Add Hugging API

data_folder/config.yaml

@@ -49,4 +49,7 @@ job_applicants_threshold:
 
 llm_model_type: openai
 llm_model: gpt-4o-mini 
+
+#llm_model_type: huggingface
+#llm_model: 'tiiuae/falcon-7b-instruct'
 # llm_api_url: https://api.pawan.krd/cosmosrp/v1 this field is optional

requirements.txt

@@ -6,6 +6,7 @@ jsonschema==4.23.0
 jsonschema-specifications==2023.12.1
 langchain==0.2.11
 langchain-anthropic
+langchain-huggingface
 langchain-community==0.2.10
 langchain-core===0.2.36
 langchain-google-genai==1.0.10

src/llm/llm_manager.py

@@ -90,6 +90,18 @@ def invoke(self, prompt: str) -> BaseMessage:
         response = self.model.invoke(prompt)
         return response
 
+class HuggingFaceModel(AIModel):
+    def __init__(self, api_key: str, llm_model: str):
+        from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
+        self.model = HuggingFaceEndpoint(repo_id=llm_model, huggingfacehub_api_token=api_key,
+                                   temperature=0.4)
+        self.chatmodel=ChatHuggingFace(llm=self.model)
+
+    def invoke(self, prompt: str) -> BaseMessage:
+        response = self.chatmodel.invoke(prompt)
+        logger.debug("Invoking Model from Hugging Face API")
+        print(response,type(response))
+        return response
 
 class AIAdapter:
     def __init__(self, config: dict, api_key: str):
@@ -111,6 +123,8 @@ def _create_model(self, config: dict, api_key: str) -> AIModel:
             return OllamaModel(llm_model, llm_api_url)
         elif llm_model_type == "gemini":
             return GeminiModel(api_key, llm_model)
+        elif llm_model_type == "huggingface":
+            return HuggingFaceModel(api_key, llm_model)        
         else:
             raise ValueError(f"Unsupported model type: {llm_model_type}")
 
@@ -286,27 +300,46 @@ def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]:
         logger.debug(f"Parsing LLM result: {llmresult}")
 
         try:
-            content = llmresult.content
-            response_metadata = llmresult.response_metadata
-            id_ = llmresult.id
-            usage_metadata = llmresult.usage_metadata
-
-            parsed_result = {
-                "content": content,
-                "response_metadata": {
-                    "model_name": response_metadata.get("model_name", ""),
-                    "system_fingerprint": response_metadata.get("system_fingerprint", ""),
-                    "finish_reason": response_metadata.get("finish_reason", ""),
-                    "logprobs": response_metadata.get("logprobs", None),
-                },
-                "id": id_,
-                "usage_metadata": {
-                    "input_tokens": usage_metadata.get("input_tokens", 0),
-                    "output_tokens": usage_metadata.get("output_tokens", 0),
-                    "total_tokens": usage_metadata.get("total_tokens", 0),
-                },
-            }
-
+            if hasattr(llmresult, 'usage_metadata '):
+                content = llmresult.content
+                response_metadata = llmresult.response_metadata
+                id_ = llmresult.id
+                usage_metadata = llmresult.usage_metadata
+
+                parsed_result = {
+                    "content": content,
+                    "response_metadata": {
+                        "model_name": response_metadata.get("model_name", ""),
+                        "system_fingerprint": response_metadata.get("system_fingerprint", ""),
+                        "finish_reason": response_metadata.get("finish_reason", ""),
+                        "logprobs": response_metadata.get("logprobs", None),
+                    },
+                    "id": id_,
+                    "usage_metadata": {
+                        "input_tokens": usage_metadata.get("input_tokens", 0),
+                        "output_tokens": usage_metadata.get("output_tokens", 0),
+                        "total_tokens": usage_metadata.get("total_tokens", 0),
+                    },
+                }
+            else :  
+                content = llmresult.content
+                response_metadata = llmresult.response_metadata
+                id_ = llmresult.id
+                token_usage = response_metadata['token_usage']
+
+                parsed_result = {
+                    "content": content,
+                    "response_metadata": {
+                        "model_name": response_metadata.get("model", ""),
+                        "finish_reason": response_metadata.get("finish_reason", ""),
+                    },
+                    "id": id_,
+                    "usage_metadata": {
+                        "input_tokens": token_usage.prompt_tokens,
+                        "output_tokens": token_usage.completion_tokens,
+                        "total_tokens": token_usage.total_tokens,
+                    },
+                }                  
             logger.debug(f"Parsed LLM result successfully: {parsed_result}")
             return parsed_result