added the Ollama Chat instances with RAG and without

scripts/BaseOllama.py

@@ -0,0 +1,30 @@
+from langchain_community.chat_models import ChatOllama
+
+class OllamaChatBot:
+    def __init__(self, base_url, model, headers):
+        self.base_url = base_url
+        self.model = model
+        self.headers = headers
+        self.messanges = []
+
+        if headers is None:
+            self.ollama = ChatOllama(
+            base_url=self.base_url,
+            model=self.model,
+        )
+        else:
+            self.ollama = ChatOllama(
+                base_url=self.base_url,
+                model=self.model,
+                headers = self.headers
+            )
+        
+
+    def get_request(self, prompt):
+        messanges = []
+        self.messanges.append(prompt)
+        if len(self.messanges) > 5:
+            messanges = messanges[:5]
+        else:
+            messanges = self.messanges
+        return self.ollama.invoke(messanges).content

scripts/Rag.py

@@ -0,0 +1,83 @@
+from langchain_community.document_loaders import UnstructuredHTMLLoader, WebBaseLoader, UnstructuredMarkdownLoader, PyPDFLoader
+from langchain_community.document_loaders.csv_loader import CSVLoader
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_community.chat_models import ChatOllama
+from langchain.chains import RetrievalQA
+from pathlib import Path
+import json
+
+
+
+class Rag:
+    def __init__(self, embeddings, model,
+                 base_url_llm,base_url_embed, base_header,
+                 embeddings_header):
+        self.retriever = None
+        self.data = None
+        self.model = model
+        
+        self.base_url_llm = base_url_llm
+        self.base_url_embed = base_url_embed
+        self.base_header = base_header
+        self.embeddings_header = embeddings_header
+        self.embeddings = OllamaEmbeddings(model=embeddings, headers=self.embeddings_header, base_url=self.base_url_embed)
+
+    def init_ollama(self):
+        self.chat_ollama =  ChatOllama(
+            base_url=self.base_url_llm,
+            model=self.model,
+            headers = self.base_header
+        )
+
+    def get_file(self, file_path):
+        # Check if the file path starts with 'https://'
+        if file_path.startswith('https://'):
+            loader = WebBaseLoader(file_path)
+            data = loader.load()
+            if data is None:
+                return False
+        else:
+            file_type = file_path.split(".")[-1]
+            try:
+                match file_type:
+                    case "csv":
+                        loader = CSVLoader(file_path=file_path)
+                        data = loader.load()
+                    case "html": # Corrected the typo in the variable name
+                        loader = UnstructuredHTMLLoader(file_path=file_path)
+                        data = loader.load()
+                    case "json":
+                        data = json.loads(Path(file_path).read_text())
+                    case "md":
+                        loader = UnstructuredMarkdownLoader(file_path=file_path)
+                        data = loader.load()
+                    case "pdf":
+                        loader = PyPDFLoader(file_path=file_path)
+                        data = loader.load_and_split()
+                    case _:
+                        loader = WebBaseLoader(file_path)
+                        data = loader.load()
+            except OSError:
+                return False
+            
+        self.data = data    
+        return True
+            
+
+
+    def receive_data(self, file_path):
+        if self.get_file(file_path):
+            text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=0)
+            splitted =  text_splitter.split_documents(self.data)
+            self.retriever = Chroma.from_documents(documents=splitted, embedding=self.embeddings).as_retriever()
+            return (False, "Success")
+        else:
+            return (True, f"'{file_path}' unsupported, read documentation for more information")    
+
+    def get_request(self, prompt):
+        qachain=RetrievalQA.from_chain_type(self.chat_ollama, retriever=self.retriever)
+        return qachain.invoke({"query": prompt})["result"]
+