337 posts · Page 26 of 29. ← Blog
A session remembers within one conversation. A ContextProvider remembers across all of them — durable facts injected into every run.
A ContextProvider holds facts that outlive any session and injects them into every run via before_run and context.extend_instructions.
An Agent is stateless — two runs are strangers. What carries history from one turn to the next is an AgentSession.
An Agent is stateless; an AgentSession carries history between turns. Thread create_session() through each run so turn two can see turn one.
A tool is just a Python function the model may call: decorate it with @tool, annotate the arguments, and the framework wires the schema, the call, and the result back into the conversation.
Turn a Python function into a tool with the @tool decorator and Annotated Field descriptions, then let the model pick the right one from the question wording.
The smallest possible Python agent: a Foundry chat client plus instructions, run once collected and once streamed.
The smallest MAF Python agent: wire a FoundryChatClient plus instructions, then run it once collected and once streamed with stream=True.
Passkeys are FIDO2; FIDO2 is the spec; Ed25519 is the signature algorithm. The full registration + assertion flow in 200 lines of stdlib Go.
Services need identity too, not just users. SPIFFE issues SVIDs (verifiable identity documents) to workloads; SPIRE is the reference issuer. The shape and the first deploy.
Two signals do most of the work for detecting compromised sessions: impossible travel between consecutive logins, and credential-stuffing density across an IP range. The Go implementation.
Vector search treats every chunk as independent. GraphRAG models the relationships between entities, communities, and concepts. For corpus-spanning questions ("what's the relationship between X and Y"), graph wins.
BigQuery has had a built-in knowledge graph since 2024. For entity resolution across millions of rows — the "is this John Smith the same as that John Smith" problem — it's the cheapest tool I've found.
Embedding a question and embedding an answer often produce different vectors. HyDE generates a hypothetical answer to the question, embeds *that*, and retrieves on it. Retrieval quality goes up disproportionately.
Naive RAG retrieves on every query. Self-RAG decides whether to retrieve. CRAG decides whether the retrieved content is good enough or needs corrective retrieval. Two papers; both worth implementing.
An Indian banking deployment needs to handle Hindi, Marathi, Tamil, Bengali, and English in the same retrieval pipeline. Bhashini (the government's language stack) plus cross-lingual embeddings make it tractable.