license

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 (https://github.com/LeonardoMdSACode)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

models/README.md

@@ -1,16 +0,0 @@
-# models/
-
-This directory should hold local model weights for running inference without cloud APIs.
-
-Recommended:
-- small quantized ggml/gguf models for llama-cpp (7B q4_0 or smaller) placed as:
-  models/ggml-model-q4_0.bin
-
-Where to get models:
-- Search Hugging Face for "ggml" or "gguf" builds (TheBloke has many community builds).
-- Always check license/usage terms before downloading.
-
-Note: large model files should not be committed to the repo. Add them to `.gitignore`:
-
-    models/*
-    !models/README.md

multi_doc_chat/logger/__init__.py

@@ -1,9 +0,0 @@
-
-from .cutom_logger import CustomLogger as _CustomLogger  # backward compat
-try:
-    from .cutom_logger import CustomLogger
-except Exception:
-    CustomLogger = _CustomLogger
-
-# Expose a global structlog-style logger used across the codebase
-GLOBAL_LOGGER = CustomLogger().get_logger(__name__)

multi_doc_chat/logger/cutom_logger.py

@@ -1,42 +0,0 @@
-import os
-import logging
-from datetime import datetime
-import structlog
-
-
-class CustomLogger:
-    def __init__(self, log_dir="logs"):
-        self.logs_dir = os.path.join(os.getcwd(), log_dir)
-        os.makedirs(self.logs_dir, exist_ok=True)
-        log_file = f"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log"
-        self.log_file_path = os.path.join(self.logs_dir, log_file)
-
-    def get_logger(self, name=__file__):
-        logger_name = os.path.basename(name)
-
-        file_handler = logging.FileHandler(self.log_file_path)
-        file_handler.setLevel(logging.INFO)
-        file_handler.setFormatter(logging.Formatter("%(message)s"))
-
-        console_handler = logging.StreamHandler()
-        console_handler.setLevel(logging.INFO)
-        console_handler.setFormatter(logging.Formatter("%(message)s"))
-
-        logging.basicConfig(
-            level=logging.INFO,
-            format="%(message)s",
-            handlers=[console_handler, file_handler]
-        )
-
-        structlog.configure(
-            processors=[
-                structlog.processors.TimeStamper(fmt="iso", utc=True, key="timestamp"),
-                structlog.processors.add_log_level,
-                structlog.processors.EventRenamer(to="event"),
-                structlog.processors.JSONRenderer()
-            ],
-            logger_factory=structlog.stdlib.LoggerFactory(),
-            cache_logger_on_first_use=True,
-        )
-
-        return structlog.get_logger(logger_name)

multi_doc_chat/prompts/prompt_library.py

@@ -1,32 +0,0 @@
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-
-contextualize_question_prompt = ChatPromptTemplate.from_messages([
-    ("system", (
-        "Given a conversation history and the most recent user query, rewrite the query as a standalone question "
-        "that makes sense without relying on the previous context. Do not provide an answer—only reformulate the "
-        "question if necessary; otherwise, return it unchanged."
-    )),
-    MessagesPlaceholder("chat_history"),
-    ("human", "{input}"),
-])
-
-# Prompt for answering based on context
-context_qa_prompt = ChatPromptTemplate.from_messages([
-    ("system", (
-        "You are an assistant designed to answer questions using the provided context. Rely only on the retrieved "
-        "information to form your response. If the answer is not found in the context, respond with 'I don't know.' "
-        "Keep your answer concise and no longer than three sentences.\n\n{context}"
-    )),
-    MessagesPlaceholder("chat_history"),
-    ("human", "{input}"),
-])
-
-# Central dictionary to register prompts
-PROMPT_REGISTRY = {
-    "contextualize_question": contextualize_question_prompt,
-    "context_qa": context_qa_prompt,
-}
-
-
-## 1. Hello thier i want to study about RAG
-## 2. what is the full form of it

templates/index.html

@@ -125,7 +125,7 @@
 </head>
 <body>
   <header>
-    <h1>MultiDocChat</h1>
+    <h1>RAG Solution</h1>
   </header>
 
   <main>