add support for mistral (#1153)

### What problem does this PR solve?

#433 

### Type of change

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

api/db/init_data.py

@@ -157,6 +157,11 @@ factory_infos = [{
     "logo": "",
     "tags": "LLM,TEXT EMBEDDING",
     "status": "1",
+},{
+    "name": "Mistral",
+    "logo": "",
+    "tags": "LLM,TEXT EMBEDDING",
+    "status": "1",
 }
     # {
     #     "name": "文心一言",
@@ -584,6 +589,63 @@ def init_llm_factory():
             "max_tokens": 8192,
             "model_type": LLMType.CHAT.value
         },
+        # ------------------------ Mistral -----------------------
+        {
+            "fid": factory_infos[14]["name"],
+            "llm_name": "open-mixtral-8x22b",
+            "tags": "LLM,CHAT,64k",
+            "max_tokens": 64000,
+            "model_type": LLMType.CHAT.value
+        },
+        {
+            "fid": factory_infos[14]["name"],
+            "llm_name": "open-mixtral-8x7b",
+            "tags": "LLM,CHAT,32k",
+            "max_tokens": 32000,
+            "model_type": LLMType.CHAT.value
+        },
+        {
+            "fid": factory_infos[14]["name"],
+            "llm_name": "open-mistral-7b",
+            "tags": "LLM,CHAT,32k",
+            "max_tokens": 32000,
+            "model_type": LLMType.CHAT.value
+        },
+        {
+            "fid": factory_infos[14]["name"],
+            "llm_name": "mistral-large-latest",
+            "tags": "LLM,CHAT,32k",
+            "max_tokens": 32000,
+            "model_type": LLMType.CHAT.value
+        },
+        {
+            "fid": factory_infos[14]["name"],
+            "llm_name": "mistral-small-latest",
+            "tags": "LLM,CHAT,32k",
+            "max_tokens": 32000,
+            "model_type": LLMType.CHAT.value
+        },
+        {
+            "fid": factory_infos[14]["name"],
+            "llm_name": "mistral-medium-latest",
+            "tags": "LLM,CHAT,32k",
+            "max_tokens": 32000,
+            "model_type": LLMType.CHAT.value
+        },
+        {
+            "fid": factory_infos[14]["name"],
+            "llm_name": "codestral-latest",
+            "tags": "LLM,CHAT,32k",
+            "max_tokens": 32000,
+            "model_type": LLMType.CHAT.value
+        },
+        {
+            "fid": factory_infos[14]["name"],
+            "llm_name": "mistral-embed",
+            "tags": "LLM,CHAT,8k",
+            "max_tokens": 8192,
+            "model_type": LLMType.EMBEDDING
+        },
     ]
     for info in factory_infos:
         try:

rag/llm/__init__.py

@@ -29,7 +29,8 @@ EmbeddingModel = {
     "Youdao": YoudaoEmbed,
     "BaiChuan": BaiChuanEmbed,
     "Jina": JinaEmbed,
-    "BAAI": DefaultEmbedding
+    "BAAI": DefaultEmbedding,
+    "Mistral": MistralEmbed
 }
 
 
@@ -52,7 +53,8 @@ ChatModel = {
     "Moonshot": MoonshotChat,
     "DeepSeek": DeepSeekChat,
     "BaiChuan": BaiChuanChat,
-    "MiniMax": MiniMaxChat
+    "MiniMax": MiniMaxChat,
+    "Mistral": MistralChat
 }
 
 

rag/llm/chat_model.py

@@ -472,3 +472,57 @@ class MiniMaxChat(Base):
         if not base_url:
             base_url="https://api.minimax.chat/v1/text/chatcompletion_v2"
         super().__init__(key, model_name, base_url)
+
+
+class MistralChat(Base):
+
+    def __init__(self, key, model_name, base_url=None):
+        from mistralai.client import MistralClient
+        self.client = MistralClient(api_key=key)
+        self.model_name = model_name
+
+    def chat(self, system, history, gen_conf):
+        if system:
+            history.insert(0, {"role": "system", "content": system})
+        for k in list(gen_conf.keys()):
+            if k not in ["temperature", "top_p", "max_tokens"]:
+                del gen_conf[k]
+        try:
+            response = self.client.chat(
+                model=self.model_name,
+                messages=history,
+                **gen_conf)
+            ans = response.choices[0].message.content
+            if response.choices[0].finish_reason == "length":
+                ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
+                    [ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
+            return ans, response.usage.total_tokens
+        except openai.APIError as e:
+            return "**ERROR**: " + str(e), 0
+
+    def chat_streamly(self, system, history, gen_conf):
+        if system:
+            history.insert(0, {"role": "system", "content": system})
+        for k in list(gen_conf.keys()):
+            if k not in ["temperature", "top_p", "max_tokens"]:
+                del gen_conf[k]
+        ans = ""
+        total_tokens = 0
+        try:
+            response = self.client.chat_stream(
+                model=self.model_name,
+                messages=history,
+                **gen_conf)
+            for resp in response:
+                if not resp.choices or not resp.choices[0].delta.content:continue
+                ans += resp.choices[0].delta.content
+                total_tokens += 1
+                if resp.choices[0].finish_reason == "length":
+                    ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
+                        [ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
+                yield ans
+
+        except openai.APIError as e:
+            yield ans + "\n**ERROR**: " + str(e)
+
+        yield total_tokens

rag/llm/embedding_model.py

@@ -344,3 +344,23 @@ class InfinityEmbed(Base):
         # Using the internal tokenizer to encode the texts and get the total
         # number of tokens
         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