities]
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D --> E[Chinese Response]
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```
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When you retain content or reflect on a query, Hindsight:
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1. **Detects the input language** automatically from the content
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2. **Extracts facts in the original language** - preserving nuance and meaning
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3. **Stores entities in their native script** - 张伟 stays 张伟, not "Zhang Wei"
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4. **Responds in the same language** - queries in Chinese get Chinese answers
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---
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## Retain with Non-English Content
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When you retain content in any language, Hindsight extracts and stores facts in that same language.
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### Example: Chinese Content
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```python
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from hindsight import Hindsight
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hindsight = Hindsight()
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# Retain Chinese content
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hindsight.retain(
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bank_id="user-123",
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content="""
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张伟是一位资深软件工程师，在腾讯工作了五年。
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他专门研究分布式系统，并领导了公司微服务架构的开发。
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""",
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context="团队概述"
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)
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# Query in Chinese - get Chinese results
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results = hindsight.recall(
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bank_id="user-123",
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query="告诉我关于张伟的信息"
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)
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# Facts are returned in Chinese:
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# - 张伟是一位资深软件工程师，在腾讯工作了五年
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# - 张伟专门研究分布式系统，并领导了公司微服务架构的开发
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```
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### Example: Japanese Content
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```python
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hindsight.retain(
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bank_id="user-123",
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content="""
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田中さんはソフトウェアエンジニアで、東京のスタートアップで働いています。
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彼女はPythonとTypeScriptが得意で、毎日コードレビューをしています。
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""",
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context="チームプロフィール"
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)
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# Query in Japanese
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results = hindsight.recall(
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bank_id="user-123",
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query="田中さんについて教えてください"
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)
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```
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---
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## Reflect with Non-English Queries
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The `reflect` operation also respects the input language, generating thoughtful responses in the same language as the query.
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### Example: Chinese Reflection
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```python
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# Store facts about team members (in Chinese)
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hindsight.retain(
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bank_id="team-eval",
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content="张伟是一位优秀的软件工程师，完成了五个重大项目。他总是按时交付，代码整洁有良好的文档。",
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context="绩效评估"
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)
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hindsight.retain(
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bank_id="team-eval",
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content="李明最近加入团队。他错过了第一个截止日期，代码有很多bug。",
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context="绩效评估"
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)
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# Reflect in Chinese
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result = hindsight.reflect(
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bank_id="team-eval",
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query="谁是更可靠的工程师？"
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)
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# Response is in Chinese:
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# "我认为张伟更可靠。张伟完成了五个重大项目，按时交付，代码质量高..."
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```
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---
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## Mixed Language Content
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Hindsight handles mixed-language content gracefully, preserving both languages where appropriate.
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### Example: Chinese Text with English Company Names
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```python
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hindsight.retain(
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bank_id="user-123",
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content="""
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王芳在Google北京办公室工作，她是一名高级产品经理。
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之前她在Microsoft和Amazon工作过。
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她负责管理YouTube在中国市场的推广策略。
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""",
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context="员工资料"
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)
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# Facts preserve both languages:
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# - 王芳在Google北京办公室工作，担任高级产品经理
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# - 王芳曾在Microsoft和Amazon工作过
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# - 王芳负责管理YouTube在中国市场的推广策略
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```
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---
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## Supported Languages
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**Hindsight's multilingual support depends entirely on your LLM's language capabilities.** Hindsight instructs the LLM to detect the input language and respond in that same language. If your LLM supports a language, Hindsight will work with it.
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Most modern LLMs (GPT-4, Claude, Gemini, Llama 3, etc.) support dozens of languages including:
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- **East Asian**: Chinese (Simplified/Traditional), Japanese, Korean
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- **European**: Spanish, French, German, Italian, Portuguese, Dutch, Polish, Russian
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- **Middle Eastern**: Arabic, Hebrew, Turkish
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- **South Asian**: Hindi, Bengali, Tamil
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- **Southeast Asian**: Thai, Vietnamese, Indonesian
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**To verify support for your target language**, test your LLM directly with content in that language. If the LLM can understand and generate text in the language, Hindsight will preserve it correctly.
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---
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## Configuring for Multilingual Use
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For optimal multilingual performance, you should configure all three components of the pipeline:
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### 1. LLM (Required)
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Your LLM must support the target languages. Most modern LLMs do, but verify with your specific model.
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### 2. Embedding Model (Recommended)
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The default embedding model (`BAAI/bge-small-en-v1.5`) is **English-only**. For multilingual content, use a multilingual embedding model:
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```bash
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# In your .env file
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HINDSIGHT_API_EMBEDDINGS_LOCAL_MODEL=BAAI/bge-m3
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```
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**Recommended multilingual embedding models:**
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| Model | Languages | Notes |
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|-------|-----------|-------|
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| `BAAI/bge-m3` | 100+ | Best overall multilingual performance |
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| `intfloat/multilingual-e5-large` | 100+ | Good alternative |
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| `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` | 50+ | Lighter weight |
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### 3. Reranker Model (Recommended)
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The default reranker (`cross-encoder/ms-marco-MiniLM-L-6-v2`) is **English-only**. For multilingual content, use a multilingual reranker:
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```bash
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# In your .env file
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HINDSIGHT_API_RERANKER_LOCAL_MODEL=BAAI/bge-reranker-v2-m3
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```
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**Recommended multilingual reranker models:**
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| Model | Languages | Notes |
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|-------|-----------|-------|
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| `BAAI/bge-reranker-v2-m3` | 100+ | Best multilingual reranking |
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| `cross-encoder/mmarco-mMiniLMv2-L12-H384-v1` | 14 | Lighter alternative |
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---
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## Best Practices
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### 1. Use Multilingual Models for Non-English Content
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If you primarily work with non-English content, configure multilingual embedding and reranker models. English-only models will still store your content correctly, but semantic search quality will be degraded.
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### 2. Keep Content in One Language Per Retain Call
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While mixed content works, keeping each `retain` call in a single language produces more consistent results.
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### 3. Query in the Same Language as Your Content
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For best results, query using the same language as your stored content. Cross-language queries (e.g., English query for Chinese content) may work but results can vary depending on your embedding model.
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---
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## Technical Details
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Multilingual support is implemented through LLM prompt instructions rather than external language detection libraries. This approach:
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- **Requires no additional dependencies**
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- **Works with any LLM** that supports multiple languages
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- **Handles edge cases** like mixed-language content naturally
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- **Preserves semantic meaning** better than rule-based translation
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The LLM is instructed to:
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1. Detect the input language
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2. Extract all facts, entities, and descriptions in that same language
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3. Never translate to English unless the input is in English
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