add support for Gemini

api/db/init_data.py

@@ -175,6 +175,11 @@ def init_superuser():
     "logo": "",
     "tags": "LLM,TEXT EMBEDDING",
     "status": "1",
+},{
+    "name": "Gemini",
+    "logo": "",
+    "tags": "LLM,TEXT EMBEDDING,IMAGE2TEXT",
+    "status": "1",
 }
     # {
     #     "name": "文心一言",
@@ -898,7 +903,37 @@ def init_llm_factory():
             "tags": "TEXT EMBEDDING",
             "max_tokens": 2048,
             "model_type": LLMType.EMBEDDING.value
-        },
+        }, {
+            "fid": factory_infos[17]["name"],
+            "llm_name": "gemini-1.5-pro-latest",
+            "tags": "LLM,CHAT,1024K",
+            "max_tokens": 1024*1024,
+            "model_type": LLMType.CHAT.value
+        }, {
+            "fid": factory_infos[17]["name"],
+            "llm_name": "gemini-1.5-flash-latest",
+            "tags": "LLM,CHAT,1024K",
+            "max_tokens": 1024*1024,
+            "model_type": LLMType.CHAT.value
+        }, {
+            "fid": factory_infos[17]["name"],
+            "llm_name": "gemini-1.0-pro",
+            "tags": "LLM,CHAT,30K",
+            "max_tokens": 30*1024,
+            "model_type": LLMType.CHAT.value
+        }, {
+            "fid": factory_infos[17]["name"],
+            "llm_name": "gemini-1.0-pro-vision-latest",
+            "tags": "LLM,IMAGE2TEXT,12K",
+            "max_tokens": 12*1024,
+            "model_type": LLMType.IMAGE2TEXT.value
+        }, {
+            "fid": factory_infos[17]["name"],
+            "llm_name": "text-embedding-004",
+            "tags": "TEXT EMBEDDING",
+            "max_tokens": 2048,
+            "model_type": LLMType.EMBEDDING.value
+        }
     ]
     for info in factory_infos:
         try:

rag/llm/chat_model.py

@@ -621,3 +621,64 @@ def chat_streamly(self, system, history, gen_conf):
             yield ans + f"ERROR: Can't invoke '{self.model_name}'. Reason: {e}"
 
         yield num_tokens_from_string(ans)
+
+class GeminiChat(Base):
+
+    def __init__(self, key, model_name,base_url=None):
+        from google.generativeai import client,GenerativeModel 
+
+        client.configure(api_key=key)
+        _client = client.get_default_generative_client()
+        self.model_name = 'models/' + model_name
+        self.model = GenerativeModel(model_name=self.model_name)
+        self.model._client = _client
+
+    def chat(self,system,history,gen_conf):
+        if system:
+            history.insert(0, {"role": "user", "parts": system})
+        if 'max_tokens' in gen_conf:
+            gen_conf['max_output_tokens'] = gen_conf['max_tokens']
+        for k in list(gen_conf.keys()):
+            if k not in ["temperature", "top_p", "max_output_tokens"]:
+                del gen_conf[k]
+        for item in history:
+            if 'role' in item and item['role'] == 'assistant':
+                item['role'] = 'model'
+            if  'content' in item :
+                item['parts'] = item.pop('content')
+
+        try:
+            response = self.model.generate_content(
+                history,
+                generation_config=gen_conf)
+            ans = response.text
+            return ans, response.usage_metadata.total_token_count
+        except Exception as e:
+            return "**ERROR**: " + str(e), 0
+
+    def chat_streamly(self, system, history, gen_conf):
+        if system:
+            history.insert(0, {"role": "user", "parts": system})
+        if 'max_tokens' in gen_conf:
+            gen_conf['max_output_tokens'] = gen_conf['max_tokens']
+        for k in list(gen_conf.keys()):
+            if k not in ["temperature", "top_p", "max_output_tokens"]:
+                del gen_conf[k]
+        for item in history:
+            if 'role' in item and item['role'] == 'assistant':
+                item['role'] = 'model'
+            if  'content' in item :
+                item['parts'] = item.pop('content')
+        ans = ""
+        try:
+            response = self.model.generate_content(
+                history,
+                generation_config=gen_conf,stream=True)
+            for resp in response:
+                ans += resp.text
+                yield ans
+
+        except Exception as e:
+            yield ans + "\n**ERROR**: " + str(e)
+
+        yield  response._chunks[-1].usage_metadata.total_token_count

rag/llm/cv_model.py

@@ -203,6 +203,29 @@ def describe(self, image, max_tokens=300):
         )
         return res.choices[0].message.content.strip(), res.usage.total_tokens
 
+class GeminiCV(Base):
+    def __init__(self, key, model_name="gemini-1.0-pro-vision-latest", lang="Chinese", **kwargs):
+        from google.generativeai import client,GenerativeModel 
+        client.configure(api_key=key)
+        _client = client.get_default_generative_client()
+        self.model_name = model_name
+        self.model = GenerativeModel(model_name=self.model_name)
+        self.model._client = _client
+        self.lang = lang 
+
+    def describe(self, image, max_tokens=2048):
+        from PIL.Image import open
+        gen_config = {'max_output_tokens':max_tokens}
+        prompt = "请用中文详细描述一下图中的内容，比如时间，地点，人物，事情，人物心情等，如果有数据请提取出数据。" if self.lang.lower() == "chinese" else \
+            "Please describe the content of this picture, like where, when, who, what happen. If it has number data, please extract them out."
+        b64 = self.image2base64(image) 
+        img = open(BytesIO(base64.b64decode(b64))) 
+        input = [prompt,img]
+        res = self.model.generate_content(
+            input,
+            generation_config=gen_config,
+        )
+        return res.text,res.usage_metadata.total_token_count
 
 class LocalCV(Base):
     def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):

rag/llm/embedding_model.py

@@ -31,7 +31,7 @@
 import asyncio
 from api.utils.file_utils import get_home_cache_dir
 from rag.utils import num_tokens_from_string, truncate
-
+import google.generativeai as genai 
 
 class Base(ABC):
     def __init__(self, key, model_name):
@@ -419,3 +419,27 @@ def encode_queries(self, text):
 
         return np.array(embeddings), token_count
 
+class GeminiEmbed(Base):
+    def __init__(self, key, model_name='models/text-embedding-004',
+                 **kwargs):
+        genai.configure(api_key=key)
+        self.model_name = 'models/' + model_name
+
+    def encode(self, texts: list, batch_size=32):
+        texts = [truncate(t, 2048) for t in texts]
+        token_count = sum(num_tokens_from_string(text) for text in texts)
+        result = genai.embed_content(
+            model=self.model_name,
+            content=texts,
+            task_type="retrieval_document",
+            title="Embedding of list of strings")
+        return np.array(result['embedding']),token_count
+
+    def encode_queries(self, text):
+        result = genai.embed_content(
+            model=self.model_name,
+            content=truncate(text,2048),
+            task_type="retrieval_document",
+            title="Embedding of single string")
+        token_count = num_tokens_from_string(text)
+        return np.array(result['embedding']),token_count

requirements.txt

@@ -147,3 +147,4 @@ markdown==3.6
 mistralai==0.4.2
 boto3==1.34.140
 duckduckgo_search==6.1.9
+google-generativeai==0.7.2

requirements_arm.txt

@@ -148,3 +148,4 @@ markdown==3.6
 mistralai==0.4.2
 boto3==1.34.140
 duckduckgo_search==6.1.9
+google-generativeai==0.7.2

requirements_dev.txt

@@ -133,3 +133,4 @@ markdown==3.6
 mistralai==0.4.2
 boto3==1.34.140
 duckduckgo_search==6.1.9
+google-generativeai==0.7.2

web/src/pages/user-setting/setting-model/index.tsx

@@ -61,6 +61,7 @@ const IconMap = {
   Mistral: 'mistral',
   'Azure-OpenAI': 'azure',
   Bedrock: 'bedrock',
+  Gemini:'gemini',
 };
 
 const LlmIcon = ({ name }: { name: string }) => {