🎉Easily expose FastAPI

README.md

@@ -27,15 +27,23 @@ bot.save_index("index.pickle")
 bot = QnABot(directory="./mydata", index="index.pickle")
 ```
 
+You can also create a FastAPI app that will expose the bot as an API. You should then be able to visit `http://localhost:8000/docs` to see the API documentation.
+
+```python
+from qnabot import QnABot, create_app
+
+app = create_app(QnABot("./examples/files"))
+```
+
 ### Features
 
 - [x] Create a question answering bot over your documents with one line of code using GPT
 - [x] Save / load index to reduce costs (Open AI embedings are used to create the index)
 - [x] Local data source (directory of documents) or S3 data source
 - [x] FAISS for storing vectors / index
+- [x] Expose bot over API using FastAPI
 - [ ] Support for other vector databases (e.g. Weaviate, Pinecone)
 - [ ] Customise prompt
-- [ ] Expose API
 - [ ] Support for LLaMA model
 - [ ] Support for Anthropic models
 - [ ] CLI / UI

qnabot/QnABot.py

@@ -6,7 +6,6 @@ from langchain.chains.qa_with_sources import load_qa_with_sources_chain
 from langchain.vectorstores.faiss import FAISS
 import pickle
 import os
-from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 
 
 class QnABot:

qnabot/__init__.py

@@ -1 +1,2 @@
 from .QnABot import QnABot
+from .api import create_app

qnabot/api.py

@@ -0,0 +1,13 @@
+from fastapi import FastAPI
+from qnabot import QnABot
+
+
+def create_app(bot: QnABot):
+    app = FastAPI()
+
+    @app.get("/v1/ask/{question}")
+    async def ask(question: str):
+        answer = bot.get_answer(question)
+        return {"answer": answer}
+
+    return app