cognee/docs/en/guides/sessions.md
HectorSin fbead80a36 docs: setup documentation structure for i18n (en/ko)
Signed-off-by: HectorSin <kkang15634@ajou.ac.kr>
2026-01-14 12:17:24 +09:00

181 lines
No EOL
6.1 KiB
Markdown

# Sessions
> Step-by-step guide to using sessions for conversational memory in Cognee
A minimal guide to enabling conversational memory with sessions. When you use the same `session_id` across searches, Cognee remembers previous questions and answers, enabling contextually aware follow-up questions.
## Before You Start
* Complete [Quickstart](../getting-started/quickstart) to understand basic operations
* Ensure you have [LLM Providers](../setup-configuration/llm-providers) configured
* Read [Sessions and Caching](../core-concepts/sessions-and-caching) for conceptual overview
* Configure your cache adapter before using sessions. See [Cache Adapters](../core-concepts/sessions-and-caching#cache-adapters) for Redis and Filesystem setup instructions.
## Code in Action
```python theme={null}
import asyncio
import cognee
from cognee import SearchType
from cognee.modules.users.methods import get_default_user
from cognee.modules.retrieval.utils.session_cache import set_session_user_context_variable
async def main():
# Prepare knowledge base
await cognee.add([
"Alice moved to Paris in 2010. She works as a software engineer.",
"Bob lives in New York. He is a data scientist.",
"Alice and Bob met at a conference in 2015."
])
await cognee.cognify()
# Set user context (required for sessions)
user = await get_default_user()
await set_session_user_context_variable(user)
# First search - starts a new session
result1 = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION,
query_text="Where does Alice live?",
session_id="conversation_1"
)
print("First answer:", result1[0])
# Follow-up search - uses conversation history
result2 = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION,
query_text="What does she do for work?",
session_id="conversation_1" # Same session
)
print("Follow-up answer:", result2[0])
# The LLM knows "she" refers to Alice from previous context
# Different session - no memory of previous conversation
result3 = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION,
query_text="What does she do for work?",
session_id="conversation_2" # New session
)
print("New session answer:", result3[0])
# This won't know who "she" refers to
asyncio.run(main())
```
<Note>
This example works with either Redis or Filesystem adapter. Configure your chosen adapter in the [Before you start](#before-you-start) section above.
</Note>
## What Just Happened
### Step 1: Prepare Knowledge Base
```python theme={null}
await cognee.add([
"Alice moved to Paris in 2010. She works as a software engineer.",
"Bob lives in New York. He is a data scientist.",
"Alice and Bob met at a conference in 2015."
])
await cognee.cognify()
```
Before you can search with sessions, you need to have data in your knowledge base. Use `cognee.add()` to ingest data and `cognee.cognify()` to build the knowledge graph.
### Step 2: Set User Context
```python theme={null}
from cognee.modules.users.methods import get_default_user
from cognee.modules.retrieval.utils.session_cache import set_session_user_context_variable
user = await get_default_user()
await set_session_user_context_variable(user)
```
Sessions require a user context to associate conversation history with a specific user. This must be set before using `session_id` in searches.
### Step 3: Use Session ID in Searches
```python theme={null}
result = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION,
query_text="Where does Alice live?",
session_id="conversation_1"
)
```
The `session_id` parameter creates or continues a conversation thread. All searches with the same `session_id` share conversation history.
### Step 4: Follow-up Questions
```python theme={null}
result = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION,
query_text="What does she do for work?",
session_id="conversation_1" # Same session
)
```
When you use the same `session_id`, Cognee automatically includes previous Q\&A turns in the LLM prompt, enabling contextual follow-up questions.
### Step 5: Multiple Sessions
```python theme={null}
# Session 1
await cognee.search(query_text="Question 1", session_id="session_1")
await cognee.search(query_text="Follow-up", session_id="session_1")
# Session 2 (independent)
await cognee.search(query_text="Question 1", session_id="session_2")
```
Each `session_id` maintains its own conversation history. Sessions are isolated from each other.
## Advanced Usage
<Accordion title="Custom Session IDs">
Use meaningful session IDs to organize conversations:
```python theme={null}
# User-specific sessions
await cognee.search(query_text="...", session_id=f"user_{user_id}_chat")
# Topic-specific sessions
await cognee.search(query_text="...", session_id="project_planning")
await cognee.search(query_text="...", session_id="bug_discussion")
```
Session IDs are arbitrary strings—use whatever naming scheme fits your application.
</Accordion>
<Accordion title="Session Expiration">
Sessions expire after 24 hours by default. To customize TTL, configure it in your cache adapter settings. Expired sessions are automatically cleaned up and won't affect new searches.
</Accordion>
<Accordion title="Disabling Sessions">
If caching is disabled or unavailable, searches work normally but without conversational memory:
```dotenv theme={null}
CACHING=false
```
Or simply omit `session_id` from search calls. The system gracefully handles missing cache backends.
</Accordion>
<Columns cols={3}>
<Card title="Sessions and Caching" icon="brain" href="/core-concepts/sessions-and-caching">
Understand how sessions work conceptually
</Card>
<Card title="Search Basics" icon="search" href="/guides/search-basics">
Learn about search parameters and types
</Card>
<Card title="Setup Configuration" icon="settings" href="/setup-configuration/overview">
Configure cache adapters and providers
</Card>
</Columns>
---
> To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt