Modify the chat_complete method to support keywords extraction.

examples/lightrag_zhipu_demo.py

@@ -0,0 +1,61 @@
+import asyncio
+import os
+import inspect
+import logging
+
+from dotenv import load_dotenv
+
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import zhipu_complete, zhipu_embedding
+from lightrag.utils import EmbeddingFunc
+
+WORKING_DIR = "./dickens"
+
+logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+api_key = os.environ.get("ZHIPUAI_API_KEY")
+if api_key is None:
+    raise Exception("Please set ZHIPU_API_KEY in your environment")
+
+
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=zhipu_complete,
+    llm_model_name="glm-4-flashx",  # Using the most cost/performance balance model, but you can change it here.
+    llm_model_max_async=4,
+    llm_model_max_token_size=32768,
+    embedding_func=EmbeddingFunc(
+        embedding_dim=2048,  # Zhipu embedding-3 dimension
+        max_token_size=8192,
+        func=lambda texts: zhipu_embedding(
+            texts
+        ),
+    ),
+)
+
+with open("./book.txt", "r", encoding="utf-8") as f:
+    rag.insert(f.read())
+
+# Perform naive search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+)
+
+# Perform local search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
+)
+
+# Perform global search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
+)
+
+# Perform hybrid search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
+)

lightrag/llm.py

@@ -4,7 +4,7 @@ import json
 import os
 import struct
 from functools import lru_cache
-from typing import List, Dict, Callable, Any, Union
+from typing import List, Dict, Callable, Any, Union, Optional
 import aioboto3
 import aiohttp
 import numpy as np
@@ -596,6 +596,179 @@ async def ollama_model_complete(
     )
 
 
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=10),
+    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
+)
+async def zhipu_complete_if_cache(
+    prompt: Union[str, List[Dict[str, str]]],
+    model: str = "glm-4-flashx", # The most cost/performance balance model in glm-4 series
+    api_key: Optional[str] = None,
+    system_prompt: Optional[str] = None,
+    history_messages: List[Dict[str, str]] = [],
+    **kwargs
+) -> str:
+    # dynamically load ZhipuAI
+    try:
+        from zhipuai import ZhipuAI
+    except ImportError:
+        raise ImportError("Please install zhipuai before initialize zhipuai backend.")
+
+    if api_key:
+        client = ZhipuAI(api_key=api_key)
+    else:
+        # please set ZHIPUAI_API_KEY in your environment
+        # os.environ["ZHIPUAI_API_KEY"]
+        client = ZhipuAI()
+
+    messages = []
+
+    if not system_prompt:
+        system_prompt = "You are a helpful assistant. 注意内容里的敏感词用***替换。"
+
+    # Add system prompt if provided
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+    messages.extend(history_messages)
+    messages.append({"role": "user", "content": prompt})
+
+    # Add debug logging
+    logger.debug("===== Query Input to LLM =====")
+    logger.debug(f"Query: {prompt}")
+    logger.debug(f"System prompt: {system_prompt}")
+
+    # Remove unsupported kwargs
+    kwargs = {k: v for k, v in kwargs.items() if k not in ['hashing_kv', 'keyword_extraction']}
+
+    response = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            **kwargs
+        )
+
+    return response.choices[0].message.content
+
+
+async def zhipu_complete(
+    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
+):
+    # Pop keyword_extraction from kwargs to avoid passing it to zhipu_complete_if_cache
+    keyword_extraction = kwargs.pop("keyword_extraction", None)
+
+    if keyword_extraction:
+        # Add a system prompt to guide the model to return JSON format
+        extraction_prompt = """You are a helpful assistant that extracts keywords from text.
+        Please analyze the content and extract two types of keywords:
+        1. High-level keywords: Important concepts and main themes
+        2. Low-level keywords: Specific details and supporting elements
+        
+        Return your response in this exact JSON format:
+        {
+            "high_level_keywords": ["keyword1", "keyword2"],
+            "low_level_keywords": ["keyword1", "keyword2", "keyword3"]
+        }
+        
+        Only return the JSON, no other text."""
+
+        # Combine with existing system prompt if any
+        if system_prompt:
+            system_prompt = f"{system_prompt}\n\n{extraction_prompt}"
+        else:
+            system_prompt = extraction_prompt
+
+        try:
+            response = await zhipu_complete_if_cache(
+                prompt=prompt,
+                system_prompt=system_prompt,
+                history_messages=history_messages,
+                **kwargs
+            )
+            
+            # Try to parse as JSON
+            try:
+                data = json.loads(response)
+                return GPTKeywordExtractionFormat(
+                    high_level_keywords=data.get("high_level_keywords", []),
+                    low_level_keywords=data.get("low_level_keywords", [])
+                )
+            except json.JSONDecodeError:
+                # If direct JSON parsing fails, try to extract JSON from text
+                match = re.search(r"\{[\s\S]*\}", response)
+                if match:
+                    try:
+                        data = json.loads(match.group())
+                        return GPTKeywordExtractionFormat(
+                            high_level_keywords=data.get("high_level_keywords", []),
+                            low_level_keywords=data.get("low_level_keywords", [])
+                        )
+                    except json.JSONDecodeError:
+                        pass
+                
+                # If all parsing fails, log warning and return empty format
+                logger.warning(f"Failed to parse keyword extraction response: {response}")
+                return GPTKeywordExtractionFormat(
+                    high_level_keywords=[], low_level_keywords=[]
+                )
+        except Exception as e:
+            logger.error(f"Error during keyword extraction: {str(e)}")
+            return GPTKeywordExtractionFormat(
+                high_level_keywords=[], low_level_keywords=[]
+            )
+    else:
+        # For non-keyword-extraction, just return the raw response string
+        return await zhipu_complete_if_cache(
+            prompt=prompt,
+            system_prompt=system_prompt,
+            history_messages=history_messages,
+            **kwargs
+        )
+
+
+@wrap_embedding_func_with_attrs(embedding_dim=1024, max_token_size=8192)
+@retry(
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=4, max=60),
+    retry=retry_if_exception_type((RateLimitError, APIConnectionError, Timeout)),
+)
+async def zhipu_embedding(
+    texts: list[str],
+    model: str = "embedding-3",
+    api_key: str = None,
+    **kwargs
+) -> np.ndarray:
+
+# dynamically load ZhipuAI
+    try:
+        from zhipuai import ZhipuAI
+    except ImportError:
+        raise ImportError("Please install zhipuai before initialize zhipuai backend.")
+    if api_key:
+        client = ZhipuAI(api_key=api_key)
+    else:
+        # please set ZHIPUAI_API_KEY in your environment
+        # os.environ["ZHIPUAI_API_KEY"]
+        client = ZhipuAI()
+
+    # Convert single text to list if needed
+    if isinstance(texts, str):
+        texts = [texts]
+
+    embeddings = []
+    for text in texts:
+        try:
+            response = client.embeddings.create(
+                model=model,
+                input=[text],
+                **kwargs
+            )
+            embeddings.append(response.data[0].embedding)
+        except Exception as e:
+            raise Exception(f"Error calling ChatGLM Embedding API: {str(e)}")
+
+    return np.array(embeddings)
+
+
 @wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8192)
 @retry(
     stop=stop_after_attempt(3),