init

.gitattributes

@@ -32,4 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1 @@
+.env

Dockerfile

@@ -0,0 +1,16 @@
+# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+#!/usr/bin/env python
+"""Example LangChain server exposes a retriever."""
+from fastapi import FastAPI
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import OllamaEmbeddings
+
+from langserve import add_routes
+
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+HF_API_KEY = os.getenv('HF_API_KEY') 
+
+vectorstore = FAISS.from_texts(
+    ["cats like fish", "dogs like sticks"], embedding=OllamaEmbeddings(
+        model="nomic-embed-text",
+        base_url="https://lintasmediadanawa-hf-llm-api.hf.space", 
+        headers={"Authorization":"Bearer "+HF_API_KEY}
+    )
+)
+retriever = vectorstore.as_retriever()
+
+app = FastAPI(
+    title="LangChain Server",
+    version="1.0",
+    description="Spin up a simple api server using Langchain's Runnable interfaces",
+)
+# Adds routes to the app for using the retriever under:
+# /invoke
+# /batch
+# /stream
+add_routes(app, retriever)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="localhost", port=8000)

requirements.txt

@@ -0,0 +1,5 @@
+fastapi
+uvicorn[standard]
+langserve[all]
+faiss-cpu
+langchain-community