File size: 8,804 Bytes
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from typing import List, Dict, Any, Tuple
from langchain_core.documents import Document
from models.llm import GroqLLM
from rag.retriever import AdvancedRetriever
from rag.memory import ConversationMemory
from rag.prompt import QA_PROMPT, REWRITE_PROMPT, MAP_PROMPT, REDUCE_PROMPT
from loaders.pdf_loader import load_multiple_pdfs
from chunking.splitter import split_documents
from utils.file_registry import (
compute_file_hash,
load_registry,
register_file,
is_duplicate,
clear_registry,
)
import time
class RAGChain:
"""
Main orchestration class for the RAG pipeline.
Handles history-aware retrieval, generation, map-reduce summarization,
and document indexing to unify the backend logic for Gradio and FastAPI.
"""
def __init__(self, retriever: AdvancedRetriever, memory: ConversationMemory):
# We lazy-load the LLM so the Gradio UI can boot up quickly.
self.llm_wrapper = None
self.langchain_llm = None
self.retriever = retriever
self.memory = memory
self.indexed_documents: List[Document] = []
def ensure_llm(self):
"""Lazy loader for the LLM."""
if self.llm_wrapper is None:
print("Initializing Groq Llama 3.1 8B...")
self.llm_wrapper = GroqLLM()
self.langchain_llm = self.llm_wrapper.get_llm()
print("Groq Llama 3.1 8B initialized.")
def index_documents(self, filepaths: List[str]) -> Dict[str, Any]:
"""
Hash-guarded ingestion pipeline.
For every supplied filepath:
1. Compute SHA-256 hash.
2. Look up hash in the on-disk registry.
3. Skip the file (no chunking, no embedding, no FAISS insertion) if
it has already been indexed.
4. Otherwise: load → chunk → embed → insert into FAISS → register.
Returns a summary dict with per-file status so callers can build
informative UI messages.
"""
registry = load_registry()
skipped: List[str] = []
indexed: List[str] = []
total_new_chunks = 0
vectorstore = self.retriever.vectorstore
for filepath in filepaths:
filename = os.path.basename(filepath)
file_hash = compute_file_hash(filepath)
if is_duplicate(registry, file_hash):
skipped.append(filename)
continue
# --- New file: run the full ingestion pipeline ---
docs = load_multiple_pdfs([filepath])
if not docs:
# Non-extractable PDF; skip silently rather than aborting the
# whole batch.
skipped.append(filename)
continue
self.indexed_documents.extend(docs)
chunks = split_documents(docs)
if vectorstore.vectorstore is None:
vectorstore.create_index(chunks)
else:
vectorstore.vectorstore.add_documents(chunks)
vectorstore.save_index()
# Persist hash → metadata so subsequent uploads are detected.
register_file(registry, file_hash, filename, len(chunks))
indexed.append(filename)
total_new_chunks += len(chunks)
# --- Build a human-readable summary message ---
parts: List[str] = []
if indexed:
parts.append(
f"Indexed {len(indexed)} new file(s) into {total_new_chunks} chunk(s): "
+ ", ".join(indexed)
)
if skipped:
parts.append(
f"Skipped {len(skipped)} duplicate file(s): " + ", ".join(skipped)
)
if not parts:
parts.append("No files were processed.")
return {
"message": " | ".join(parts),
"num_new_chunks": total_new_chunks,
"indexed": indexed,
"skipped": skipped,
}
def rewrite_query(self, query: str) -> str:
"""Rewrites the query into a standalone question using chat history."""
self.ensure_llm()
history_str = self.memory.get_history_string()
if history_str == "No previous history.":
return query # No rewrite needed for the first turn
prompt_text = REWRITE_PROMPT.format(chat_history=history_str, question=query)
rewritten = self.langchain_llm.invoke(prompt_text)
# Clean up output if the model rambles
content = rewritten.content if hasattr(rewritten, 'content') else str(rewritten)
return content.strip().split("\n")[0]
def ask(self, query: str, stream: bool = True) -> Dict[str, Any]:
"""
Main Q&A function.
1. Rewrite query if there's history.
2. Retrieve and rerank context.
3. Generate answer (streaming or synchronous).
"""
self.ensure_llm()
start_time = time.time()
# 1. Query Rewrite
standalone_query = self.rewrite_query(query)
# 2. Retrieval
docs_with_scores = self.retriever.retrieve(standalone_query)
context_str = self.retriever.format_docs(docs_with_scores) if docs_with_scores else ""
# 3. Prompt Construction
prompt_text = QA_PROMPT.format(context=context_str, question=standalone_query)
response_data = {
"query": query,
"standalone_query": standalone_query,
"context_docs": docs_with_scores,
"time_taken": 0.0
}
# 4. Generation
if stream:
# Note: For streaming, we return the streamer object. The caller needs to iterate over it
# and update the memory after streaming finishes.
streamer = self.llm_wrapper.generate_stream(prompt_text)
response_data["streamer"] = streamer
else:
answer = self.langchain_llm.invoke(prompt_text)
answer_text = answer.content if hasattr(answer, 'content') else str(answer)
response_data["answer"] = answer_text.strip()
self.memory.add_user_message(query)
self.memory.add_assistant_message(response_data["answer"])
response_data["time_taken"] = time.time() - start_time
return response_data
def summarize(self) -> str:
"""
Map-Reduce Summarization:
1. Map: Summarize each chunk individually.
2. Reduce: Combine chunk summaries into a final structured summary.
"""
if not self.indexed_documents:
raise ValueError("No documents indexed to summarize.")
self.ensure_llm()
# 1. Map phase
intermediate_summaries = []
for doc in self.indexed_documents:
map_prompt_text = MAP_PROMPT.format(text=doc.page_content)
chunk_summary = self.langchain_llm.invoke(map_prompt_text)
chunk_text = chunk_summary.content if hasattr(chunk_summary, 'content') else str(chunk_summary)
intermediate_summaries.append(chunk_text.strip())
# 2. Reduce phase
combined_summaries = "\n\n".join(intermediate_summaries)
reduce_prompt_text = REDUCE_PROMPT.format(text=combined_summaries)
final_summary = self.langchain_llm.invoke(reduce_prompt_text)
final_text = final_summary.content if hasattr(final_summary, 'content') else str(final_summary)
return final_text.strip()
# ---------------------------------------------------------------------------
# Module-level utility — does NOT touch LLM, memory, or retriever wiring
# ---------------------------------------------------------------------------
def reset_vector_store(rag_chain: "RAGChain") -> None:
"""
Hard-reset the vector database.
Actions performed (in order):
1. Delete every file inside the FAISS index directory.
2. Remove the processed-files registry (processed_files.json).
3. Set the in-memory vectorstore to None.
4. Clear the cached list of indexed documents.
The Groq LLM, conversation memory, embeddings model, reranker, and all
UI components are left entirely unaffected.
Args:
rag_chain: The live RAGChain instance to reset.
"""
vs_wrapper = rag_chain.retriever.vectorstore
index_path = vs_wrapper.index_path
# 1. Delete FAISS index files from disk
if os.path.isdir(index_path):
for fname in os.listdir(index_path):
fpath = os.path.join(index_path, fname)
if os.path.isfile(fpath):
os.remove(fpath)
# 2. Delete the hash registry
clear_registry()
# 3. Reset in-memory state
vs_wrapper.vectorstore = None
rag_chain.indexed_documents = []
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