Harrison/chain lab (#156)

docs/examples/model_laboratory.ipynb

@@ -42,7 +42,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_lab = ModelLaboratory(llms)"
+    "model_lab = ModelLaboratory.from_llms(llms)"
    ]
   },
   {
@@ -60,19 +60,19 @@
       "\n",
       "\u001b[1mOpenAI\u001b[0m\n",
       "Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n",
-      "\u001b[104m\n",
+      "\u001b[36;1m\u001b[1;3m\n",
       "\n",
       "Flamingos are pink.\u001b[0m\n",
       "\n",
       "\u001b[1mCohere\u001b[0m\n",
       "Params: {'model': 'command-xlarge-20221108', 'max_tokens': 20, 'temperature': 0.0, 'k': 0, 'p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}\n",
-      "\u001b[103m\n",
+      "\u001b[33;1m\u001b[1;3m\n",
       "\n",
       "Pink\u001b[0m\n",
       "\n",
       "\u001b[1mHuggingFaceHub\u001b[0m\n",
       "Params: {'repo_id': 'google/flan-t5-xl', 'temperature': 1}\n",
-      "\u001b[101mpink\u001b[0m\n",
+      "\u001b[38;5;200m\u001b[1;3mpink\u001b[0m\n",
       "\n"
      ]
     }
@@ -89,7 +89,7 @@
    "outputs": [],
    "source": [
     "prompt = Prompt(template=\"What is the capital of {state}?\", input_variables=[\"state\"])\n",
-    "model_lab_with_prompt = ModelLaboratory(llms, prompt=prompt)"
+    "model_lab_with_prompt = ModelLaboratory.from_llms(llms, prompt=prompt)"
    ]
   },
   {
@@ -107,19 +107,19 @@
       "\n",
       "\u001b[1mOpenAI\u001b[0m\n",
       "Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n",
-      "\u001b[104m\n",
+      "\u001b[36;1m\u001b[1;3m\n",
       "\n",
       "The capital of New York is Albany.\u001b[0m\n",
       "\n",
       "\u001b[1mCohere\u001b[0m\n",
       "Params: {'model': 'command-xlarge-20221108', 'max_tokens': 20, 'temperature': 0.0, 'k': 0, 'p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}\n",
-      "\u001b[103m\n",
+      "\u001b[33;1m\u001b[1;3m\n",
       "\n",
       "The capital of New York is Albany.\u001b[0m\n",
       "\n",
       "\u001b[1mHuggingFaceHub\u001b[0m\n",
       "Params: {'repo_id': 'google/flan-t5-xl', 'temperature': 1}\n",
-      "\u001b[101mst john s\u001b[0m\n",
+      "\u001b[38;5;200m\u001b[1;3mst john s\u001b[0m\n",
       "\n"
      ]
     }
@@ -130,10 +130,103 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "54336dbf",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "from langchain import SelfAskWithSearchChain, SerpAPIChain\n",
+    "\n",
+    "open_ai_llm = OpenAI(temperature=0)\n",
+    "search = SerpAPIChain()\n",
+    "self_ask_with_search_openai = SelfAskWithSearchChain(llm=open_ai_llm, search_chain=search, verbose=True)\n",
+    "\n",
+    "cohere_llm = Cohere(temperature=0, model=\"command-xlarge-20221108\")\n",
+    "search = SerpAPIChain()\n",
+    "self_ask_with_search_cohere = SelfAskWithSearchChain(llm=cohere_llm, search_chain=search, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6a50a9f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chains = [self_ask_with_search_openai, self_ask_with_search_cohere]\n",
+    "names = [str(open_ai_llm), str(cohere_llm)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d3549e99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_lab = ModelLaboratory(chains, names=names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "362f7f57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1mInput:\u001b[0m\n",
+      "What is the hometown of the reigning men's U.S. Open champion?\n",
+      "\n",
+      "\u001b[1mOpenAI\u001b[0m\n",
+      "Params: {'model': 'text-davinci-002', 'temperature': 0.0, 'max_tokens': 256, 'top_p': 1, 'frequency_penalty': 0, 'presence_penalty': 0, 'n': 1, 'best_of': 1}\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "What is the hometown of the reigning men's U.S. Open champion?\n",
+      "Are follow up questions needed here:\u001b[32;1m\u001b[1;3m Yes.\n",
+      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
+      "Intermediate answer: \u001b[33;1m\u001b[1;3mCarlos Alcaraz.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "Follow up: Where is Carlos Alcaraz from?\u001b[0m\n",
+      "Intermediate answer: \u001b[33;1m\u001b[1;3mEl Palmar, Spain.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "So the final answer is: El Palmar, Spain\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[36;1m\u001b[1;3m\n",
+      "So the final answer is: El Palmar, Spain\u001b[0m\n",
+      "\n",
+      "\u001b[1mCohere\u001b[0m\n",
+      "Params: {'model': 'command-xlarge-20221108', 'max_tokens': 256, 'temperature': 0.0, 'k': 0, 'p': 1, 'frequency_penalty': 0, 'presence_penalty': 0}\n",
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new chain...\u001b[0m\n",
+      "What is the hometown of the reigning men's U.S. Open champion?\n",
+      "Are follow up questions needed here:\u001b[32;1m\u001b[1;3m Yes.\n",
+      "Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
+      "Intermediate answer: \u001b[33;1m\u001b[1;3mCarlos Alcaraz.\u001b[0m\u001b[32;1m\u001b[1;3m\n",
+      "So the final answer is:\n",
+      "\n",
+      "Carlos Alcaraz\u001b[0m\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3m\n",
+      "So the final answer is:\n",
+      "\n",
+      "Carlos Alcaraz\u001b[0m\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_lab.compare(\"What is the hometown of the reigning men's U.S. Open champion?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94159131",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],

langchain/chains/vector_db_qa/base.py

@@ -27,6 +27,8 @@ class VectorDBQA(Chain, BaseModel):
     """LLM wrapper to use."""
     vectorstore: VectorStore
     """Vector Database to connect to."""
+    k: int = 4
+    """Number of documents to query for."""
     input_key: str = "query"  #: :meta private:
     output_key: str = "result"  #: :meta private:
 
@@ -55,7 +57,7 @@ class VectorDBQA(Chain, BaseModel):
     def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
         question = inputs[self.input_key]
         llm_chain = LLMChain(llm=self.llm, prompt=prompt)
-        docs = self.vectorstore.similarity_search(question)
+        docs = self.vectorstore.similarity_search(question, k=self.k)
         contexts = []
         for j, doc in enumerate(docs):
             contexts.append(f"Context {j}:\n{doc.page_content}")

langchain/model_laboratory.py

@@ -1,6 +1,7 @@
 """Experiment with different models."""
-from typing import List, Optional
+from typing import List, Optional, Sequence
 
+from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.input import get_color_mapping, print_text
 from langchain.llms.base import LLM
@@ -10,7 +11,41 @@ from langchain.prompts.prompt import Prompt
 class ModelLaboratory:
     """Experiment with different models."""
 
-    def __init__(self, llms: List[LLM], prompt: Optional[Prompt] = None):
+    def __init__(self, chains: Sequence[Chain], names: Optional[List[str]] = None):
+        """Initialize with chains to experiment with.
+
+        Args:
+            chains: list of chains to experiment with.
+        """
+        if not isinstance(chains[0], Chain):
+            raise ValueError(
+                "ModelLaboratory should now be initialized with Chains. "
+                "If you want to initialize with LLMs, use the `from_llms` method "
+                "instead (`ModelLaboratory.from_llms(...)`)"
+            )
+        for chain in chains:
+            if len(chain.input_keys) != 1:
+                raise ValueError(
+                    "Currently only support chains with one input variable, "
+                    f"got {chain.input_keys}"
+                )
+            if len(chain.output_keys) != 1:
+                raise ValueError(
+                    "Currently only support chains with one output variable, "
+                    f"got {chain.output_keys}"
+                )
+        if names is not None:
+            if len(names) != len(chains):
+                raise ValueError("Length of chains does not match length of names.")
+        self.chains = chains
+        chain_range = [str(i) for i in range(len(self.chains))]
+        self.chain_colors = get_color_mapping(chain_range)
+        self.names = names
+
+    @classmethod
+    def from_llms(
+        cls, llms: List[LLM], prompt: Optional[Prompt] = None
+    ) -> "ModelLaboratory":
         """Initialize with LLMs to experiment with and optional prompt.
 
         Args:
@@ -18,18 +53,11 @@ class ModelLaboratory:
             prompt: Optional prompt to use to prompt the LLMs. Defaults to None.
                 If a prompt was provided, it should only have one input variable.
         """
-        self.llms = llms
-        llm_range = [str(i) for i in range(len(self.llms))]
-        self.llm_colors = get_color_mapping(llm_range)
         if prompt is None:
-            self.prompt = Prompt(input_variables=["_input"], template="{_input}")
-        else:
-            if len(prompt.input_variables) != 1:
-                raise ValueError(
-                    "Currently only support prompts with one input variable, "
-                    f"got {prompt}"
-                )
-            self.prompt = prompt
+            prompt = Prompt(input_variables=["_input"], template="{_input}")
+        chains = [LLMChain(llm=llm, prompt=prompt) for llm in llms]
+        names = [str(llm) for llm in llms]
+        return cls(chains, names=names)
 
     def compare(self, text: str) -> None:
         """Compare model outputs on an input text.
@@ -42,9 +70,11 @@ class ModelLaboratory:
             text: input text to run all models on.
         """
         print(f"\033[1mInput:\033[0m\n{text}\n")
-        for i, llm in enumerate(self.llms):
-            print_text(str(llm), end="\n")
-            chain = LLMChain(llm=llm, prompt=self.prompt)
-            llm_inputs = {self.prompt.input_variables[0]: text}
-            output = chain.predict(**llm_inputs)
-            print_text(output, color=self.llm_colors[str(i)], end="\n\n")
+        for i, chain in enumerate(self.chains):
+            if self.names is not None:
+                name = self.names[i]
+            else:
+                name = str(chain)
+            print_text(name, end="\n")
+            output = chain.run(text)
+            print_text(output, color=self.chain_colors[str(i)], end="\n\n")