Memvid vs Vector Databases

# Exact lexical search (instant, no embeddings needed)
results = mem.find("handleAuthentication", k=5)

# Temporal queries (unique to Memvid)
results = mem.timeline("2024-01-01", "2024-01-31")

# Entity state (knowledge graph)
alice = mem.state("Alice")  # {employer: "Anthropic", role: "Engineer"}

# Semantic search (when you need it)
results = mem.find("cost reduction strategies", mode="vec")

# Hybrid search (best of both)
results = mem.find("budget optimization", mode="auto")

# 1. Sign up at pinecone.io
# 2. Create a project
# 3. Get API key
# 4. Install SDK
pip install pinecone-client

# 5. Initialize
import pinecone
pinecone.init(api_key="your-api-key", environment="us-west1-gcp")

# 6. Create index (wait for provisioning...)
pinecone.create_index("my-index", dimension=1536, metric="cosine")

# 7. Wait for index to be ready
import time
while not pinecone.describe_index("my-index").status["ready"]:
    time.sleep(1)

# 8. Connect to index
index = pinecone.Index("my-index")

# 9. Now you need to embed your data before inserting...

# 1. Install
pip install chromadb

# 2. Initialize
import chromadb
client = chromadb.Client()

# 3. Create collection
collection = client.create_collection("my-collection")

# 4. Add documents (ChromaDB embeds automatically, but still takes time)
collection.add(
    documents=["doc1", "doc2", "doc3"],
    ids=["id1", "id2", "id3"]
)
# This step embeds all documents - can take minutes for large datasets

npm install -g memvid-cli
memvid create knowledge.mv2
echo "Your document content" | memvid put knowledge.mv2
memvid find knowledge.mv2 --query "document"

from memvid_sdk import create

mem = create('knowledge.mv2')
mem.put(title="Doc", label="docs", metadata={}, text="Your document content")
results = mem.find("document", k=5)

# Finding a specific function (exact match)
memvid find codebase.mv2 --query "handleWebSocketConnection"
# → Returns exact matches instantly

# With vector search, you might get:
# - "processNetworkRequest" (semantically similar, wrong function)
# - "WebSocket" documentation (not the function)
# - "connectionHandler" (close but not exact)

# Enable semantic search
memvid doctor knowledge.mv2 --rebuild-vec-index

# Semantic mode
memvid find knowledge.mv2 --query "cost reduction strategies" --mode vec

# Hybrid mode (auto-selects best approach)
memvid find knowledge.mv2 --query "budget optimization" --mode auto

# Share your entire knowledge base
cp knowledge.mv2 /team/shared/

# Version control it
git add knowledge.mv2 && git commit -m "Updated docs"

# Backup
cp knowledge.mv2 knowledge.mv2.backup

import pinecone
from memvid_sdk import create

# Export from Pinecone
pinecone.init(api_key="...")
index = pinecone.Index("my-index")

# Create Memvid memory
mem = create("knowledge.mv2")

# Fetch and migrate (you'll need your original documents)
# Pinecone doesn't store original text, only vectors
# This is why Memvid stores everything in one place

import chromadb
from memvid_sdk import create

# Export from ChromaDB
client = chromadb.Client()
collection = client.get_collection("my-collection")
results = collection.get(include=["documents", "metadatas"])

# Create Memvid memory
mem = create("knowledge.mv2")

# Migrate documents
for doc, meta in zip(results["documents"], results["metadatas"]):
    mem.put(
        title=meta.get("title", "Untitled"),
        label="migrated",
        metadata=meta,
        text=doc
    )

mem.seal()

# Install (10 seconds)
npm install -g memvid-cli

# Create and search (10 more seconds)
memvid create test.mv2
echo "The quick brown fox jumps over the lazy dog" | memvid put test.mv2
memvid find test.mv2 --query "quick fox"

# That's it. No API keys. No embedding wait. Just search.