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refactor(llm): rename LlmConfig to LLMConfig for consistency

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Rename LlmConfig to LLMConfig across the codebase to follow consistent naming conventions.
Update all imports and usages to use the new name.
Update documentation and examples to reflect the change.

BREAKING CHANGE: LlmConfig has been renamed to LLMConfig. Users need to update their imports and usage.
This commit is contained in:
UncleCode
2025-03-05 14:17:04 +08:00
parent e896c08f9c
commit baee4949d3
33 changed files with 362 additions and 174 deletions
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8
docs/md_v2/api/parameters.md
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@@ -245,8 +245,8 @@ run_config = CrawlerRunConfig(
)
```
# 3. **LlmConfig** - Setting up LLM providers
LlmConfig is useful to pass LLM provider config to strategies and functions that rely on LLMs to do extraction, filtering, schema generation etc. Currently it can be used in the following -
# 3. **LLMConfig** - Setting up LLM providers
LLMConfig is useful to pass LLM provider config to strategies and functions that rely on LLMs to do extraction, filtering, schema generation etc. Currently it can be used in the following -
1. LLMExtractionStrategy
2. LLMContentFilter
@@ -262,7 +262,7 @@ LlmConfig is useful to pass LLM provider config to strategies and functions that
## 3.2 Example Usage
```python
llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
```
## 4. Putting It All Together
@@ -270,7 +270,7 @@ llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI
- **Use** `BrowserConfig` for **global** browser settings: engine, headless, proxy, user agent.
- **Use** `CrawlerRunConfig` for each crawl’s **context**: how to filter content, handle caching, wait for dynamic elements, or run JS.
- **Pass** both configs to `AsyncWebCrawler` (the `BrowserConfig`) and then to `arun()` (the `CrawlerRunConfig`).
- **Use** `LlmConfig` for LLM provider configurations that can be used across all extraction, filtering, schema generation tasks. Can be used in - `LLMExtractionStrategy`, `LLMContentFilter`, `JsonCssExtractionStrategy.generate_schema` & `JsonXPathExtractionStrategy.generate_schema`
- **Use** `LLMConfig` for LLM provider configurations that can be used across all extraction, filtering, schema generation tasks. Can be used in - `LLMExtractionStrategy`, `LLMContentFilter`, `JsonCssExtractionStrategy.generate_schema` & `JsonXPathExtractionStrategy.generate_schema`
```python
# Create a modified copy with the clone() method

8
docs/md_v2/api/strategies.md
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@@ -131,7 +131,7 @@ OverlappingWindowChunking(
```python
from pydantic import BaseModel
from crawl4ai.extraction_strategy import LLMExtractionStrategy
from crawl4ai.async_configs import LlmConfig
from crawl4ai.types import LLMConfig
# Define schema
class Article(BaseModel):
@@ -141,7 +141,7 @@ class Article(BaseModel):
# Create strategy
strategy = LLMExtractionStrategy(
llmConfig = LlmConfig(provider="ollama/llama2"),
llm_config = LLMConfig(provider="ollama/llama2"),
schema=Article.schema(),
instruction="Extract article details"
)
@@ -198,7 +198,7 @@ result = await crawler.arun(
```python
from crawl4ai.chunking_strategy import OverlappingWindowChunking
from crawl4ai.async_configs import LlmConfig
from crawl4ai.types import LLMConfig
# Create chunking strategy
chunker = OverlappingWindowChunking(
@@ -208,7 +208,7 @@ chunker = OverlappingWindowChunking(
# Use with extraction strategy
strategy = LLMExtractionStrategy(
llmConfig = LlmConfig(provider="ollama/llama2"),
llm_config = LLMConfig(provider="ollama/llama2"),
chunking_strategy=chunker
)