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examples/langchain-python-rag-privategpt/README.md deleted 100644 → 0
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# PrivateGPT with Llama 2 uncensored
https://github.com/ollama/ollama/assets/3325447/20cf8ec6-ff25-42c6-bdd8-9be594e3ce1b
> Note: this example is a slightly modified version of PrivateGPT using models such as Llama 2 Uncensored. All credit for PrivateGPT goes to Iván Martínez who is the creator of it, and you can find his GitHub repo [here](https://github.com/imartinez/privateGPT).
### Setup
Set up a virtual environment (optional):
```
python3 -m venv .venv
source .venv/bin/activate
```
Install the Python dependencies:
```shell
pip install -r requirements.txt
```
Pull the model you'd like to use:
```
ollama pull llama2-uncensored
```
### Getting WeWork's latest quarterly earnings report (10-Q)
```
mkdir source_documents
curl https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf -o source_documents/wework.pdf
```
### Ingesting files
```shell
python ingest.py
```
Output should look like this:
```shell
Creating new vectorstore
Loading documents from source_documents
Loading new documents: 100%|██████████████████████| 1/1 [00:01<00:00, 1.73s/it]
Loaded 1 new documents from source_documents
Split into 90 chunks of text (max. 500 tokens each)
Creating embeddings. May take some minutes...
Using embedded DuckDB with persistence: data will be stored in: db
Ingestion complete! You can now run privateGPT.py to query your documents
```
### Ask questions
```shell
python privateGPT.py
Enter a query: How many locations does WeWork have?
> Answer (took 17.7 s.):
As of June 2023, WeWork has 777 locations worldwide, including 610 Consolidated Locations (as defined in the section entitled Key Performance Indicators).
```
### Try a different model:
```
ollama pull llama2:13b
MODEL=llama2:13b python privateGPT.py
```
## Adding more files
Put any and all your files into the `source_documents` directory
The supported extensions are:
- `.csv`: CSV,
- `.docx`: Word Document,
- `.doc`: Word Document,
- `.enex`: EverNote,
- `.eml`: Email,
- `.epub`: EPub,
- `.html`: HTML File,
- `.md`: Markdown,
- `.msg`: Outlook Message,
- `.odt`: Open Document Text,
- `.pdf`: Portable Document Format (PDF),
- `.pptx` : PowerPoint Document,
- `.ppt` : PowerPoint Document,
- `.txt`: Text file (UTF-8),
examples/langchain-python-rag-privategpt/constants.py deleted 100644 → 0
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import os
from chromadb.config import Settings
# Define the folder for storing database
PERSIST_DIRECTORY = os.environ.get('PERSIST_DIRECTORY', 'db')
# Define the Chroma settings
CHROMA_SETTINGS = Settings(
persist_directory=PERSIST_DIRECTORY,
anonymized_telemetry=False
)
examples/langchain-python-rag-privategpt/ingest.py deleted 100755 → 0
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#!/usr/bin/env python3
import os
import glob
from typing import List
from multiprocessing import Pool
from tqdm import tqdm
from langchain.document_loaders import (
CSVLoader,
EverNoteLoader,
PyMuPDFLoader,
TextLoader,
UnstructuredEmailLoader,
UnstructuredEPubLoader,
UnstructuredHTMLLoader,
UnstructuredMarkdownLoader,
UnstructuredODTLoader,
UnstructuredPowerPointLoader,
UnstructuredWordDocumentLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.docstore.document import Document
from constants import CHROMA_SETTINGS
# Load environment variables
persist_directory = os.environ.get('PERSIST_DIRECTORY', 'db')
source_directory = os.environ.get('SOURCE_DIRECTORY', 'source_documents')
embeddings_model_name = os.environ.get('EMBEDDINGS_MODEL_NAME', 'all-MiniLM-L6-v2')
chunk_size = 500
chunk_overlap = 50
# Custom document loaders
class MyElmLoader(UnstructuredEmailLoader):
"""Wrapper to fallback to text/plain when default does not work"""
def load(self) -> List[Document]:
"""Wrapper adding fallback for elm without html"""
try:
try:
doc = UnstructuredEmailLoader.load(self)
except ValueError as e:
if 'text/html content not found in email' in str(e):
# Try plain text
self.unstructured_kwargs["content_source"]="text/plain"
doc = UnstructuredEmailLoader.load(self)
else:
raise
except Exception as e:
# Add file_path to exception message
raise type(e)(f"{self.file_path}: {e}") from e
return doc
# Map file extensions to document loaders and their arguments
LOADER_MAPPING = {
".csv": (CSVLoader, {}),
# ".docx": (Docx2txtLoader, {}),
".doc": (UnstructuredWordDocumentLoader, {}),
".docx": (UnstructuredWordDocumentLoader, {}),
".enex": (EverNoteLoader, {}),
".eml": (MyElmLoader, {}),
".epub": (UnstructuredEPubLoader, {}),
".html": (UnstructuredHTMLLoader, {}),
".md": (UnstructuredMarkdownLoader, {}),
".odt": (UnstructuredODTLoader, {}),
".pdf": (PyMuPDFLoader, {}),
".ppt": (UnstructuredPowerPointLoader, {}),
".pptx": (UnstructuredPowerPointLoader, {}),
".txt": (TextLoader, {"encoding": "utf8"}),
# Add more mappings for other file extensions and loaders as needed
}
def load_single_document(file_path: str) -> List[Document]:
if os.path.getsize(file_path) != 0:
filename, ext = os.path.splitext(file_path)
if ext in LOADER_MAPPING:
loader_class, loader_args = LOADER_MAPPING[ext]
try:
loader = loader_class(file_path, **loader_args)
if loader:
return loader.load()
except:
print(f"Corrupted file {file_path}. Ignoring it.")
else:
print(f"Unsupported file {file_path}. Ignoring it.")
else:
print(f"Empty file {file_path}. Ignoring it.")
def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]:
"""
Loads all documents from the source documents directory, ignoring specified files
"""
all_files = []
for ext in LOADER_MAPPING:
all_files.extend(
glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True)
)
filtered_files = [file_path for file_path in all_files if file_path not in ignored_files]
with Pool(processes=os.cpu_count()) as pool:
results = []
with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar:
for i, docs in enumerate(pool.imap_unordered(load_single_document, filtered_files)):
if docs:
results.extend(docs)
pbar.update()
return results
def process_documents(ignored_files: List[str] = []) -> List[Document]:
"""
Load documents and split in chunks
"""
print(f"Loading documents from {source_directory}")
documents = load_documents(source_directory, ignored_files)
if not documents:
print("No new documents to load")
exit(0)
print(f"Loaded {len(documents)} new documents from {source_directory}")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
texts = text_splitter.split_documents(documents)
print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)")
return texts
def does_vectorstore_exist(persist_directory: str) -> bool:
"""
Checks if vectorstore exists
"""
if os.path.exists(os.path.join(persist_directory, 'index')):
if os.path.exists(os.path.join(persist_directory, 'chroma-collections.parquet')) and os.path.exists(os.path.join(persist_directory, 'chroma-embeddings.parquet')):
list_index_files = glob.glob(os.path.join(persist_directory, 'index/*.bin'))
list_index_files += glob.glob(os.path.join(persist_directory, 'index/*.pkl'))
# At least 3 documents are needed in a working vectorstore
if len(list_index_files) > 3:
return True
return False
def main():
# Create embeddings
embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
if does_vectorstore_exist(persist_directory):
# Update and store locally vectorstore
print(f"Appending to existing vectorstore at {persist_directory}")
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
collection = db.get()
texts = process_documents([metadata['source'] for metadata in collection['metadatas']])
print(f"Creating embeddings. May take some minutes...")
db.add_documents(texts)
else:
# Create and store locally vectorstore
print("Creating new vectorstore")
texts = process_documents()
print(f"Creating embeddings. May take some minutes...")
db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)
db.persist()
db = None
print(f"Ingestion complete! You can now run privateGPT.py to query your documents")
if __name__ == "__main__":
main()
examples/langchain-python-rag-privategpt/poetry.lock deleted 100644 → 0
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examples/langchain-python-rag-privategpt/privateGPT.py deleted 100755 → 0
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#!/usr/bin/env python3
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from langchain.llms import Ollama
import chromadb
import os
import argparse
import time
model = os.environ.get("MODEL", "llama2-uncensored")
# For embeddings model, the example uses a sentence-transformers model
# https://www.sbert.net/docs/pretrained_models.html
# "The all-mpnet-base-v2 model provides the best quality, while all-MiniLM-L6-v2 is 5 times faster and still offers good quality."
embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME", "all-MiniLM-L6-v2")
persist_directory = os.environ.get("PERSIST_DIRECTORY", "db")
target_source_chunks = int(os.environ.get('TARGET_SOURCE_CHUNKS',4))
from constants import CHROMA_SETTINGS
def main():
# Parse the command line arguments
args = parse_arguments()
embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
# activate/deactivate the streaming StdOut callback for LLMs
callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]
llm = Ollama(model=model, callbacks=callbacks)
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents= not args.hide_source)
# Interactive questions and answers
while True:
query = input("\nEnter a query: ")
if query == "exit":
break
if query.strip() == "":
continue
# Get the answer from the chain
start = time.time()
res = qa(query)
answer, docs = res['result'], [] if args.hide_source else res['source_documents']
end = time.time()
# Print the result
print("\n\n> Question:")
print(query)
print(answer)
# Print the relevant sources used for the answer
for document in docs:
print("\n> " + document.metadata["source"] + ":")
print(document.page_content)
def parse_arguments():
parser = argparse.ArgumentParser(description='privateGPT: Ask questions to your documents without an internet connection, '
'using the power of LLMs.')
parser.add_argument("--hide-source", "-S", action='store_true',
help='Use this flag to disable printing of source documents used for answers.')
parser.add_argument("--mute-stream", "-M",
action='store_true',
help='Use this flag to disable the streaming StdOut callback for LLMs.')
return parser.parse_args()
if __name__ == "__main__":
main()
examples/langchain-python-rag-privategpt/pyproject.toml deleted 100644 → 0
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[tool.poetry]
name = "privategpt"
version = "0.1.0"
description = ""
authors = ["Ivan Martinez <ivanmartit@gmail.com>"]
license = "Apache Version 2.0"
readme = "README.md"
[tool.poetry.dependencies]
python = "^3.10"
langchain = "0.0.261"
gpt4all = "^1.0.3"
chromadb = "^0.3.26"
PyMuPDF = "^1.22.5"
python-dotenv = "^1.0.0"
unstructured = "^0.8.0"
extract-msg = "^0.41.5"
tabulate = "^0.9.0"
pandoc = "^2.3"
pypandoc = "^1.11"
tqdm = "^4.65.0"
sentence-transformers = "^2.2.2"
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
examples/langchain-python-rag-privategpt/requirements.txt deleted 100644 → 0
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langchain==0.0.274
gpt4all==1.0.8
chromadb==0.5.0
llama-cpp-python==0.1.81
urllib3==2.0.4
PyMuPDF==1.23.5
python-dotenv==1.0.0
unstructured==0.10.8
extract-msg==0.45.0
tabulate==0.9.0
pandoc==2.3
pypandoc==1.11
tqdm==4.66.1
sentence_transformers==2.2.2
numpy>=1.22.2 # not directly required, pinned by Snyk to avoid a vulnerability
examples/langchain-python-rag-websummary/README.md deleted 100644 → 0
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# LangChain Web Summarization
This example summarizes the website, [https://ollama.com/blog/run-llama2-uncensored-locally](https://ollama.com/blog/run-llama2-uncensored-locally)
## Running the Example
1. Ensure you have the `llama3.2` model installed:
```bash
ollama pull llama3.2
```
2. Install the Python Requirements.
```bash
pip install -r requirements.txt
```
3. Run the example:
```bash
python main.py
```
examples/langchain-python-rag-websummary/main.py deleted 100644 → 0
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from langchain_community.llms import Ollama
from langchain_community.document_loaders import WebBaseLoader
from langchain.chains.summarize import load_summarize_chain
loader = WebBaseLoader("https://ollama.com/blog/run-llama2-uncensored-locally")
docs = loader.load()
llm = Ollama(model="llama3.2")
chain = load_summarize_chain(llm, chain_type="stuff")
result = chain.invoke(docs)
print(result)
examples/langchain-python-simple/README.md deleted 100644 → 0
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# LangChain
This example is a basic "hello world" of using LangChain with Ollama.
## Running the Example
1. Ensure you have the `llama3.2` model installed:
```bash
ollama pull llama3.2
```
2. Install the Python Requirements.
```bash
pip install -r requirements.txt
```
3. Run the example:
```bash
python main.py
```
examples/langchain-python-simple/main.py deleted 100644 → 0
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from langchain.llms import Ollama
input = input("What is your question?\n> ")
llm = Ollama(model="llama3.2")
res = llm.invoke(input)
print (res)
examples/langchain-typescript-simple/README.md deleted 100644 → 0
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# LangChain
This example is a basic "hello world" of using LangChain with Ollama using Node.js and Typescript.
## Running the Example
1. Install the prerequisites:
```bash
npm install
```
2. Ensure the `mistral` model is available:
```bash
ollama pull mistral
```
3. Run the example:
```bash
npm start
```
examples/langchain-typescript-simple/main.ts deleted 100644 → 0
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import { Ollama } from 'langchain/llms/ollama';
import * as readline from "readline";
async function main() {
const ollama = new Ollama({
model: 'mistral'
// other parameters can be found at https://js.langchain.com/docs/api/llms_ollama/classes/Ollama
});
const rl = readline.createInterface({
input: process.stdin,
output: process.stdout,
});
rl.question("What is your question: \n", async (user_input) => {
const stream = await ollama.stream(user_input);
for await (const chunk of stream) {
process.stdout.write(chunk);
}
rl.close();
})
}
main();
\ No newline at end of file
examples/langchain-typescript-simple/package-lock.json deleted 100644 → 0
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{
"name": "langchain-typescript-simple",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"dependencies": {
"langchain": "^0.0.165"
},
"devDependencies": {
"typescript": "^5.2.2"
}
},
"node_modules/@anthropic-ai/sdk": {
"version": "0.6.2",
"resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.6.2.tgz",
"integrity": "sha512-fB9PUj9RFT+XjkL+E9Ol864ZIJi+1P8WnbHspN3N3/GK2uSzjd0cbVIKTGgf4v3N8MwaQu+UWnU7C4BG/fap/g==",
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examples/langchain-typescript-simple/package.json deleted 100644 → 0
View file @ 17fcdea6
{
"scripts": {
"start": "tsx main.ts"
},
"devDependencies": {
"tsx": "^4.6.2",
"typescript": "^5.3.3"
},
"dependencies": {
"langchain": "^0.0.165",
"readline": "^1.3.0"
}
}
examples/modelfile-mario/Modelfile deleted 100644 → 0
View file @ 17fcdea6
FROM llama3.2
PARAMETER temperature 1
SYSTEM """
You are Mario from super mario bros, acting as an assistant.
"""
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