feat: FastEmbed embedding support (#291)

### Description

Following up on https://github.com/infiniflow/ragflow/pull/275, this PR
adds support for FastEmbed model configurations.

The options are not exhaustive. You can find the full list
[here](https://qdrant.github.io/fastembed/examples/Supported_Models/).

P.S. I ran into OOM issues when building the image.

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

---------

Co-authored-by: KevinHuSh <kevinhu.sh@gmail.com>

api/db/init_data.py

     "logo": "",
     "tags": "LLM,TEXT EMBEDDING",
     "status": "1",
+}, {
+    "name": "FastEmbed",
+    "logo": "",
+    "tags": "TEXT EMBEDDING",
+    "status": "1",
 },
     {
     "name": "Xinference",
             "max_tokens": 128 * 1000,
             "model_type": LLMType.CHAT.value
         },
+        # ------------------------ FastEmbed -----------------------
+        {
+            "fid": factory_infos[5]["name"],
+            "llm_name": "BAAI/bge-small-en-v1.5",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 512,
+            "model_type": LLMType.EMBEDDING.value
+        }, {
+            "fid": factory_infos[5]["name"],
+            "llm_name": "BAAI/bge-small-zh-v1.5",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 512,
+            "model_type": LLMType.EMBEDDING.value
+        }, {
+        }, {
+            "fid": factory_infos[5]["name"],
+            "llm_name": "BAAI/bge-base-en-v1.5",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 512,
+            "model_type": LLMType.EMBEDDING.value
+        }, {
+        }, {
+            "fid": factory_infos[5]["name"],
+            "llm_name": "BAAI/bge-large-en-v1.5",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 512,
+            "model_type": LLMType.EMBEDDING.value
+        }, {
+            "fid": factory_infos[5]["name"],
+            "llm_name": "sentence-transformers/all-MiniLM-L6-v2",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 512,
+            "model_type": LLMType.EMBEDDING.value
+        }, {
+            "fid": factory_infos[5]["name"],
+            "llm_name": "nomic-ai/nomic-embed-text-v1.5",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 8192,
+            "model_type": LLMType.EMBEDDING.value
+        }, {
+            "fid": factory_infos[5]["name"],
+            "llm_name": "jinaai/jina-embeddings-v2-small-en",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 2147483648,
+            "model_type": LLMType.EMBEDDING.value
+        }, {
+            "fid": factory_infos[5]["name"],
+            "llm_name": "jinaai/jina-embeddings-v2-base-en",
+            "tags": "TEXT EMBEDDING,",
+            "max_tokens": 2147483648,
+            "model_type": LLMType.EMBEDDING.value
+        },
     ]
     for info in factory_infos:
         try:

rag/llm/__init__.py

     "Xinference": XinferenceEmbed,
     "Tongyi-Qianwen": HuEmbedding, #QWenEmbed,
     "ZHIPU-AI": ZhipuEmbed,
-    "Moonshot": HuEmbedding
+    "Moonshot": HuEmbedding,
+    "FastEmbed": FastEmbed
 }
 
 

rag/llm/embedding_model.py

 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
+from typing import Optional
 from zhipuai import ZhipuAI
 import os
 from abc import ABC
 from ollama import Client
 import dashscope
 from openai import OpenAI
+from fastembed import TextEmbedding
 from FlagEmbedding import FlagModel
 import torch
 import numpy as np
         return np.array(res["embedding"]), 128
 
 
+class FastEmbed(Base):
+    def __init__(
+        self,
+        key: Optional[str] = None,
+        model_name: str = "BAAI/bge-small-en-v1.5",
+        cache_dir: Optional[str] = None,
+        threads: Optional[int] = None,
+        **kwargs,
+    ):
+        self._model = TextEmbedding(model_name, cache_dir, threads, **kwargs)
+
+    def encode(self, texts: list, batch_size=32):
+        # Using the internal tokenizer to encode the texts and get the total number of tokens
+        encodings = self._model.model.tokenizer.encode_batch(texts)
+        total_tokens = sum(len(e) for e in encodings)
+
+        embeddings = [e.tolist() for e in self._model.embed(texts, batch_size)]
+
+        return np.array(embeddings), total_tokens
+
+    def encode_queries(self, text: str):
+        # Using the internal tokenizer to encode the texts and get the total number of tokens
+        encoding = self._model.model.tokenizer.encode(text)
+        embedding = next(self._model.query_embed(text)).tolist()
+
+        return np.array(embedding), len(encoding.ids)
+
+
 class XinferenceEmbed(Base):
     def __init__(self, key, model_name="", base_url=""):
         self.client = OpenAI(api_key="xxx", base_url=base_url)
         res = self.client.embeddings.create(input=[text],
                                             model=self.model_name)
         return np.array(res.data[0].embedding), res.usage.total_tokens
+

requirements.txt

 elasticsearch-dsl==8.12.0
 et-xmlfile==1.1.0
 filelock==3.13.1
+fastembed==0.2.6
 FlagEmbedding==1.2.5
 Flask==3.0.2
 Flask-Cors==4.0.0