truncate text to fitin embedding model (#692)

### What problem does this PR solve?


### Type of change

- [x] Refactoring

rag/llm/embedding_model.py

@@ -27,8 +27,7 @@ import torch
 import numpy as np
 
 from api.utils.file_utils import get_project_base_directory, get_home_cache_dir
-from rag.utils import num_tokens_from_string
+from rag.utils import num_tokens_from_string, truncate
-
 
 try:
     flag_model = FlagModel(os.path.join(get_home_cache_dir(), "bge-large-zh-v1.5"),
@@ -70,7 +69,7 @@ class DefaultEmbedding(Base):
         self.model = flag_model
 
     def encode(self, texts: list, batch_size=32):
-        texts = [t[:2000] for t in texts]
+        texts = [truncate(t, 2048) for t in texts]
         token_count = 0
         for t in texts:
             token_count += num_tokens_from_string(t)
@@ -93,12 +92,14 @@ class OpenAIEmbed(Base):
         self.model_name = model_name
 
     def encode(self, texts: list, batch_size=32):
+        texts = [truncate(t, 8196) for t in texts]
         res = self.client.embeddings.create(input=texts,
                                             model=self.model_name)
-        return np.array([d.embedding for d in res.data]), res.usage.total_tokens
+        return np.array([d.embedding for d in res.data]
+                        ), res.usage.total_tokens
 
     def encode_queries(self, text):
-        res = self.client.embeddings.create(input=[text],
+        res = self.client.embeddings.create(input=[truncate(text, 8196)],
                                             model=self.model_name)
         return np.array(res.data[0].embedding), res.usage.total_tokens
 
@@ -112,7 +113,7 @@ class QWenEmbed(Base):
         import dashscope
         res = []
         token_count = 0
-        texts = [txt[:2048] for txt in texts]
+        texts = [truncate(t, 2048) for t in texts]
         for i in range(0, len(texts), batch_size):
             resp = dashscope.TextEmbedding.call(
                 model=self.model_name,

rag/utils/__init__.py

@@ -63,3 +63,7 @@ def num_tokens_from_string(string: str) -> int:
     num_tokens = len(encoder.encode(string))
     return num_tokens
 
+
+def truncate(string: str, max_len: int) -> int:
+    """Returns truncated text if the length of text exceed max_len."""
+    return encoder.decode(encoder.encode(string)[:max_len])