Verwendung
- Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
- Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
- Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/nousresearch/hermes-agent --skill pinecone-researchVor dem Start
- Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
- Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument
Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.
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)
