Build agent memory with Pinecone by storing searchable representations of conversations and documents. Retrieve relevant past context for answers, organize memory by session or user, and prototype semantic search that finds information by meaning.
中文简介正在准备中。当前显示英语简介。
Agent RAG and long-term memory with Pinecone.
安装前可以查看交给 AI 代理的指令内容。
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.
Use when:
Use the mlops/pinecone skill instead when:
pip install pinecone-client langchain-pinecone langchain-openai
Set your API key:
export PINECONE_API_KEY="your-api-key"
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?")
# 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)
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Set up the 1Password CLI and use stored passwords or API keys in commands and configuration templates. Choose an authentication method, retrieve secret references, and pass credentials to processes through environment variables.
中文简介正在准备中。当前显示英语简介。
Build an Excel workbook linking the income statement, balance sheet, and cash flow statement. Populate historical figures, create formula-based forecasts, compare scenarios, and check that balances and cash totals agree across all three statements.
中文简介正在准备中。当前显示英语简介。
Adapt existing PyTorch training scripts to use multiple GPUs or machines with limited code changes. The skill guides configuration, launch commands, mixed precision, which uses lower-precision numbers, and DeepSpeed or FSDP settings for distributing training work and memory.
中文简介正在准备中。当前显示英语简介。
Configure Hermes to run AI models through Actual Computer on your own hardware. Set up either the hosted relay or a local connection, select models, verify responses, and troubleshoot request errors or empty output.
中文简介正在准备中。当前显示英语简介。
Test an app from the perspective of a frustrated, less technical user. Find confusing steps and barriers, separate actionable usability problems from personal complaints, and produce screenshots, suggested fixes, and prioritized tickets.
中文简介正在准备中。当前显示英语简介。
Resolve Git merge conflicts between two agents by comparing both changes and their stated goals. Combine compatible work, explain choices where designs differ, and verify the merged result with project builds or tests.
中文简介正在准备中。当前显示英语简介。