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Conversation History

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Overview

RAGLight supports full multi-turn conversations across all LLM providers. User and assistant messages from previous turns are automatically injected into each new request — the model sees the full context of the conversation. This works identically for generate() and generate_streaming(), and is compatible with all providers: Ollama, OpenAI, Mistral, Gemini, LMStudio, and AWS Bedrock.

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How it works

Each call to generate() or generate_streaming() accepts an optional history field — a list of {"role": ..., "content": ...} messages. RAGLight injects them into the LLM prompt before the current question. RAGPipeline manages this history automatically. You just call generate() repeatedly and history is accumulated for you.

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Usage

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Automatic history with RAGPipeline

from raglight.rag.simple_rag_api import RAGPipeline
from raglight.config.rag_config import RAGConfig
from raglight.config.vector_store_config import VectorStoreConfig
from raglight.config.settings import Settings

config = RAGConfig(
    llm=Settings.DEFAULT_LLM,
    provider=Settings.OLLAMA,
)

vector_store_config = VectorStoreConfig(
    embedding_model=Settings.DEFAULT_EMBEDDINGS_MODEL,
    provider=Settings.HUGGINGFACE,
    database=Settings.CHROMA,
    persist_directory="./myDb",
    collection_name="my_collection",
)

pipeline = RAGPipeline(config, vector_store_config)
pipeline.build()

# Each call automatically includes previous turns
response1 = pipeline.generate("What is RAGLight?")
print(response1)

response2 = pipeline.generate("Can you give me a code example?")
print(response2)  # The model knows the previous question was about RAGLight

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Capping history length

To limit memory usage in long conversations, set max_history in RAGConfig: 
config = RAGConfig(
    llm=Settings.DEFAULT_LLM,
    provider=Settings.OLLAMA,
    max_history=10,  # Keep the last 10 messages
)
When the cap is reached, the oldest messages are dropped automatically.

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Resetting history

pipeline.reset_history()

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Manual history with the Builder API

When using the RAG object directly, pass history explicitly: 
rag = (
    Builder()
    .with_embeddings(Settings.HUGGINGFACE, model_name=Settings.DEFAULT_EMBEDDINGS_MODEL)
    .with_vector_store(Settings.CHROMA, persist_directory="./myDb", collection_name="my_collection")
    .with_llm(Settings.OLLAMA, model_name=Settings.DEFAULT_LLM)
    .build_rag(k=5)
)

history = []

question1 = "What is RAGLight?"
response1 = rag.generate({"question": question1, "history": history})
history.append({"role": "user", "content": question1})
history.append({"role": "assistant", "content": response1})

question2 = "Can you give me a code example?"
response2 = rag.generate({"question": question2, "history": history})

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History in the REST API

The /generate and /generate/stream endpoints accept a history field: 
{
  "question": "Can you give me a code example?",
  "history": [
    {"role": "user", "content": "What is RAGLight?"},
    {"role": "assistant", "content": "RAGLight is a lightweight Python RAG framework..."}
  ]
}
The Streamlit chat UI (raglight serve --ui) manages this automatically.

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Summary

  • RAGPipeline accumulates history automatically across generate() calls
  • Use max_history in RAGConfig to cap the number of messages kept
  • Use pipeline.reset_history() to start a fresh conversation
  • All providers support history — same behavior regardless of backend
  • Works identically with generate() and generate_streaming()