Spaces:
Running
Running
feat(rag): working rag tool with sources
Browse files- .gitattributes +1 -0
- .python-version +1 -0
- README.md +2 -2
- app.py +132 -0
- requirements.txt +4 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.pdf filter=lfs diff=lfs merge=lfs -text
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.python-version
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rag-tool-template
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README.md
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---
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title: Rag Tool Template
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emoji: π
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colorFrom: indigo
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colorTo: blue
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@@ -9,4 +9,4 @@ app_file: app.py
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pinned: false
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---
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---
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title: Rag Conmmunity Tool Template
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emoji: π
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colorFrom: indigo
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colorTo: blue
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pinned: false
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---
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Clone this space, add your documents to the `sources` folder and use your space directly from HuggingChat!
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app.py
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import gradio as gr
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import spaces
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import subprocess
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import os
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import shutil
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import string
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import random
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import glob
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from pypdf import PdfReader
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from sentence_transformers import SentenceTransformer
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model_name = os.environ.get("MODEL", "Snowflake/snowflake-arctic-embed-m")
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chunk_size = int(os.environ.get("CHUNK_SIZE", 128))
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default_max_characters = int(os.environ.get("DEFAULT_MAX_CHARACTERS", 258))
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model = SentenceTransformer(model_name)
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# model.to(device="cuda")
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@spaces.GPU
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def embed(queries, chunks) -> dict[str, list[tuple[str, float]]]:
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query_embeddings = model.encode(queries, prompt_name="query")
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document_embeddings = model.encode(chunks)
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scores = query_embeddings @ document_embeddings.T
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results = {}
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for query, query_scores in zip(queries, scores):
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chunk_idxs = [i for i in range(len(chunks))]
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# Get a structure like {query: [(chunk_idx, score), (chunk_idx, score), ...]}
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results[query] = list(zip(chunk_idxs, query_scores))
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return results
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def extract_text_from_pdf(reader):
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full_text = ""
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for idx, page in enumerate(reader.pages):
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text = page.extract_text()
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if len(text) > 0:
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full_text += f"---- Page {idx} ----\n" + page.extract_text() + "\n\n"
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return full_text.strip()
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def convert(filename) -> str:
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plain_text_filetypes = [
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".txt",
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".csv",
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".tsv",
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".md",
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".yaml",
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".toml",
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".json",
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".json5",
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".jsonc",
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]
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# Already a plain text file that wouldn't benefit from pandoc so return the content
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if any(filename.endswith(ft) for ft in plain_text_filetypes):
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with open(filename, "r") as f:
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return f.read()
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if filename.endswith(".pdf"):
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return extract_text_from_pdf(PdfReader(filename))
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raise ValueError(f"Unsupported file type: {filename}")
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def chunk_to_length(text, max_length=512):
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chunks = []
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while len(text) > max_length:
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chunks.append(text[:max_length])
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text = text[max_length:]
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chunks.append(text)
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return chunks
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@spaces.GPU
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def predict(query, max_characters) -> str:
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# Embed the query
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query_embedding = model.encode(query, prompt_name="query")
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# Initialize a list to store all chunks and their similarities across all documents
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all_chunks = []
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# Iterate through all documents
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for filename, doc in docs.items():
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# Calculate dot product between query and document embeddings
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similarities = doc["embeddings"] @ query_embedding.T
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# Add chunks and similarities to the all_chunks list
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all_chunks.extend([(filename, chunk, sim) for chunk, sim in zip(doc["chunks"], similarities)])
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# Sort all chunks by similarity
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all_chunks.sort(key=lambda x: x[2], reverse=True)
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# Initialize a dictionary to store relevant chunks for each document
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relevant_chunks = {}
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# Add most relevant chunks until max_characters is reached
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total_chars = 0
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for filename, chunk, _ in all_chunks:
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if total_chars + len(chunk) <= max_characters:
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if filename not in relevant_chunks:
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relevant_chunks[filename] = []
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relevant_chunks[filename].append(chunk)
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total_chars += len(chunk)
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else:
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break
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return relevant_chunks
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docs = {}
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for filename in glob.glob("sources/*"):
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converted_doc = convert(filename)
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chunks = chunk_to_length(converted_doc, chunk_size)
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embeddings = model.encode(chunks)
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docs[filename] = {
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"chunks": chunks,
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"embeddings": embeddings,
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}
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gr.Interface(
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predict,
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inputs=[
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gr.Textbox(label="Query asked about the documents"),
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gr.Number(label="Max output characters", value=default_max_characters),
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],
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outputs=[gr.JSON(label="Relevant chunks")],
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).launch()
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requirements.txt
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pypdf==4.2.0
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sentence-transformers==3.0.0
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gradio
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spaces
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