diff --git a/local_rag.py b/local_rag.py
index 2f62ebb..4745412 100644
--- a/local_rag.py
+++ b/local_rag.py
@@ -1,5 +1,5 @@
"""
-Local RAG setup with LangChain, Ollama, and FAISS
+Local RAG setup with LangChain, Ollama/OpenAI, and FAISS
Minimal dependencies, simple code
"""
import os
@@ -9,34 +9,51 @@ from langchain_community.document_loaders import PyPDFLoader, TextLoader
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_ollama import ChatOllama
+from langchain_openai import ChatOpenAI
from langchain_text_splitters import RecursiveCharacterTextSplitter
class LocalRAG:
- def __init__(self, vectorstore_path="./vectorstore", ollama_model="mistral:7b"):
- """Initialize local RAG system"""
+ def __init__(
+ self,
+ vectorstore_path="./vectorstore",
+ llm_provider="ollama",
+ ollama_model="gpt-oss:20b",
+ openai_model="gpt-5.2",
+ ollama_base_url="http://localhost:11434",
+ ):
+ """Initialize local RAG system. llm_provider: 'ollama' or 'openai'."""
self.vectorstore_path = vectorstore_path
- self.ollama_model = ollama_model
-
+ self.llm_provider = llm_provider
+
# Embeddings
print("Loading embeddings model...")
self.embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
-
+
# Text splitter
self.text_splitter = RecursiveCharacterTextSplitter(
chunk_size=2000,
chunk_overlap=400
)
-
- # Ollama LLM
- print(f"Connecting to Ollama (model: {ollama_model})...")
- self.llm = ChatOllama(
- model=ollama_model,
- base_url="http://localhost:11434"
- )
-
+
+ # LLM (Ollama or OpenAI)
+ if llm_provider == "openai":
+ api_key = os.environ.get("OPENAI_API_KEY")
+ if not api_key:
+ raise ValueError(
+ "OPENAI_API_KEY environment variable is required when llm_provider='openai'"
+ )
+ print(f"Using OpenAI (model: {openai_model})...")
+ self.llm = ChatOpenAI(model=openai_model, api_key=api_key)
+ else:
+ print(f"Using Ollama (model: {ollama_model})...")
+ self.llm = ChatOllama(
+ model=ollama_model,
+ base_url=ollama_base_url
+ )
+
# Vector store (load if exists, otherwise None)
self.vectorstore = None
self._load_vectorstore()
diff --git a/requirements.txt b/requirements.txt
index 153a7cb..b1c1fdb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,7 @@
langchain
langchain-community
langchain-ollama
+langchain-openai
langchain-text-splitters
langchain-huggingface
faiss-cpu
diff --git a/server.py b/server.py
index 3e9979a..92d9556 100644
--- a/server.py
+++ b/server.py
@@ -10,10 +10,18 @@ from pydantic import BaseModel
from local_rag import LocalRAG
-# Initialize RAG once at startup
-VECTORSTORE_PATH = "./vectorstore"
+# LLM provider: "ollama" or "openai"
+LLM_PROVIDER = "openai"
OLLAMA_MODEL = "gpt-oss:20b"
-rag = LocalRAG(vectorstore_path=VECTORSTORE_PATH, ollama_model=OLLAMA_MODEL)
+OPENAI_MODEL = "gpt-5.2"
+
+VECTORSTORE_PATH = "./vectorstore"
+rag = LocalRAG(
+ vectorstore_path=VECTORSTORE_PATH,
+ llm_provider=LLM_PROVIDER,
+ ollama_model=OLLAMA_MODEL,
+ openai_model=OPENAI_MODEL,
+)
app = FastAPI(title="Local RAG Chat", version="1.0.0")
@@ -64,15 +72,16 @@ def chat(request: ChatRequest):
answer = result["answer"]
retrieved = result.get("retrieved", [])
- # Server-side console trace: log retrieved chunks before LLM answer
+ # Server-side console trace: shorter chunk logs + raw LLM response
if retrieved:
- print(f"\n[RAG] Retrieved {len(retrieved)} chunk(s) for query: {request.message[:80]!r}")
+ print(f"\n[RAG] Retrieved {len(retrieved)} chunk(s)")
for i, chunk in enumerate(retrieved):
content = chunk.get("content", "")
- preview = (content[:1000] + "...") if len(content) > 1000 else content
- print(f" [{i + 1}] source={chunk.get('source', '')} page={chunk.get('page')} | {preview!r}")
+ preview = (content[:80] + "...") if len(content) > 80 else content
+ print(f" [{i + 1}] {chunk.get('source', '')} p.{chunk.get('page', '?')} s={chunk.get('score')} | {preview!r}")
else:
- print(f"\n[RAG] Retrieved 0 chunks for query: {request.message[:80]!r}")
+ print(f"\n[RAG] Retrieved 0 chunks")
+ print(f"[RAG] LLM response:\n{answer}")
return ChatResponse(answer=answer, retrieved=retrieved)
except Exception as e:
diff --git a/templates/chat.html b/templates/chat.html
index 22267f7..16654e3 100644
--- a/templates/chat.html
+++ b/templates/chat.html
@@ -45,9 +45,45 @@
border-radius: 8px;
font-size: 0.9rem;
line-height: 1.5;
- white-space: pre-wrap;
word-break: break-word;
}
+ .msg.user, .msg.error {
+ white-space: pre-wrap;
+ }
+ .msg.assistant .markdown-body {
+ white-space: normal;
+ }
+ .msg.assistant .markdown-body h1, .msg.assistant .markdown-body h2, .msg.assistant .markdown-body h3 {
+ margin: 0.75em 0 0.35em;
+ font-size: 1em;
+ font-weight: 600;
+ }
+ .msg.assistant .markdown-body h1:first-child, .msg.assistant .markdown-body h2:first-child, .msg.assistant .markdown-body h3:first-child { margin-top: 0; }
+ .msg.assistant .markdown-body p { margin: 0.5em 0; }
+ .msg.assistant .markdown-body p:first-child { margin-top: 0; }
+ .msg.assistant .markdown-body p:last-child { margin-bottom: 0; }
+ .msg.assistant .markdown-body pre {
+ margin: 0.5em 0;
+ padding: 0.6rem;
+ background: #18181b;
+ border-radius: 6px;
+ overflow-x: auto;
+ font-size: 0.85em;
+ }
+ .msg.assistant .markdown-body code {
+ background: #18181b;
+ padding: 0.15em 0.35em;
+ border-radius: 4px;
+ font-size: 0.9em;
+ }
+ .msg.assistant .markdown-body pre code {
+ padding: 0;
+ background: none;
+ }
+ .msg.assistant .markdown-body ul, .msg.assistant .markdown-body ol { margin: 0.5em 0; padding-left: 1.4em; }
+ .msg.assistant .markdown-body li { margin: 0.25em 0; }
+ .msg.assistant .markdown-body a { color: #60a5fa; text-decoration: none; }
+ .msg.assistant .markdown-body a:hover { text-decoration: underline; }
.msg.user {
align-self: flex-end;
background: #3f3f46;
@@ -120,11 +156,13 @@
color: #71717a;
}
+
+
Local RAG Chat
- Ask questions about your documents. Answers are generated from the vector store + Ollama.
+ Ask questions about your documents. Answers are generated from the vector store + Ollama / OpenAI.
@@ -143,10 +181,18 @@
const chatHistory = [];
function appendMessage(role, text, isError = false) {
+ text = text ?? '';
const div = document.createElement('div');
div.className = 'msg ' + (isError ? 'error' : role);
const label = role === 'user' ? 'You' : 'RAG';
- div.innerHTML = '' + label + '' + escapeHtml(text);
+ let body;
+ if (role === 'assistant' && !isError) {
+ const rawHtml = marked.parse(text, { gfm: true, breaks: true });
+ body = '' + DOMPurify.sanitize(rawHtml) + '
';
+ } else {
+ body = escapeHtml(text);
+ }
+ div.innerHTML = '' + label + '' + body;
messagesEl.appendChild(div);
messagesEl.scrollTop = messagesEl.scrollHeight;
chatHistory.push({ role: role, content: text });