Create utils.py

utils.py

@@ -0,0 +1,143 @@
+# utils 
+
+from langchain_chroma import Chroma
+from langchain_nomic.embeddings import NomicEmbeddings
+from langchain_core.documents import Document
+from langchain.retrievers.document_compressors import CohereRerank
+#from langchain_core import CohereRerank
+#from langchain_cohere import CohereRerank
+
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain.retrievers import EnsembleRetriever
+from langchain.retrievers import BM25Retriever
+from langchain_groq import ChatGroq
+
+from dotenv import load_dotenv
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import Runnable, RunnableMap
+from langchain.schema import BaseRetriever
+from qdrant_client import models
+
+
+from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+
+load_dotenv()
+
+import os
+LANGCHAIN_API_KEY = os.getenv('LANGCHAIN_API_KEY')
+
+#Retriever
+
+def get_retriever(n_docs=5):  # renamed function
+    vector_database_path = "db"
+    
+    embedding_model = NomicEmbeddings(model="nomic-embed-text-v1.5", inference_mode="local")
+    
+    vectorstore = Chroma(collection_name="chromadb3",
+                        persist_directory=vector_database_path,
+                        embedding_function=embedding_model)
+    
+    vs_retriever = vectorstore.as_retriever(k=n_docs)
+    
+    # Get documents from vector store
+    try:
+        store_data = vectorstore.get()
+        texts = store_data['documents']
+        metadatas = store_data['metadatas']
+        
+        if not texts:  # If no documents found
+            print("Warning: No documents found in vector store. Using vector retriever only.")
+            return vs_retriever
+            
+        # Create documents with explicit IDs
+        documents = []
+        for i, (text, metadata) in enumerate(zip(texts, metadatas)):
+            doc = Document(
+                page_content=text,
+                metadata=metadata if metadata else {},
+                id_=str(i)  # Add explicit ID
+            )
+            documents.append(doc)
+        
+        # Create BM25 retriever with explicit document handling
+        keyword_retriever = BM25Retriever.from_texts(
+            texts=[doc.page_content for doc in documents],
+            metadatas=[doc.metadata for doc in documents],
+            ids=[doc.id_ for doc in documents]
+        )
+        keyword_retriever.k = n_docs
+        
+        ensemble_retriever = EnsembleRetriever(
+            retrievers=[vs_retriever, keyword_retriever],
+            weights=[0.5, 0.5]
+        )
+        
+        compressor = CohereRerank(model="rerank-english-v3.0")
+        compression_retriever = ContextualCompressionRetriever(
+            base_compressor=compressor,
+            base_retriever=ensemble_retriever
+        )
+        
+        return compression_retriever
+        
+    except Exception as e:
+        print(f"Warning: Error creating combined retriever ({str(e)}). Using vector retriever only.")
+        return vs_retriever
+
+#Retriever prompt
+rag_prompt = """You are a medical chatbot designed to answer health-related questions.
+The questions you will receive will primarily focus on medical topics and patient care.
+Here is the context to use to answer the question:
+{context}
+Think carefully about the above context.
+Now, review the user question:
+{input}
+Provide an answer to this question using only the above context.
+Answer:"""
+
+# Post-processing
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+#RAG chain
+def get_expression_chain(retriever: BaseRetriever, model_name="llama-3.1-70b-versatile", temp=0 ) -> Runnable:
+    """Return a chain defined primarily in LangChain Expression Language"""
+    def retrieve_context(input_text):
+        # Use the retriever to fetch relevant documents
+        docs = retriever.get_relevant_documents(input_text)
+        return format_docs(docs)
+    
+    ingress = RunnableMap(
+        {
+            "input": lambda x: x["input"],
+            "context": lambda x: retrieve_context(x["input"]),
+        }
+    )
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                rag_prompt
+            )
+        ]
+    )
+    llm = ChatGroq(model=model_name,api_key="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", temperature=temp)
+
+    chain = ingress | prompt | llm
+    return chain
+
+embedding_model = NomicEmbeddings(model="nomic-embed-text-v1.5", inference_mode="local")
+#embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+
+#Generate embeddings for a given text
+def get_embeddings(text):
+    return embedding_model.embed([text], task_type='search_document')[0]
+
+
+# Create or connect to a Qdrant collection
+def create_qdrant_collection(client, collection_name):
+    if collection_name not in client.get_collections().collections:
+        client.create_collection(
+            collection_name=collection_name,
+            vectors_config=models.VectorParams(size=768, distance=models.Distance.COSINE)
+        )