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import os
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from dotenv import load_dotenv
load_dotenv()
# Build FAISS retriever from raw text
def build_retriever_from_text(text: str, chunk_size: int = 800, overlap: int = 100, k: int = 3):
splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=overlap)
chunks = splitter.split_text(text)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vs = FAISS.from_texts(chunks, embeddings)
return vs.as_retriever(search_kwargs={"k": k})
# Together.ai LLM (LLaMA) factory
def together_llm(model: str = "meta-llama/Llama-Vision-Free", temperature: float = 0.2, max_tokens: int = 512):
return ChatOpenAI(
model=model,
temperature=temperature,
max_tokens=max_tokens,
openai_api_key=os.getenv("TOGETHER_API_KEY"),
openai_api_base="https://api.together.xyz/v1"
)
# Q&A over PDF (RAG)
def rag_qa(text: str, question: str, model: str = "meta-llama/Llama-Vision-Free"):
retriever = build_retriever_from_text(text)
llm = together_llm(model=model)
qa = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
return_source_documents=True,
chain_type="stuff"
)
result = qa({"query": question})
return result["result"], result.get("source_documents", [])
# Summarize PDF text
def summarize_text(text: str, model: str = "meta-llama/Llama-Vision-Free"):
prompt = (
"You are a concise technical summarizer. Summarize the following document in 6-10 bullet points, "
"preserving key facts, numbers, and definitions. Text:\n\n"
f"{text}"
)
llm = together_llm(model=model, temperature=0.2, max_tokens=400)
output = llm.invoke(prompt)
return output.content.strip()
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