Update autism_chatbot.py

autism_chatbot.py

@@ -1,194 +1,194 @@
-from langchain.chains import ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
-from langchain.prompts import PromptTemplate
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.llms.base import LLM
-from groq import Groq
-from typing import Any, List, Optional, Dict
-from pydantic import Field, BaseModel
-import os
-
-
-class GroqLLM(LLM, BaseModel):
-    groq_api_key: str = Field(..., description="Groq API Key")
-    model_name: str = Field(default="llama-3.3-70b-versatile", description="Model name to use")
-    client: Optional[Any] = None
-
-    def __init__(self, **data):
-        super().__init__(**data)
-        self.client = Groq(api_key=self.groq_api_key)
-    
-    @property
-    def _llm_type(self) -> str:
-        return "groq"
-
-    def _call(self, prompt: str, stop: Optional[List[str]] = None, **kwargs: Any) -> str:
-        completion = self.client.chat.completions.create(
-            messages=[{"role": "user", "content": prompt}],
-            model=self.model_name,
-            **kwargs
-        )
-        return completion.choices[0].message.content
-    
-    @property
-    def _identifying_params(self) -> Dict[str, Any]:
-        """Get the identifying parameters."""
-        return {
-            "model_name": self.model_name
-        }
-
-
-class AutismResearchBot:
-    def __init__(self, groq_api_key: str, index_path: str = "faiss_index"):
-        # Initialize the Groq LLM
-        self.llm = GroqLLM(
-            groq_api_key=groq_api_key,
-            model_name="llama-3.3-70b-versatile"  # You can adjust the model as needed
-        )
-        
-        # Load the FAISS index
-        self.embeddings = HuggingFaceEmbeddings(
-            model_name="pritamdeka/S-PubMedBert-MS-MARCO",
-            model_kwargs={'device': 'cpu'}
-        )
-
-        self.db = FAISS.load_local(index_path, self.embeddings, allow_dangerous_deserialization = True)
-        
-        # Initialize memory
-        self.memory = ConversationBufferMemory(
-            memory_key="chat_history",
-            return_messages=True,
-            output_key = "answer"
-        )
-        
-        # Create the RAG chain
-        self.qa_chain = self._create_qa_chain()
-    
-    def _create_qa_chain(self):
-        # Define the prompt template
-        template = """You are an expert AI assistant specialized in autism research and diagnostics. You have access to a database of scientific papers, research documents, and diagnostic tools about autism. Use this knowledge to ask targeted questions, gather relevant information, and provide an accurate, evidence-based assessment of the type of autism the person may have. Finally, offer appropriate therapy recommendations.
-
-Context from scientific papers use these context details only when you will at the end provide therapies don't dicusss these midway betwenn the conversation:
-
-{context}
-
-Chat History:
-{chat_history}
-
-Objective:
-
-Ask a series of insightful, diagnostic questions to gather comprehensive information about the individual's or their child's behaviors, challenges, and strengths.
-Analyze the responses given to these questions using knowledge from the provided research context.
-Determine the type of autism the individual may have based on the gathered data.
-Offer evidence-based therapy recommendations tailored to the identified type of autism.
-Instructions:
-
-Introduce yourself in the initial message. Please note not to reintroduce yourself in subsequent messages within the same chat.
-Each question should be clear, accessible, and empathetic while maintaining scientific accuracy.
-Ensure responses and questions demonstrate sensitivity to the diverse experiences of individuals with autism and their families.
-Cite specific findings or conclusions from the research context where relevant.
-Acknowledge any limitations or uncertainties in the research when analyzing responses.
-Aim for conciseness in responses, ensuring clarity and brevity without losing essential details.
-Initial Introduction:
-“”"
-
-Hello, I am an AI assistant specialized in autism research and diagnostics. I am here to gather some information to help provide an evidence-based assessment and recommend appropriate therapies.
-
-“”"
-
-Initial Diagnostic Question:
-“”"
-
-To begin, can you describe some of the behaviors or challenges that prompted you to seek this assessment?
-
-“”"
-
-Subsequent Questions: (Questions should follow based on the user's answers, aiming to gather necessary details concisely)
-
-question :
-{question}
-
-Answer:"""
-
-        PROMPT = PromptTemplate(
-            template=template,
-            input_variables=["context", "chat_history", "question"]
-        )
-        
-        # Create the chain
-        chain = ConversationalRetrievalChain.from_llm(
-            llm=self.llm,
-            chain_type="stuff",
-            retriever=self.db.as_retriever(
-                search_type="similarity",
-                search_kwargs={"k": 3}
-            ),
-            memory=self.memory,
-            combine_docs_chain_kwargs={
-                "prompt": PROMPT
-            },
-            # verbose = True,
-            return_source_documents=True
-        )
-        
-        return chain
-    
-    def answer_question(self, question: str):
-        """
-        Process a question and return the answer along with source documents
-        """
-        result = self.qa_chain({"question": question})
-        
-        # Extract answer and sources
-        answer = result['answer']
-        sources = result['source_documents']
-        
-        # Format sources for reference
-        source_info = []
-        for doc in sources:
-            source_info.append({
-                'content': doc.page_content[:200] + "...",
-                'metadata': doc.metadata
-            })
-        
-        return {
-            'answer': answer,
-            'sources': source_info
-        }
-
-# Example usage
-if __name__ == "__main__":
-    groq_api_key = "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"
-    
-    # Initialize the bot
-    bot = AutismResearchBot(groq_api_key=groq_api_key)
-    
-    # Example question
-    # question = "What are the latest findings regarding sensory processing in autism?"
-    # response = bot.answer_question(question)
-    while(1):
-        print("*"*40)
-        print("*"*40)
-        print("*"*40)
-        question = input("Enter your question (or 'quit' to exit): ")
-        if question.lower() == 'quit':
-            break
-        response = bot.answer_question(question)
-        print("\nAnswer:")
-        print(response['answer'])
-        # print("\nSources used:")
-        # for source in response['sources']:
-        #     print(f"\nSource metadata: {source['metadata']}")
-        #     print(f"Content preview: {source['content']}")
-        #     bot.answer_question
-    # Print response
-    # print("\nAnswer:")
-    # print(response['answer'])
-    # print("\nSources used:")
-    # for source in response['sources']:
-    #     print(f"\nSource metadata: {source['metadata']}")
-    #     print(f"Content preview: {source['content']}")
-
-
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.llms.base import LLM
+from groq import Groq
+from typing import Any, List, Optional, Dict
+from pydantic import Field, BaseModel
+import os
+
+
+class GroqLLM(LLM, BaseModel):
+    groq_api_key: str = Field(..., description="Groq API Key")
+    model_name: str = Field(default="llama-3.3-70b-versatile", description="Model name to use")
+    client: Optional[Any] = None
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        self.client = Groq(api_key=self.groq_api_key)
+    
+    @property
+    def _llm_type(self) -> str:
+        return "groq"
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None, **kwargs: Any) -> str:
+        completion = self.client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model=self.model_name,
+            **kwargs
+        )
+        return completion.choices[0].message.content
+    
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            "model_name": self.model_name
+        }
+
+
+class AutismResearchBot:
+    def __init__(self, groq_api_key: str, index_path: str = "index.faiss"):
+        # Initialize the Groq LLM
+        self.llm = GroqLLM(
+            groq_api_key=groq_api_key,
+            model_name="llama-3.3-70b-versatile"  # You can adjust the model as needed
+        )
+        
+        # Load the FAISS index
+        self.embeddings = HuggingFaceEmbeddings(
+            model_name="pritamdeka/S-PubMedBert-MS-MARCO",
+            model_kwargs={'device': 'cpu'}
+        )
+
+        self.db = FAISS.load_local(index_path, self.embeddings, allow_dangerous_deserialization = True)
+        
+        # Initialize memory
+        self.memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            return_messages=True,
+            output_key = "answer"
+        )
+        
+        # Create the RAG chain
+        self.qa_chain = self._create_qa_chain()
+    
+    def _create_qa_chain(self):
+        # Define the prompt template
+        template = """You are an expert AI assistant specialized in autism research and diagnostics. You have access to a database of scientific papers, research documents, and diagnostic tools about autism. Use this knowledge to ask targeted questions, gather relevant information, and provide an accurate, evidence-based assessment of the type of autism the person may have. Finally, offer appropriate therapy recommendations.
+
+Context from scientific papers use these context details only when you will at the end provide therapies don't dicusss these midway betwenn the conversation:
+
+{context}
+
+Chat History:
+{chat_history}
+
+Objective:
+
+Ask a series of insightful, diagnostic questions to gather comprehensive information about the individual's or their child's behaviors, challenges, and strengths.
+Analyze the responses given to these questions using knowledge from the provided research context.
+Determine the type of autism the individual may have based on the gathered data.
+Offer evidence-based therapy recommendations tailored to the identified type of autism.
+Instructions:
+
+Introduce yourself in the initial message. Please note not to reintroduce yourself in subsequent messages within the same chat.
+Each question should be clear, accessible, and empathetic while maintaining scientific accuracy.
+Ensure responses and questions demonstrate sensitivity to the diverse experiences of individuals with autism and their families.
+Cite specific findings or conclusions from the research context where relevant.
+Acknowledge any limitations or uncertainties in the research when analyzing responses.
+Aim for conciseness in responses, ensuring clarity and brevity without losing essential details.
+Initial Introduction:
+“”"
+
+Hello, I am an AI assistant specialized in autism research and diagnostics. I am here to gather some information to help provide an evidence-based assessment and recommend appropriate therapies.
+
+“”"
+
+Initial Diagnostic Question:
+“”"
+
+To begin, can you describe some of the behaviors or challenges that prompted you to seek this assessment?
+
+“”"
+
+Subsequent Questions: (Questions should follow based on the user's answers, aiming to gather necessary details concisely)
+
+question :
+{question}
+
+Answer:"""
+
+        PROMPT = PromptTemplate(
+            template=template,
+            input_variables=["context", "chat_history", "question"]
+        )
+        
+        # Create the chain
+        chain = ConversationalRetrievalChain.from_llm(
+            llm=self.llm,
+            chain_type="stuff",
+            retriever=self.db.as_retriever(
+                search_type="similarity",
+                search_kwargs={"k": 3}
+            ),
+            memory=self.memory,
+            combine_docs_chain_kwargs={
+                "prompt": PROMPT
+            },
+            # verbose = True,
+            return_source_documents=True
+        )
+        
+        return chain
+    
+    def answer_question(self, question: str):
+        """
+        Process a question and return the answer along with source documents
+        """
+        result = self.qa_chain({"question": question})
+        
+        # Extract answer and sources
+        answer = result['answer']
+        sources = result['source_documents']
+        
+        # Format sources for reference
+        source_info = []
+        for doc in sources:
+            source_info.append({
+                'content': doc.page_content[:200] + "...",
+                'metadata': doc.metadata
+            })
+        
+        return {
+            'answer': answer,
+            'sources': source_info
+        }
+
+# Example usage
+if __name__ == "__main__":
+    groq_api_key = "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"
+    
+    # Initialize the bot
+    bot = AutismResearchBot(groq_api_key=groq_api_key)
+    
+    # Example question
+    # question = "What are the latest findings regarding sensory processing in autism?"
+    # response = bot.answer_question(question)
+    while(1):
+        print("*"*40)
+        print("*"*40)
+        print("*"*40)
+        question = input("Enter your question (or 'quit' to exit): ")
+        if question.lower() == 'quit':
+            break
+        response = bot.answer_question(question)
+        print("\nAnswer:")
+        print(response['answer'])
+        # print("\nSources used:")
+        # for source in response['sources']:
+        #     print(f"\nSource metadata: {source['metadata']}")
+        #     print(f"Content preview: {source['content']}")
+        #     bot.answer_question
+    # Print response
+    # print("\nAnswer:")
+    # print(response['answer'])
+    # print("\nSources used:")
+    # for source in response['sources']:
+    #     print(f"\nSource metadata: {source['metadata']}")
+    #     print(f"Content preview: {source['content']}")
+
+