updated pip package readme & fix bugs with GH action (dgarnitz#89)

updated pip package readme & fix bugs with GH action

.github/workflows/run_unittests.yml → .github/workflows/unittests.yml

@@ -1,5 +1,5 @@
 
-name: Vectorflow_Unittests
+name: Unittests
 
 on:
   pull_request:
@@ -8,12 +8,9 @@ on:
 
 
 jobs:
-  build:
-
+  unittests:
+    name: Unit Tests
     runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.9", "3.10"]
 
     env:
       LOCAL_VECTOR_DB: qdrant 
@@ -23,7 +20,7 @@ jobs:
       - name: Setup Python # Set Python version
         uses: actions/setup-python@v4
         with:
-          python-version: ${{ matrix.python-version }}
+          python-version: 3.9
 
       - name: Install dependencies - Api
         run: |

README.md

@@ -35,19 +35,21 @@ cd vectorflow
 ```
 
 #### Embed Documents with Client
-To start embedding documents locally, [install the VectorFlow Client python library](https://pypi.org/project/vectorflow-client/)
+To start embedding documents locally, [install the VectorFlow Client python library](https://pypi.org/project/vectorflow-client/) in your python application's virtual environment. 
 ```
 pip install vectorflow-client
 ```
 then run the following
 ```
+from vectorflow-client.client.vectorflow import Vectorflow
+
 vectorflow = Vectorflow()
 vectorflow.embeddings_api_key = os.getenv("OPEN_AI_KEY")
-paths = ['path_to_your_file']
+paths = ['path_to_your_file1', 'path_to_your_file2', ...]
 response = vectorflow.upload(paths)
 ```
 
-for more instructions see the `README.md` in the `client` directory.
+You do **not** need to clone the VectorFlow repo to utilize the client functionality via pip. For more instructions see the `README.md` in the `client` directory.
 
 See the appendix for details on how to use the `testing_clients` scripts.
 

client/README.md

@@ -1,15 +1,12 @@
 # VectorFlow Python Client
 Use this Python client to embed documents with VectorFlow and check on the status of those embeddings. 
 
-
-
-
 ### How to Use
 The client has 2 methods for uploading documents to embed and 2 for checking statuses, listed below. All four methods return a python `response` object from the python `requests` library. You must parse the response using the `.json()` method. 
 
 #### Initialize
 ```
-from client.vectorflow import Vectorflow
+from vectorflow-client.client.vectorflow import Vectorflow
 
 vectorflow = Vectorflow()
 vectorflow.embedding_api_key = "YOUR_OPEN_AI_KEY"

setup.py

@@ -1,22 +1,18 @@
-from setuptools import setup
+from setuptools import setup, find_packages
 
 # Read the content of your README file
-with open("README.md", "r", encoding="utf-8") as fh:
+with open("./client/README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()
 
 setup(
-    name="vectorflow-ai",
-    version="0.1.1",  # Update version as needed
+    name="vectorflow-client",
+    version="0.0.5",
+    packages=find_packages(where="client") + ["shared"],
+    package_dir={"": "client", "shared": "src/shared"},
     install_requires=[
-        # No dependencies right now since this is only for setting up docker-compose
+        'requests==2.31.0'
     ],
-    entry_points={
-        'console_scripts': [
-            'vectorflow=vectorflow:main',
-        ],
-    },
-    py_modules=["vectorflow"],
-    include_package_data=True,  # This ensures non-python files (like your setup.sh) are included
+    include_package_data=True,  # This is the line that reads the MANIFEST.in
     long_description=long_description,
-    long_description_content_type="text/markdown",  # This is important! It tells PyPI the README is in Markdown
+    long_description_content_type="text/markdown",
 )