Rename embedchain to mem0 and open sourcing code for long term memory (#1474)
Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>
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co-authored by
Deshraj Yadav
parent
83e8c97295
commit
f842a92e25
@@ -0,0 +1 @@
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OPENAI_API_KEY=sk-xxx
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@@ -0,0 +1 @@
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.env
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from fastapi import FastAPI, responses
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from pydantic import BaseModel
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from embedchain import App
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app = FastAPI(title="Embedchain FastAPI App")
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embedchain_app = App()
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class SourceModel(BaseModel):
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source: str
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class QuestionModel(BaseModel):
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question: str
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@app.post("/add")
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async def add_source(source_model: SourceModel):
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"""
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Adds a new source to the EmbedChain app.
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Expects a JSON with a "source" key.
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"""
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source = source_model.source
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embedchain_app.add(source)
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return {"message": f"Source '{source}' added successfully."}
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@app.post("/query")
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async def handle_query(question_model: QuestionModel):
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"""
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Handles a query to the EmbedChain app.
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Expects a JSON with a "question" key.
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"""
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question = question_model.question
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answer = embedchain_app.query(question)
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return {"answer": answer}
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@app.post("/chat")
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async def handle_chat(question_model: QuestionModel):
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"""
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Handles a chat request to the EmbedChain app.
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Expects a JSON with a "question" key.
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"""
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question = question_model.question
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response = embedchain_app.chat(question)
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return {"response": response}
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@app.get("/")
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async def root():
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return responses.RedirectResponse(url="/docs")
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@@ -0,0 +1,16 @@
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services:
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- type: web
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name: ec-render-app
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runtime: python
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repo: https://github.com/<your-username>/<repo-name>
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scaling:
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minInstances: 1
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maxInstances: 3
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targetMemoryPercent: 60 # optional if targetCPUPercent is set
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targetCPUPercent: 60 # optional if targetMemory is set
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buildCommand: pip install -r requirements.txt
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startCommand: uvicorn app:app --host 0.0.0.0
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envVars:
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- key: OPENAI_API_KEY
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value: sk-xxx
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autoDeploy: false # optional
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@@ -0,0 +1,4 @@
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fastapi==0.104.0
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uvicorn==0.23.2
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embedchain
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beautifulsoup4
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