├── rag ├── __init__.py └── pipeline.py ├── .gitattributes ├── data └── invoice_1.pdf ├── requirements.txt ├── config.yml ├── README.md ├── main.py ├── ingest.py ├── prompts_zephyr.txt ├── prompts_starling.txt ├── .gitignore └── LICENSE /rag/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /data/invoice_1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/katanaml/llm-ollama-invoice-cpu/HEAD/data/invoice_1.pdf -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | langchain==0.0.344 2 | langchain-experimental==0.0.43 3 | chromadb==0.4.18 4 | sentence_transformers 5 | pypdf 6 | python-box -------------------------------------------------------------------------------- /config.yml: -------------------------------------------------------------------------------- 1 | CHUNK_SIZE: 1000 2 | CHUNK_OVERLAP: 30 3 | NUM_RESULTS: 1 4 | DATA_PATH: 'data/' 5 | EMBEDDINGS: 'sentence-transformers/all-mpnet-base-v2' 6 | VECTOR_DB: 'vectorstore/sparrow' 7 | NORMALIZE_EMBEDDINGS: True 8 | COLLECTION_NAME: 'sparrow' 9 | DEVICE: 'cpu' 10 | VECTOR_SPACE: 'cosine' 11 | LLM: 'starling-lm:7b-alpha-q5_K_M' 12 | #LLM: 'zephyr:7b-alpha-q5_K_M' -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Invoice data processing LLM RAG on CPU with Ollama and ChromaDB 2 | 3 | 4 | Easy-to-Follow RAG Pipeline Tutorial: Invoice Processing with ChromaDB & LangChain 5 | 6 | Secure and Private: On-Premise Invoice Processing with LangChain and Ollama RAG 7 | 8 | ___ 9 | 10 | ## Quickstart 11 | 12 | ### RAG runs offline on local CPU 13 | 14 | 1. Install the requirements: 15 | 16 | ``` 17 | pip install -r requirements.txt 18 | ``` 19 | 20 | 2. Install Ollama and pull LLM model specified in config.yml 21 | 22 | 3. Copy text PDF files to the `data` folder. 23 | 24 | 4. Run the script, to convert text to vector embeddings and save in Chroma vector storage: 25 | 26 | ``` 27 | python ingest.py 28 | ``` 29 | 30 | 5. Run the script, to process data with LLM RAG and return the answer: 31 | 32 | ``` 33 | python main.py "What is the invoice number value?" 34 | ``` 35 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import timeit 2 | import argparse 3 | from rag.pipeline import build_rag_pipeline 4 | import json 5 | 6 | 7 | def get_rag_response(query, chain): 8 | response = chain({'query': query}) 9 | 10 | res = response['result'] 11 | 12 | start_index = res.find('{') 13 | end_index = res.rfind('}') 14 | 15 | if start_index != -1 and end_index != -1 and end_index > start_index: 16 | json_fragment = res[start_index:end_index + 1] 17 | try: 18 | # Convert the extracted string to JSON 19 | json_data = json.loads(json_fragment) 20 | return json_data 21 | except json.JSONDecodeError as e: 22 | print(f"Error parsing JSON: {e}") 23 | else: 24 | print("No JSON object found in the string.") 25 | 26 | return res 27 | 28 | 29 | if __name__ == "__main__": 30 | parser = argparse.ArgumentParser() 31 | parser.add_argument('input', 32 | type=str, 33 | default='What is the invoice number value?', 34 | help='Enter the query to pass into the LLM') 35 | args = parser.parse_args() 36 | 37 | start = timeit.default_timer() 38 | 39 | qa_chain = build_rag_pipeline() 40 | print('Retrieving answer...') 41 | answer = get_rag_response(args.input, qa_chain) 42 | 43 | end = timeit.default_timer() 44 | 45 | print(f'\nAnswer:\n {answer}') 46 | print('=' * 50) 47 | 48 | print(f"Time to retrieve answer: {end - start}") -------------------------------------------------------------------------------- /ingest.py: -------------------------------------------------------------------------------- 1 | from langchain.vectorstores import Chroma 2 | from langchain.text_splitter import RecursiveCharacterTextSplitter 3 | from langchain.document_loaders import PyPDFLoader, DirectoryLoader 4 | from langchain.embeddings import HuggingFaceEmbeddings 5 | import shutil 6 | import box 7 | import yaml 8 | import warnings 9 | 10 | 11 | warnings.filterwarnings("ignore", category=DeprecationWarning) 12 | 13 | 14 | def run_ingest(): 15 | # Import config vars 16 | with open('config.yml', 'r', encoding='utf8') as ymlfile: 17 | cfg = box.Box(yaml.safe_load(ymlfile)) 18 | 19 | loader = DirectoryLoader(cfg.DATA_PATH, 20 | glob='*.pdf', 21 | loader_cls=PyPDFLoader) 22 | 23 | documents = loader.load() 24 | text_splitter = RecursiveCharacterTextSplitter(chunk_size=cfg.CHUNK_SIZE, 25 | chunk_overlap=cfg.CHUNK_OVERLAP) 26 | texts = text_splitter.split_documents(documents) 27 | print(f"Loaded {len(texts)} splits") 28 | 29 | embeddings = HuggingFaceEmbeddings(model_name=cfg.EMBEDDINGS, 30 | model_kwargs={'device': cfg.DEVICE}, 31 | encode_kwargs={'normalize_embeddings': cfg.NORMALIZE_EMBEDDINGS}) 32 | 33 | shutil.rmtree(cfg.VECTOR_DB, ignore_errors=True) 34 | 35 | vector_store = Chroma.from_documents(texts, 36 | embeddings, 37 | collection_name=cfg.COLLECTION_NAME, 38 | collection_metadata={"hnsw:space": cfg.VECTOR_SPACE}, 39 | persist_directory=cfg.VECTOR_DB) 40 | 41 | print(f"Vector store created at {cfg.VECTOR_DB}") 42 | 43 | 44 | if __name__ == "__main__": 45 | run_ingest() -------------------------------------------------------------------------------- /rag/pipeline.py: -------------------------------------------------------------------------------- 1 | from langchain.vectorstores import Chroma 2 | from langchain.embeddings import HuggingFaceEmbeddings 3 | from langchain.prompts import PromptTemplate 4 | from langchain.chains import RetrievalQA 5 | from langchain.llms import Ollama 6 | import box 7 | import yaml 8 | import warnings 9 | 10 | 11 | warnings.filterwarnings("ignore", category=DeprecationWarning) 12 | 13 | 14 | def load_embedding_model(model_name, normalize_embedding=True, device='cpu'): 15 | return HuggingFaceEmbeddings( 16 | model_name=model_name, 17 | model_kwargs={'device': device}, 18 | encode_kwargs={ 19 | 'normalize_embeddings': normalize_embedding 20 | } 21 | ) 22 | 23 | 24 | def load_retriever(embeddings, store_path, collection_name, vector_space, num_results=1): 25 | vector_store = Chroma(collection_name=collection_name, 26 | persist_directory=store_path, 27 | collection_metadata={"hnsw:space": vector_space}, 28 | embedding_function=embeddings) 29 | retriever = vector_store.as_retriever(search_kwargs={"k": num_results}) 30 | 31 | return retriever 32 | 33 | 34 | def load_prompt_template(): 35 | template = """Use the following pieces of information to answer the user's question. 36 | If you don't know the answer, just say that you don't know, don't try to make up an answer. 37 | 38 | Context: {context} 39 | Question: {question} 40 | 41 | Only return the helpful answer below and nothing else. 42 | Helpful answer: 43 | """ 44 | 45 | prompt = PromptTemplate.from_template(template) 46 | 47 | return prompt 48 | 49 | 50 | def load_qa_chain(retriever, llm, prompt): 51 | return RetrievalQA.from_chain_type( 52 | llm=llm, 53 | retriever=retriever, 54 | chain_type="stuff", 55 | return_source_documents=True, 56 | chain_type_kwargs={'prompt': prompt} 57 | ) 58 | 59 | 60 | def build_rag_pipeline(): 61 | # Import config vars 62 | with open('config.yml', 'r', encoding='utf8') as ymlfile: 63 | cfg = box.Box(yaml.safe_load(ymlfile)) 64 | 65 | print("Loading embedding model...") 66 | embeddings = load_embedding_model(model_name=cfg.EMBEDDINGS, 67 | normalize_embedding=cfg.NORMALIZE_EMBEDDINGS, 68 | device=cfg.DEVICE) 69 | 70 | print("Loading vector store and retriever...") 71 | retriever = load_retriever(embeddings, 72 | cfg.VECTOR_DB, 73 | cfg.COLLECTION_NAME, 74 | cfg.VECTOR_SPACE, 75 | cfg.NUM_RESULTS) 76 | 77 | print("Loading prompt template...") 78 | prompt = load_prompt_template() 79 | 80 | print("Loading Ollama...") 81 | llm = Ollama(model=cfg.LLM, verbose=False, temperature=0) 82 | 83 | print("Loading QA chain...") 84 | qa_chain = load_qa_chain(retriever, llm, prompt) 85 | 86 | return qa_chain 87 | 88 | 89 | -------------------------------------------------------------------------------- /prompts_zephyr.txt: -------------------------------------------------------------------------------- 1 | python main.py "retrieve one value: invoice number. format response as following {\"invoice_number\": {}}" 2 | 3 | {'invoice_number': '61356291'} 4 | ================================================== 5 | Time to retrieve answer: 103.90454878797755 6 | 7 | 8 | python main.py "retrieve one value: invoice date. format response as following {\"invoice_date\": {}}" 9 | 10 | {'invoice_date': {'09/06/2012': {}}} 11 | ================================================== 12 | Time to retrieve answer: 104.87561345699942 13 | 14 | 15 | python main.py "retrieve three values: client name, complete address and tax ID. format response as following {\"client_name\": {},\"address\": {},\"tax_id\": {}}" 16 | 17 | {'client_name': 'Rodriguez-Stevens', 'address': '2280 Angela Plain\nHortonshire, MS 93248', 'tax_id': '939-98-8477'} 18 | ================================================== 19 | Time to retrieve answer: 108.91416592500173 20 | 21 | 22 | python main.py "retrieve three values: seller name, complete address and tax ID. format response as following {\"seller_name\": {},\"address\": {},\"tax_id\": {}}" 23 | 24 | {'seller_name': 'Chapman, Kim and Green', 'address': '64731 James Branch\nSmithmouth, NC 26872', 'tax_id': '949-84-9105'} 25 | ================================================== 26 | Time to retrieve answer: 113.15477752301376 27 | 28 | 29 | python main.py "retrieve one value: invoice iban. format response as following {\"invoice_iban\": {}}" 30 | 31 | - 32 | 33 | 34 | python main.py "retrieve two values: net price and gross worth for the second invoice item from the table. format response as following {\"net_price\": {},\"gross_worth\": {}}" 35 | 36 | {'net_price': 280.8, 'gross_worth': 315.4} 37 | ================================================== 38 | Time to retrieve answer: 129.65302655799314 39 | 40 | 41 | python main.py "retrieve gross worth value for each invoice item from the table. format response as following {\"gross_worth\": []}" 42 | 43 | {'gross_worth': [6600, 12355, 825, 1429]} 44 | ================================================== 45 | Time to retrieve answer: 107.56224957900122 46 | 47 | 48 | python main.py "retrieve names of invoice items included into this invoice. format response as following {\"item_name\": []}" 49 | 50 | {'item_name': ['Wine Glasses Goblets Pair Clear', 'With Hooks Stemware Storage', 'Replacement Corkscrew Parts Spiral Worm Wine Opener Bottle Houdini', 'HOME ESSENTIALS GRADIENT STEMLESS WINE GLASSES SET OF 4 20 FL OZ (591 ml) NEW']} 51 | ================================================== 52 | Time to retrieve answer: 135.3677220880054 53 | 54 | 55 | python main.py "retrieve invoice total info. use this format for the answer {\"invoice_total\": {}}" 56 | 57 | {'invoice_total': {'net_worth': 192.81, 'vat': 19.28, 'gross_worth': 212.09}} 58 | ================================================== 59 | Time to retrieve answer: 111.45547287299996 60 | 61 | 62 | python main.py "retrieve three values: total gross worth, invoice number and invoice date. use this format for the response {\"total_gross_worth\": {}, \"invoice_number\": {}, \"invoice_date\": {}}" 63 | 64 | {'total_gross_worth': 21209, 'invoice_number': '61356291', 'invoice_date': '09/06/2012'} 65 | ================================================== 66 | Time to retrieve answer: 122.91637057694606 67 | -------------------------------------------------------------------------------- /prompts_starling.txt: -------------------------------------------------------------------------------- 1 | python main.py "retrieve one value: invoice number. format response as following {\"invoice_number\": {}}" 2 | 3 | {'invoice_number': 61356291} 4 | ================================================== 5 | Time to retrieve answer: 97.6245634490042 6 | 7 | 8 | python main.py "retrieve one value: invoice date. format response as following {\"invoice_date\": {}}" 9 | 10 | {'invoice_date': '09/06/2012'} 11 | ================================================== 12 | Time to retrieve answer: 97.94699124002364 13 | 14 | 15 | python main.py "retrieve three values: client name, complete address and tax ID. format response as following {\"client_name\": {},\"address\": {},\"tax_id\": {}}" 16 | 17 | {'client_name': 'Rodriguez-Stevens', 'address': '2280 Angela Plain', 'tax_id': '939-98-8477'} 18 | ================================================== 19 | Time to retrieve answer: 102.29442486795597 20 | 21 | 22 | python main.py "retrieve three values: seller name, complete address and tax ID. format response as following {\"seller_name\": {},\"address\": {},\"tax_id\": {}}" 23 | 24 | {'seller_name': 'Chapman, Kim and Green', 'address': '64731 James Branch, Smithmouth, NC 26872', 'tax_id': '949-84-9105'} 25 | ================================================== 26 | Time to retrieve answer: 114.44599518104224 27 | 28 | 29 | python main.py "retrieve one value: invoice iban. format response as following {\"invoice_iban\": {}}" 30 | 31 | {'invoice_iban': 'GB50ACIE59715038217063'} 32 | ================================================== 33 | Time to retrieve answer: 100.62414190900745 34 | 35 | 36 | python main.py "retrieve two values: net price and gross worth for the second invoice item from the table. format response as following {\"net_price\": {},\"gross_worth\": {}}" 37 | 38 | {'net_price': 123.55, 'gross_worth': 246.7} 39 | ================================================== 40 | Time to retrieve answer: 103.98144886799855 41 | 42 | 43 | python main.py "retrieve gross worth value for each invoice item from the table. format response as following {\"gross_worth\": []}" 44 | 45 | {'gross_worth': [66.0, 123.55, 8.25, 14.29]} 46 | ================================================== 47 | Time to retrieve answer: 99.5734898429946 48 | 49 | 50 | python main.py "retrieve names of invoice items included into this invoice. format response as following {\"item_name\": []}" 51 | 52 | {'item_name': ['Wine Glasses Goblets Pair Clear Glass', 'With Hooks Stemware Storage Multiple Uses Iron Wine Rack Hanging Glass', 'Replacement Corkscrew Parts Spiral Worm Wine Opener Bottle Houdini', 'HOME ESSENTIALS GRADIENT STEMLESS WINE GLASSES SET OF 4 20 FL OZ (591 ml) NEW']} 53 | ================================================== 54 | Time to retrieve answer: 110.7761614319752 55 | 56 | 57 | python main.py "retrieve invoice total info. use this format for the answer {\"invoice_total\": {}}" 58 | 59 | {'invoice_total': 212.09} 60 | ================================================== 61 | Time to retrieve answer: 102.51593980001053 62 | 63 | 64 | python main.py "retrieve three values: total gross worth, invoice number and invoice date. use this format for the response {\"total_gross_worth\": {}, \"invoice_number\": {}, \"invoice_date\": {}}" 65 | 66 | {'total_gross_worth': 212.09, 'invoice_number': 61356291, 'invoice_date': '09/06/2012'} 67 | ================================================== 68 | Time to retrieve answer: 109.37831377796829 -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # 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