Function load_llm

chains.py:63–98

(llm_name: str, logger=BaseLogger(), config={})

def load_llm(llm_name: str, logger=BaseLogger(), config={}):
    if llm_name in ["gpt-4", "gpt-4o", "gpt-4-turbo"]:
        logger.info("LLM: Using GPT-4")
        return ChatOpenAI(temperature=0, model_name=llm_name, streaming=True)
    elif llm_name == "gpt-3.5":
        logger.info("LLM: Using GPT-3.5")
        return ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", streaming=True)
    elif llm_name == "claudev2":
        logger.info("LLM: ClaudeV2")
        return ChatBedrock(
            model_id="anthropic.claude-v2",
            model_kwargs={"temperature": 0.0, "max_tokens_to_sample": 1024},
            streaming=True,
        )
    elif llm_name.startswith(AWS_MODELS):
        logger.info(f"LLM: {llm_name}")
        return ChatBedrock(
            model_id=llm_name,
            model_kwargs={"temperature": 0.0, "max_tokens_to_sample": 1024},
            streaming=True,
        )

    elif len(llm_name):
        logger.info(f"LLM: Using Ollama: {llm_name}")
        return ChatOllama(
            temperature=0,
            base_url=config["ollama_base_url"],
            model=llm_name,
            streaming=True,
            # seed=2,
            top_k=10,  # A higher value (100) will give more diverse answers, while a lower value (10) will be more conservative.
            top_p=0.3,  # Higher value (0.95) will lead to more diverse text, while a lower value (0.5) will generate more focused text.
            num_ctx=3072,  # Sets the size of the context window used to generate the next token.
        )
    logger.info("LLM: Using GPT-3.5")
    return ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", streaming=True)


def configure_llm_only_chain(llm):