Upload query.py

query.py

@@ -0,0 +1,198 @@
+import requests
+import json
+import re
+from urllib.parse import quote
+
+def extract_between_tags(text, start_tag, end_tag):
+    start_index = text.find(start_tag)
+    end_index = text.find(end_tag, start_index)
+    return text[start_index+len(start_tag):end_index-len(end_tag)]
+
+class CitationNormalizer():
+
+    def __init__(self, responses, docs):
+        self.docs = docs
+        self.responses = responses
+        self.refs = []
+
+    def normalize_citations(self, summary):
+        start_tag = "%START_SNIPPET%"
+        end_tag = "%END_SNIPPET%"
+
+        # find all references in the summary
+        pattern = r'\[\d{1,2}\]'
+        matches = [match.span() for match in re.finditer(pattern, summary)]
+
+        # figure out unique list of references
+        for match in matches:
+            start, end = match
+            response_num = int(summary[start+1:end-1])
+            doc_num = self.responses[response_num-1]['documentIndex']
+            metadata = {item['name']: item['value'] for item in self.docs[doc_num]['metadata']}
+            text = extract_between_tags(self.responses[response_num-1]['text'], start_tag, end_tag)
+            if 'url' in metadata.keys():
+                url = f"{metadata['url']}#:~:text={quote(text)}"
+                if url not in self.refs:
+                    self.refs.append(url)
+
+        # replace references with markdown links
+        refs_dict = {url:(inx+1) for inx,url in enumerate(self.refs)}
+        for match in reversed(matches):
+            start, end = match
+            response_num = int(summary[start+1:end-1])
+            doc_num = self.responses[response_num-1]['documentIndex']
+            metadata = {item['name']: item['value'] for item in self.docs[doc_num]['metadata']}
+            text = extract_between_tags(self.responses[response_num-1]['text'], start_tag, end_tag)
+            if 'url' in metadata.keys():
+                url = f"{metadata['url']}#:~:text={quote(text)}"
+                citation_inx = refs_dict[url]
+                summary = summary[:start] + f'[\[{citation_inx}\]]({url})' + summary[end:]
+            else:
+                summary = summary[:start] + summary[end:]
+
+        return summary
+
+class VectaraQuery():
+    def __init__(self, api_key: str, customer_id: str, corpus_ids: list[str], prompt_name: str = None):
+        self.customer_id = customer_id
+        self.corpus_ids = corpus_ids
+        self.api_key = api_key
+        self.prompt_name = prompt_name if prompt_name else "vectara-experimental-summary-ext-2023-12-11-sml"
+        self.conv_id = None
+
+    def get_body(self, query_str: str):
+        corpora_key_list = [{
+                'customer_id': self.customer_id, 'corpus_id': corpus_id, 'lexical_interpolation_config': {'lambda': 0.025}
+            } for corpus_id in self.corpus_ids
+        ]
+
+        return {
+            'query': [
+                { 
+                    'query': query_str,
+                    'start': 0,
+                    'numResults': 50,
+                    'corpusKey': corpora_key_list,
+                    'context_config': {
+                        'sentences_before': 2,
+                        'sentences_after': 2,
+                        'start_tag': "%START_SNIPPET%",
+                        'end_tag': "%END_SNIPPET%",
+                    },
+                    'rerankingConfig':
+                    {
+                        'rerankerId': 272725718,
+                        'mmrConfig': {
+                            'diversityBias': 0.3
+                        }
+                    },
+                    'summary': [
+                        {
+                            'responseLang': 'eng',
+                            'maxSummarizedResults': 5,
+                            'summarizerPromptName': self.prompt_name,
+                            'chat': {
+                                'store': True,
+                                'conversationId': self.conv_id
+                            },
+                        }
+                    ]
+                } 
+            ]
+        }
+
+    def get_headers(self):
+        return {
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+            "customer-id": self.customer_id,
+            "x-api-key": self.api_key,
+            "grpc-timeout": "60S"
+        }
+
+    def submit_query(self, query_str: str):
+
+        endpoint = f"https://api.vectara.io/v1/query"
+        body = self.get_body(query_str)
+
+        response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_headers())    
+        if response.status_code != 200:
+            print(f"Query failed with code {response.status_code}, reason {response.reason}, text {response.text}")
+            return "Sorry, something went wrong in my brain. Please try again later."
+
+        res = response.json()
+
+        top_k = 10
+        summary = res['responseSet'][0]['summary'][0]['text']
+        responses = res['responseSet'][0]['response'][:top_k]
+        docs = res['responseSet'][0]['document']
+        chat = res['responseSet'][0]['summary'][0].get('chat', None)
+
+        if chat and chat['status'] is not None:
+            st_code = chat['status']
+            print(f"Chat query failed with code {st_code}")
+            if st_code == 'RESOURCE_EXHAUSTED':
+                self.conv_id = None
+                return 'Sorry, Vectara chat turns exceeds plan limit.'
+            return 'Sorry, something went wrong in my brain. Please try again later.'
+        
+        self.conv_id = chat['conversationId'] if chat else None
+        summary = CitationNormalizer(responses, docs).normalize_citations(summary)        
+        return summary
+
+    def submit_query_streaming(self, query_str: str):
+
+        endpoint = f"https://api.vectara.io/v1/stream-query"
+        body = self.get_body(query_str)
+
+        response = requests.post(endpoint, data=json.dumps(body), verify=True, headers=self.get_headers(), stream=True) 
+        if response.status_code != 200:
+            print(f"Query failed with code {response.status_code}, reason {response.reason}, text {response.text}")
+            return "Sorry, something went wrong in my brain. Please try again later."
+
+        chunks = []
+        accumulated_text = ""  # Initialize text accumulation
+        pattern_max_length = 50  # Example heuristic
+        for line in response.iter_lines():
+            if line:  # filter out keep-alive new lines
+                data = json.loads(line.decode('utf-8'))
+                res = data['result']
+                response_set = res['responseSet']                
+                if response_set is None:
+                    # grab next chunk and yield it as output
+                    summary = res.get('summary', None)
+                    if summary is None or len(summary)==0:
+                        continue
+                    else:
+                        chat = summary.get('chat', None)
+                        if chat and chat.get('status', None):
+                            st_code = chat['status']
+                            print(f"Chat query failed with code {st_code}")
+                            if st_code == 'RESOURCE_EXHAUSTED':
+                                self.conv_id = None
+                                return 'Sorry, Vectara chat turns exceeds plan limit.'
+                            return 'Sorry, something went wrong in my brain. Please try again later.'
+                        conv_id = chat.get('conversationId', None) if chat else None
+                        if conv_id:
+                            self.conv_id = conv_id
+                        
+                    chunk = summary['text']
+                    accumulated_text += chunk  # Append current chunk to accumulation
+                    if len(accumulated_text) > pattern_max_length:
+                        accumulated_text = re.sub(r"\[\d+\]", "", accumulated_text)
+                        accumulated_text = re.sub(r"\s+\.", ".", accumulated_text)
+                        out_chunk = accumulated_text[:-pattern_max_length]
+                        chunks.append(out_chunk)
+                        yield out_chunk
+                        accumulated_text = accumulated_text[-pattern_max_length:]
+
+                    if summary['done']:
+                        break
+
+        # yield the last piece
+        if len(accumulated_text) > 0:
+            accumulated_text = re.sub(r" \[\d+\]\.", ".", accumulated_text)
+            chunks.append(accumulated_text)
+            yield accumulated_text        
+        
+        return ''.join(chunks)