add support for mistral (#1153)

### What problem does this PR solve?

#433 

### Type of change

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

rag/llm/embedding_model.py

@@ -343,4 +343,24 @@ class InfinityEmbed(Base):
     def encode_queries(self, text: str) -> tuple[np.ndarray, int]:
         # Using the internal tokenizer to encode the texts and get the total
         # number of tokens
-        return self.encode([text])
+        return self.encode([text])
+
+
+class MistralEmbed(Base):
+    def __init__(self, key, model_name="mistral-embed",
+                 base_url=None):
+        from mistralai.client import MistralClient
+        self.client = MistralClient(api_key=key)
+        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(input=texts,
+                                            model=self.model_name)
+        return np.array([d.embedding for d in res.data]
+                        ), res.usage.total_tokens
+
+    def encode_queries(self, text):
+        res = self.client.embeddings(input=[truncate(text, 8196)],
+                                            model=self.model_name)
+        return np.array(res.data[0].embedding), res.usage.total_tokens