langchain（3）：链

llm chain：它会在后台格式化提示词，然后将格式化后的提示词传给llm

prompt = ChatPromptTemplate.from_template(
    "What is the best name to describe \
    a company that makes {product}?"
)
chain = LLMChain(llm=llm, prompt=prompt)
product = "Queen Size Sheet Set"
chain.run(product)

简单顺序链 simple sequent chain：将一系列链一个一个的执行

# prompt template 1
first_prompt = ChatPromptTemplate.from_template(
    "What is the best name to describe \
    a company that makes {product}?"
)
# Chain 1
chain_one = LLMChain(llm=llm, prompt=first_prompt)

# prompt template 2
second_prompt = ChatPromptTemplate.from_template(
    "Write a 20 words description for the following \
    company:{company_name}"
)
# chain 2
chain_two = LLMChain(llm=llm, prompt=second_prompt)

overall_simple_chain = SimpleSequentialChain(chains=[chain_one, chain_two],
                                             verbose=True
                                            )
overall_simple_chain.run(product)

顺序链sequent chain：当存在多个输入输出时，使用不同的变量名标记，切忌变量名错误，通过改变LLMChain方法中的{}的输入和output_key来确定变量名

# prompt template 1: translate to english
first_prompt = ChatPromptTemplate.from_template(
    "Translate the following review to english:"
    "\n\n{Review}"
)
# chain 1: input= Review and output= English_Review
chain_one = LLMChain(llm=llm, prompt=first_prompt, 
                     output_key="English_Review"
                    )
                    
# prompt template 2: summarize                    
second_prompt = ChatPromptTemplate.from_template(
    "Can you summarize the following review in 1 sentence:"
    "\n\n{English_Review}"
)
# chain 2: input= English_Review and output= summary
chain_two = LLMChain(llm=llm, prompt=second_prompt, 
                     output_key="summary"
                    )

# prompt template 3: translate to english
third_prompt = ChatPromptTemplate.from_template(
    "What language is the following review:\n\n{Review}"
)
# chain 3: input= Review and output= language
chain_three = LLMChain(llm=llm, prompt=third_prompt,
                       output_key="language"
                      )
                      
# prompt template 4: follow up message
fourth_prompt = ChatPromptTemplate.from_template(
    "Write a follow up response to the following "
    "summary in the specified language:"
    "\n\nSummary: {summary}\n\nLanguage: {language}"
)
# chain 4: input= summary, language and output= followup_message
chain_four = LLMChain(llm=llm, prompt=fourth_prompt,
                      output_key="followup_message"
                     )
                     
# overall_chain: input= Review 
# and output= English_Review,summary, followup_message
# 可以将其中任何过程的链结果输出
overall_chain = SequentialChain(
    chains=[chain_one, chain_two, chain_three, chain_four],
    input_variables=["Review"],
    output_variables=["English_Review", "summary","followup_message"],
    verbose=True
)

路由链route chain：如果你有多条子链，每条子链专门负责处理某种特定类型的输入，这种情况可以使用

# 设置不同的prompt模板作为不同route方向
physics_template = """You are a very smart physics professor. \
You are great at answering questions about physics in a concise\
and easy to understand manner. \
When you don't know the answer to a question you admit\
that you don't know.

Here is a question:
{input}"""

math_template = """You are a very good mathematician. \
You are great at answering math questions. \
You are so good because you are able to break down \
hard problems into their component parts, 
answer the component parts, and then put them together\
to answer the broader question.

Here is a question:
{input}"""

history_template = """You are a very good historian. \
You have an excellent knowledge of and understanding of people,\
events and contexts from a range of historical periods. \
You have the ability to think, reflect, debate, discuss and \
evaluate the past. You have a respect for historical evidence\
and the ability to make use of it to support your explanations \
and judgements.

Here is a question:
{input}"""

computerscience_template = """ You are a successful computer scientist.\
You have a passion for creativity, collaboration,\
forward-thinking, confidence, strong problem-solving capabilities,\
understanding of theories and algorithms, and excellent communication \
skills. You are great at answering coding questions. \
You are so good because you know how to solve a problem by \
describing the solution in imperative steps \
that a machine can easily interpret and you know how to \
choose a solution that has a good balance between \
time complexity and space complexity. 

Here is a question:
{input}"""

# 给每个模板起名，并加上描述，然后传递给路由链，由它决定什么时候用哪条子链
prompt_infos = [
    {
        "name": "physics", 
        "description": "Good for answering questions about physics", 
        "prompt_template": physics_template
    },
    {
        "name": "math", 
        "description": "Good for answering math questions", 
        "prompt_template": math_template
    },
    {
        "name": "History", 
        "description": "Good for answering history questions", 
        "prompt_template": history_template
    },
    {
        "name": "computer science", 
        "description": "Good for answering computer science questions", 
        "prompt_template": computerscience_template
    }
]

# 创建目标链
llm = ChatOpenAI(temperature=0, model=llm_model)

destination_chains = {}
for p_info in prompt_infos:
    name = p_info["name"]
    prompt_template = p_info["prompt_template"]
    prompt = ChatPromptTemplate.from_template(template=prompt_template)
    chain = LLMChain(llm=llm, prompt=prompt)
    destination_chains[name] = chain  

# 创建默认链，其他链调用不到时备用 
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)

# 创建提示词模板，完成任务的说明，输出内容格式
MULTI_PROMPT_ROUTER_TEMPLATE = """Given a raw text input to a \
language model select the model prompt best suited for the input. \
You will be given the names of the available prompts and a \
description of what the prompt is best suited for. \
You may also revise the original input if you think that revising\
it will ultimately lead to a better response from the language model.

<< FORMATTING >>
Return a markdown code snippet with a JSON object formatted to look like:
```json
{{{{
    "destination": string \ name of the prompt to use or "DEFAULT"
    "next_inputs": string \ a potentially modified version of the original input
}}}}

REMEMBER: "destination" MUST be one of the candidate prompt
names specified below OR it can be "DEFAULT" if the input is not
well suited for any of the candidate prompts.
REMEMBER: "next_inputs" can just be the original input
if you don't think any modifications are needed.

<< CANDIDATE PROMPTS >>

<< INPUT >>

<< OUTPUT (remember to include the ```json)>>"""

```python
# 创建路由链
# 1.使用之前定义的prompt模板创建完整路由模板
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
    destinations=destinations_str
)
# 2.为模板创建提示词模板
# RouterOutputParser方法帮助route chain在哪条子链中路由
router_prompt = PromptTemplate(
    template=router_template,
    input_variables=["input"],
    output_parser=RouterOutputParser(),
)
# 3.传入llm和整个route提示词，创建route chain
router_chain = LLMRouterChain.from_llm(llm, router_prompt)

# 组建路由链route chain
# 路由链->目标链->默认链(没有目标)
chain = MultiPromptChain(router_chain=router_chain, 
                         destination_chains=destination_chains, 
                         default_chain=default_chain, verbose=True
                        )