Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/nousresearch/hermes-agent --skill pinecone-researchAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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-openaiSet 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)
