├── .gitignore ├── LICENSE ├── README.md ├── config.yml ├── data ├── invoice_1.jpg └── invoice_1.pdf ├── docker-compose.yml ├── ingest.py ├── main.py ├── prompts.txt ├── rag ├── __init__.py └── pipeline.py └── requirements.txt /.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 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 105 | __pypackages__/ 106 | 107 | # Celery stuff 108 | celerybeat-schedule 109 | celerybeat.pid 110 | 111 | # SageMath parsed files 112 | *.sage.py 113 | 114 | # Environments 115 | .env 116 | .venv 117 | env/ 118 | venv/ 119 | ENV/ 120 | env.bak/ 121 | venv.bak/ 122 | 123 | # Spyder project settings 124 | .spyderproject 125 | .spyproject 126 | 127 | # Rope project settings 128 | .ropeproject 129 | 130 | # mkdocs documentation 131 | /site 132 | 133 | # mypy 134 | .mypy_cache/ 135 | .dmypy.json 136 | dmypy.json 137 | 138 | # Pyre type checker 139 | .pyre/ 140 | 141 | # pytype static type analyzer 142 | .pytype/ 143 | 144 | # Cython debug symbols 145 | cython_debug/ 146 | 147 | # PyCharm 148 | # JetBrains specific template is maintainted in a separate JetBrains.gitignore that can 149 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 150 | # and can be added to the global gitignore or merged into this file. 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It offers a starting point for building your own local RAG pipeline, independent of online APIs and cloud-based LLM services like OpenAI. This empowers developers to experiment and deploy RAG applications in controlled environments. 4 | 5 | A fullstack UI application generated using [create-llama](https://blog.llamaindex.ai/create-llama-a-command-line-tool-to-generate-llamaindex-apps-8f7683021191) and customised for this project can be found at https://github.com/tyrell/llm-ollama-llamaindex-bootstrap-ui 6 | 7 | [My blog post](https://www.tyrell.co/2023/12/weaving-path-to-relevance-leveraging.html) provides more context, motivation and thinking behind these projects. 8 | 9 | ## The Stack 10 | 11 | This RAG application runs entirely offline, utilizing your local CPU to generate/retrieve/rank responses without needing internet access. This RAG deployment relies solely on your local CPU for computation. Please note that processing large datasets or using resource-intensive models might slow down performance. 12 | 13 | 1. Large Language Model - We use Ollama (https://ollama.ai/) to run our LLM locally. Any model supported by Ollama can be configured to be used using the config.yml file found in this application. 14 | 2. Vector Store - We use Weaviate (https://weaviate.io) as the Vector Store. We run Weaviate as a Docker container. The URL of the Weaviate instance can be configured using the config.yml file. 15 | 3. Index - We use LlamaIndex (https://www.llamaindex.ai) as the core of this RAG application acting as the index of our private data structures. The sample code provided by the template ingests a file into the index. 16 | 4. Vector Embeddings - We use Langchain (https://www.langchain.com) and HuggingFace (https://huggingface.co) to maintain a local embedding model. 17 | 18 | ___ 19 | 20 | ## Quickstart 21 | 22 | 1. Run the Weaviate local Vector Store with Docker: 23 | 24 | ``` 25 | docker compose up -d 26 | ``` 27 | 28 | 2. Install Python requirements: 29 | 30 | ``` 31 | pip install -r requirements.txt 32 | ``` 33 | 34 | 3. Install Ollama and pull the preferred LLM model specified in config.yml 35 | 36 | 4. Copy text PDF files to the `data` folder 37 | 38 | 5. Run the script, to convert text to vector embeddings and save in Weaviate: 39 | 40 | ``` 41 | python ingest.py 42 | ``` 43 | 44 | 1. Run main.py to process data with LLM RAG pipeline defined in pipeline.py and return the answer: 45 | 46 | ``` 47 | python main.py "Who are you?" 48 | ``` 49 | 50 | Answer: 51 | 52 | ``` 53 | Answer: 54 | I am an AI language model, designed to assist and provide information based on the context provided. In this case, the context is related to an invoice from Chapman, Kim and Green to Rodriguez-Stevens for various items such as wine glasses, stemware storage, corkscrew parts, and stemless wine glasses. 55 | 56 | Here are some key details from the invoice: 57 | - Invoice number: 61356291 58 | - Date of issue: 09/06/2012 59 | - Seller: Chapman, Kim and Green 60 | - Buyer: Rodriguez-Stevens 61 | - VAT rate: 10% 62 | 63 | The invoice includes several items with their respective quantities, unit measures (UM), net prices, net worth, gross worth, and taxes. The summary section provides the total net worth, VAT amount, and gross worth of the invoice. 64 | ================================================== 65 | Time to retrieve answer: 37.36918904201593 66 | 67 | ``` 68 | 69 | You can find more prompts in prompts.txt to test the template application. Once yo have read through the codebase, expand the RAG to your specific needs. 70 | 71 | # License 72 | Apache 2.0 73 | 74 | 75 | ~ Tyrell Perera 76 | -------------------------------------------------------------------------------- /config.yml: -------------------------------------------------------------------------------- 1 | DATA_PATH: 'data/' 2 | LLM: 'starling-lm:7b-alpha-q5_K_M' 3 | EMBEDDINGS: 'sentence-transformers/all-mpnet-base-v2' 4 | WEAVIATE_URL: 'http://localhost:8080' 5 | INDEX_NAME: 'Tyrell' 6 | CHUNK_SIZE: 1000 7 | -------------------------------------------------------------------------------- /data/invoice_1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tyrell/llm-ollama-llamaindex-bootstrap/502f70a4f8118834c4b641d0dc4c9f82398cc170/data/invoice_1.jpg -------------------------------------------------------------------------------- /data/invoice_1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tyrell/llm-ollama-llamaindex-bootstrap/502f70a4f8118834c4b641d0dc4c9f82398cc170/data/invoice_1.pdf -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | --- 2 | version: '3.4' 3 | services: 4 | weaviate: 5 | container_name: weaviate-db 6 | command: 7 | - --host 8 | - 0.0.0.0 9 | - --port 10 | - '8080' 11 | - --scheme 12 | - http 13 | image: semitechnologies/weaviate:1.22.4 14 | ports: 15 | - 8080:8080 16 | - 50051:50051 17 | volumes: 18 | - weaviate_data:/var/lib/weaviate 19 | restart: on-failure:0 20 | environment: 21 | QUERY_DEFAULTS_LIMIT: 25 22 | AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true' 23 | PERSISTENCE_DATA_PATH: '/var/lib/weaviate' 24 | DEFAULT_VECTORIZER_MODULE: 'none' 25 | ENABLE_MODULES: '' 26 | CLUSTER_HOSTNAME: 'node1' 27 | volumes: 28 | weaviate_data: 29 | ... -------------------------------------------------------------------------------- /ingest.py: -------------------------------------------------------------------------------- 1 | import weaviate 2 | from llama_index import StorageContext, SimpleDirectoryReader, ServiceContext, VectorStoreIndex 3 | from llama_index.vector_stores import WeaviateVectorStore 4 | from llama_index.embeddings import LangchainEmbedding 5 | from langchain.embeddings.huggingface import HuggingFaceEmbeddings 6 | import box 7 | import yaml 8 | 9 | import warnings 10 | warnings.filterwarnings("ignore", category=DeprecationWarning) 11 | 12 | 13 | def load_documents(docs_path): 14 | """ 15 | This function retrieves and returns a list of JPEG documents from a specified directory. 16 | 17 | It utilizes the `SimpleDirectoryReader` class from the `pathlib` module to efficiently iterate through all files 18 | with the `.jpg` extension within the provided directory. The function then prints the total number of loaded documents 19 | before returning them as a list. 20 | 21 | Args: 22 | docs_path: The absolute or relative path to the directory containing the target JPEG documents. 23 | 24 | Returns: 25 | A list of `SimpleDirectoryReader` objects, each representing an individual JPEG file within the directory. 26 | 27 | Raises: 28 | FileNotFoundError: If the specified directory path is invalid or inaccessible. 29 | OSError: If an error occurs during file iteration or processing. 30 | 31 | """ 32 | documents = SimpleDirectoryReader(docs_path, required_exts=[".jpg"]).load_data() 33 | print(f"Loaded {len(documents)} documents") 34 | return documents 35 | 36 | 37 | def load_embedding_model(model_name): 38 | """ 39 | Creates and returns a LangchainEmbedding object based on a specified Hugging Face model name. 40 | 41 | Args: 42 | model_name: The string identifier of the desired pre-trained embedding model from Hugging Face. 43 | 44 | Returns: 45 | A LangchainEmbedding object configured with the chosen Hugging Face model. 46 | 47 | Notes: 48 | This function utilizes the `LangchainEmbedding` wrapper around the underlying `HuggingFaceEmbeddings` class. 49 | 50 | """ 51 | embeddings = LangchainEmbedding( 52 | HuggingFaceEmbeddings(model_name=model_name) 53 | ) 54 | return embeddings 55 | 56 | 57 | def build_index(weaviate_client, embed_model, documents, index_name): 58 | """ 59 | Constructs and populates a Weaviate Vector Store index with embedded document representations. 60 | 61 | This function takes several inputs and performs the following tasks: 62 | 63 | 1. **Context Creation:** 64 | - Constructs a `ServiceContext` object using the provided embedding model and sets `llm` to None (assuming no language model involved). 65 | - Creates a `WeaviateVectorStore` object for accessing the specified Weaviate client and index name. 66 | - Builds a `StorageContext` based on the generated vector store. 67 | 68 | 2. **Index Building:** 69 | - Utilizes the `VectorStoreIndex.from_documents` method to construct an index from the provided list of documents. 70 | - This process involves embedding each document using the injected `embed_model` within the service context. 71 | - The generated vector representations are then stored in the specified Weaviate index through the storage context. 72 | 73 | 3. **Index Return:** 74 | - Finally, the function returns the constructed `VectorStoreIndex` object, representing the populated Weaviate index. 75 | 76 | Args: 77 | weaviate_client: An instance of the Weaviate client for accessing the target server. 78 | embed_model: An object capable of generating vector representations for the documents. 79 | documents: A list of documents to be indexed in Weaviate. 80 | index_name: The name of the Weaviate Vector Store index to populate. 81 | 82 | Returns: 83 | A `VectorStoreIndex` object representing the newly created and populated Weaviate index. 84 | 85 | Notes: 86 | This function assumes that the `embed_model` and provided documents are compatible for generating suitable vector representations. 87 | Additionally, ensure the Weaviate client has access and appropriate permissions to the specified index. 88 | 89 | """ 90 | service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=None) 91 | vector_store = WeaviateVectorStore(weaviate_client=weaviate_client, index_name=index_name) 92 | storage_context = StorageContext.from_defaults(vector_store=vector_store) 93 | 94 | index = VectorStoreIndex.from_documents( 95 | documents, 96 | service_context=service_context, 97 | storage_context=storage_context, 98 | ) 99 | 100 | return index 101 | 102 | 103 | if __name__ == "__main__": 104 | 105 | # Import configuration specified in config.yml 106 | with open('config.yml', 'r', encoding='utf8') as configuration: 107 | cfg = box.Box(yaml.safe_load(configuration)) 108 | 109 | print("Connecting to Weaviate") 110 | client = weaviate.Client(cfg.WEAVIATE_URL) 111 | 112 | print("Loading documents...") 113 | documents = load_documents(cfg.DATA_PATH) 114 | 115 | print("Loading embedding model...") 116 | embeddings = load_embedding_model(model_name=cfg.EMBEDDINGS) 117 | 118 | print("Building index...") 119 | index = build_index(client, embeddings, documents, cfg.INDEX_NAME) 120 | 121 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import timeit 2 | import argparse 3 | 4 | from rag.pipeline import build_rag_pipeline 5 | 6 | import warnings 7 | warnings.filterwarnings("ignore", category=DeprecationWarning) 8 | 9 | 10 | def get_rag_response(query, rag_chain): 11 | """ 12 | Queries the RAG pipeline with the provided query and retrieves the generated response. 13 | 14 | Args: 15 | query: The textual query to be submitted to the RAG pipeline. 16 | rag_chain: The pre-built RAG pipeline object for processing the query. 17 | 18 | Returns: 19 | The response text generated by the RAG pipeline based on the input query. 20 | 21 | """ 22 | result = rag_chain.query(query) 23 | return result 24 | 25 | 26 | if __name__ == "__main__": 27 | parser = argparse.ArgumentParser() 28 | parser.add_argument('input', 29 | type=str, 30 | default='What is the invoice number value?', 31 | help='Enter the query to pass into the LLM') 32 | args = parser.parse_args() 33 | 34 | start = timeit.default_timer() 35 | 36 | rag_chain = build_rag_pipeline() 37 | 38 | print('Retrieving answer...') 39 | answer = get_rag_response(args.input, rag_chain) 40 | answer = str(answer).strip() 41 | 42 | end = timeit.default_timer() 43 | 44 | print(f'\nAnswer:\n{answer}') 45 | print('=' * 50) 46 | 47 | print(f"Time to retrieve answer: {end - start}") -------------------------------------------------------------------------------- /prompts.txt: -------------------------------------------------------------------------------- 1 | python main.py "retrieve invoice number" 2 | 3 | 61356291 4 | ================================================== 5 | Time to retrieve answer: 94.57260256999871 6 | 7 | 8 | python main.py "retrieve invoice date" 9 | 10 | 09/06/2012 11 | ================================================== 12 | Time to retrieve answer: 98.17322501199669 13 | 14 | 15 | python main.py "retrieve three values: client name, complete address and tax ID" 16 | 17 | Client Name: Rodriguez-Stevens 18 | Complete Address: 2280 Angela Plain, Hortonshire, MS 93248 19 | Tax ID: 939-98-8477 20 | ================================================== 21 | Time to retrieve answer: 117.18188980000559 22 | 23 | 24 | python main.py "retrieve three values: seller name, complete address and tax ID" 25 | 26 | Seller Name: Chapman, Kim and Green 27 | Complete Address: 64731 James Branch, Smithmouth, NC 26872, USA 28 | Tax ID: 949-84-9105 29 | ================================================== 30 | Time to retrieve answer: 103.867440025002 31 | 32 | 33 | python main.py "retrieve invoice iban" 34 | 35 | GB50ACIE59715038217063 36 | ================================================== 37 | Time to retrieve answer: 96.29626568200183 38 | 39 | 40 | python main.py "retrieve two values for the second invoice item: net price and gross worth" 41 | 42 | The net price for the second invoice item is $28.08 and the gross worth is $123.55. 43 | ================================================== 44 | Time to retrieve answer: 98.65136093599722 45 | 46 | 47 | python main.py "retrieve gross worth value for each invoice item" 48 | 49 | 66.0, 123.55, 8.25, 14.29 50 | ================================================== 51 | Time to retrieve answer: 106.47106381098274 52 | 53 | 54 | python main.py "retrieve names of invoice items" 55 | 56 | 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 57 | ================================================== 58 | Time to retrieve answer: 117.21840551000787 59 | 60 | 61 | python main.py "retrieve invoice total info" 62 | 63 | The total invoice amount is $212.09, with a net worth of $192.81 and VAT of $19.28. 64 | ================================================== 65 | Time to retrieve answer: 97.72127992598689 66 | 67 | 68 | python main.py "retrieve three values: total gross worth, invoice number and invoice date" 69 | 70 | Total gross worth: $212.09, Invoice number: 61356291, Invoice date: 09/06/2012 71 | ================================================== 72 | Time to retrieve answer: 110.59500618302263 73 | 74 | 75 | python main.py "Who are you?" 76 | 77 | Answer: 78 | I am an AI language model, designed to assist and provide information based on the context provided. In this case, the context is related to an invoice from Chapman, Kim and Green to Rodriguez-Stevens for various items such as wine glasses, stemware storage, corkscrew parts, and stemless wine glasses. 79 | 80 | Here are some key details from the invoice: 81 | - Invoice number: 61356291 82 | - Date of issue: 09/06/2012 83 | - Seller: Chapman, Kim and Green 84 | - Buyer: Rodriguez-Stevens 85 | - VAT rate: 10% 86 | 87 | The invoice includes several items with their respective quantities, unit measures (UM), net prices, net worth, gross worth, and taxes. The summary section provides the total net worth, VAT amount, and gross worth of the invoice. 88 | ================================================== 89 | Time to retrieve answer: 37.36918904201593 90 | 91 | 92 | python main.py "Please summarize this invoice document." 93 | 94 | Answer: 95 | This invoice is issued by Chapman, Kim and Green on 09/06/2012 to Rodriguez-Stevens. The total net worth of the items sold is $192.81, with a VAT rate of 10%. The gross worth of the items after adding VAT is $212.09. The items purchased include 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, and HOME ESSENTIALS GRADIENT STEMLESS WINE GLASSES SET OF 4 20 FL OZ (591 ml) NEW. 96 | ================================================== 97 | Time to retrieve answer: 27.147464457899332 98 | 99 | 100 | python main.py "What's a Wine Glasses Goblets?" 101 | 102 | Answer: 103 | A Wine Glasses Goblets is a set of wine glasses with goblet-style bowls, designed for holding and drinking wine. These glasses typically have a large, round bowl that narrows towards the top, providing a generous surface area for swirling and sniffing the wine's aromas. They are often used for red wines, as their shape allows for better aeration of the wine, which can enhance its flavors and aromas. 104 | ================================================== 105 | Time to retrieve answer: 21.826263291062787 106 | -------------------------------------------------------------------------------- /rag/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tyrell/llm-ollama-llamaindex-bootstrap/502f70a4f8118834c4b641d0dc4c9f82398cc170/rag/__init__.py -------------------------------------------------------------------------------- /rag/pipeline.py: -------------------------------------------------------------------------------- 1 | from llama_index import VectorStoreIndex, ServiceContext 2 | from llama_index.embeddings import LangchainEmbedding 3 | from langchain.embeddings.huggingface import HuggingFaceEmbeddings 4 | from llama_index.llms import Ollama 5 | from llama_index.vector_stores import WeaviateVectorStore 6 | import weaviate 7 | import box 8 | import yaml 9 | 10 | 11 | def load_embedding_model(model_name): 12 | embeddings = LangchainEmbedding( 13 | HuggingFaceEmbeddings(model_name=model_name) 14 | ) 15 | return embeddings 16 | 17 | 18 | def load_index(chunk_size, llm, embed_model, weaviate_client, index_name): 19 | service_context = ServiceContext.from_defaults( 20 | chunk_size=chunk_size, 21 | llm=llm, 22 | embed_model=embed_model 23 | ) 24 | 25 | vector_store = WeaviateVectorStore(weaviate_client=weaviate_client, index_name=index_name) 26 | 27 | index = VectorStoreIndex.from_vector_store( 28 | vector_store, service_context=service_context 29 | ) 30 | 31 | return index 32 | 33 | def build_rag_pipeline(): 34 | """ 35 | Constructs and configures a RAG pipeline for retrieval-augmented generation tasks. 36 | 37 | This function performs the following steps to set up the RAG pipeline: 38 | 39 | 1. **Configuration Loading:** 40 | - Reads configuration variables from a specified YAML file (`config.yml`). 41 | - Stores the loaded configuration as a `box.Box` object for convenient access. 42 | 43 | 2. **Weaviate Connection:** 44 | - Establishes a connection to the Weaviate server using the provided URL in the configuration. 45 | - Creates a Weaviate client object for interacting with the Weaviate database. 46 | 47 | 3. **LLAMA Model Loading:** 48 | - Loads the specified Ollama language model based on the `LLM` key in the configuration. 49 | - Sets the model temperature to 0 for a more deterministic response generation. 50 | 51 | 4. **Embedding Model Loading:** 52 | - Utilizes the `load_embedding_model` function to retrieve a pre-trained Hugging Face model configured for Langchain. 53 | - This model will be used to embed documents and queries for efficient search and retrieval. 54 | 55 | 5. **Vector Store Index Loading:** 56 | - Fetches the pre-built Weaviate Vector Store index named in the configuration (`INDEX_NAME`). 57 | - Connects the index to the Weaviate client and embeds relevant context using the selected service context. 58 | 59 | 6. **Query Engine Construction:** 60 | - Converts the loaded Vector Store index into a dedicated query engine for efficient retrieval. 61 | - Sets the `streaming` flag to `False` to return the final response after the entire query is processed. 62 | 63 | 7. **Pipeline Return:** 64 | - Returns the fully constructed and configured RAG pipeline represented by the `query_engine` object. 65 | 66 | Notes: 67 | - This function relies on a separate `config.yml` file for storing configuration values. 68 | - Ensure that the configuration file contains valid values for all required keys. 69 | 70 | """ 71 | # Import configuration specified in config.yml 72 | with open('config.yml', 'r', encoding='utf8') as ymlfile: 73 | cfg = box.Box(yaml.safe_load(ymlfile)) 74 | 75 | print("Connecting to Weaviate") 76 | client = weaviate.Client(cfg.WEAVIATE_URL) 77 | 78 | print("Loading Ollama...") 79 | llm = Ollama(model=cfg.LLM, temperature=0) 80 | 81 | print("Loading embedding model...") 82 | embeddings = load_embedding_model(model_name=cfg.EMBEDDINGS) 83 | 84 | print("Loading index...") 85 | index = load_index(cfg.CHUNK_SIZE, llm, embeddings, client, cfg.INDEX_NAME) 86 | 87 | print("Constructing query engine...") 88 | query_engine = index.as_query_engine(streaming=False) 89 | 90 | return query_engine 91 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | llama-index==0.9.13 2 | langchain==0.0.345 3 | sentence-transformers 4 | weaviate-client 5 | pypdf 6 | python-box --------------------------------------------------------------------------------