Update _langchain.py with [KEYWORDS] tag option (#1871)

bertopic/representation/_langchain.py

@@ -23,8 +23,14 @@ class LangChain(BaseRepresentation):
                Output key must be `output_text`.
         prompt: The prompt to be used in the model. If no prompt is given,
                 `self.default_prompt_` is used instead.
-        nr_docs: The number of documents to pass to LangChain if a prompt
-                 with the `["DOCUMENTS"]` tag is used.
+                 NOTE: Use `"[KEYWORDS]"` in the prompt
+                 to decide where the keywords need to be
+                 inserted. Keywords won't be included unless
+                 indicated. Unlike other representation models,
+                 Langchain does not use the `"[DOCUMENTS]"` tag
+                 to insert documents into the prompt. The load_qa_chain function
+                 formats the representative documents within the prompt.
+        nr_docs: The number of documents to pass to LangChain
         diversity: The diversity of documents to pass to LangChain.
                    Accepts values between 0 and 1. A higher 
                    values results in passing more diverse documents
@@ -185,10 +191,24 @@ def extract_topics(self,
         ]
 
         # `self.chain` must take `input_documents` and `question` as input keys
-        inputs = [
-            {"input_documents": docs, "question": self.prompt}
-            for docs in chain_docs
-        ]
+        # Use a custom prompt that leverages keywords, using the tag: [KEYWORDS]
+        if "[KEYWORDS]" in self.prompt:
+            prompts = []
+            for topic in topics:
+                keywords = list(zip(*topics[topic]))[0]
+                prompt = self.prompt.replace("[KEYWORDS]", ", ".join(keywords))
+                prompts.append(prompt)
+
+            inputs = [
+                {"input_documents": docs, "question": prompt}
+                for docs, prompt in zip(chain_docs, prompts)
+            ]
+
+        else:
+            inputs = [
+                {"input_documents": docs, "question": self.prompt}
+                for docs in chain_docs
+            ]
 
         # `self.chain` must return a dict with an `output_text` key
         # same output key as the `StuffDocumentsChain` returned by `load_qa_chain`