pinecone-research

사용 방법

설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.

npx skills add https://github.com/nousresearch/hermes-agent --skill pinecone-research

시작 전 확인

설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.

원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

Pinecone Research — Agent RAG & Long-Term Memory

Use Pinecone as a retrieval-augmented generation (RAG) backend for agent conversations: persist embeddings, retrieve relevant context from past sessions, and build long-term memory.

When to use this skill

Use when:

Building agent RAG pipelines with Pinecone as the vector store
Need persistent long-term memory across agent sessions
Combining retrieval with agent tool use
Researching or prototyping semantic search workflows

Use the mlops/pinecone skill instead when:

Need a general Pinecone reference (index management, CRUD, hybrid search)
Working on production infrastructure without agent integration

Quick start

Setup

bash

pip install pinecone-client langchain-pinecone langchain-openai

Set your API key:

bash

export PINECONE_API_KEY="your-api-key"

Basic RAG pipeline

python

from pinecone import Pinecone, ServerlessSpec
from langchain_pinecone import PineconeVectorStore
from langchain_openai import OpenAIEmbeddings

# Initialize Pinecone
pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])

# Create or connect to index
index_name = "agent-memory"
if index_name not in [i.name for i in pc.list_indexes()]:
    pc.create_index(
        name=index_name,
        dimension=1536,
        metric="cosine",
        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
    )

# Build vector store
vectorstore = PineconeVectorStore.from_documents(
    documents=docs,
    embedding=OpenAIEmbeddings(),
    index_name=index_name,
)

# Retrieve relevant context
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
results = retriever.invoke("What did the agent discuss yesterday?")

Namespace-based session memory

python

# Store per-session memory
vectorstore = PineconeVectorStore(
    index=pc.Index(index_name),
    embedding=OpenAIEmbeddings(),
    namespace=f"session-{session_id}",
)

# Query across all sessions (no namespace filter)
all_memory = PineconeVectorStore(
    index=pc.Index(index_name),
    embedding=OpenAIEmbeddings(),
)
results = all_memory.similarity_search("relevant query", k=10)

Best practices

Namespace by session or user — isolate data for multi-tenant agents
Batch upserts — 100–200 vectors per batch for efficiency
Metadata filtering — tag vectors with session ID, timestamp, topic
Prune old memory — delete stale namespaces to control costs
Use serverless — auto-scaling, pay-per-use pricing

Resources

Pinecone Docs: https://docs.pinecone.io
LangChain Integration: https://python.langchain.com/docs/integrations/vectorstores/pinecone
Free Tier: 1 index, 100K vectors (1536 dimensions)