Custom prompt option for llm_bash and api chains (#612)

Co-authored-by: lesscomfortable <pancho_ingham@hotmail.com>

docs/modules/chains/examples/llm_bash.ipynb

@@ -28,7 +28,7 @@
       "\n",
       "Answer: \u001b[33;1m\u001b[1;3mHello World\n",
       "\u001b[0m\n",
-      "\u001b[1m> Finished LLMBashChain chain.\u001b[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
@@ -55,12 +55,83 @@
     "bash_chain.run(text)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customize Prompt\n",
+    "You can also customize the prompt that is used. Here is an example prompting to avoid using the 'echo' utility"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "\n",
+    "_PROMPT_TEMPLATE = \"\"\"If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put \"#!/bin/bash\" in your answer. Make sure to reason step by step, using this format:\n",
+    "Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"\n",
+    "I need to take the following actions:\n",
+    "- List all files in the directory\n",
+    "- Create a new directory\n",
+    "- Copy the files from the first directory into the second directory\n",
+    "```bash\n",
+    "ls\n",
+    "mkdir myNewDirectory\n",
+    "cp -r target/* myNewDirectory\n",
+    "```\n",
+    "\n",
+    "Do not use 'echo' when writing the script.\n",
+    "\n",
+    "That is the format. Begin!\n",
+    "Question: {question}\"\"\"\n",
+    "\n",
+    "PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
+      "Please write a bash script that prints 'Hello World' to the console.\u001b[32;1m\u001b[1;3m\n",
+      "\n",
+      "```bash\n",
+      "printf \"Hello World\\n\"\n",
+      "```\u001b[0m['```bash', 'printf \"Hello World\\\\n\"', '```']\n",
+      "\n",
+      "Answer: \u001b[33;1m\u001b[1;3mHello World\n",
+      "\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Hello World\\n'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bash_chain = LLMBashChain(llm=llm, prompt=PROMPT, verbose=True)\n",
+    "\n",
+    "text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
+    "\n",
+    "bash_chain.run(text)"
+   ]
   }
  ],
  "metadata": {
@@ -79,7 +150,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,

langchain/chains/api/base.py

@@ -9,6 +9,7 @@ from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.llms.base import BaseLLM
+from langchain.prompts import BasePromptTemplate
 from langchain.requests import RequestsWrapper
 
 
@@ -80,12 +81,18 @@ class APIChain(Chain, BaseModel):
 
     @classmethod
     def from_llm_and_api_docs(
-        cls, llm: BaseLLM, api_docs: str, headers: Optional[dict] = None, **kwargs: Any
+        cls,
+        llm: BaseLLM,
+        api_docs: str,
+        headers: Optional[dict] = None,
+        api_url_prompt: BasePromptTemplate = API_URL_PROMPT,
+        api_response_prompt: BasePromptTemplate = API_RESPONSE_PROMPT,
+        **kwargs: Any,
     ) -> APIChain:
         """Load chain from just an LLM and the api docs."""
-        get_request_chain = LLMChain(llm=llm, prompt=API_URL_PROMPT)
+        get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt)
         requests_wrapper = RequestsWrapper(headers=headers)
-        get_answer_chain = LLMChain(llm=llm, prompt=API_RESPONSE_PROMPT)
+        get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt)
         return cls(
             api_request_chain=get_request_chain,
             api_answer_chain=get_answer_chain,

langchain/chains/llm_bash/base.py

@@ -7,6 +7,7 @@ from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_bash.prompt import PROMPT
 from langchain.llms.base import BaseLLM
+from langchain.prompts.base import BasePromptTemplate
 from langchain.utilities.bash import BashProcess
 
 
@@ -24,6 +25,7 @@ class LLMBashChain(Chain, BaseModel):
     """LLM wrapper to use."""
     input_key: str = "question"  #: :meta private:
     output_key: str = "answer"  #: :meta private:
+    prompt: BasePromptTemplate = PROMPT
 
     class Config:
         """Configuration for this pydantic object."""
@@ -48,7 +50,7 @@ class LLMBashChain(Chain, BaseModel):
         return [self.output_key]
 
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
-        llm_executor = LLMChain(prompt=PROMPT, llm=self.llm)
+        llm_executor = LLMChain(prompt=self.prompt, llm=self.llm)
         bash_executor = BashProcess()
         if self.verbose:
             self.callback_manager.on_text(inputs[self.input_key])