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📖 docs: Update litellm.md with Redis and Langfuse (#1618)
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@ -105,9 +105,9 @@ Overall, LiteLLM Server offers a comprehensive suite of tools for managing, depl
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## Ollama
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Use [Ollama](https://ollama.ai/) for
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* Run large language models on local hardware
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* Host multiple models
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* Dynamically load the model upon request
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* Run large language models on local hardware
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* Host multiple models
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* Dynamically load the model upon request
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### docker-compose.yaml with GPU
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```yaml
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@ -155,4 +155,101 @@ Add the below lines to the config to access the Ollama models
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stream: True
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```
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## Caching with Redis
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Litellm supports in-memory, redis, and s3 caching. Note: Caching currently only works with exact matching.
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### Update docker-compose.yaml to enable Redis
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Add the below service to your docker-compose.yaml
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```yaml
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redis:
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image: redis:7-alpine
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command:
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- sh
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- -c # this is to evaluate the $REDIS_PASSWORD from the env
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- redis-server --appendonly yes --requirepass $$REDIS_PASSWORD ## $$ because of docker-compose
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environment:
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REDIS_PASSWORD: RedisChangeMe
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volumes:
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- ./redis:/data
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```
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Add the following to the environment variables in the litellm service inside the docker-compose.yaml
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```yaml
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litellm:
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image: ghcr.io/berriai/litellm:main-latest
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volumes:
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- ./litellm/litellm-config.yaml:/app/config.yaml
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command: [ "--config", "/app/config.yaml", "--port", "8000", "--num_workers", "8" ]
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environment:
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REDIS_HOST: redis
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REDIS_PORT: 6379
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REDIS_PASSWORD: RedisChangeMe
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```
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### Update Litellm Config File
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Add the below options to the litellm config file
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```yaml
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litellm_settings: # module level litellm settings - https://github.com/BerriAI/litellm/blob/main/litellm/__init__.py
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cache: True # set cache responses to True, litellm defaults to using a redis cache
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cache_params: # cache_params are optional
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type: "redis" # The type of cache to initialize. Can be "local" or "redis". Defaults to "local".
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# Optional configurations
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supported_call_types: ["acompletion", "completion", "embedding", "aembedding"] # defaults to all litellm call types
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```
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## Performance Monitoring with Langfuse
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Litellm supports various logging and observability options. The settings below will enable Langfuse which will provide a cache_hit tag showing which conversations used cache.
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### Update docker-compose.yaml to enable Langfuse
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Langfuse requires a postgres database, so add both postgres and langfuse services to the docker-compose.yaml
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```yaml
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langfuse-server:
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image: ghcr.io/langfuse/langfuse:latest
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depends_on:
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- db
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ports:
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- "3000:3000"
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environment:
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- NODE_ENV=production
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- DATABASE_URL=postgresql://postgres:PostgresChangeMe@db:5432/postgres
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- NEXTAUTH_SECRET=ChangeMe
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- SALT=ChangeMe
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- NEXTAUTH_URL=http://localhost:3000
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- TELEMETRY_ENABLED=${TELEMETRY_ENABLED:-true}
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- NEXT_PUBLIC_SIGN_UP_DISABLED=${NEXT_PUBLIC_SIGN_UP_DISABLED:-false}
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- LANGFUSE_ENABLE_EXPERIMENTAL_FEATURES=${LANGFUSE_ENABLE_EXPERIMENTAL_FEATURES:-false}
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db:
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image: postgres
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restart: always
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environment:
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- POSTGRES_USER=postgres
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- POSTGRES_PASSWORD=PostgresChangeMe
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- POSTGRES_DB=postgres
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volumes:
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- ./postgres:/var/lib/postgresql/data
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```
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Once Langfuse is running, create an account by accessing the web interface on port 3000. Create a new project to obtain the needed public and private key used by the litellm config
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Add environement variable within the litellm service within docker-compose.yaml
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```yaml
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litellm:
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image: ghcr.io/berriai/litellm:main-latest
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ports:
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- "8000:8000"
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volumes:
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- /srv/litellm/config/litellm-config.yaml:/app/config.yaml
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command: [ "--config", "/app/config.yaml", "--port", "8000", "--num_workers", "8" ]
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environment:
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LANGFUSE_PUBLIC_KEY: pk-lf-RandomStringFromLangfuseWebInterface
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LANGFUSE_SECRET_KEY: sk-lf-RandomStringFromLangfuseWebInterface
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LANGFUSE_HOST: http://langfuse-server:3000
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```
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### Update litellm config file
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```yaml
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litellm_settings:
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success_callback: ["langfuse"]
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```
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