Merge pull request #1594 from arc53/googleai-compatability-tools

Googleai compatability tools

application/cache.py

@@ -33,7 +33,7 @@ def gen_cache_key(messages, model="docgpt", tools=None):
     if not all(isinstance(msg, dict) for msg in messages):
         raise ValueError("All messages must be dictionaries.")
     messages_str = json.dumps(messages)
-    tools_str = json.dumps(tools) if tools else ""
+    tools_str = json.dumps(str(tools)) if tools else ""
     combined = f"{model}_{messages_str}_{tools_str}"
     cache_key = get_hash(combined)
     return cache_key
@@ -68,8 +68,8 @@ def wrapper(self, model, messages, stream, tools=None, *args, **kwargs):
 
 
 def stream_cache(func):
-    def wrapper(self, model, messages, stream, *args, **kwargs):
-        cache_key = gen_cache_key(messages)
+    def wrapper(self, model, messages, stream, tools=None, *args, **kwargs):
+        cache_key = gen_cache_key(messages, model, tools)
         logger.info(f"Stream cache key: {cache_key}")
 
         redis_client = get_redis_instance()
@@ -86,7 +86,7 @@ def wrapper(self, model, messages, stream, *args, **kwargs):
             except redis.ConnectionError as e:
                 logger.error(f"Redis connection error: {e}")
 
-        result = func(self, model, messages, stream, *args, **kwargs)
+        result = func(self, model, messages, stream, tools=tools, *args, **kwargs)
         stream_cache_data = []
 
         for chunk in result:

application/llm/base.py

@@ -1,6 +1,7 @@
 from abc import ABC, abstractmethod
+
+from application.cache import gen_cache, stream_cache
 from application.usage import gen_token_usage, stream_token_usage
-from application.cache import stream_cache, gen_cache
 
 
 class BaseLLM(ABC):
@@ -18,18 +19,38 @@ def _raw_gen(self, model, messages, stream, tools, *args, **kwargs):
 
     def gen(self, model, messages, stream=False, tools=None, *args, **kwargs):
         decorators = [gen_token_usage, gen_cache]
-        return self._apply_decorator(self._raw_gen, decorators=decorators, model=model, messages=messages, stream=stream, tools=tools, *args, **kwargs)
+        return self._apply_decorator(
+            self._raw_gen,
+            decorators=decorators,
+            model=model,
+            messages=messages,
+            stream=stream,
+            tools=tools,
+            *args,
+            **kwargs
+        )
 
     @abstractmethod
     def _raw_gen_stream(self, model, messages, stream, *args, **kwargs):
         pass
 
-    def gen_stream(self, model, messages, stream=True, *args, **kwargs):
+    def gen_stream(self, model, messages, stream=True, tools=None, *args, **kwargs):
         decorators = [stream_cache, stream_token_usage]
-        return self._apply_decorator(self._raw_gen_stream, decorators=decorators, model=model, messages=messages, stream=stream, *args, **kwargs)
-
+        return self._apply_decorator(
+            self._raw_gen_stream,
+            decorators=decorators,
+            model=model,
+            messages=messages,
+            stream=stream,
+            tools=tools,
+            *args,
+            **kwargs
+        )
+
     def supports_tools(self):
-        return hasattr(self, '_supports_tools') and callable(getattr(self, '_supports_tools'))
+        return hasattr(self, "_supports_tools") and callable(
+            getattr(self, "_supports_tools")
+        )
 
     def _supports_tools(self):
-        raise NotImplementedError("Subclass must implement _supports_tools method")
+        raise NotImplementedError("Subclass must implement _supports_tools method")

application/llm/google_ai.py

@@ -1,48 +1,149 @@
+from google import genai
+from google.genai import types
+
 from application.llm.base import BaseLLM
 
-class GoogleLLM(BaseLLM):
 
+class GoogleLLM(BaseLLM):
     def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
-
         super().__init__(*args, **kwargs)
         self.api_key = api_key
         self.user_api_key = user_api_key
 
     def _clean_messages_google(self, messages):
-        return [
-            {
-                "role": "model" if message["role"] == "system" else message["role"],
-                "parts": [message["content"]],
-            }
-            for message in messages[1:]
-        ]
+        cleaned_messages = []
+        for message in messages:
+            role = message.get("role")
+            content = message.get("content")
+
+            if role == "assistant":
+                role = "model"
+
+            parts = []
+            if role and content is not None:
+                if isinstance(content, str):
+                    parts = [types.Part.from_text(content)]
+                elif isinstance(content, list):
+                    for item in content:
+                        if "text" in item:
+                            parts.append(types.Part.from_text(item["text"]))
+                        elif "function_call" in item:
+                            parts.append(
+                                types.Part.from_function_call(
+                                    name=item["function_call"]["name"],
+                                    args=item["function_call"]["args"],
+                                )
+                            )
+                        elif "function_response" in item:
+                            parts.append(
+                                types.Part.from_function_response(
+                                    name=item["function_response"]["name"],
+                                    response=item["function_response"]["response"],
+                                )
+                            )
+                        else:
+                            raise ValueError(
+                                f"Unexpected content dictionary format:{item}"
+                            )
+                else:
+                    raise ValueError(f"Unexpected content type: {type(content)}")
+
+                cleaned_messages.append(types.Content(role=role, parts=parts))
+
+        return cleaned_messages
+
+    def _clean_tools_format(self, tools_list):
+        genai_tools = []
+        for tool_data in tools_list:
+            if tool_data["type"] == "function":
+                function = tool_data["function"]
+                genai_function = dict(
+                    name=function["name"],
+                    description=function["description"],
+                    parameters={
+                        "type": "OBJECT",
+                        "properties": {
+                            k: {
+                                **v,
+                                "type": v["type"].upper() if v["type"] else None,
+                            }
+                            for k, v in function["parameters"]["properties"].items()
+                        },
+                        "required": (
+                            function["parameters"]["required"]
+                            if "required" in function["parameters"]
+                            else []
+                        ),
+                    },
+                )
+                genai_tool = types.Tool(function_declarations=[genai_function])
+                genai_tools.append(genai_tool)
+
+        return genai_tools
 
     def _raw_gen(
         self,
         baseself,
         model,
         messages,
         stream=False,
-        **kwargs
-    ):  
-        import google.generativeai as genai
-        genai.configure(api_key=self.api_key)
-        model = genai.GenerativeModel(model, system_instruction=messages[0]["content"])
-        response = model.generate_content(self._clean_messages_google(messages))
-        return response.text
+        tools=None,
+        formatting="openai",
+        **kwargs,
+    ):
+        client = genai.Client(api_key=self.api_key)
+        if formatting == "openai":
+            messages = self._clean_messages_google(messages)
+        config = types.GenerateContentConfig()
+        if messages[0].role == "system":
+            config.system_instruction = messages[0].parts[0].text
+            messages = messages[1:]
+
+        if tools:
+            cleaned_tools = self._clean_tools_format(tools)
+            config.tools = cleaned_tools
+            response = client.models.generate_content(
+                model=model,
+                contents=messages,
+                config=config,
+            )
+            return response
+        else:
+            response = client.models.generate_content(
+                model=model, contents=messages, config=config
+            )
+            return response.text
 
     def _raw_gen_stream(
         self,
         baseself,
         model,
         messages,
         stream=True,
-        **kwargs
-    ):  
-        import google.generativeai as genai
-        genai.configure(api_key=self.api_key)
-        model = genai.GenerativeModel(model, system_instruction=messages[0]["content"])
-        response = model.generate_content(self._clean_messages_google(messages), stream=True)
-        for line in response:
-            if line.text is not None:
-                yield line.text
+        tools=None,
+        formatting="openai",
+        **kwargs,
+    ):
+        client = genai.Client(api_key=self.api_key)
+        if formatting == "openai":
+            messages = self._clean_messages_google(messages)
+        config = types.GenerateContentConfig()
+        if messages[0].role == "system":
+            config.system_instruction = messages[0].parts[0].text
+            messages = messages[1:]
+
+        if tools:
+            cleaned_tools = self._clean_tools_format(tools)
+            config.tools = cleaned_tools
+
+        response = client.models.generate_content_stream(
+            model=model,
+            contents=messages,
+            config=config,
+        )
+        for chunk in response:
+            if chunk.text is not None:
+                yield chunk.text
+
+    def _supports_tools(self):
+        return True

application/llm/openai.py

@@ -1,6 +1,5 @@
-from application.llm.base import BaseLLM
 from application.core.settings import settings
-
+from application.llm.base import BaseLLM
 
 
 class OpenAILLM(BaseLLM):
@@ -10,10 +9,7 @@ def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
 
         super().__init__(*args, **kwargs)
         if settings.OPENAI_BASE_URL:
-            self.client = OpenAI(
-                api_key=api_key,
-                base_url=settings.OPENAI_BASE_URL
-            )
+            self.client = OpenAI(api_key=api_key, base_url=settings.OPENAI_BASE_URL)
         else:
             self.client = OpenAI(api_key=api_key)
         self.api_key = api_key
@@ -27,8 +23,8 @@ def _raw_gen(
         stream=False,
         tools=None,
         engine=settings.AZURE_DEPLOYMENT_NAME,
-        **kwargs
-    ):          
+        **kwargs,
+    ):
         if tools:
             response = self.client.chat.completions.create(
                 model=model, messages=messages, stream=stream, tools=tools, **kwargs
@@ -48,18 +44,16 @@ def _raw_gen_stream(
         stream=True,
         tools=None,
         engine=settings.AZURE_DEPLOYMENT_NAME,
-        **kwargs
-    ):  
+        **kwargs,
+    ):
         response = self.client.chat.completions.create(
             model=model, messages=messages, stream=stream, **kwargs
         )
 
         for line in response:
-            # import sys
-            # print(line.choices[0].delta.content, file=sys.stderr)
             if line.choices[0].delta.content is not None:
                 yield line.choices[0].delta.content
-    
+
     def _supports_tools(self):
         return True
 

application/requirements.txt

@@ -14,6 +14,8 @@ esutils==1.0.1
 Flask==3.1.0
 faiss-cpu==1.9.0.post1
 flask-restx==1.3.0
+google-genai==0.5.0
+google-generativeai==0.8.3
 gTTS==2.5.4
 gunicorn==23.0.0
 html2text==2024.2.26

application/retriever/brave_search.py

@@ -74,12 +74,10 @@ def gen(self):
         if len(self.chat_history) > 1:
             for i in self.chat_history:
                 if "prompt" in i and "response" in i:
-                        messages_combine.append(
-                            {"role": "user", "content": i["prompt"]}
-                        )
-                        messages_combine.append(
-                            {"role": "system", "content": i["response"]}
-                        )
+                    messages_combine.append({"role": "user", "content": i["prompt"]})
+                    messages_combine.append(
+                        {"role": "assistant", "content": i["response"]}
+                    )
         messages_combine.append({"role": "user", "content": self.question})
 
         llm = LLMCreator.create_llm(

application/retriever/classic_rag.py

@@ -5,7 +5,6 @@
 from application.vectorstore.vector_creator import VectorCreator
 
 
-
 class ClassicRAG(BaseRetriever):
 
     def __init__(
@@ -74,13 +73,11 @@ def gen(self):
 
         if len(self.chat_history) > 1:
             for i in self.chat_history:
-                    if "prompt" in i and "response" in i:
-                        messages_combine.append(
-                            {"role": "user", "content": i["prompt"]}
-                        )
-                        messages_combine.append(
-                            {"role": "system", "content": i["response"]}
-                        )
+                if "prompt" in i and "response" in i:
+                    messages_combine.append({"role": "user", "content": i["prompt"]})
+                    messages_combine.append(
+                        {"role": "assistant", "content": i["response"]}
+                    )
         messages_combine.append({"role": "user", "content": self.question})
         # llm = LLMCreator.create_llm(
         #     settings.LLM_NAME, api_key=settings.API_KEY, user_api_key=self.user_api_key

application/retriever/duckduck_search.py

@@ -91,11 +91,9 @@ def gen(self):
         if len(self.chat_history) > 1:      
             for i in self.chat_history:
                     if "prompt" in i and "response" in i:
+                        messages_combine.append({"role": "user", "content": i["prompt"]})
                         messages_combine.append(
-                            {"role": "user", "content": i["prompt"]}
-                        )
-                        messages_combine.append(
-                            {"role": "system", "content": i["response"]}
+                            {"role": "assistant", "content": i["response"]}
                         )
         messages_combine.append({"role": "user", "content": self.question})