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ollama/examples/kubernetes/gpu.yaml 0 → 100755
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---
apiVersion: v1
kind: Namespace
metadata:
name: ollama
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: ollama
namespace: ollama
spec:
strategy:
type: Recreate
selector:
matchLabels:
name: ollama
template:
metadata:
labels:
name: ollama
spec:
containers:
- name: ollama
image: ollama/ollama:latest
env:
- name: PATH
value: /usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
- name: LD_LIBRARY_PATH
value: /usr/local/nvidia/lib:/usr/local/nvidia/lib64
- name: NVIDIA_DRIVER_CAPABILITIES
value: compute,utility
ports:
- name: http
containerPort: 11434
protocol: TCP
resources:
limits:
nvidia.com/gpu: 1
tolerations:
- key: nvidia.com/gpu
operator: Exists
effect: NoSchedule
---
apiVersion: v1
kind: Service
metadata:
name: ollama
namespace: ollama
spec:
type: ClusterIP
selector:
name: ollama
ports:
- port: 80
name: http
targetPort: http
protocol: TCP
ollama/examples/langchain-python-rag-document/README.md 0 → 100755
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# LangChain Document QA
This example provides an interface for asking questions to a PDF document.
## Setup
1. Ensure you have the `llama3.1` model installed:
```
ollama pull llama3.1
```
2. Install the Python Requirements.
```
pip install -r requirements.txt
```
## Run
```
python main.py
```
A prompt will appear, where questions may be asked:
```
Query: How many locations does WeWork have?
```
ollama/examples/langchain-python-rag-document/main.py 0 → 100755
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from langchain.document_loaders import OnlinePDFLoader
from langchain.vectorstores import Chroma
from langchain.embeddings import GPT4AllEmbeddings
from langchain import PromptTemplate
from langchain.llms import Ollama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import RetrievalQA
import sys
import os
class SuppressStdout:
def __enter__(self):
self._original_stdout = sys.stdout
self._original_stderr = sys.stderr
sys.stdout = open(os.devnull, 'w')
sys.stderr = open(os.devnull, 'w')
def __exit__(self, exc_type, exc_val, exc_tb):
sys.stdout.close()
sys.stdout = self._original_stdout
sys.stderr = self._original_stderr
# load the pdf and split it into chunks
loader = OnlinePDFLoader("https://d18rn0p25nwr6d.cloudfront.net/CIK-0001813756/975b3e9b-268e-4798-a9e4-2a9a7c92dc10.pdf")
data = loader.load()
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)
with SuppressStdout():
vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())
while True:
query = input("\nQuery: ")
if query == "exit":
break
if query.strip() == "":
continue
# Prompt
template = """Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Use three sentences maximum and keep the answer as concise as possible.
{context}
Question: {question}
Helpful Answer:"""
QA_CHAIN_PROMPT = PromptTemplate(
input_variables=["context", "question"],
template=template,
)
llm = Ollama(model="llama3.1", callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
qa_chain = RetrievalQA.from_chain_type(
llm,
retriever=vectorstore.as_retriever(),
chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},
)
result = qa_chain({"query": query})
ollama/examples/langchain-python-rag-document/requirements.txt 0 → 100755
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absl-py==1.4.0
aiohttp==3.8.5
aiosignal==1.3.1
anyio==3.7.1
astunparse==1.6.3
async-timeout==4.0.3
attrs==23.1.0
backoff==2.2.1
beautifulsoup4==4.12.2
bs4==0.0.1
cachetools==5.3.1
certifi==2023.7.22
cffi==1.15.1
chardet==5.2.0
charset-normalizer==3.2.0
Chroma==0.2.0
chroma-hnswlib==0.7.2
chromadb==0.4.5
click==8.1.6
coloredlogs==15.0.1
cryptography==41.0.3
dataclasses-json==0.5.14
fastapi==0.99.1
filetype==1.2.0
flatbuffers==23.5.26
frozenlist==1.4.0
gast==0.4.0
google-auth==2.22.0
google-auth-oauthlib==1.0.0
google-pasta==0.2.0
gpt4all==1.0.8
grpcio==1.57.0
h11==0.14.0
h5py==3.9.0
httptools==0.6.0
humanfriendly==10.0
idna==3.4
importlib-resources==6.0.1
joblib==1.3.2
keras==2.13.1
langchain==0.0.261
langsmith==0.0.21
libclang==16.0.6
lxml==4.9.3
Markdown==3.4.4
MarkupSafe==2.1.3
marshmallow==3.20.1
monotonic==1.6
mpmath==1.3.0
multidict==6.0.4
mypy-extensions==1.0.0
nltk==3.8.1
numexpr==2.8.5
numpy==1.24.3
oauthlib==3.2.2
onnxruntime==1.15.1
openapi-schema-pydantic==1.2.4
opt-einsum==3.3.0
overrides==7.4.0
packaging==23.1
pdf2image==1.16.3
pdfminer==20191125
pdfminer.six==20221105
Pillow==10.0.0
posthog==3.0.1
protobuf==4.24.0
pulsar-client==3.2.0
pyasn1==0.5.0
pyasn1-modules==0.3.0
pycparser==2.21
pycryptodome==3.18.0
pydantic==1.10.12
PyPika==0.48.9
python-dateutil==2.8.2
python-dotenv==1.0.0
python-magic==0.4.27
PyYAML==6.0.1
regex==2023.8.8
requests==2.31.0
requests-oauthlib==1.3.1
rsa==4.9
six==1.16.0
sniffio==1.3.0
soupsieve==2.4.1
SQLAlchemy==2.0.19
starlette==0.27.0
sympy==1.12
tabulate==0.9.0
tenacity==8.2.2
tensorboard==2.13.0
tensorboard-data-server==0.7.1
tensorflow==2.13.0
tensorflow-estimator==2.13.0
tensorflow-hub==0.14.0
tensorflow-macos==2.13.0
termcolor==2.3.0
tokenizers==0.13.3
tqdm==4.66.1
typing-inspect==0.9.0
typing_extensions==4.5.0
unstructured==0.9.2
urllib3==1.26.16
uvicorn==0.23.2
uvloop==0.17.0
watchfiles==0.19.0
websockets==11.0.3
Werkzeug==2.3.6
wrapt==1.15.0
yarl==1.9.2
ollama/examples/langchain-python-rag-privategpt/.gitignore 0 → 100755
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# OSX
.DS_STORE
# Models
models/
# Local Chroma db
.chroma/
db/
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
ollama/examples/langchain-python-rag-privategpt/LICENSE 0 → 100755
View file @ 95005046
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ollama/examples/langchain-python-rag-privategpt/README.md 0 → 100755
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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),
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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
)
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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()
ollama/examples/langchain-python-rag-privategpt/poetry.lock 0 → 100755
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This source diff could not be displayed because it is too large. You can view the blob instead.
ollama/examples/langchain-python-rag-privategpt/privateGPT.py 0 → 100755
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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()
ollama/examples/langchain-python-rag-privategpt/pyproject.toml 0 → 100755
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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"
ollama/examples/langchain-python-rag-privategpt/requirements.txt 0 → 100755
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langchain==0.0.274
gpt4all==1.0.8
chromadb==0.4.7
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
\ No newline at end of file
ollama/examples/langchain-python-rag-websummary/README.md 0 → 100755
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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 `llama3.1` model installed:
```bash
ollama pull llama3.1
```
2. Install the Python Requirements.
```bash
pip install -r requirements.txt
```
3. Run the example:
```bash
python main.py
```
ollama/examples/langchain-python-rag-websummary/main.py 0 → 100755
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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="llama3.1")
chain = load_summarize_chain(llm, chain_type="stuff")
result = chain.invoke(docs)
print(result)
ollama/examples/langchain-python-simple/README.md 0 → 100755
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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 `llama3.1` model installed:
```bash
ollama pull llama3.1
```
2. Install the Python Requirements.
```bash
pip install -r requirements.txt
```
3. Run the example:
```bash
python main.py
```
ollama/examples/langchain-python-simple/main.py 0 → 100755
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from langchain.llms import Ollama
input = input("What is your question?")
llm = Ollama(model="llama3.1")
res = llm.predict(input)
print (res)
ollama/examples/langchain-typescript-simple/README.md 0 → 100755
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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
```
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