Resolves infiniflow#2905 openai compatible model provider add llama.c…

…pp rerank support (infiniflow#2906)

### What problem does this PR solve?
Resolve infiniflow#2905 



due to the in-consistent of token size, I make it safe to limit 500 in
code, since there is no config param to control

my llama.cpp run set -ub to 1024:

${llama_path}/bin/llama-server --host 0.0.0.0 --port 9901 -ub 1024 -ngl
99 -m $gguf_file --reranking "$@"





### Type of change

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

Here is my test Ragflow use llama.cpp

```
lot update_slots: id  0 | task 458 | prompt done, n_past = 416, n_tokens = 416
slot      release: id  0 | task 458 | stop processing: n_past = 416, truncated = 0
slot launch_slot_: id  0 | task 459 | processing task
slot update_slots: id  0 | task 459 | tokenizing prompt, len = 2
slot update_slots: id  0 | task 459 | prompt tokenized, n_ctx_slot = 8192, n_keep = 0, n_prompt_tokens = 111
slot update_slots: id  0 | task 459 | kv cache rm [0, end)
slot update_slots: id  0 | task 459 | prompt processing progress, n_past = 111, n_tokens = 111, progress = 1.000000
slot update_slots: id  0 | task 459 | prompt done, n_past = 111, n_tokens = 111
slot      release: id  0 | task 459 | stop processing: n_past = 111, truncated = 0
srv  update_slots: all slots are idle
request: POST /rerank 172.23.0.4 200

```

rag/llm/rerank_model.py

@@ -242,10 +242,46 @@ def similarity(self, query: str, texts: list):
 
 class OpenAI_APIRerank(Base):
     def __init__(self, key, model_name, base_url):
-        pass
+        if base_url.find("/rerank") == -1:
+            self.base_url = urljoin(base_url, "/rerank")
+        else:
+            self.base_url = base_url
+        self.headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {key}"
+        }
+        self.model_name = model_name
 
     def similarity(self, query: str, texts: list):
-        raise NotImplementedError("The api has not been implement")
+        # noway to config Ragflow , use fix setting
+        texts = [truncate(t, 500) for t in texts]
+        data = {
+            "model": self.model_name,
+            "query": query,
+            "documents": texts,
+            "top_n": len(texts),
+        }
+        token_count = 0
+        for t in texts:
+            token_count += num_tokens_from_string(t)
+        res = requests.post(self.base_url, headers=self.headers, json=data).json()
+        rank = np.zeros(len(texts), dtype=float)
+        if 'results' not in res:
+            raise ValueError("response not contains results\n" + str(res))
+        for d in res["results"]:
+            rank[d["index"]] = d["relevance_score"]
+
+        # Normalize the rank values to the range 0 to 1
+        min_rank = np.min(rank)
+        max_rank = np.max(rank)
+
+        # Avoid division by zero if all ranks are identical
+        if max_rank - min_rank != 0:
+            rank = (rank - min_rank) / (max_rank - min_rank)
+        else:
+            rank = np.zeros_like(rank)
+
+        return rank, token_count
 
 
 class CoHereRerank(Base):