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session_id ties multiple pipeline runs together for tracing and analytics, but it does NOT give LLM nodes conversational memory. Pipeline LLM nodes only see the current prompt — they don’t look up prior messages from the session. For conversational context with pipelines, accumulate history client-side and pass it as part of the prompt input on each run. (Alternatively, use a Chatbot, which manages history via conversation_id.) 
from typing import Any, cast

from vectorshift.pipeline import Pipeline

# ── Build and deploy a simple LLM pipeline ──────────────────────────
PIPELINE_NAME = "session_overrides_example"
try:
    pipeline = Pipeline.fetch(name=PIPELINE_NAME)
    print(f"Pipeline fetched: id={pipeline.id}, branch_id={pipeline.branch_id}")
except Exception as e:
    print(f"Error fetching pipeline: {e}")
    pipeline = Pipeline.new(name=PIPELINE_NAME)
    print(f"Pipeline created: id={pipeline.id}, branch_id={pipeline.branch_id}")
inp = pipeline.add(name="input_0", id="input_0").input(input_type="string")
llm = pipeline.add(name="llm", id="llm").llm(
    provider="openai",
    model="gpt-4o",
    system="You are a helpful assistant.",
    prompt=inp.text,
)
out = pipeline.add(name="output_0", id="output_0").output(
    output_type="string", value=llm.response
)
pipeline.save(deploy=True)

# ── 1. session_id: run grouping / tracing (not chat memory) ────────
# Both runs share a session_id so they're grouped in the span system,
# but the LLM node in the second run has no memory of the first.
result1 = cast(
    dict[str, Any],
    pipeline.run(
        inputs={"input_0": "My name is Alice."},
        session_id="session-001",
    ),
)
print("Run 1:", result1.get("outputs"))

result2 = cast(
    dict[str, Any],
    pipeline.run(
        inputs={"input_0": "What is my name?"},
        session_id="session-001",
    ),
)
# The LLM will NOT know the name "Alice" — session_id doesn't carry history.
print("Run 2 (no memory):", result2.get("outputs"))

# ── 2. Client-side conversation history ─────────────────────────────
# To give the LLM conversational context, accumulate messages yourself
# and pass the full history as the prompt input each run.
history: list[str] = []

prompts = ["My name is Alice.", "What is my name?"]
for user_msg in prompts:
    history.append(f"User: {user_msg}")
    conversation = "\n".join(history)

    result = cast(
        dict[str, Any],
        pipeline.run(
            inputs={"input_0": conversation},
            session_id="session-001",
        ),
    )
    assistant_reply = result.get("outputs", {}).get("output_0", "")
    history.append(f"Assistant: {assistant_reply}")
    print(f"With history: {assistant_reply}")

# ── 3. node_input_overrides: change node inputs at runtime ──────────
result3 = cast(
    dict[str, Any],
    pipeline.run(
        inputs={"input_0": "What is your favorite thing to do?"},
        node_input_overrides={
            "llm.system": "You are a pirate. Answer everything like a pirate.",
        },
    ),
)
print("Overridden:", result3.get("outputs"))
Source: examples/pipelines/session_and_overrides.py in the SDK repo.