Spaces:
Build error
Build error
File size: 7,412 Bytes
c5e4524 1a08523 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 |
def query_pinecone_sparse(
dense_vec,
sparse_vec,
top_k,
index,
year,
quarter,
ticker,
participant_type,
threshold=0.25,
):
if participant_type == "Company Speaker":
participant = "Answer"
else:
participant = "Question"
if year == "All":
if quarter == "All":
xc = index.query(
vector=dense_vec,
sparse_vector=sparse_vec,
top_k=top_k,
filter={
"Year": {
"$in": [
int("2020"),
int("2019"),
int("2018"),
int("2017"),
int("2016"),
]
},
"Quarter": {"$in": ["Q1", "Q2", "Q3", "Q4"]},
"Ticker": {"$eq": ticker},
"QA_Flag": {"$eq": participant},
},
include_metadata=True,
)
else:
xc = index.query(
vector=dense_vec,
sparse_vector=sparse_vec,
top_k=top_k,
filter={
"Year": {
"$in": [
int("2020"),
int("2019"),
int("2018"),
int("2017"),
int("2016"),
]
},
"Quarter": {"$eq": quarter},
"Ticker": {"$eq": ticker},
"QA_Flag": {"$eq": participant},
},
include_metadata=True,
)
else:
# search pinecone index for context passage with the answer
xc = index.query(
vector=dense_vec,
sparse_vector=sparse_vec,
top_k=top_k,
filter={
"Year": int(year),
"Quarter": {"$eq": quarter},
"Ticker": {"$eq": ticker},
"QA_Flag": {"$eq": participant},
},
include_metadata=True,
)
# filter the context passages based on the score threshold
filtered_matches = []
for match in xc["matches"]:
if match["score"] >= threshold:
filtered_matches.append(match)
xc["matches"] = filtered_matches
return xc
def query_pinecone(
dense_vec,
top_k,
index,
year,
quarter,
ticker,
participant_type,
threshold=0.25,
):
if participant_type == "Company Speaker":
participant = "Answer"
else:
participant = "Question"
if year == "All":
if quarter == "All":
xc = index.query(
vector=dense_vec,
top_k=top_k,
filter={
"Year": {
"$in": [
int("2020"),
int("2019"),
int("2018"),
int("2017"),
int("2016"),
]
},
"Quarter": {"$in": ["Q1", "Q2", "Q3", "Q4"]},
"Ticker": {"$eq": ticker},
"QA_Flag": {"$eq": participant},
},
include_metadata=True,
)
else:
xc = index.query(
vector=dense_vec,
top_k=top_k,
filter={
"Year": {
"$in": [
int("2020"),
int("2019"),
int("2018"),
int("2017"),
int("2016"),
]
},
"Quarter": {"$eq": quarter},
"Ticker": {"$eq": ticker},
"QA_Flag": {"$eq": participant},
},
include_metadata=True,
)
else:
# search pinecone index for context passage with the answer
xc = index.query(
vector=dense_vec,
top_k=top_k,
filter={
"Year": int(year),
"Quarter": {"$eq": quarter},
"Ticker": {"$eq": ticker},
"QA_Flag": {"$eq": participant},
},
include_metadata=True,
)
# filter the context passages based on the score threshold
filtered_matches = []
for match in xc["matches"]:
if match["score"] >= threshold:
filtered_matches.append(match)
xc["matches"] = filtered_matches
return xc
def format_query(query_results):
# extract passage_text from Pinecone search result
context = [
result["metadata"]["Text"] for result in query_results["matches"]
]
return context
def sentence_id_combine(data, query_results, lag=1):
# Extract sentence IDs from query results
ids = [
result["metadata"]["Sentence_id"]
for result in query_results["matches"]
]
# Generate new IDs by adding a lag value to the original IDs
new_ids = [id + i for id in ids for i in range(-lag, lag + 1)]
# Remove duplicates and sort the new IDs
new_ids = sorted(set(new_ids))
# Create a list of lookup IDs by grouping the new IDs in groups of lag*2+1
lookup_ids = [
new_ids[i : i + (lag * 2 + 1)]
for i in range(0, len(new_ids), lag * 2 + 1)
]
# Create a list of context sentences by joining the sentences
# corresponding to the lookup IDs
context_list = [
" ".join(
data.loc[data["Sentence_id"].isin(lookup_id), "Text"].to_list()
)
for lookup_id in lookup_ids
]
return context_list
def text_lookup(data, sentence_ids):
context = ". ".join(data.iloc[sentence_ids].to_list())
return context
def year_quarter_range(start_quarter, start_year, end_quarter, end_year):
"""Creates a list of all (year, quarter) pairs that lie in the range including the start and end quarters."""
start_year = int(start_year)
end_year = int(end_year)
quarters = (
[("Q1", "Q2", "Q3", "Q4")] * (end_year - start_year)
+ [("Q1", "Q2", "Q3" if end_quarter == "Q4" else "Q4")]
* (end_quarter == "Q4")
+ [
(
"Q1"
if start_quarter == "Q1"
else "Q2"
if start_quarter == "Q2"
else "Q3"
if start_quarter == "Q3"
else "Q4",
)
* (end_year - start_year)
]
)
years = list(range(start_year, end_year + 1))
list_year_quarter = [
(y, q) for y in years for q in quarters[years.index(y)]
]
# Remove duplicate pairs
seen = set()
list_year_quarter_cleaned = []
for tup in list_year_quarter:
if tup not in seen:
seen.add(tup)
list_year_quarter_cleaned.append(tup)
return list_year_quarter_cleaned
def multi_document_query(
dense_query_embedding,
sparse_query_embedding,
num_results,
pinecone_index,
start_quarter,
start_year,
end_quarter,
end_year,
ticker,
participant_type,
threshold,
):
pass
|