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2 changes: 2 additions & 0 deletions .github/workflows/test.yml
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Expand Up @@ -7,6 +7,8 @@ on:
push:
branches:
- main
schedule:
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Adding this in some places just to keep us honest

- cron: '0 0 * * 1' # Runs every Monday

jobs:
test:
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -85,6 +85,7 @@ An estimated 31% of LLM queries are potentially redundant ([source](https://arxi
| --- | --- |
[/agents/00_langgraph_redis_agentic_rag.ipynb](python-recipes/agents/00_langgraph_redis_agentic_rag.ipynb) | Notebook to get started with lang-graph and agents |
[/agents/01_crewai_langgraph_redis.ipynb](python-recipes/agents/01_crewai_langgraph_redis.ipynb) | Notebook to get started with lang-graph and agents |
[/agents/02_full_featured_agent.ipynb](python-recipes/agents/02_full_featured_agent.ipynb) | Notebook builds full tool calling agent with semantic cache and router |

### Computer Vision
| Recipe | Description |
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1 change: 1 addition & 0 deletions propositions.json

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750 changes: 628 additions & 122 deletions python-recipes/RAG/04_advanced_redisvl.ipynb

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20 changes: 10 additions & 10 deletions python-recipes/RAG/06_ragas_evaluation.ipynb
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Expand Up @@ -105,22 +105,22 @@
"outputs": [],
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain.document_loaders import UnstructuredFileLoader\n",
"from langchain_community.document_loaders import PyPDFLoader\n",
"\n",
"CHUNK_SIZE = 2500\n",
"CHUNK_OVERLAP = 0\n",
"\n",
"source_doc = \"resources/nke-10k-2023.pdf\"\n",
"# pdf to load\n",
"path = 'resources/nke-10k-2023.pdf'\n",
"assert os.path.exists(path), f\"File not found: {path}\"\n",
"\n",
"loader = UnstructuredFileLoader(\n",
" source_doc, mode=\"single\", strategy=\"fast\"\n",
")\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(\n",
" chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP\n",
")\n",
"# load and split\n",
"loader = PyPDFLoader(path)\n",
"pages = loader.load()\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)\n",
"chunks = text_splitter.split_documents(pages)\n",
"\n",
"chunks = loader.load_and_split(text_splitter)"
"print(\"Done preprocessing. Created\", len(chunks), \"chunks of the original pdf\", path)"
]
},
{
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