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Update multi query template README, ntbk (#12356)
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@ -14,3 +14,38 @@ This template uses Pinecone as a vectorstore and requires that `PINECONE_API_KEY
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Be sure that `OPENAI_API_KEY` is set in order to the OpenAI models.
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## App
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Example `server.py`:
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```
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from fastapi import FastAPI
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from langserve import add_routes
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from rag_pinecone_multi_query.chain import chain
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app = FastAPI()
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# Edit this to add the chain you want to add
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add_routes(app, chain, path="rag_pinecone_multi_query")
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8001)
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```
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Run:
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```
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python app/server.py
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```
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Check endpoint:
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```
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http://0.0.0.0:8001/docs
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```
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See `rag_pinecone_multi_query.ipynb` for example usage -
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```
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from langserve.client import RemoteRunnable
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rag_app_pinecone = RemoteRunnable('http://0.0.0.0:8001/rag_pinecone_multi_query')
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rag_app_pinecone.invoke("What are the different types of agent memory")
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```
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@ -11,13 +11,24 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 8,
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"id": "d774be2a",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'The different types of agent memory mentioned in the context are short-term memory, long-term memory, explicit/declarative memory, and implicit/procedural memory.'"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langserve.client import RemoteRunnable\n",
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"rag_app_pinecone = RemoteRunnable('http://localhost:8000/rag-pinecone-multi-query')\n",
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"rag_app_pinecone = RemoteRunnable('http://0.0.0.0:8001/rag_pinecone_multi_query')\n",
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"rag_app_pinecone.invoke(\"What are the different types of agent memory\")"
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]
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}
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