Adding an in-context QA evaluation chain + chain of thought reasoning chain for improved accuracy (#2444)

Right now, eval chains require an answer for every question. It's
cumbersome to collect this ground truth so getting around this issue
with 2 things:

* Adding a context param in `ContextQAEvalChain` and simply evaluating
if the question is answered accurately from context
* Adding chain of though explanation prompting to improve the accuracy
of this w/o GT.

This also gets to feature parity with openai/evals which has the same
contextual eval w/o GT.

TODO in follow-up:
* Better prompt inheritance. No need for seperate prompt for CoT
reasoning. How can we merge them together

---------

Co-authored-by: Vashisht Madhavan <vashishtmadhavan@Vashs-MacBook-Pro.local>

docs/use_cases/evaluation/question_answering.ipynb

@@ -234,6 +234,93 @@
     "evalchain.evaluate(examples, predictions, question_key=\"question\", answer_key=\"answer\", prediction_key=\"text\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cb1cf335",
+   "metadata": {},
+   "source": [
+    "## Evaluation without Ground Truth\n",
+    "Its possible to evaluate question answering systems without ground truth. You would need a `\"context\"` input that reflects what the information the LLM uses to answer the question. This context can be obtained by any retreival system. Here's an example of how it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c59293f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "context_examples = [\n",
+    "    {\n",
+    "        \"question\": \"How old am I?\",\n",
+    "        \"context\": \"I am 30 years old. I live in New York and take the train to work everyday.\",\n",
+    "    },\n",
+    "    {\n",
+    "        \"question\": 'Who won the NFC championship game in 2023?\"',\n",
+    "        \"context\": \"NFC Championship Game 2023: Philadelphia Eagles 31, San Francisco 49ers 7\"\n",
+    "    }\n",
+    "]\n",
+    "QA_PROMPT = \"Answer the question based on the  context\\nContext:{context}\\nQuestion:{question}\\nAnswer:\"\n",
+    "template = PromptTemplate(input_variables=[\"context\", \"question\"], template=QA_PROMPT)\n",
+    "qa_chain = LLMChain(llm=llm, prompt=template)\n",
+    "predictions = qa_chain.apply(context_examples)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e500d0cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'text': 'You are 30 years old.'},\n",
+       " {'text': ' The Philadelphia Eagles won the NFC championship game in 2023.'}]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6d8cbc1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.evaluation.qa import ContextQAEvalChain\n",
+    "eval_chain = ContextQAEvalChain.from_llm(llm)\n",
+    "graded_outputs = eval_chain.evaluate(context_examples, predictions, question_key=\"question\", prediction_key=\"text\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6c5262d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'text': ' CORRECT'}, {'text': ' CORRECT'}]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "graded_outputs"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "aaa61f0c",
@@ -329,7 +416,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.16"
   },
   "vscode": {
    "interpreter": {