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Pinecone Research — Agent RAG and long-term memory with Pinecone

website/docs/user-guide/skills/optional/research/research-pinecone-research.md

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Pinecone Research

Agent RAG and long-term memory with Pinecone.

Skill metadata

SourceOptional — install with hermes skills install official/research/pinecone-research
Pathoptional-skills/research/pinecone-research
Version1.0.0
Authorimmuhammadfurqan
LicenseMIT
Dependenciespinecone-client, langchain-pinecone
Platformslinux, macos, windows
TagsRAG, Pinecone, Memory, Research, Vector Database, Agent, Retrieval

Reference: full SKILL.md

:::info The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active. :::

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

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

Resources